System

Abstract

An object of a system according to an embodiment is to efficiently collect and analyze securities reports of listed companies and provide appropriate advice to investors.SOLUTION: A system includes a report collection part, a database storage part, a data transmission part, an analysis part, and an advice provision part. A report collection part collects securities reports of each listed company. The database storage unit stores the securities report collected by the report collection unit in the database. The data-transmitting unit transmits the database stored by the database-storing unit to the generation AI. The analysis unit analyzes the AI transmitted by the generation unit. The advice providing unit provides advice to the investor based on the data analyzed by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to efficiently collect and analyze securities reports from listed companies and provide appropriate advice to investors.

[0005] The system according to the embodiment aims to efficiently collect and analyze securities reports of listed companies and provide appropriate advice to investors. [Means for solving the problem]

[0006] The system according to the embodiment includes a report collection unit, a database storage unit, a data transmission unit, an analysis unit, and an advice provision unit. The report collection unit collects securities reports from each listed company. The database storage unit stores the securities reports collected by the report collection unit in a database. The data transmission unit transmits the data stored by the database storage unit to the generation AI. The analysis unit analyzes the data transmitted by the generation AI. The advice provision unit provides advice to investors based on the data analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently collect and analyze securities reports of listed companies and provide appropriate advice to investors. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The automated analysis system according to an embodiment of the present invention collects securities reports from listed companies, analyzes the data using a generation AI, and provides useful advice to investors. This allows the automated analysis system to analyze financial and performance data from companies and provide useful advice to investors.

[0029] The automated analysis system according to the embodiment includes a report collection unit, a database storage unit, a data transmission unit, an analysis unit, and an advice provision unit. The report collection unit collects securities reports from listed companies. For example, it downloads securities reports from company websites or financial institution databases. The report collection unit can also collect related information from social media and news sites and use it as supplementary information for the reports. The database storage unit stores the securities reports collected by the report collection unit in a database. For example, it stores the collected securities reports in digital format for later analysis. The data transmission unit transmits the data stored by the database storage unit to the generation AI. For example, it transmits the company's financial data and performance data to the generation AI to use as the basis for analysis. The analysis unit analyzes the data transmitted by the generation AI. For example, the generation AI analyzes the company's sales and profit margins, the increase in off-balance sheet liabilities, the identities of major customers, and suspected related-party transactions. The advice provision unit provides advice to investors based on the data analyzed by the analysis unit. For example, the generation AI can point out high-risk points to investors based on the analysis results. This allows the automated analysis system according to the embodiment to analyze a company's financial data and performance data and provide useful advice to investors.

[0030] The report collection unit can be equipped with a function to automatically collect securities reports from corporate websites or financial institution databases. The report collection unit, for example, builds a system that automatically collects securities reports from corporate websites and financial institution databases. In addition, information related to the company is collected from social media and news sites and stored in a database as supplemental information for the report. Securities reports are downloaded from the company's official website or financial institution database using an automatic collection system. In addition, the latest information about the company is collected from social media and news sites to supplement the content of the report. When collecting securities reports from corporate websites and financial institution databases, a function to collect related information from social media and news sites is added. This allows for a more detailed understanding of the content of the report. This improves the efficiency of data collection by automatically collecting securities reports from corporate websites and financial institution databases.

[0031] The database storage unit may have a function of referring to authentication data from a third party and assigning a reliability score to evaluate the reliability of securities reports. The database storage unit, for example, builds a system that refers to authentication data from a third party to evaluate the reliability of collected securities reports. For example, it assigns a reliability score based on the authentication data and stores it in a database. To evaluate the reliability of securities reports, it automatically collects authentication data from a third party and assigns a reliability score to the reports. This allows highly reliable reports to be analyzed preferentially. A system is developed that refers to authentication data from a third party for collected securities reports and assigns a reliability score to them. For example, it evaluates the reliability of the reports based on the authentication data and stores it in a database. This allows highly reliable data to be analyzed preferentially by evaluating the reliability of collected securities reports.

[0032] The data transmission unit can have a function to include at least one of the following information in the data sent to the generation AI: a company's sales or profit margin, an increase in off-balance sheet liabilities, the identities of major customers, and suspected related-party transactions. The data transmission unit, for example, has a function to include information such as a company's sales or profit margin, an increase in off-balance sheet liabilities, the identities of major customers, and suspected related-party transactions in the data sent to the generation AI. For example, a sentiment estimation function is used to analyze market sentiment toward the contents of a report and evaluate the importance of the collected data. A system is constructed that uses the sentiment estimation function to analyze market sentiment toward the contents of collected securities reports. For example, positive and negative sentiment toward the contents of the report is quantified and the importance of the data is evaluated. A system is developed that analyzes market sentiment toward the contents of securities reports in real time and evaluates the importance of collected data based on the results. For example, reports with high sentiment scores are prioritized for analysis. A system is constructed that uses the sentiment estimation function to analyze market sentiment toward the contents of collected securities reports and dynamically update the importance of the data. For example, reports are prioritized based on the sentiment score. This enables more accurate analysis by including important information in the data sent to the generation AI.

[0033] The analysis unit may be equipped with a function for analyzing at least one of a company's sales or profit margin, the increase in off-balance sheet liabilities, the identity of major customers, and suspected related-party transactions, and pointing out high-risk areas to investors. The analysis unit may be equipped with a function for analyzing, for example, a company's sales or profit margin, the increase in off-balance sheet liabilities, the identity of major customers, and suspected related-party transactions, and pointing out high-risk areas to investors. For example, the collection of securities reports may be expanded to include not only listed companies but also unlisted companies and startups. A system may be built that expands the collection of securities reports to include not only listed companies but also unlisted companies and startups. For example, financial data and performance data of unlisted companies may be collected and stored in a database. A function may be added to collect securities reports of unlisted companies and startups in addition to listed companies. This allows for the analysis of a wider range of corporate data. A system may be developed that expands the collection of securities reports to include unlisted companies and startups. For example, reports may be collected from the websites and databases of unlisted companies. This allows us to analyze a company's risk factors and point out high-risk points to investors, thereby improving the accuracy of investment decisions.

[0034] The advice providing unit can have a function to provide investors with specific action plans based on the analysis results of the generation AI. The advice providing unit, for example, has a function to provide investors with specific action plans (for example, buying and selling timing and risk management measures) based on the analysis results of the generation AI. For example, a system is built that includes specific action plans in the advice provided by the generation AI. For example, the timing of buying and selling and risk management measures are specifically presented. By including specific action plans when providing advice, a system is developed that provides information in a form that is easy for investors to implement. For example, specific buying and selling instructions and risk avoidance measures are presented. By including specific action plans in the advice provided by the generation AI, a system is built that enables investors to act quickly. For example, the timing of buying and selling and risk management measures are explained in detail. In this way, by providing specific action plans, investors are able to act quickly.

[0035] The advice providing unit may have a function to evaluate the success rate of past advice and assign a reliability score when providing advice by the generation AI. The advice providing unit, for example, has a function to evaluate the success rate of past advice and assign a reliability score when providing advice by the generation AI. For example, a system is constructed that evaluates the success rate of advice provided by the generation AI and assigns a reliability score. For example, reliability is evaluated based on the track record of past advice. A system is developed that allows investors to obtain reliable information when providing advice by evaluating the past success rate and assigning a reliability score. For example, advice with a high success rate is provided preferentially. A system is constructed that allows investors to accept advice with confidence by evaluating the success rate of advice provided by the generation AI and assigning a reliability score. For example, the priority of advice is determined based on the reliability score. In this way, by evaluating the success rate of past advice and assigning a reliability score, investors can obtain reliable information.

[0036] The advice providing unit can have a function to customize the advice provided by the generating AI according to different investment styles. The advice providing unit, for example, has a function to customize the advice provided by the generating AI according to different investment styles (for example, short-term investment and long-term investment). For example, a system is built that customizes the advice provided by the generating AI according to short-term and long-term investment styles. For example, advice is provided separately for short-term buying and selling timing and long-term growth strategies. A system is developed that meets the needs of investors by customizing advice according to different investment styles. For example, advice is provided separately for short-term profit pursuit and long-term asset formation. A system is built that allows investors to select the optimal investment strategy by customizing the advice provided by the generating AI according to different investment styles. For example, advice is provided by comparing the risks and returns of short-term and long-term investments. This makes it possible to meet the needs of investors by customizing advice according to different investment styles.

[0037] The advice providing unit may have a function to enable the format of advice provided by the generation AI to be not only text but also at least one of audio and visual notes. The advice providing unit may, for example, have a function to enable the format of advice provided by the generation AI to be not only text but also various other formats such as audio and visual notes. For example, a system may be built that enables the format of advice provided by the generation AI to be not only text but also various other formats such as audio and visual notes. For example, advice may be provided using a voice assistant or visual notes. By diversifying the format of advice provided, a system may be developed that allows investors to obtain information in a format that is easier to understand. For example, advice may be provided using audio guidance or visual notes. A system may be built that diversifies the format of advice provided by the generation AI and provides it in formats such as text, audio, and visual notes. For example, the format of advice may be made selectable according to user preference. In this way, by diversifying the format of advice provided, investors may obtain information in a format that is easier to understand.

[0038] The analysis unit can be equipped with a function to automatically generate a risk assessment report of an investment target based on the advice provided by the generation AI and provide it to investors. The analysis unit, for example, is equipped with a function to automatically generate a risk assessment report of an investment target based on the advice provided by the generation AI and provide it to investors. For example, a system is built that automatically generates a risk assessment report of an investment target based on the advice provided by the generation AI. For example, a risk assessment report is analyzed to create a risk assessment report. A system is developed that automatically generates a risk assessment report of an investment target and provides it to investors. For example, a risk assessment report is created based on the advice provided by the generation AI and sent to investors. A system is built that automatically generates a risk assessment report of an investment target based on the advice provided by the generation AI and provides it to investors. For example, a risk assessment report is generated in real time and provided to investors. This allows investors to quickly assess risk by automatically generating a risk assessment report based on the advice provided by the generation AI.

[0039] The analysis unit may have a function to integrate different data sources and perform comprehensive analysis when analyzing information about investment targets. The analysis unit may have a function to integrate different data sources (e.g., news articles and social media posts) and perform comprehensive analysis when analyzing information about investment targets. For example, a system may be built that integrates different data sources such as news articles and social media posts when analyzing information about investment targets. For example, news articles and social media posts about companies may be collected and stored in a database. A system may be developed that integrates different data sources and performs comprehensive information analysis. For example, news articles and social media posts may be analyzed to evaluate risk factors and growth opportunities for the company. A system may be built that integrates different data sources such as news articles and social media posts when analyzing information about investment targets and performs comprehensive analysis. For example, information from different data sources may be integrated to perform a comprehensive risk assessment. In this way, by integrating different data sources and performing comprehensive information analysis, it is possible to more accurately evaluate the risk factors and growth opportunities of investment targets.

[0040] The analysis unit can have a function to customize the information analysis of investment targets for different regions and industries, and to identify region-specific risks and industry-specific trends. The analysis unit, for example, has a function to customize the information analysis of investment targets for different regions and industries, and to identify region-specific risks and industry-specific trends. For example, a system is built to customize the information analysis of investment targets for each region and to identify region-specific risks. For example, the economic situation and market trends for each region are analyzed. A system is developed to customize the information analysis for each industry and to identify industry-specific trends. For example, the growth rate and risk factors for each industry are analyzed. A system is built to customize the information analysis of investment targets for each region and industry, and to identify region-specific risks and industry-specific trends. For example, risk factors for each region and growth opportunities for each industry are analyzed. This allows investors to make more accurate investment decisions by identifying region-specific risks and industry-specific trends.

[0041] The analysis unit can have a function to personalize the results of information analysis according to the investor's portfolio and propose an optimal investment strategy. The analysis unit, for example, has a function to personalize the results of information analysis according to the investor's portfolio and propose an optimal investment strategy. For example, a system is constructed that personalizes the results of information analysis according to the investor's portfolio. For example, an optimal investment strategy is proposed based on the investor's assets and risk tolerance. A system is developed that customizes the results of information analysis according to the investor's portfolio and proposes an optimal investment strategy. For example, advice is provided according to the investor's goals and investment style. A system is constructed that personalizes the results of information analysis according to the investor's portfolio and proposes an optimal investment strategy. For example, investment destinations are selected based on the investor's risk profile. In this way, the results of information analysis can be personalized according to the investor's portfolio, making it possible to propose an optimal investment strategy.

[0042] The data transmission unit can be equipped with a function to include a company's non-financial data in the data sent to the generation AI. The data transmission unit, for example, has a function to include a company's non-financial data (e.g., employee satisfaction and customer satisfaction) in the data sent to the generation AI. For example, a system can be built that adds a company's non-financial data to the input data to the generation AI. For example, employee satisfaction and customer satisfaction can be included in the analysis. A system can be developed that adds non-financial data to the input data to the generation AI and performs more multifaceted analysis. For example, analyzing a company's social responsibility and environmental performance. A system can be built that performs more comprehensive analysis by including a company's non-financial data in the input data to the generation AI. For example, analyzing employee work environment and customer satisfaction. In this way, the inclusion of a company's non-financial data enables more multifaceted analysis.

[0043] The analysis unit can be equipped with a function to ensure transparency by including an explanation of the data points and algorithms that form the basis of the analysis in the output results of the generation AI. The analysis unit can be equipped with a function to ensure transparency by including an explanation of the data points and algorithms that form the basis of the analysis in the output results of the generation AI, for example. For example, a system can be built in which an explanation of the data points and algorithms that form the basis of the analysis is included in the output results of the generation AI. For example, the source of the data used in the analysis and specific numerical values ​​can be displayed. To ensure transparency of the output results, a function can be added that provides detailed explanations of the data points and algorithms used by the generation AI. For example, specific items and values ​​of the data used in the analysis can be displayed. A system can be developed in which an explanation of the data points and algorithms that form the basis of the analysis is included in the output results of the generation AI, allowing users to check the contents. For example, a detailed explanation of the data used in the analysis can be provided. This ensures transparency of the output results by including an explanation of the data points and algorithms that form the basis of the analysis.

[0044] The data transmission unit can have a function to accommodate different formats for the data sent to the generation AI. The data transmission unit, for example, has a function to accommodate different formats for the data sent to the generation AI (for example, image data or audio data). For example, a system is built that accommodates different formats for the input data of the generation AI. For example, image data and audio data are included in the analysis. In order to utilize diverse data sources, a system is developed that accommodates different formats for the input data of the generation AI. For example, data is collected using image recognition or audio analysis. By adapting the input data of the generation AI to different formats, a system is built that performs more comprehensive analysis. For example, image data and audio data are integrated with text data for analysis. This allows for adaptation to data in different formats, making it possible to utilize a wider variety of data sources.

[0045] The advice providing unit can have a function to customize the output results of the generation AI according to different user segments. The advice providing unit, for example, has a function to customize the output results of the generation AI according to different user segments (e.g., individual investors and institutional investors). For example, a system is built that customizes the output results of the generation AI according to different user segments. For example, advice is provided in different formats for individual investors and institutional investors. A system is developed that customizes the output results of the generation AI to meet user needs. For example, the content of advice is adjusted based on the investor's profile. A system is built that customizes the output results of the generation AI according to different user segments and meets user needs. For example, concise advice is provided for individual investors and detailed analysis is provided for institutional investors. This makes it possible to meet user needs by customizing the output results according to different user segments.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The Report Collection Department not only collects securities reports from corporate websites and financial institution databases, but can also collect data from corporate internal databases and ERP systems. For example, it can directly obtain financial and performance data from a corporate internal database to gather more detailed information. It can also obtain production and inventory data from ERP systems to gain a more accurate understanding of the company's operating status. Furthermore, the data collected from a corporate internal database and ERP system can be stored in the Database Storage Department and used for analysis by the Analysis Department. This allows for the use of corporate internal data to gather more detailed and accurate information and provide useful advice to investors.

[0048] The Report Collection Department not only collects securities reports from corporate websites and financial institution databases, but can also collect data from corporate internal databases and ERP systems. For example, it can directly obtain financial and performance data from a corporate internal database to gather more detailed information. It can also obtain production and inventory data from ERP systems to gain a more accurate understanding of the company's operating status. Furthermore, the data collected from a corporate internal database and ERP system can be stored in the Database Storage Department and used for analysis by the Analysis Department. This allows for the use of corporate internal data to gather more detailed and accurate information and provide useful advice to investors.

[0049] To evaluate the reliability of collected securities reports, the database storage unit can refer not only to certified data from third-party organizations, but also to a company's internal audit reports and external audit reports. For example, by collecting a company's internal audit reports and assigning a reliability score, it is possible to use more reliable data for analysis by collecting external audit reports and assigning a reliability score. Furthermore, internal audit reports and external audit reports can be stored in the database and used for analysis by the analysis unit. In this way, by utilizing internal audit reports and external audit reports, it is possible to collect more reliable data and provide useful advice to investors.

[0050] The data transmission unit can include information such as a company's sales and profit margins, the increase in off-balance-sheet liabilities, the identities of major customers, and suspected related-party transactions in the data sent to the generation AI, as well as non-financial data about the company (e.g., employee satisfaction and customer satisfaction). For example, including employee satisfaction and customer satisfaction in the analysis can evaluate the overall health of the company. Also, including corporate social responsibility and environmental performance in the analysis can evaluate the company's sustainability. Furthermore, adding non-financial data to the input data of the generation AI enables a more comprehensive analysis. This allows for a more multifaceted analysis and provides useful advice to investors.

[0051] The analysis unit can not only analyze a company's sales and profit margins, the increase in off-balance-sheet liabilities, the identity of major customers, and suspected related-party transactions, but also analyze a company's non-financial data (e.g., employee satisfaction and customer satisfaction). For example, analyzing employee satisfaction and customer satisfaction can assess a company's overall health. Also, analyzing corporate social responsibility and environmental performance can assess a company's sustainability. Furthermore, including non-financial data in the analysis allows for a more comprehensive analysis. This allows for a more multifaceted analysis and provides useful advice to investors.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The Report Collection Department collects securities reports from each listed company. For example, they download securities reports from company websites or financial institution databases. The Report Collection Department can also collect related information from social media and news sites to use as supplemental information for the reports. Step 2: The database storage unit stores the securities reports collected by the report collection unit in a database. For example, the collected securities reports are stored in a digital format for later analysis. Step 3: The data transmission unit transmits the data stored by the database storage unit to the generation AI. For example, the data may be a company's financial data or performance data, which will be used as the basis for analysis. Step 4: The analysis unit analyzes the data sent by the generation AI. For example, the generation AI analyzes the company's sales and profit margins, the increase in off-balance-sheet liabilities, the identities of major customers, and suspected related-party transactions. Step 5: The advice provider provides advice to investors based on the data analyzed by the analysis unit. For example, it may point out high-risk points to investors based on the results of the analysis by the generation AI.

[0054] (Example 2) The automated analysis system according to an embodiment of the present invention collects securities reports from listed companies, analyzes the data using a generation AI, and provides useful advice to investors. This allows the automated analysis system to analyze financial and performance data from companies and provide useful advice to investors.

[0055] The automated analysis system according to the embodiment includes a report collection unit, a database storage unit, a data transmission unit, an analysis unit, and an advice provision unit. The report collection unit collects securities reports from listed companies. For example, it downloads securities reports from company websites or financial institution databases. The report collection unit can also collect related information from social media and news sites and use it as supplementary information for the reports. The database storage unit stores the securities reports collected by the report collection unit in a database. For example, it stores the collected securities reports in digital format for later analysis. The data transmission unit transmits the data stored by the database storage unit to the generation AI. For example, it transmits the company's financial data and performance data to the generation AI to use as the basis for analysis. The analysis unit analyzes the data transmitted by the generation AI. For example, the generation AI analyzes the company's sales and profit margins, the increase in off-balance sheet liabilities, the identities of major customers, and suspected related-party transactions. The advice provision unit provides advice to investors based on the data analyzed by the analysis unit. For example, the generation AI can point out high-risk points to investors based on the analysis results. This allows the automated analysis system according to the embodiment to analyze a company's financial data and performance data and provide useful advice to investors.

[0056] The report collection unit can be equipped with a function to automatically collect securities reports from corporate websites or financial institution databases. The report collection unit, for example, builds a system that automatically collects securities reports from corporate websites and financial institution databases. In addition, information related to the company is collected from social media and news sites and stored in a database as supplemental information for the report. Securities reports are downloaded from the company's official website or financial institution database using an automatic collection system. In addition, the latest information about the company is collected from social media and news sites to supplement the content of the report. When collecting securities reports from corporate websites and financial institution databases, a function to collect related information from social media and news sites is added. This allows for a more detailed understanding of the content of the report. This improves the efficiency of data collection by automatically collecting securities reports from corporate websites and financial institution databases.

[0057] The database storage unit may have a function of referring to authentication data from a third party and assigning a reliability score to evaluate the reliability of securities reports. The database storage unit, for example, builds a system that refers to authentication data from a third party to evaluate the reliability of collected securities reports. For example, it assigns a reliability score based on the authentication data and stores it in a database. To evaluate the reliability of securities reports, it automatically collects authentication data from a third party and assigns a reliability score to the reports. This allows highly reliable reports to be analyzed preferentially. A system is developed that refers to authentication data from a third party for collected securities reports and assigns a reliability score to them. For example, it evaluates the reliability of the reports based on the authentication data and stores it in a database. This allows highly reliable data to be analyzed preferentially by evaluating the reliability of collected securities reports.

[0058] The data transmission unit can have a function to include at least one of the following information in the data sent to the generation AI: a company's sales or profit margin, an increase in off-balance sheet liabilities, the identities of major customers, and suspected related-party transactions. The data transmission unit, for example, has a function to include information such as a company's sales or profit margin, an increase in off-balance sheet liabilities, the identities of major customers, and suspected related-party transactions in the data sent to the generation AI. For example, a sentiment estimation function is used to analyze market sentiment toward the contents of a report and evaluate the importance of the collected data. A system is constructed that uses the sentiment estimation function to analyze market sentiment toward the contents of collected securities reports. For example, positive and negative sentiment toward the contents of the report is quantified and the importance of the data is evaluated. A system is developed that analyzes market sentiment toward the contents of securities reports in real time and evaluates the importance of collected data based on the results. For example, reports with high sentiment scores are prioritized for analysis. A system is constructed that uses the sentiment estimation function to analyze market sentiment toward the contents of collected securities reports and dynamically update the importance of the data. For example, reports are prioritized based on the sentiment score. This enables more accurate analysis by including important information in the data sent to the generation AI.

[0059] The analysis unit may be equipped with a function for analyzing at least one of a company's sales or profit margin, the increase in off-balance sheet liabilities, the identity of major customers, and suspected related-party transactions, and pointing out high-risk areas to investors. The analysis unit may be equipped with a function for analyzing, for example, a company's sales or profit margin, the increase in off-balance sheet liabilities, the identity of major customers, and suspected related-party transactions, and pointing out high-risk areas to investors. For example, the collection of securities reports may be expanded to include not only listed companies but also unlisted companies and startups. A system may be built that expands the collection of securities reports to include not only listed companies but also unlisted companies and startups. For example, financial data and performance data of unlisted companies may be collected and stored in a database. A function may be added to collect securities reports of unlisted companies and startups in addition to listed companies. This allows for the analysis of a wider range of corporate data. A system may be developed that expands the collection of securities reports to include unlisted companies and startups. For example, reports may be collected from the websites and databases of unlisted companies. This allows us to analyze a company's risk factors and point out high-risk points to investors, thereby improving the accuracy of investment decisions.

[0060] The advice providing unit can have a function to provide investors with specific action plans based on the analysis results of the generation AI. The advice providing unit, for example, has a function to provide investors with specific action plans (for example, buying and selling timing and risk management measures) based on the analysis results of the generation AI. For example, a system is built that includes specific action plans in the advice provided by the generation AI. For example, the timing of buying and selling and risk management measures are specifically presented. By including specific action plans when providing advice, a system is developed that provides information in a form that is easy for investors to implement. For example, specific buying and selling instructions and risk avoidance measures are presented. By including specific action plans in the advice provided by the generation AI, a system is built that enables investors to act quickly. For example, the timing of buying and selling and risk management measures are explained in detail. In this way, by providing specific action plans, investors are able to act quickly.

[0061] The advice providing unit may have a function to evaluate the success rate of past advice and assign a reliability score when providing advice by the generation AI. The advice providing unit, for example, has a function to evaluate the success rate of past advice and assign a reliability score when providing advice by the generation AI. For example, a system is constructed that evaluates the success rate of advice provided by the generation AI and assigns a reliability score. For example, reliability is evaluated based on the track record of past advice. A system is developed that allows investors to obtain reliable information when providing advice by evaluating the past success rate and assigning a reliability score. For example, advice with a high success rate is provided preferentially. A system is constructed that allows investors to accept advice with confidence by evaluating the success rate of advice provided by the generation AI and assigning a reliability score. For example, the priority of advice is determined based on the reliability score. In this way, by evaluating the success rate of past advice and assigning a reliability score, investors can obtain reliable information.

[0062] The advice providing unit can have a function to customize the advice provided by the generating AI according to different investment styles. The advice providing unit, for example, has a function to customize the advice provided by the generating AI according to different investment styles (for example, short-term investment and long-term investment). For example, a system is built that customizes the advice provided by the generating AI according to short-term and long-term investment styles. For example, advice is provided separately for short-term buying and selling timing and long-term growth strategies. A system is developed that meets the needs of investors by customizing advice according to different investment styles. For example, advice is provided separately for short-term profit pursuit and long-term asset formation. A system is built that allows investors to select the optimal investment strategy by customizing the advice provided by the generating AI according to different investment styles. For example, advice is provided by comparing the risks and returns of short-term and long-term investments. This makes it possible to meet the needs of investors by customizing advice according to different investment styles.

[0063] The advice providing unit may have a function to enable the format of advice provided by the generation AI to be not only text but also at least one of audio and visual notes. The advice providing unit may, for example, have a function to enable the format of advice provided by the generation AI to be not only text but also various other formats such as audio and visual notes. For example, a system may be built that enables the format of advice provided by the generation AI to be not only text but also various other formats such as audio and visual notes. For example, advice may be provided using a voice assistant or visual notes. By diversifying the format of advice provided, a system may be developed that allows investors to obtain information in a format that is easier to understand. For example, advice may be provided using audio guidance or visual notes. A system may be built that diversifies the format of advice provided by the generation AI and provides it in formats such as text, audio, and visual notes. For example, the format of advice may be made selectable according to user preference. In this way, by diversifying the format of advice provided, investors may obtain information in a format that is easier to understand.

[0064] The advice providing unit can be equipped with a function that uses an emotion estimation function to monitor investors' emotional reactions to advice in real time and optimize the timing of providing the advice. The advice providing unit, for example, uses the emotion estimation function to monitor investors' emotional reactions to advice in real time and optimize the timing of providing the advice. For example, a system is built that uses the emotion estimation function to monitor investors' emotional reactions to advice provided by the generation AI in real time. For example, the timing of providing the advice is optimized based on the emotion score. A system is developed that analyzes investors' emotional reactions in real time and optimizes the timing of providing the advice based on the results. For example, advice is provided at times when positive emotions are strong. A system is built that uses the emotion estimation function to monitor investors' emotional reactions to advice provided by the generation AI in real time and optimizes the timing of providing the advice. For example, the priority of advice is determined based on the emotion score. In this way, more effective advice can be provided by monitoring investors' emotional reactions in real time and optimizing the timing of providing the advice.

[0065] The analysis unit can be equipped with a function to automatically generate a risk assessment report of an investment target based on the advice provided by the generation AI and provide it to investors. The analysis unit, for example, is equipped with a function to automatically generate a risk assessment report of an investment target based on the advice provided by the generation AI and provide it to investors. For example, a system is built that automatically generates a risk assessment report of an investment target based on the advice provided by the generation AI. For example, a risk assessment report is analyzed to create a risk assessment report. A system is developed that automatically generates a risk assessment report of an investment target and provides it to investors. For example, a risk assessment report is created based on the advice provided by the generation AI and sent to investors. A system is built that automatically generates a risk assessment report of an investment target based on the advice provided by the generation AI and provides it to investors. For example, a risk assessment report is generated in real time and provided to investors. This allows investors to quickly assess risk by automatically generating a risk assessment report based on the advice provided by the generation AI.

[0066] The analysis unit may have a function to integrate different data sources and perform comprehensive analysis when analyzing information about investment targets. The analysis unit may have a function to integrate different data sources (e.g., news articles and social media posts) and perform comprehensive analysis when analyzing information about investment targets. For example, a system may be built that integrates different data sources such as news articles and social media posts when analyzing information about investment targets. For example, news articles and social media posts about companies may be collected and stored in a database. A system may be developed that integrates different data sources and performs comprehensive information analysis. For example, news articles and social media posts may be analyzed to evaluate risk factors and growth opportunities for the company. A system may be built that integrates different data sources such as news articles and social media posts when analyzing information about investment targets and performs comprehensive analysis. For example, information from different data sources may be integrated to perform a comprehensive risk assessment. In this way, by integrating different data sources and performing comprehensive information analysis, it is possible to more accurately evaluate the risk factors and growth opportunities of investment targets.

[0067] The analysis unit can be provided with a function that uses an emotion estimation function to analyze market sentiment toward information about an investment target and reflect that in risk assessment. The analysis unit, for example, is provided with a function that uses the emotion estimation function to analyze market sentiment toward information about an investment target and reflect that in risk assessment. For example, a system that uses the emotion estimation function to analyze market sentiment toward information about an investment target is constructed. For example, the emotion estimation function is used to analyze sentiment in the market toward information about an investment target. For example, the emotion scores of news articles and social media posts about a company are analyzed and reflected in risk assessment. A system that analyzes market sentiment in real time and performs risk assessment of investment targets based on the results is developed. For example, companies with strong positive sentiment are prioritized in evaluation. A system that uses the emotion estimation function to analyze market sentiment toward information about an investment target and reflect that in risk assessment is constructed. For example, the risk factors of a company are evaluated based on the emotion score. In this way, by analyzing market sentiment and reflecting it in risk assessment, more accurate risk assessment is possible.

[0068] The analysis unit can have a function to customize the information analysis of investment targets for different regions and industries, and to identify region-specific risks and industry-specific trends. The analysis unit, for example, has a function to customize the information analysis of investment targets for different regions and industries, and to identify region-specific risks and industry-specific trends. For example, a system is built to customize the information analysis of investment targets for each region and to identify region-specific risks. For example, the economic situation and market trends for each region are analyzed. A system is developed to customize the information analysis for each industry and to identify industry-specific trends. For example, the growth rate and risk factors for each industry are analyzed. A system is built to customize the information analysis of investment targets for each region and industry, and to identify region-specific risks and industry-specific trends. For example, risk factors for each region and growth opportunities for each industry are analyzed. This allows investors to make more accurate investment decisions by identifying region-specific risks and industry-specific trends.

[0069] The analysis unit can have a function to personalize the results of information analysis according to the investor's portfolio and propose an optimal investment strategy. The analysis unit, for example, has a function to personalize the results of information analysis according to the investor's portfolio and propose an optimal investment strategy. For example, a system is constructed that personalizes the results of information analysis according to the investor's portfolio. For example, an optimal investment strategy is proposed based on the investor's assets and risk tolerance. A system is developed that customizes the results of information analysis according to the investor's portfolio and proposes an optimal investment strategy. For example, advice is provided according to the investor's goals and investment style. A system is constructed that personalizes the results of information analysis according to the investor's portfolio and proposes an optimal investment strategy. For example, investment destinations are selected based on the investor's risk profile. In this way, the results of information analysis can be personalized according to the investor's portfolio, making it possible to propose an optimal investment strategy.

[0070] The data transmission unit can be equipped with a function to include a company's non-financial data in the data sent to the generation AI. The data transmission unit, for example, has a function to include a company's non-financial data (e.g., employee satisfaction and customer satisfaction) in the data sent to the generation AI. For example, a system can be built that adds a company's non-financial data to the input data to the generation AI. For example, employee satisfaction and customer satisfaction can be included in the analysis. A system can be developed that adds non-financial data to the input data to the generation AI and performs more multifaceted analysis. For example, analyzing a company's social responsibility and environmental performance. A system can be built that performs more comprehensive analysis by including a company's non-financial data in the input data to the generation AI. For example, analyzing employee work environment and customer satisfaction. In this way, the inclusion of a company's non-financial data enables more multifaceted analysis.

[0071] The analysis unit can be equipped with a function to ensure transparency by including an explanation of the data points and algorithms that form the basis of the analysis in the output results of the generation AI. The analysis unit can be equipped with a function to ensure transparency by including an explanation of the data points and algorithms that form the basis of the analysis in the output results of the generation AI, for example. For example, a system can be built in which an explanation of the data points and algorithms that form the basis of the analysis is included in the output results of the generation AI. For example, the source of the data used in the analysis and specific numerical values ​​can be displayed. To ensure transparency of the output results, a function can be added that provides detailed explanations of the data points and algorithms used by the generation AI. For example, specific items and values ​​of the data used in the analysis can be displayed. A system can be developed in which an explanation of the data points and algorithms that form the basis of the analysis is included in the output results of the generation AI, allowing users to check the contents. For example, a detailed explanation of the data used in the analysis can be provided. This ensures transparency of the output results by including an explanation of the data points and algorithms that form the basis of the analysis.

[0072] The analysis unit can be provided with a function that uses an emotion estimation function to analyze market sentiment toward the output results and evaluate the reliability of the output results. The analysis unit, for example, is provided with a function that uses the emotion estimation function to analyze market sentiment toward the output results and evaluate the reliability of the output results. For example, a system that uses the emotion estimation function to analyze market sentiment toward the output results of the generation AI is constructed. For example, positive and negative sentiment toward the output results is quantified and reliability is evaluated. A system that analyzes market sentiment toward the output results in real time and evaluates the reliability of the output results based on the results is developed. For example, output results with a high emotion score are preferentially displayed. A system that uses the emotion estimation function to analyze market sentiment toward the output results of the generation AI and evaluate their reliability is constructed. For example, the priority of output results is determined based on the emotion score. In this way, more reliable information can be provided by analyzing market sentiment and evaluating the reliability of the output results.

[0073] The data transmission unit can have a function to accommodate different formats for the data sent to the generation AI. The data transmission unit, for example, has a function to accommodate different formats for the data sent to the generation AI (for example, image data or audio data). For example, a system is built that accommodates different formats for the input data of the generation AI. For example, image data and audio data are included in the analysis. In order to utilize diverse data sources, a system is developed that accommodates different formats for the input data of the generation AI. For example, data is collected using image recognition or audio analysis. By adapting the input data of the generation AI to different formats, a system is built that performs more comprehensive analysis. For example, image data and audio data are integrated with text data for analysis. This allows for adaptation to data in different formats, making it possible to utilize a wider variety of data sources.

[0074] The advice providing unit can have a function to customize the output results of the generation AI according to different user segments. The advice providing unit, for example, has a function to customize the output results of the generation AI according to different user segments (e.g., individual investors and institutional investors). For example, a system is built that customizes the output results of the generation AI according to different user segments. For example, advice is provided in different formats for individual investors and institutional investors. A system is developed that customizes the output results of the generation AI to meet user needs. For example, the content of advice is adjusted based on the investor's profile. A system is built that customizes the output results of the generation AI according to different user segments and meets user needs. For example, concise advice is provided for individual investors and detailed analysis is provided for institutional investors. This makes it possible to meet user needs by customizing the output results according to different user segments.

[0075] The analysis unit can be equipped with a function that uses an emotion estimation function to monitor investors' emotional reactions to the output results in real time and optimize the display order of the output results. The analysis unit, for example, uses the emotion estimation function to monitor investors' emotional reactions to the output results in real time and optimize the display order of the output results. For example, a system is constructed that uses the emotion estimation function to monitor investors' emotional reactions to the output results of the generation AI in real time. For example, the display order of the output results is optimized based on the emotion score. A system is developed that analyzes investors' emotional reactions in real time and optimizes the display order of the output results based on the results. For example, output results with stronger positive emotions are preferentially displayed. A system is constructed that uses the emotion estimation function to monitor investors' emotional reactions to the output results of the generation AI in real time and optimizes the display order of the output results. For example, the priority of the output results is dynamically changed based on the emotion score. This makes it possible to provide more effective information by monitoring investors' emotional reactions in real time and optimizing the display order of the output results.

[0076] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0077] The Report Collection Department not only collects securities reports from corporate websites and financial institution databases, but can also collect data from corporate internal databases and ERP systems. For example, it can directly obtain financial and performance data from a corporate internal database to gather more detailed information. It can also obtain production and inventory data from ERP systems to gain a more accurate understanding of the company's operating status. Furthermore, the data collected from a corporate internal database and ERP system can be stored in the Database Storage Department and used for analysis by the Analysis Department. This allows for the use of corporate internal data to gather more detailed and accurate information and provide useful advice to investors.

[0078] The Report Collection Department not only collects securities reports from corporate websites and financial institution databases, but can also collect data from corporate internal databases and ERP systems. For example, it can directly obtain financial and performance data from a corporate internal database to gather more detailed information. It can also obtain production and inventory data from ERP systems to gain a more accurate understanding of the company's operating status. Furthermore, the data collected from a corporate internal database and ERP system can be stored in the Database Storage Department and used for analysis by the Analysis Department. This allows for the use of corporate internal data to gather more detailed and accurate information and provide useful advice to investors.

[0079] To evaluate the reliability of collected securities reports, the database storage unit can refer not only to certified data from third-party organizations, but also to a company's internal audit reports and external audit reports. For example, by collecting a company's internal audit reports and assigning a reliability score, it is possible to use more reliable data for analysis by collecting external audit reports and assigning a reliability score. Furthermore, internal audit reports and external audit reports can be stored in the database and used for analysis by the analysis unit. In this way, by utilizing internal audit reports and external audit reports, it is possible to collect more reliable data and provide useful advice to investors.

[0080] The data transmission unit can include information such as a company's sales and profit margins, the increase in off-balance-sheet liabilities, the identities of major customers, and suspected related-party transactions in the data sent to the generation AI, as well as non-financial data about the company (e.g., employee satisfaction and customer satisfaction). For example, including employee satisfaction and customer satisfaction in the analysis can evaluate the overall health of the company. Also, including corporate social responsibility and environmental performance in the analysis can evaluate the company's sustainability. Furthermore, adding non-financial data to the input data of the generation AI enables a more comprehensive analysis. This allows for a more multifaceted analysis and provides useful advice to investors.

[0081] The analysis unit can not only analyze a company's sales and profit margins, the increase in off-balance-sheet liabilities, the identity of major customers, and suspected related-party transactions, but also analyze a company's non-financial data (e.g., employee satisfaction and customer satisfaction). For example, analyzing employee satisfaction and customer satisfaction can assess a company's overall health. Also, analyzing corporate social responsibility and environmental performance can assess a company's sustainability. Furthermore, including non-financial data in the analysis allows for a more comprehensive analysis. This allows for a more multifaceted analysis and provides useful advice to investors.

[0082] The advice providing unit not only provides specific action plans (e.g., buying and selling timing and risk management measures) to investors based on the analysis results of the generative AI, but also monitors the investor's emotional reactions in real time and adjusts the content of the advice. For example, it uses the emotion estimation function to monitor the investor's emotional reactions in real time and provides advice to take risks when emotions are strong positive, and advice to avoid risks when emotions are strong negative. It can also use the emotion estimation function to optimize the timing of providing advice based on the investor's emotional reactions. This makes it possible to provide more effective advice by monitoring the investor's emotional reactions in real time and optimizing the content and timing of advice.

[0083] When providing advice using the generation AI, the advice providing unit not only evaluates the success rate of past advice and assigns a reliability score, but also monitors the investor's emotional response in real time and adjusts the content of the advice. For example, the emotion estimation function can be used to monitor the investor's emotional response in real time, and if the investor's emotional response is strong, advice to take risks is provided, and if the investor's emotional response is strong, advice to avoid risks is provided. The emotion estimation function can also be used to optimize the timing of providing advice based on the investor's emotional response. This makes it possible to provide more effective advice by monitoring the investor's emotional response in real time and optimizing the content and timing of advice.

[0084] When providing advice using the generation AI, the advice providing unit can monitor the investor's emotional reactions in real time and adjust the content of the advice. For example, the emotion estimation function can be used to monitor the investor's emotional reactions in real time, and if the investor's emotions are strong positive, advice to take risks is provided, and if the investor's emotions are strong negative, advice to avoid risks is provided. The emotion estimation function can also be used to optimize the timing of providing advice based on the investor's emotional reactions. This makes it possible to provide more effective advice by monitoring the investor's emotional reactions in real time and optimizing the content and timing of advice.

[0085] When providing advice using the generation AI, the advice providing unit can monitor the investor's emotional reactions in real time and adjust the content of the advice. For example, the emotion estimation function can be used to monitor the investor's emotional reactions in real time, and if the investor's emotions are strong positive, advice to take risks is provided, and if the investor's emotions are strong negative, advice to avoid risks is provided. The emotion estimation function can also be used to optimize the timing of providing advice based on the investor's emotional reactions. This makes it possible to provide more effective advice by monitoring the investor's emotional reactions in real time and optimizing the content and timing of advice.

[0086] When providing advice using the generation AI, the advice providing unit can monitor the investor's emotional reactions in real time and adjust the content of the advice. For example, the emotion estimation function can be used to monitor the investor's emotional reactions in real time, and if the investor's emotions are strong positive, advice to take risks is provided, and if the investor's emotions are strong negative, advice to avoid risks is provided. The emotion estimation function can also be used to optimize the timing of providing advice based on the investor's emotional reactions. This makes it possible to provide more effective advice by monitoring the investor's emotional reactions in real time and optimizing the content and timing of advice.

[0087] The processing flow of the second embodiment will be briefly explained below.

[0088] Step 1: The Report Collection Department collects securities reports from each listed company. For example, they download securities reports from company websites or financial institution databases. The Report Collection Department can also collect related information from social media and news sites to use as supplemental information for the reports. Step 2: The database storage unit stores the securities reports collected by the report collection unit in a database. For example, the collected securities reports are stored in a digital format for later analysis. Step 3: The data transmission unit transmits the data stored by the database storage unit to the generation AI. For example, the data may be a company's financial data or performance data, which will be used as the basis for analysis. Step 4: The analysis unit analyzes the data sent by the generation AI. For example, the generation AI analyzes the company's sales and profit margins, the increase in off-balance-sheet liabilities, the identities of major customers, and suspected related-party transactions. Step 5: The advice provider provides advice to investors based on the data analyzed by the analysis unit. For example, it may point out high-risk points to investors based on the results of the analysis by the generation AI.

[0089] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0090] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0091] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0092] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0093] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0094] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0095] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0096] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0097] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0098] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0099] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0100] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0101] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0102] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0103] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0104] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0105] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0106] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0107] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0108] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0109] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0121] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0123] 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.

[0124] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0125] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0126] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0127] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0128] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0129] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0130] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0139] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0140] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0141] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0142] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0143] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0144] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0145] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0146] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0147] 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.

[0148] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0149] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0150] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0151] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0152] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0153] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0154] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0155] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0156] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The Report Collection Department collects the securities reports of listed companies, a database storage unit that stores the securities reports collected by the report collection unit in a database; a data transmission unit that transmits the data stored by the database storage unit to the generation AI; an analysis unit that analyzes the data transmitted by the generation AI; an advice providing unit that provides advice to investors based on the data analyzed by the analysis unit; A system characterized by:

2. The report collection unit It has the ability to automatically collect securities reports from company websites or financial institution databases.

2. The system of claim 1.

3. The database storage unit To evaluate the reliability of the securities report, the system has the function of referencing certified data from a third-party organization and assigning a reliability score.

2. The system of claim 1.

4. The data transmission unit The data sent to the generation AI has the function of including at least one of the following information: company sales or profit margin, increase in off-balance sheet liabilities, identity of major customers, and suspected related party transactions.

2. The system of claim 1.

5. The analysis unit The system has the function of analyzing at least one of the following: a company's sales or profit margin, the increase in off-balance-sheet debt, the identity of major customers, and suspected related-party transactions, and pointing out high-risk points to the investor.

2. The system of claim 1.

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