system

Abstract

The system according to this embodiment aims to provide highly reliable investment information. [Solution] The system according to the embodiment comprises a submission reception unit, an information analysis unit, a selection unit, and an information provision unit. The submission reception unit receives opinions and information from users regarding investments. The information analysis unit analyzes the information submitted by the submission reception unit. The selection unit identifies trends and recommended stocks based on the information analyzed by the information analysis unit. The information provision unit provides the trends and recommended stocks identified by the selection unit to the user.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to find a reliable investment information source and the authenticity of social media information was unclear.

[0005] The system according to the embodiment aims to provide highly reliable investment information.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a submission reception unit, an information analysis unit, a selection unit, and an information provision unit. The submission reception unit receives opinions and information about investments from users. The information analysis unit analyzes the information submitted by the submission reception unit. The selection unit identifies trends and recommended stocks based on the information analyzed by the information analysis unit. The information provision unit provides the trends and recommended stocks identified by the selection unit to the user. [Effects of the Invention]

[0007] The system according to this embodiment can provide highly reliable investment information. [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, etc. 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) The community-driven investment insight platform according to an embodiment of the present invention is a system in which users post opinions and information on investments, and AI analyzes this information to identify trends and recommended stocks. This system analyzes community feedback using AI to identify trends and recommended stocks. It also leverages collective intelligence to provide investment insights by analyzing influential statements on social media and evaluating their reliability. For example, a community platform is developed that allows users to post opinions and information on investments. Next, the AI ​​analyzes the posted information to identify trends and recommended stocks. Furthermore, it analyzes influential statements on social media and evaluates their reliability. As a result, users can obtain highly reliable information that integrates a large number of opinions, improving their investment decisions. In addition, two-way interaction within the community can be expected to deepen investment knowledge. For example, a user posts opinions and information on investments to the community platform. In this case, the user can freely express their opinions and engage in discussions with other users. For example, opinions on a particular stock or advice on investment strategies may be posted. Next, the AI ​​analyzes the posted information. The AI ​​analyzes the posted information using natural language processing technology to identify trends and recommended stocks. For example, if there are many positive opinions about a particular stock, that stock will be identified as a recommended stock. Similarly, if a particular investment strategy is supported by many users, that strategy will be identified as a trend. Furthermore, influential statements on social media are analyzed. The AI ​​collects social media posts and evaluates their influence. For example, statements from users with a large number of followers or posts that have been shared (e.g., reposted) many times are evaluated as influential. This allows for the identification of highly reliable information. This system enables users to obtain reliable information that integrates multiple opinions, improving their investment decisions. For example, if there are many positive opinions about a particular stock, the likelihood of profiting from investing in that stock increases. Additionally, interactive community engagement can deepen investment knowledge. For example, users can learn new investment strategies through discussions with other users.Thus, community-driven investment insight platforms can acquire reliable information and improve investment decisions. This allows community-driven investment insight platforms to enhance users' investment decisions.

[0029] The community-driven investment insight platform according to this embodiment comprises a submission reception unit, an information analysis unit, a selection unit, and an information provision unit. The submission reception unit receives opinions and information from users regarding investments. The opinions and information posted by users include, but are not limited to, investment strategies, stock information, and market forecasts. The submission reception unit allows users to freely express their opinions and engage in discussions with other users. The information analysis unit analyzes the information posted by the submission reception unit. The information analysis unit analyzes the posted information using, for example, natural language processing technology. For example, the information analysis unit analyzes the posted information using text mining technology to identify trends and recommended stocks. The information analysis unit can also analyze the sentiment of the posted information using sentiment analysis technology. For example, the information analysis unit analyzes positive and negative opinions in the posted information to identify trends and recommended stocks. The selection unit identifies trends and recommended stocks based on the information analyzed by the information analysis unit. For example, if there are many positive opinions about a particular stock, the selection unit identifies that stock as a recommended stock. The Identification Unit can also identify a particular investment strategy as a trend if it is supported by many users. For example, the Identification Unit identifies trends and recommended stocks based on information analyzed by the Information Analysis Unit. The Information Provision Unit provides the trends and recommended stocks identified by the Identification Unit to users. The Information Provision Unit provides the identified trends and recommended stocks to users in the form of notifications, reports, dashboards, etc. The Information Provision Unit can also provide the identified trends and recommended stocks to users through web applications or mobile applications. For example, the Information Provision Unit provides the identified trends and recommended stocks to users as notifications. The Information Provision Unit can also provide the identified trends and recommended stocks to users as reports. The Information Provision Unit can also provide the identified trends and recommended stocks to users as dashboards. As a result, the community-driven investment insight platform according to this embodiment can efficiently analyze users' opinions and information regarding investments and provide reliable investment insights.

[0030] The posting section allows users to post opinions and information related to investments. These opinions and information may include, but are not limited to, investment strategies, stock information, and market forecasts. The posting section allows users to freely express their opinions and engage in discussions with other users. Specifically, the posting section provides an easily accessible interface with features such as text input and file uploads. Users can post in text format to share their investment experiences and market views. They can also attach images, graphs, and links, enabling posts that include visual information. Furthermore, the posting section provides comment and "like" functions to facilitate communication among users, allowing them to express opinions and support for other posts. To maintain the quality of posted content, the posting section includes spam filtering and automatic detection of inappropriate content, ensuring the reliability and integrity of information on the platform.

[0031] The Information Analysis Department analyzes information submitted by the Submission Department. For example, the Information Analysis Department uses natural language processing techniques to analyze submitted information. Specifically, it uses text mining techniques to analyze submitted information and identify trends and recommended stocks. The Information Analysis Department can also analyze the sentiment of submitted information using sentiment analysis techniques. For example, it analyzes positive and negative opinions in submitted information to identify trends and recommended stocks. First, the Information Analysis Department preprocesses the submitted text data to remove unnecessary information and noise. Next, it uses natural language processing techniques to analyze the meaning of the submitted content, extracting keywords and classifying topics. This allows it to identify the main themes and concerns of the submitted content. Furthermore, it uses sentiment analysis techniques to analyze the sentiment of the submitted content and classify it into positive, negative, and neutral sentiments. This allows it to understand the user sentiment trends towards specific stocks or markets. Based on these analysis results, the Information Analysis Department provides data to identify trends and recommended stocks.

[0032] The Identification Department identifies trends and recommended stocks based on information analyzed by the Information Analysis Department. For example, if there are many positive opinions about a particular stock, the Identification Department will identify that stock as a recommended stock. The Identification Department can also identify a particular investment strategy as a trend if it is supported by many users. For example, the Identification Department identifies trends and recommended stocks based on information analyzed by the Information Analysis Department. Specifically, the Identification Department analyzes the trends in user opinions on specific stocks and investment strategies based on data provided by the Information Analysis Department. For example, if there are many positive opinions about a particular stock, it will identify that stock as a recommended stock. Also, if a particular investment strategy is supported by many users, it will identify that strategy as a trend. Based on these identification results, the Identification Department provides users with reliable investment insights.

[0033] The Information Provider Department provides users with trends and recommended stocks identified by the Specialized Department. The Information Provider Department provides identified trends and recommended stocks to users in various forms, such as notifications, reports, and dashboards. The Information Provider Department can also provide identified trends and recommended stocks to users through web and mobile applications. For example, the Information Provider Department can provide identified trends and recommended stocks to users as notifications. The Information Provider Department can also provide identified trends and recommended stocks to users as reports. The Information Provider Department can also provide identified trends and recommended stocks to users as dashboards. Specifically, the Information Provider Department provides trend and recommended stock information from the Specialized Department in a format that is easily accessible to users. For example, it can provide a real-time updated dashboard through web and mobile applications, allowing users to check the latest investment insights. It can also create regular reports and distribute them to users via email or in-app notifications. This ensures that users can always make investment decisions based on the latest information. Furthermore, the Information Provider Department can collect user feedback and continuously improve the accuracy and usefulness of the information it provides.

[0034] The community-driven investment insights platform further includes a speech analysis unit that collects social media posts and evaluates their influence. The speech analysis unit, for example, collects social media posts and evaluates their influence. For instance, it evaluates posts from users with a large number of followers or posts that have been shared many times as influential. The speech analysis unit uses metrics such as follower count, post shares, and engagement rate to collect social media posts and evaluate their influence. This allows the speech analysis unit to identify reliable information by evaluating influential social media posts. For example, the speech analysis unit can collect posts from users with a large number of followers and evaluate their influence. It can also collect posts that have been shared many times and evaluate their influence. It can also collect posts with high engagement rates and evaluate their influence. This allows the speech analysis unit to identify reliable information by evaluating influential social media posts.

[0035] The community-driven investment insights platform further includes an influence assessment unit that evaluates statements from users with a large number of followers or statements that have been shared many times as influential statements. For example, the influence assessment unit evaluates statements from users with a large number of followers as influential statements. The influence assessment unit also evaluates statements that have been shared many times as influential statements. The influence assessment unit evaluates influential statements using metrics such as follower count, number of shares, and engagement rate. This allows the influence assessment unit to identify reliable information by evaluating influential statements. For example, the influence assessment unit evaluates statements from users with a large number of followers as influential statements. The influence assessment unit can also evaluate statements that have been shared many times as influential statements. The influence assessment unit can also evaluate statements with high engagement rates as influential statements. This allows the influence assessment unit to identify reliable information by evaluating influential statements.

[0036] The submission reception unit can analyze a user's past posting history and select the optimal posting method. For example, the submission reception unit can prioritize suggesting posting formats (text, images, videos, etc.) that the user has frequently used in the past. The submission reception unit can also analyze the user's past posting content and automatically suggest relevant topics and keywords. The submission reception unit can also suggest the optimal posting timing based on the user's posting frequency. In this way, the submission reception unit can select the optimal posting method by analyzing the user's past posting history. Some or all of the above processing in the submission reception unit may be performed using AI, for example, or not using AI. For example, the submission reception unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal posting method.

[0037] The submission receiving unit can filter submissions based on the user's current investment status and areas of interest. For example, the submission receiving unit can accept only relevant investment information based on the user's portfolio information. The submission receiving unit can also prioritize submissions related to the user's areas of interest (technology, healthcare, etc.). The submission receiving unit can also analyze the user's investment history and prioritize submissions related to past successful investment strategies. In this way, the submission receiving unit can prioritize submissions that are highly relevant by filtering based on the user's current investment status and areas of interest. Some or all of the above processing in the submission receiving unit may be performed using AI, for example, or not using AI. For example, the submission receiving unit can input the user's investment status data into a generating AI and have the generating AI perform the filtering.

[0038] The submission reception unit can prioritize accepting submissions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the submission reception unit can prioritize accepting investment information related to that region. Based on the user's location, the submission reception unit can also prioritize accepting submissions related to the region's economic conditions and market trends. If the user is traveling, the submission reception unit can also prioritize accepting investment information for their destination. In this way, the submission reception unit can prioritize accepting investment information related to a region by taking into account the user's geographical location. Some or all of the above processing in the submission reception unit may be performed using AI, for example, or not using AI. For example, the submission reception unit can input the user's geographical location data into a generating AI and have the generating AI perform filtering of highly relevant submissions.

[0039] The submission reception unit can analyze a user's social media activity and accept relevant posts when a submission is received. For example, the submission reception unit can prioritize accepting important posts based on the user's number of followers and influence on social media. The submission reception unit can also analyze a user's social media activity history and prioritize accepting posts that contain relevant topics and keywords. The submission reception unit can also prioritize accepting influential posts based on the user's social media engagement rate. In this way, the submission reception unit can prioritize accepting relevant posts by analyzing the user's social media activity. Some or all of the above processing in the submission reception unit may be performed using AI, for example, or not using AI. For example, the submission reception unit can input the user's social media activity data into a generating AI and have the generating AI perform filtering of relevant posts.

[0040] The information analysis unit can adjust the level of detail of its analysis based on the importance of the posts during the analysis process. For example, the information analysis unit can perform a detailed analysis on important posts to provide deeper insights. For general posts, the information analysis unit can perform a concise analysis to provide essential information. For urgent posts, the information analysis unit can perform a rapid analysis and provide immediate results. This allows the information analysis unit to perform detailed analysis on important posts by adjusting the level of detail based on the importance of the posts. Some or all of the above processes in the information analysis unit may be performed using AI, for example, or without AI. For example, the information analysis unit can input post importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0041] The information analysis unit can apply different analysis algorithms depending on the category of the post during information analysis. For example, the information analysis unit can apply a technical analysis algorithm to technology-related posts. It can also apply a medical data analysis algorithm to healthcare-related posts. It can also apply an economic data analysis algorithm to finance-related posts. By applying different analysis algorithms depending on the category of the post, the information analysis unit can provide more accurate analysis results. Some or all of the above processing in the information analysis unit may be performed using AI, for example, or without AI. For example, the information analysis unit can input post category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0042] The information analysis unit can determine the priority of analysis based on the submission date of the posts during information analysis. For example, the information analysis unit can prioritize the analysis of the most recent posts and provide results quickly. The information analysis unit can also adjust the priority of analysis for past posts according to their importance. The information analysis unit can also immediately analyze and provide results for posts of high urgency. In this way, the information analysis unit can perform analysis quickly on the most recent posts by determining the priority of analysis based on the submission date of the posts. Some or all of the above processing in the information analysis unit may be performed using AI, for example, or not using AI. For example, the information analysis unit can input submission date data of posts into a generating AI and have the generating AI perform the determination of analysis priorities.

[0043] The identification unit can improve the accuracy of identification by considering the interrelationships of posts during the identification process. For example, the identification unit can evaluate the relevance between posts and perform identification based on related posts. The identification unit can also analyze the interrelationships of posts and integrate relevant information to improve the accuracy of identification. The identification unit can also consider the interrelationships of posts and extract important information to perform identification. In this way, the identification unit can improve the accuracy of identification by considering the interrelationships of posts. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input interrelationship data of posts into a generating AI and have the generating AI perform the improvement of the accuracy of identification.

[0044] The identification unit can perform identification while considering the poster's attribute information. For example, the identification unit can identify highly reliable information based on the poster's expertise and experience. The identification unit can also identify highly reliable information by analyzing the poster's past posting history. The identification unit can also improve the accuracy of identification by considering the poster's attribute information (occupation, age, region, etc.). As a result, the identification unit can identify highly reliable information by considering the poster's attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the poster's attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of identification.

[0045] The information provision unit can select the optimal information provision method by referring to the user's past information acquisition history when providing information. For example, the information provision unit may prioritize providing information in formats (text, images, videos, etc.) that the user has frequently used in the past. The information provision unit can also analyze the user's past information acquisition history and provide information that includes relevant topics and keywords. The information provision unit can also suggest the optimal timing for providing information based on the user's information acquisition frequency. In this way, the information provision unit can select the optimal information provision method by referring to the user's past information acquisition history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the user's information acquisition history data into a generating AI and have the generating AI select the optimal information provision method.

[0046] The speech analysis unit can adjust the level of detail of its analysis based on the impact of each statement. For example, it can perform a detailed analysis of high-impact statements to provide deeper insights. For general statements, it can perform a concise analysis to provide key information. For urgent statements, it can perform a rapid analysis and provide immediate results. This allows the speech analysis unit to perform detailed analysis on important statements by adjusting the level of detail based on their impact. Some or all of the above processes in the speech analysis unit may be performed using AI, for example, or not. For example, the speech analysis unit can input statement impact data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0047] The utterance analysis unit can determine the priority of analysis based on when the utterances were submitted. For example, the utterance analysis unit can prioritize the analysis of the most recent utterances and provide results quickly. The utterance analysis unit can also adjust the priority of analysis for past utterances according to their importance. For utterances of high urgency, the utterance analysis unit can perform an immediate analysis and provide results. In this way, the utterance analysis unit can perform a rapid analysis of the most recent utterances by determining the priority of analysis based on when the utterances were submitted. Some or all of the above processing in the utterance analysis unit may be performed using AI, for example, or not using AI. For example, the utterance analysis unit can input utterance submission time data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0048] The influence assessment unit can adjust the level of detail of its assessment based on the impact of each statement during the influence assessment process. For example, the influence assessment unit can perform a detailed assessment of highly influential statements to provide deeper insights. For general statements, the influence assessment unit can also perform a concise assessment to provide concise information. For urgent statements, the influence assessment unit can perform a rapid assessment and provide immediate results. This allows the influence assessment unit to perform detailed assessments of important statements by adjusting the level of detail based on the impact of each statement. Some or all of the above processes in the influence assessment unit may be performed using AI, for example, or not. For example, the influence assessment unit can input statement impact data into a generating AI and have the generating AI perform the adjustment of the level of detail of the assessment.

[0049] The influence assessment unit can determine the priority of evaluations based on the timing of the submission of statements during the influence assessment process. For example, the influence assessment unit can prioritize evaluations of recent statements and provide results quickly. The influence assessment unit can also adjust the priority of evaluations of past statements according to their importance. For statements of high urgency, the influence assessment unit can perform evaluations immediately and provide results immediately. This allows the influence assessment unit to quickly evaluate recent statements by determining the priority of evaluations based on the timing of the submission of statements. Some or all of the above processes in the influence assessment unit may be performed using AI, for example, or not. For example, the influence assessment unit can input statement submission timing data into a generating AI and have the generating AI perform the determination of evaluation priorities.

[0050] The influence assessment unit can adjust the order of evaluation based on the relevance of statements during the influence assessment process. For example, the influence assessment unit can prioritize the evaluation of highly relevant statements and provide results quickly. The influence assessment unit can also postpone the evaluation of less relevant statements. The influence assessment unit can also evaluate the relevance of statements and adjust the order of evaluation according to their importance. This allows the influence assessment unit to prioritize the evaluation of highly relevant statements by adjusting the order of evaluation based on the relevance of statements. Some or all of the above processing in the influence assessment unit may be performed using AI, for example, or without AI. For example, the influence assessment unit can input statement relevance data into a generating AI and have the generating AI perform the adjustment of the evaluation order.

[0051] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0052] The submission system can automatically suggest relevant news articles and market reports based on user submissions. For example, if a user submits a post about a specific stock, it will suggest the latest news articles related to that stock. The submission system can also suggest market reports related to a specific investment strategy if a user submits one. Furthermore, if a user submits a post about a specific market forecast, the submission system can suggest economic indicators and statistical data related to that forecast. In this way, the submission system can support users' investment decisions by providing relevant information based on their submissions.

[0053] The information analysis unit can automatically search for and present relevant past posts based on the user's posts. For example, if a user posts about a specific stock, the unit can search for past posts related to that stock and present them to the user. The information analysis unit can also search for and present past posts related to a specific investment strategy if the user posts about that strategy. Furthermore, if the information analysis unit posts about a specific market forecast, it can search for and present past posts related to that forecast. In this way, the information analysis unit can support users' investment decisions by providing relevant past posts based on the user's posts.

[0054] The system can automatically suggest expert opinions and advice based on user posts. For example, if a user posts about a specific stock, it will suggest expert opinions and advice on that stock. If a user posts about a specific investment strategy, the system can also suggest expert opinions and advice on that strategy. Furthermore, if a user posts about a specific market forecast, the system can suggest expert opinions and advice on that forecast. In this way, the system can support users' investment decisions by providing relevant expert opinions and advice based on user posts.

[0055] The information provision department can automatically suggest relevant educational content and learning resources based on user posts. For example, if a user posts about a specific stock, it will suggest educational content and learning resources related to that stock. The information provision department can also suggest educational content and learning resources related to an investment strategy if a user posts about that strategy. Furthermore, if a user posts about a specific market forecast, the information provision department can suggest educational content and learning resources related to that forecast. In this way, the information provision department can improve users' investment knowledge by providing relevant educational content and learning resources based on user posts.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The posting section allows users to post their opinions and information about investments. These posts can include investment strategies, stock information, and market forecasts. The posting section allows users to freely express their opinions and engage in discussions with other users. Step 2: The Information Analysis Department analyzes the information submitted by the Submission Department. The Information Analysis Department uses natural language processing and text mining technologies to analyze the submitted information and identify trends and recommended stocks. It can also analyze the sentiment of the submitted information using sentiment analysis technology. Step 3: The Identification Department identifies trends and recommended stocks based on the information analyzed by the Information Analysis Department. If there are many positive opinions about a particular stock, the Identification Department identifies that stock as a recommended stock. Also, if a particular investment strategy is supported by many users, that strategy is identified as a trend. Step 4: The Information Provision Department provides users with the trends and recommended stocks identified by the Identification Department. The Information Provision Department provides users with the identified trends and recommended stocks in the form of notifications, reports, dashboards, etc. The Information Provision Department can also provide information to users through web applications and mobile applications.

[0058] (Example of form 2) The community-driven investment insight platform according to an embodiment of the present invention is a system in which users post opinions and information on investments, and AI analyzes this information to identify trends and recommended stocks. This system analyzes community feedback using AI to identify trends and recommended stocks. It also leverages collective intelligence to provide investment insights by analyzing influential statements on social media and evaluating their reliability. For example, a community platform is developed that allows users to post opinions and information on investments. Next, the AI ​​analyzes the posted information to identify trends and recommended stocks. Furthermore, it analyzes influential statements on social media and evaluates their reliability. As a result, users can obtain highly reliable information that integrates a large number of opinions, improving their investment decisions. In addition, two-way interaction within the community can be expected to deepen investment knowledge. For example, a user posts opinions and information on investments to the community platform. In this case, the user can freely express their opinions and engage in discussions with other users. For example, opinions on a particular stock or advice on investment strategies may be posted. Next, the AI ​​analyzes the posted information. The AI ​​analyzes the posted information using natural language processing technology to identify trends and recommended stocks. For example, if there are many positive opinions about a particular stock, that stock will be identified as a recommended stock. Similarly, if a particular investment strategy is supported by many users, that strategy will be identified as a trend. Furthermore, influential statements on social media are analyzed. The AI ​​collects social media posts and evaluates their influence. For example, statements from users with a large number of followers or posts that have been shared (e.g., reposted) many times are evaluated as influential. This allows for the identification of highly reliable information. This system enables users to obtain reliable information that integrates multiple opinions, improving their investment decisions. For example, if there are many positive opinions about a particular stock, the likelihood of profiting from investing in that stock increases. Additionally, interactive community engagement can deepen investment knowledge. For example, users can learn new investment strategies through discussions with other users.Thus, community-driven investment insight platforms can acquire reliable information and improve investment decisions. This allows community-driven investment insight platforms to enhance users' investment decisions.

[0059] The community-driven investment insight platform according to this embodiment comprises a submission reception unit, an information analysis unit, a selection unit, and an information provision unit. The submission reception unit receives opinions and information from users regarding investments. The opinions and information posted by users include, but are not limited to, investment strategies, stock information, and market forecasts. The submission reception unit allows users to freely express their opinions and engage in discussions with other users. The information analysis unit analyzes the information posted by the submission reception unit. The information analysis unit analyzes the posted information using, for example, natural language processing technology. For example, the information analysis unit analyzes the posted information using text mining technology to identify trends and recommended stocks. The information analysis unit can also analyze the sentiment of the posted information using sentiment analysis technology. For example, the information analysis unit analyzes positive and negative opinions in the posted information to identify trends and recommended stocks. The selection unit identifies trends and recommended stocks based on the information analyzed by the information analysis unit. For example, if there are many positive opinions about a particular stock, the selection unit identifies that stock as a recommended stock. The Identification Unit can also identify a particular investment strategy as a trend if it is supported by many users. For example, the Identification Unit identifies trends and recommended stocks based on information analyzed by the Information Analysis Unit. The Information Provision Unit provides the trends and recommended stocks identified by the Identification Unit to users. The Information Provision Unit provides the identified trends and recommended stocks to users in the form of notifications, reports, dashboards, etc. The Information Provision Unit can also provide the identified trends and recommended stocks to users through web applications or mobile applications. For example, the Information Provision Unit provides the identified trends and recommended stocks to users as notifications. The Information Provision Unit can also provide the identified trends and recommended stocks to users as reports. The Information Provision Unit can also provide the identified trends and recommended stocks to users as dashboards. As a result, the community-driven investment insight platform according to this embodiment can efficiently analyze users' opinions and information regarding investments and provide reliable investment insights.

[0060] The posting section allows users to post opinions and information related to investments. These opinions and information may include, but are not limited to, investment strategies, stock information, and market forecasts. The posting section allows users to freely express their opinions and engage in discussions with other users. Specifically, the posting section provides an easily accessible interface with features such as text input and file uploads. Users can post in text format to share their investment experiences and market views. They can also attach images, graphs, and links, enabling posts that include visual information. Furthermore, the posting section provides comment and "like" functions to facilitate communication among users, allowing them to express opinions and support for other posts. To maintain the quality of posted content, the posting section includes spam filtering and automatic detection of inappropriate content, ensuring the reliability and integrity of information on the platform.

[0061] The Information Analysis Department analyzes information submitted by the Submission Department. For example, the Information Analysis Department uses natural language processing techniques to analyze submitted information. Specifically, it uses text mining techniques to analyze submitted information and identify trends and recommended stocks. The Information Analysis Department can also analyze the sentiment of submitted information using sentiment analysis techniques. For example, it analyzes positive and negative opinions in submitted information to identify trends and recommended stocks. First, the Information Analysis Department preprocesses the submitted text data to remove unnecessary information and noise. Next, it uses natural language processing techniques to analyze the meaning of the submitted content, extracting keywords and classifying topics. This allows it to identify the main themes and concerns of the submitted content. Furthermore, it uses sentiment analysis techniques to analyze the sentiment of the submitted content and classify it into positive, negative, and neutral sentiments. This allows it to understand the user sentiment trends towards specific stocks or markets. Based on these analysis results, the Information Analysis Department provides data to identify trends and recommended stocks.

[0062] The Identification Department identifies trends and recommended stocks based on information analyzed by the Information Analysis Department. For example, if there are many positive opinions about a particular stock, the Identification Department will identify that stock as a recommended stock. The Identification Department can also identify a particular investment strategy as a trend if it is supported by many users. For example, the Identification Department identifies trends and recommended stocks based on information analyzed by the Information Analysis Department. Specifically, the Identification Department analyzes the trends in user opinions on specific stocks and investment strategies based on data provided by the Information Analysis Department. For example, if there are many positive opinions about a particular stock, it will identify that stock as a recommended stock. Also, if a particular investment strategy is supported by many users, it will identify that strategy as a trend. Based on these identification results, the Identification Department provides users with reliable investment insights.

[0063] The Information Provider Department provides users with trends and recommended stocks identified by the Specialized Department. The Information Provider Department provides identified trends and recommended stocks to users in various forms, such as notifications, reports, and dashboards. The Information Provider Department can also provide identified trends and recommended stocks to users through web and mobile applications. For example, the Information Provider Department can provide identified trends and recommended stocks to users as notifications. The Information Provider Department can also provide identified trends and recommended stocks to users as reports. The Information Provider Department can also provide identified trends and recommended stocks to users as dashboards. Specifically, the Information Provider Department provides trend and recommended stock information from the Specialized Department in a format that is easily accessible to users. For example, it can provide a real-time updated dashboard through web and mobile applications, allowing users to check the latest investment insights. It can also create regular reports and distribute them to users via email or in-app notifications. This ensures that users can always make investment decisions based on the latest information. Furthermore, the Information Provider Department can collect user feedback and continuously improve the accuracy and usefulness of the information it provides.

[0064] The community-driven investment insights platform further includes a speech analysis unit that collects social media posts and evaluates their influence. The speech analysis unit, for example, collects social media posts and evaluates their influence. For instance, it evaluates posts from users with a large number of followers or posts that have been shared many times as influential. The speech analysis unit uses metrics such as follower count, post shares, and engagement rate to collect social media posts and evaluate their influence. This allows the speech analysis unit to identify reliable information by evaluating influential social media posts. For example, the speech analysis unit can collect posts from users with a large number of followers and evaluate their influence. It can also collect posts that have been shared many times and evaluate their influence. It can also collect posts with high engagement rates and evaluate their influence. This allows the speech analysis unit to identify reliable information by evaluating influential social media posts.

[0065] The community-driven investment insights platform further includes an influence assessment unit that evaluates statements from users with a large number of followers or statements that have been shared many times as influential statements. For example, the influence assessment unit evaluates statements from users with a large number of followers as influential statements. The influence assessment unit also evaluates statements that have been shared many times as influential statements. The influence assessment unit evaluates influential statements using metrics such as follower count, number of shares, and engagement rate. This allows the influence assessment unit to identify reliable information by evaluating influential statements. For example, the influence assessment unit evaluates statements from users with a large number of followers as influential statements. The influence assessment unit can also evaluate statements that have been shared many times as influential statements. The influence assessment unit can also evaluate statements with high engagement rates as influential statements. This allows the influence assessment unit to identify reliable information by evaluating influential statements.

[0066] The submission reception unit can estimate the user's emotions and adjust the timing of submissions based on the estimated emotions. For example, if the user is stressed, the submission reception unit can temporarily delay submission to give the user time to relax. If the user is excited, the submission reception unit can also immediately accept submissions and collect opinions while emotions are heightened. If the user is tired, the submission reception unit can simplify the submission process to allow for quick submissions. This allows the submission reception unit to accept submissions at a more appropriate time by adjusting the timing of submissions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 submission reception unit may be performed using AI or not using AI. For example, the submission reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0067] The submission reception unit can analyze a user's past posting history and select the optimal posting method. For example, the submission reception unit can prioritize suggesting posting formats (text, images, videos, etc.) that the user has frequently used in the past. The submission reception unit can also analyze the user's past posting content and automatically suggest relevant topics and keywords. The submission reception unit can also suggest the optimal posting timing based on the user's posting frequency. In this way, the submission reception unit can select the optimal posting method by analyzing the user's past posting history. Some or all of the above processing in the submission reception unit may be performed using AI, for example, or not using AI. For example, the submission reception unit can input the user's past posting history data into a generating AI and have the generating AI select the optimal posting method.

[0068] The submission receiving unit can filter submissions based on the user's current investment status and areas of interest. For example, the submission receiving unit can accept only relevant investment information based on the user's portfolio information. The submission receiving unit can also prioritize submissions related to the user's areas of interest (technology, healthcare, etc.). The submission receiving unit can also analyze the user's investment history and prioritize submissions related to past successful investment strategies. In this way, the submission receiving unit can prioritize submissions that are highly relevant by filtering based on the user's current investment status and areas of interest. Some or all of the above processing in the submission receiving unit may be performed using AI, for example, or not using AI. For example, the submission receiving unit can input the user's investment status data into a generating AI and have the generating AI perform the filtering.

[0069] The submission reception unit can estimate a user's emotions and prioritize submissions based on the estimated emotions. For example, if a user is excited, the submission reception unit will prioritize accepting that submission and quickly share it with other users. If a user is calm, the submission reception unit can also review the content of the submission in detail and categorize it appropriately. If a user is feeling anxious, the submission reception unit can prioritize accepting that submission and provide support. This allows the submission reception unit to quickly accept important submissions by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 submission reception unit may be performed using AI or not. For example, the submission reception unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation.

[0070] The submission reception unit can prioritize accepting submissions that are highly relevant, taking into account the user's geographical location. For example, if the user is in a specific region, the submission reception unit can prioritize accepting investment information related to that region. Based on the user's location, the submission reception unit can also prioritize accepting submissions related to the region's economic conditions and market trends. If the user is traveling, the submission reception unit can also prioritize accepting investment information for their destination. In this way, the submission reception unit can prioritize accepting investment information related to a region by taking into account the user's geographical location. Some or all of the above processing in the submission reception unit may be performed using AI, for example, or not using AI. For example, the submission reception unit can input the user's geographical location data into a generating AI and have the generating AI perform filtering of highly relevant submissions.

[0071] The submission reception unit can analyze a user's social media activity and accept relevant posts when a submission is received. For example, the submission reception unit can prioritize accepting important posts based on the user's number of followers and influence on social media. The submission reception unit can also analyze a user's social media activity history and prioritize accepting posts that contain relevant topics and keywords. The submission reception unit can also prioritize accepting influential posts based on the user's social media engagement rate. In this way, the submission reception unit can prioritize accepting relevant posts by analyzing the user's social media activity. Some or all of the above processing in the submission reception unit may be performed using AI, for example, or not using AI. For example, the submission reception unit can input the user's social media activity data into a generating AI and have the generating AI perform filtering of relevant posts.

[0072] The information 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 relaxed, the information analysis unit can provide detailed analysis results to gain deeper insights. If the user is in a hurry, the information analysis unit can also provide concise analysis results that get straight to the point. If the user is excited, the information analysis unit can also provide analysis results using visually appealing graphs and charts. In this way, the information analysis unit can provide more appropriate analysis results by adjusting the presentation of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 information analysis unit may be performed using AI, for example, or not using AI. For example, the information analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis based on emotions.

[0073] The information analysis unit can adjust the level of detail of its analysis based on the importance of the posts during the analysis process. For example, the information analysis unit can perform a detailed analysis on important posts to provide deeper insights. For general posts, the information analysis unit can perform a concise analysis to provide essential information. For urgent posts, the information analysis unit can perform a rapid analysis and provide immediate results. This allows the information analysis unit to perform detailed analysis on important posts by adjusting the level of detail based on the importance of the posts. Some or all of the above processes in the information analysis unit may be performed using AI, for example, or without AI. For example, the information analysis unit can input post importance data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0074] The information analysis unit can apply different analysis algorithms depending on the category of the post during information analysis. For example, the information analysis unit can apply a technical analysis algorithm to technology-related posts. It can also apply a medical data analysis algorithm to healthcare-related posts. It can also apply an economic data analysis algorithm to finance-related posts. By applying different analysis algorithms depending on the category of the post, the information analysis unit can provide more accurate analysis results. Some or all of the above processing in the information analysis unit may be performed using AI, for example, or without AI. For example, the information analysis unit can input post category data into a generating AI and have the generating AI execute the application of different analysis algorithms.

[0075] The information analysis unit can determine the priority of analysis based on the submission date of the posts during information analysis. For example, the information analysis unit can prioritize the analysis of the most recent posts and provide results quickly. The information analysis unit can also adjust the priority of analysis for past posts according to their importance. The information analysis unit can also immediately analyze and provide results for posts of high urgency. In this way, the information analysis unit can perform analysis quickly on the most recent posts by determining the priority of analysis based on the submission date of the posts. Some or all of the above processing in the information analysis unit may be performed using AI, for example, or not using AI. For example, the information analysis unit can input submission date data of posts into a generating AI and have the generating AI perform the determination of analysis priorities.

[0076] The identification unit can estimate the user's emotions and adjust specific criteria based on the estimated emotions. For example, if the user is relaxed, the identification unit can use detailed criteria for identification. If the user is in a hurry, the identification unit can also use concise criteria for quick identification. If the user is excited, the identification unit can also use visually appealing criteria for identification. This allows the identification unit to make more appropriate identifications by adjusting specific criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 identification unit may be performed using AI, for example, or not using AI. For example, the identification unit can input user emotion data into a generative AI and have the generative AI perform adjustments to specific criteria.

[0077] The identification unit can improve the accuracy of identification by considering the interrelationships of posts during the identification process. For example, the identification unit can evaluate the relevance between posts and perform identification based on related posts. The identification unit can also analyze the interrelationships of posts and integrate relevant information to improve the accuracy of identification. The identification unit can also consider the interrelationships of posts and extract important information to perform identification. In this way, the identification unit can improve the accuracy of identification by considering the interrelationships of posts. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input interrelationship data of posts into a generating AI and have the generating AI perform the improvement of the accuracy of identification.

[0078] The identification unit can perform identification while considering the poster's attribute information. For example, the identification unit can identify highly reliable information based on the poster's expertise and experience. The identification unit can also identify highly reliable information by analyzing the poster's past posting history. The identification unit can also improve the accuracy of identification by considering the poster's attribute information (occupation, age, region, etc.). As a result, the identification unit can identify highly reliable information by considering the poster's attribute information. Some or all of the above processing in the identification unit may be performed using AI, for example, or without AI. For example, the identification unit can input the poster's attribute information data into a generating AI and have the generating AI perform the task of improving the accuracy of identification.

[0079] The information delivery unit can estimate the user's emotions and adjust the method of information delivery based on the estimated emotions. For example, if the user is relaxed, the information delivery unit may provide detailed information to enable deeper insights. If the user is in a hurry, the information delivery unit may also provide concise information that gets straight to the point. If the user is excited, the information delivery unit may also provide information using visually appealing graphs and charts. In this way, the information delivery unit can provide more appropriate information by adjusting the method of information delivery according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 information delivery unit may be performed using AI or not using AI. For example, the information delivery unit can input user emotion data into a generative AI and have the generative AI adjust the method of information delivery.

[0080] The information provision unit can select the optimal information provision method by referring to the user's past information acquisition history when providing information. For example, the information provision unit may prioritize providing information in formats (text, images, videos, etc.) that the user has frequently used in the past. The information provision unit can also analyze the user's past information acquisition history and provide information that includes relevant topics and keywords. The information provision unit can also suggest the optimal timing for providing information based on the user's information acquisition frequency. In this way, the information provision unit can select the optimal information provision method by referring to the user's past information acquisition history. Some or all of the above processing in the information provision unit may be performed using AI, for example, or without AI. For example, the information provision unit can input the user's information acquisition history data into a generating AI and have the generating AI select the optimal information provision method.

[0081] The speech analysis unit can estimate the user's emotions and adjust the speech analysis method based on the estimated emotions. For example, if the user is relaxed, the speech analysis unit can perform a detailed analysis to gain deeper insights. If the user is in a hurry, the speech analysis unit can also perform a concise analysis that gets straight to the point. If the user is excited, the speech analysis unit can provide the analysis results using visually appealing graphs and charts. In this way, the speech analysis unit can provide more appropriate analysis results by adjusting the speech analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 speech analysis unit may be performed using AI or not using AI. For example, the speech analysis unit can input user emotion data into a generative AI and have the generative AI adjust the speech analysis method.

[0082] The speech analysis unit can adjust the level of detail of its analysis based on the impact of each statement. For example, it can perform a detailed analysis of high-impact statements to provide deeper insights. For general statements, it can perform a concise analysis to provide key information. For urgent statements, it can perform a rapid analysis and provide immediate results. This allows the speech analysis unit to perform detailed analysis on important statements by adjusting the level of detail based on their impact. Some or all of the above processes in the speech analysis unit may be performed using AI, for example, or not. For example, the speech analysis unit can input statement impact data into a generating AI and have the generating AI adjust the level of detail of the analysis.

[0083] The speech analysis unit can estimate the user's emotions and prioritize speeches based on the estimated emotions. For example, if a user is excited, the speech analysis unit can prioritize analyzing that speech and quickly share it with other users. If a user is calm, the speech analysis unit can also examine the content of the speech in detail and categorize it appropriately. If a user is feeling anxious, the speech analysis unit can prioritize analyzing that speech and provide support. This allows the speech analysis unit to quickly analyze important speeches by prioritizing them according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 speech analysis unit may be performed using AI or not. For example, the speech analysis unit can input user emotion data into a generative AI and have the generative AI perform the task of determining speech priorities.

[0084] The utterance analysis unit can determine the priority of analysis based on when the utterances were submitted. For example, the utterance analysis unit can prioritize the analysis of the most recent utterances and provide results quickly. The utterance analysis unit can also adjust the priority of analysis for past utterances according to their importance. For utterances of high urgency, the utterance analysis unit can perform an immediate analysis and provide results. In this way, the utterance analysis unit can perform a rapid analysis of the most recent utterances by determining the priority of analysis based on when the utterances were submitted. Some or all of the above processing in the utterance analysis unit may be performed using AI, for example, or not using AI. For example, the utterance analysis unit can input utterance submission time data into a generating AI and have the generating AI perform the determination of analysis priorities.

[0085] The influence assessment unit can estimate the user's emotions and adjust the criteria for influence assessment based on the estimated user emotions. For example, if the user is relaxed, the influence assessment unit can assess influence using detailed criteria. If the user is in a hurry, the influence assessment unit can also quickly assess influence using concise criteria. If the user is excited, the influence assessment unit can also assess influence using visually appealing criteria. This allows the influence assessment unit to perform more appropriate influence assessments by adjusting the criteria for influence assessment according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 influence assessment unit may be performed using AI or not using AI. For example, the influence assessment unit can input user emotion data into a generative AI and have the generative AI adjust the criteria for influence assessment.

[0086] The influence assessment unit can adjust the level of detail of its assessment based on the impact of each statement during the influence assessment process. For example, the influence assessment unit can perform a detailed assessment of highly influential statements to provide deeper insights. For general statements, the influence assessment unit can also perform a concise assessment to provide concise information. For urgent statements, the influence assessment unit can perform a rapid assessment and provide immediate results. This allows the influence assessment unit to perform detailed assessments of important statements by adjusting the level of detail based on the impact of each statement. Some or all of the above processes in the influence assessment unit may be performed using AI, for example, or not. For example, the influence assessment unit can input statement impact data into a generating AI and have the generating AI perform the adjustment of the level of detail of the assessment.

[0087] The influence assessment unit can estimate the user's emotions and determine the priority of influence assessments based on the estimated user emotions. For example, if the user is excited, the influence assessment unit can prioritize the impact of that statement and quickly share it with other users. If the user is calm, the influence assessment unit can also examine the content of the statement in detail and categorize it appropriately. If the user is feeling anxious, the influence assessment unit can prioritize the impact of that statement and provide support. In this way, the influence assessment unit can quickly assess the impact of important statements by determining the priority of influence assessments according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI 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 influence assessment unit may be performed using AI, for example, or not using AI. For example, the influence assessment unit can input user emotion data into a generative AI and have the generative AI perform the determination of influence assessment priorities.

[0088] The influence assessment unit can determine the priority of evaluations based on the timing of the submission of statements during the influence assessment process. For example, the influence assessment unit can prioritize evaluations of recent statements and provide results quickly. The influence assessment unit can also adjust the priority of evaluations of past statements according to their importance. For statements of high urgency, the influence assessment unit can perform evaluations immediately and provide results immediately. This allows the influence assessment unit to quickly evaluate recent statements by determining the priority of evaluations based on the timing of the submission of statements. Some or all of the above processes in the influence assessment unit may be performed using AI, for example, or not. For example, the influence assessment unit can input statement submission timing data into a generating AI and have the generating AI perform the determination of evaluation priorities.

[0089] The influence assessment unit can adjust the order of evaluation based on the relevance of statements during the influence assessment process. For example, the influence assessment unit can prioritize the evaluation of highly relevant statements and provide results quickly. The influence assessment unit can also postpone the evaluation of less relevant statements. The influence assessment unit can also evaluate the relevance of statements and adjust the order of evaluation according to their importance. This allows the influence assessment unit to prioritize the evaluation of highly relevant statements by adjusting the order of evaluation based on the relevance of statements. Some or all of the above processing in the influence assessment unit may be performed using AI, for example, or without AI. For example, the influence assessment unit can input statement relevance data into a generating AI and have the generating AI perform the adjustment of the evaluation order.

[0090] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0091] The submission system can automatically suggest relevant news articles and market reports based on user submissions. For example, if a user submits a post about a specific stock, it will suggest the latest news articles related to that stock. The submission system can also suggest market reports related to a specific investment strategy if a user submits one. Furthermore, if a user submits a post about a specific market forecast, the submission system can suggest economic indicators and statistical data related to that forecast. In this way, the submission system can support users' investment decisions by providing relevant information based on their submissions.

[0092] The information analysis unit can automatically search for and present relevant past posts based on the user's posts. For example, if a user posts about a specific stock, the unit can search for past posts related to that stock and present them to the user. The information analysis unit can also search for and present past posts related to a specific investment strategy if the user posts about that strategy. Furthermore, if the information analysis unit posts about a specific market forecast, it can search for and present past posts related to that forecast. In this way, the information analysis unit can support users' investment decisions by providing relevant past posts based on the user's posts.

[0093] The system can automatically suggest expert opinions and advice based on user posts. For example, if a user posts about a specific stock, it will suggest expert opinions and advice on that stock. If a user posts about a specific investment strategy, the system can also suggest expert opinions and advice on that strategy. Furthermore, if a user posts about a specific market forecast, the system can suggest expert opinions and advice on that forecast. In this way, the system can support users' investment decisions by providing relevant expert opinions and advice based on user posts.

[0094] The information provision department can automatically suggest relevant educational content and learning resources based on user posts. For example, if a user posts about a specific stock, it will suggest educational content and learning resources related to that stock. The information provision department can also suggest educational content and learning resources related to an investment strategy if a user posts about that strategy. Furthermore, if a user posts about a specific market forecast, the information provision department can suggest educational content and learning resources related to that forecast. In this way, the information provision department can improve users' investment knowledge by providing relevant educational content and learning resources based on user posts.

[0095] The submission reception system can estimate the user's emotions and adjust the content of the post based on those emotions. For example, if the user is stressed, the submission reception system can summarize the content concisely and present it in an easy-to-understand format. If the user is excited, the submission reception system can also provide detailed information to capture the user's interest. Furthermore, if the user is anxious, the submission reception system can provide reassuring information. In this way, the submission reception system can support users' investment decisions by adjusting the content of posts according to their emotions.

[0096] The information analysis unit can estimate the user's emotions and adjust how the analysis results are presented based on those emotions. For example, if the user is relaxed, it can provide detailed analysis results to gain deeper insights. If the user is in a hurry, the information analysis unit can also provide concise analysis results that get straight to the point. Furthermore, if the user is excited, the information analysis unit can present the analysis results using visually appealing graphs and charts. In this way, the information analysis unit can support the user's investment decisions by adjusting how the analysis results are presented according to the user's emotions.

[0097] The identification unit can estimate the user's emotions and adjust specific criteria based on those emotions. For example, if the user is relaxed, it can use detailed criteria for identification. If the user is in a hurry, it can use concise criteria for quick identification. Furthermore, if the user is excited, it can use visually appealing criteria for identification. In this way, the identification unit can support the user's investment decisions by adjusting specific criteria according to the user's emotions.

[0098] The information delivery department can estimate the user's emotions and adjust the way information is delivered based on those estimates. For example, if the user is relaxed, it can provide detailed information to help them gain deeper insights. If the user is in a hurry, the information delivery department can also provide concise information that gets straight to the point. Furthermore, if the user is excited, the information delivery department can use visually appealing graphs and charts to deliver information. In this way, the information delivery department can support the user's investment decisions by adjusting the way information is delivered according to the user's emotions.

[0099] The speech analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is relaxed, it can perform a detailed analysis to gain deeper insights. If the user is in a hurry, the speech analysis unit can perform a concise analysis that gets straight to the point. Furthermore, if the user is excited, the speech analysis unit can provide the analysis results using visually appealing graphs and charts. In this way, the speech analysis unit can support the user's investment decisions by adjusting the analysis method according to the user's emotions.

[0100] The influence assessment unit can estimate the user's emotions and adjust the influence assessment criteria based on those emotions. For example, if the user is relaxed, it can use detailed criteria to assess influence. If the user is in a hurry, the influence assessment unit can also use concise criteria to quickly assess influence. Furthermore, if the user is excited, the influence assessment unit can use visually appealing criteria to assess influence. In this way, the influence assessment unit can support the user's investment decisions by adjusting the influence assessment criteria according to the user's emotions.

[0101] The following briefly describes the processing flow for example form 2.

[0102] Step 1: The posting section allows users to post their opinions and information about investments. These posts can include investment strategies, stock information, and market forecasts. The posting section allows users to freely express their opinions and engage in discussions with other users. Step 2: The Information Analysis Department analyzes the information submitted by the Submission Department. The Information Analysis Department uses natural language processing and text mining technologies to analyze the submitted information and identify trends and recommended stocks. It can also analyze the sentiment of the submitted information using sentiment analysis technology. Step 3: The Identification Department identifies trends and recommended stocks based on the information analyzed by the Information Analysis Department. If there are many positive opinions about a particular stock, the Identification Department identifies that stock as a recommended stock. Also, if a particular investment strategy is supported by many users, that strategy is identified as a trend. Step 4: The Information Provision Department provides users with the trends and recommended stocks identified by the Identification Department. The Information Provision Department provides users with the identified trends and recommended stocks in the form of notifications, reports, dashboards, etc. The Information Provision Department can also provide information to users through web applications and mobile applications.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] Each of the multiple elements described above, including the submission reception unit, information analysis unit, identification unit, and information provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the submission reception unit is implemented by the control unit 46A of the smart device 14, allowing users to submit opinions and information regarding investments. The information analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, and analyzes the submitted information. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, and identifies trends and recommended stocks based on the analyzed information. The information provision unit is implemented by the control unit 46A of the smart device 14, for example, and provides the identified trends and recommended stocks to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0107] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the submission reception unit, information analysis unit, identification unit, and information provision unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the submission reception unit is implemented by the control unit 46A of the smart glasses 214, allowing users to submit opinions and information regarding investments. The information analysis unit is implemented by the identification processing unit 290 of the data processing device 12, for example, and analyzes the submitted information. The identification unit is implemented by the identification processing unit 290 of the data processing device 12, for example, and identifies trends and recommended stocks based on the analyzed information. The information provision unit is implemented by the control unit 46A of the smart glasses 214, for example, and provides the identified trends and recommended stocks to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0123] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the submission reception unit, information analysis unit, identification unit, and information provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the submission reception unit is implemented by the control unit 46A of the headset terminal 314, allowing users to submit opinions and information regarding investments. The information analysis unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, and analyzes the submitted information. The identification unit is implemented by the identification processing unit 290 of the data processing unit 12, for example, and identifies trends and recommended stocks based on the analyzed information. The information provision unit is implemented by the control unit 46A of the headset terminal 314, for example, and provides the identified trends and recommended stocks to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0139] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] Each of the multiple elements described above, including the submission reception unit, information analysis unit, identification unit, and information provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the submission reception unit is implemented by the control unit 46A of the robot 414, allowing users to submit opinions and information regarding investments. The information analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and analyzes the submitted information. The identification unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12, and identifies trends and recommended stocks based on the analyzed information. The information provision unit is implemented by, for example, the control unit 46A of the robot 414, and provides the identified trends and recommended stocks to the user. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] (Note 1) A submission area where users can post their opinions and information about investments, The information analysis unit analyzes the information submitted by the aforementioned submission reception unit, Based on the information analyzed by the aforementioned information analysis unit, the identification unit identifies trends and recommended stocks, The system includes an information provision unit that provides users with trends and recommended stocks identified by the aforementioned specific unit. A system characterized by the following features. (Note 2) We will further develop a department that collects statements on social media and evaluates the influence of those statements through statement analysis. The system described in Appendix 1, characterized by the features described herein. (Note 3) It also includes an influence assessment section that evaluates statements from users with a large number of followers and statements that have been shared many times as influential statements. The system described in Appendix 2, characterized by the features described herein. (Note 4) The aforementioned submission reception department is: The system estimates user sentiment and adjusts the timing of submissions based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned submission reception department is: Analyze the user's past posting history and select the optimal posting method. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned submission reception department is: When submitting a post, it will be filtered based on the user's current investment status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned submission reception department is: It estimates the user's sentiment and determines the priority of posts to accept based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned submission reception department is: When accepting submissions, the system prioritizes submissions that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned submission reception department is: When accepting submissions, the system analyzes the user's social media activity and accepts relevant submissions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned information analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned information analysis unit, When analyzing information, adjust the level of detail of the analysis based on the importance of the post. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned information analysis unit, When analyzing information, different analysis algorithms are applied depending on the category of the post. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned information analysis unit, When analyzing information, the priority of analysis is determined based on the submission date of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 14) The specified part is, It estimates the user's emotions and adjusts certain criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The specified part is, When specific situations arise, the interrelationships between posts are taken into consideration to improve the accuracy of the selection process. The system described in Appendix 1, characterized by the features described herein. (Note 16) The specified part is, When identifying a poster, the poster's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned information provision unit, It estimates the user's emotions and adjusts the way information is provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned information provision unit, When providing information, the system selects the most suitable method of delivery by referring to the user's past information acquisition history. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned speech analysis department, We estimate the user's emotions and adjust the analysis method of their statements based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 20) The aforementioned speech analysis department, When analyzing statements, adjust the level of detail based on the impact of the statement. The system described in Appendix 2, characterized by the features described herein. (Note 21) The aforementioned speech analysis department, It estimates the user's emotions and determines the priority of comments based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 22) The aforementioned speech analysis department, When analyzing statements, prioritize the analysis based on when the statements were submitted. The system described in Appendix 2, characterized by the features described herein. (Note 23) The aforementioned influence assessment unit, We estimate user sentiment and adjust the criteria for influence assessment based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 24) The aforementioned influence assessment unit, When assessing influence, adjust the level of detail in the assessment based on the impact of the statement. The system described in Appendix 3, characterized by the features described herein. (Note 25) The aforementioned influence assessment unit, The system estimates user sentiment and determines the priority of influence evaluation based on the estimated user sentiment. The system described in Appendix 3, characterized by the features described herein. (Note 26) The aforementioned influence assessment unit, When assessing influence, prioritizing evaluations is determined based on when the statements were submitted. The system described in Appendix 3, characterized by the features described herein. (Note 27) The aforementioned influence assessment unit, When assessing influence, the order of evaluation is adjusted based on the relevance of the statements. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0175] 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 submission area where users can post their opinions and information about investments, The information analysis unit analyzes the information submitted by the aforementioned submission reception unit, Based on the information analyzed by the aforementioned information analysis unit, the identification unit identifies trends and recommended stocks, The system includes an information provision unit that provides users with trends and recommended stocks identified by the aforementioned specific unit. A system characterized by the following features.

2. We will further develop a department that collects statements on social media and evaluates the influence of those statements through statement analysis. The system according to feature 1.

3. It also includes an influence assessment section that evaluates statements from users with a large number of followers and statements that have been shared many times as influential statements. The system according to feature 2.

4. The aforementioned submission reception department is: The system estimates user sentiment and adjusts the timing of submissions based on the estimated sentiment. The system according to feature 1.

5. The aforementioned submission reception department is: Analyze the user's past posting history and select the optimal posting method. The system according to feature 1.

6. The aforementioned submission reception department is: When submitting a post, it will be filtered based on the user's current investment status and areas of interest. The system according to feature 1.

7. The aforementioned submission reception department is: It estimates the user's sentiment and determines the priority of posts to accept based on the estimated user sentiment. The system according to feature 1.

8. The aforementioned submission reception department is: When accepting submissions, the system prioritizes submissions that are highly relevant, taking into account the user's geographical location. The system according to feature 1.

9. The aforementioned submission reception department is: When accepting submissions, the system analyzes the user's social media activity and accepts relevant submissions. The system according to feature 1.

10. The aforementioned information analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system according to feature 1.

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