System

Abstract

An object of a system according to an embodiment is to automate collection and analysis of financial data and improve work efficiency.SOLUTION: A system includes a financial data collection part and a financial data analysis part. A financial data collection part automatically collects financial data of a company. The financial data analysis unit performs analysis based on the financial data collected by the financial data collection 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, collecting and analyzing financial data requires a great deal of time and effort, which can lead to discrepancies in the analysis results.

[0005] The system according to the embodiment aims to automate the collection and analysis of financial data and improve business efficiency. [Means for solving the problem]

[0006] The system according to the embodiment includes a financial data collection unit and a financial data analysis unit. The financial data collection unit automatically collects financial data of a company. The financial data analysis unit performs analysis based on the financial data collected by the financial data collection unit. [Effects of the Invention]

[0007] The system according to the embodiment can automate the collection and analysis of financial data and improve business efficiency. [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 financial analysis AI system according to the embodiment of the present invention is a system that automatically collects corporate financial data and analyzes it using a generation AI. This allows the financial analysis AI system to automate the collection and analysis of financial data, thereby improving business efficiency.

[0029] A financial analysis AI system according to an embodiment includes a financial data collection unit and a financial data analysis unit. The financial data collection unit automatically collects financial data from a company. For example, the financial data collection unit uses an API to obtain financial statements and transaction data from the company. The financial data collection unit can also collect financial data from websites using scraping technology. The financial data collection unit can also automatically extract data from a company's internal systems. For example, the financial data collection unit uses an API to obtain financial data from a company's accounting system. Scraping technology analyzes the HTML structure of a website to extract the necessary data. Data extraction from a company's internal systems can be performed using database queries. The financial data analysis unit performs analysis based on the collected financial data. For example, the financial data analysis unit performs a profitability analysis to evaluate a company's profit margin. The financial data analysis unit can also perform a liquidity analysis to evaluate a company's short-term solvency. The financial data analysis unit can also perform an efficiency analysis to evaluate the efficiency of a company's asset utilization. For example, the profitability analysis calculates the return on sales and net profit margin. The liquidity analysis calculates the current ratio and quick ratio. Efficiency analysis calculates total asset turnover and inventory turnover. As a result, the financial analysis AI system according to the embodiment automates the collection and analysis of financial data, improving business efficiency. For example, managers can make quick and accurate decisions based on reports generated by the financial analysis AI system. Furthermore, finance department personnel are freed from manual data collection and analysis tasks, allowing them to focus on more strategic business operations.

[0030] The financial data collection unit can collect financial statements and transaction data from the past few years in bulk and store it in a database. For example, the financial data collection unit collects financial statements and transaction data from the past few years in bulk and stores it in a database. For example, the financial data collection unit uses an API to obtain financial statements from the past five years and stores it in a database. The financial data collection unit can also use scraping technology to collect past transaction data and store it in a database. The financial data collection unit can also extract past data from a company's internal system and store it in a database. For example, an API is used to obtain financial data from a company's accounting system for the past five years. Scraping technology analyzes the HTML structure of a website and extracts past transaction data. Data extraction from a company's internal system can be performed using a database query. This allows past financial data to be efficiently collected and stored in a database, eliminating the need for manual data collection.

[0031] The financial data analysis unit can calculate various financial indicators, such as profitability analysis, liquidity analysis, and efficiency analysis, to evaluate the financial situation of a company. The financial data analysis unit, for example, performs profitability analysis to evaluate the profit margin of a company. For example, the financial data analysis unit calculates the profit margin on sales and the net profit margin. The financial data analysis unit also performs liquidity analysis to evaluate the short-term solvency of a company. For example, the financial data analysis unit calculates the current ratio and the quick ratio. The financial data analysis unit also performs efficiency analysis to evaluate the efficiency of asset utilization of a company. For example, the financial data analysis unit calculates the total asset turnover rate and the inventory turnover rate. This enables detailed financial analysis by calculating various financial indicators and evaluating the financial situation of a company.

[0032] The financial data analysis department can identify profitable departments and departments that need improvement, and create a report that includes proposals based on the identified departments. The financial data analysis department, for example, identifies profitable departments and creates a report that includes proposals based on the identified departments. For example, the financial data analysis department calculates profit margins by department and identifies profitable departments. The financial data analysis department also identifies departments that need improvement and creates a report that includes proposals based on the identified departments. For example, the financial data analysis department analyzes costs by department and identifies departments that need improvement. The financial data analysis department also identifies profitable departments and departments that need improvement and creates a report that includes proposals based on the identified departments. For example, the financial data analysis department analyzes sales and profit margins by department and identifies profitable departments and departments that need improvement. This allows management to make quick and accurate decisions by identifying profitable departments and departments that need improvement and creating a report that includes proposals based on the identified departments.

[0033] The financial data analysis unit can graph trends in sales and profit margins over the past five years and make future predictions. For example, the financial data analysis unit graphs trends in sales and profit margins over the past five years and makes future predictions. For example, the financial data analysis unit displays trends in sales over the past five years in a line graph and predicts future sales. The financial data analysis unit also displays trends in profit margins over the past five years in a bar graph and predicts future profit margins. The financial data analysis unit also graphs trends in sales and profit margins over the past five years and makes future predictions. For example, the financial data analysis unit performs a regression analysis based on sales and profit margin data over the past five years to predict future sales and profit margins. In this way, graphing trends in past sales and profit margins and making future predictions makes it easier for managers to grasp the future financial situation.

[0034] The financial data analysis unit can detect a sudden decrease in sales or an increase in costs and issue an alert. For example, the financial data analysis unit detects a sudden decrease in sales and issues an alert. For example, the financial data analysis unit monitors sales data in real time and issues an alert when the sales data falls below a certain threshold. The financial data analysis unit also detects a sudden increase in costs and issues an alert. For example, the financial data analysis unit monitors cost data in real time and issues an alert when the cost data exceeds a certain threshold. The financial data analysis unit also detects a sudden decrease in sales or an increase in costs and issues an alert. For example, the financial data analysis unit monitors sales data and cost data in real time and issues an alert when an abnormal value is detected. In this way, by detecting a sudden decrease in sales or an increase in costs and issuing an alert, management can respond quickly.

[0035] The financial data collection unit can use the generation AI to evaluate the reliability of data and automatically exclude unreliable data. The financial data collection unit, for example, uses the generation AI to evaluate the reliability of each data point. For example, the generation AI checks the origin and consistency of the data and automatically excludes unreliable data. The financial data collection unit also evaluates the reliability of financial data collected using the generation AI in real time and filters out unreliable data. For example, the generation AI detects and excludes outliers and inconsistent data. The financial data collection unit also uses the generation AI to score the reliability of data and automatically excludes data below a certain score. For example, the generation AI calculates a score based on the origin and past reliability history of the data. This evaluates the reliability of the data and automatically excludes unreliable data, thereby improving the accuracy of the analysis results.

[0036] The financial data collection unit updates the collected data in real time, allowing it to constantly reflect the latest financial situation. For example, the financial data collection unit automatically updates the collected financial data each time new data is added to the database. The financial data collection unit also analyzes the collected financial data in real time using a generation AI to reflect the latest financial situation. For example, the generation AI periodically crawls the data to obtain the latest information. The financial data collection unit also develops an API for updating financial data in real time, instantly reflecting data from external systems. For example, it obtains transaction data and market data in real time and reflects it in the database. This allows the collected data to be updated in real time and constantly reflect the latest financial situation, allowing management to make decisions based on the latest information.

[0037] The financial data collection department can expand the scope of financial data collection to include other departments within the company, enabling comprehensive corporate analysis. For example, the financial data collection department expands the scope of financial data collection to include other departments within the company and performs comprehensive corporate analysis. For example, the financial data collection department collects marketing data and human resources data and integrates it with financial data. The financial data collection department also uses generative AI to collect data from different departments within the company and performs comprehensive corporate analysis. For example, it analyzes the effectiveness of marketing campaigns and the impact of personnel changes in relation to financial data. The financial data collection department also expands the scope of financial data collection and develops a system for integrated analysis of data from the entire company. For example, it collects data from each department in real time and evaluates overall corporate performance. This allows the scope of financial data collection to be expanded to include other departments within the company and enables comprehensive corporate analysis, thereby enabling the evaluation of overall corporate performance.

[0038] The financial data collection unit can collect financial data from different industries and perform comparative analysis between industries. For example, the financial data collection unit collects financial data from different industries and performs comparative analysis between industries. For example, the financial data collection unit compares financial data from the manufacturing industry and the service industry and analyzes industry-specific trends. The financial data collection unit also uses generative AI to collect financial data from different industries and perform comparative analysis between industries. For example, it compares the profitability and efficiency of each industry and sets benchmarks. The financial data collection unit also collects financial data from different industries and builds a database for comparative analysis between industries. For example, it collects financial indicators for each industry in a unified format and performs comparative analysis. This makes it possible to understand industry-specific trends and benchmarks by collecting financial data from different industries and performing comparative analysis between industries.

[0039] The financial data analysis department can use generative AI to perform risk assessments based on the analysis results and identify high-risk areas. The financial data analysis department, for example, analyzes financial data collected using generative AI and builds a system for risk assessment. For example, it identifies areas of low profitability and liquidity and clearly indicates high-risk areas. The financial data analysis department also performs risk assessments based on the results of financial data analysis and identifies high-risk areas. For example, it detects outliers and trend changes and issues risk alerts. The financial data analysis department also develops a system that uses generative AI to perform risk assessments based on the results of financial data analysis and identifies high-risk areas. For example, it calculates a risk score and visualizes high-risk areas. This allows management to effectively manage risk by performing risk assessments based on the analysis results and identifying high-risk areas.

[0040] The financial data analysis department can automatically detect abnormal values ​​and outliers in financial data analysis and reflect them in the analysis results. For example, the financial data analysis department uses generative AI to build a system that automatically detects abnormal values ​​and outliers in financial data analysis. For example, it analyzes the consistency and patterns of data and identifies outliers. The financial data analysis department also automatically detects abnormal values ​​and outliers in financial data analysis and reflects them in the analysis results. For example, it excludes or corrects outliers. The financial data analysis department also develops a system that automatically detects abnormal values ​​and outliers in financial data analysis and reflects them in the analysis results using generative AI. For example, it introduces an algorithm to minimize the impact of outliers. This makes it possible to automatically detect abnormal values ​​and outliers and reflect them in the analysis results, thereby improving the accuracy of the analysis results.

[0041] The financial data analysis department can compare the results of financial data analysis with data from other companies and perform benchmark analysis. For example, the financial data analysis department builds a system that compares the results of collected financial data analysis with data from other companies and performs benchmark analysis. For example, it compares the results with financial indicators from competitors to evaluate the company's performance. The financial data analysis department also uses generative AI to compare the results of financial data analysis with data from other companies and performs benchmark analysis. For example, it compares the results with industry averages or data from top companies to identify areas for improvement. The financial data analysis department also develops a system that compares the results of financial data analysis with data from other companies and performs benchmark analysis. For example, it collects financial data from competitors and performs comparative analysis. This allows the company to compare the results of financial data analysis with data from other companies and perform benchmark analysis to evaluate the company's performance and identify areas for improvement.

[0042] The financial data analysis department can predict future financial conditions based on the analysis results and perform scenario analysis. For example, the financial data analysis department uses generative AI to build a system that predicts future financial conditions based on the analysis results of financial data collected. For example, it makes future predictions of sales and profit margins. The financial data analysis department also predicts future financial conditions based on the analysis results of financial data and performs scenario analysis. For example, it sets multiple scenarios and makes financial predictions based on each scenario. The financial data analysis department also develops a system that uses generative AI to predict future financial conditions based on the analysis results of financial data and performs scenario analysis. For example, it performs scenario analysis that takes into account different economic situations and market conditions. In this way, by predicting future financial conditions based on the analysis results and performing scenario analysis, management can understand future risks and opportunities and make strategic decisions.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The financial data collection unit automatically collects a company's financial data. For example, the financial data collection unit may use an API to obtain a company's financial statements and transaction data. The financial data collection unit may also use scraping technology to collect financial data from websites. The financial data collection unit may also automatically extract data from a company's internal systems. For example, an API may be used to obtain financial data from a company's accounting system. Scraping technology analyzes a website's HTML structure and extracts the necessary data. Data extraction from a company's internal systems can be performed using database queries. The financial data analysis unit performs analysis based on the collected financial data. For example, the financial data analysis unit may perform a profitability analysis to evaluate a company's profit margin. The financial data analysis unit may also perform a liquidity analysis to evaluate a company's short-term solvency. The financial data analysis unit may also perform an efficiency analysis to evaluate a company's asset utilization efficiency. For example, a profitability analysis may calculate the return on sales and net profit margin. A liquidity analysis may calculate the current ratio and quick ratio. An efficiency analysis may calculate the total asset turnover and inventory turnover. As a result, the financial analysis AI system according to the embodiment automates the collection and analysis of financial data, thereby improving business efficiency. For example, managers can make quick and accurate decisions based on reports generated by the financial analysis AI system. Furthermore, financial department personnel are freed from manual data collection and analysis tasks, allowing them to focus on more strategic work.

[0045] The financial data collection department can collect financial statements and transaction data from the past few years in bulk and store it in a database. For example, the financial data collection department can use an API to obtain financial statements from the past five years and store it in a database. The financial data collection department can also use scraping technology to collect past transaction data and store it in a database. The financial data collection department can also extract past data from a company's internal system and store it in a database. For example, an API can be used to obtain financial data from a company's accounting system from the past five years. Scraping technology analyzes the HTML structure of a website and extracts past transaction data. Data extraction from a company's internal system can be performed using a database query. This allows past financial data to be collected efficiently and stored in a database, eliminating the need for manual data collection.

[0046] The financial data analysis section calculates various financial indicators, such as profitability analysis, liquidity analysis, and efficiency analysis, to evaluate a company's financial situation. For example, the financial data analysis section performs profitability analysis to evaluate a company's profit margin. For example, the financial data analysis section calculates the return on sales and net profit margin. The financial data analysis section also performs liquidity analysis to evaluate a company's short-term solvency. For example, the financial data analysis section calculates the current ratio and quick ratio. The financial data analysis section also performs efficiency analysis to evaluate the efficiency of a company's asset utilization. For example, the financial data analysis section calculates the total asset turnover rate and inventory turnover rate. This enables detailed financial analysis by calculating various financial indicators and evaluating a company's financial situation.

[0047] The financial data analysis department can identify profitable departments and departments that need improvement, and create reports that include proposals based on that. For example, the financial data analysis department calculates profit margins by department and identifies profitable departments. The financial data analysis department also identifies departments that need improvement, and creates reports that include proposals based on that. For example, the financial data analysis department analyzes costs by department and identifies departments that need improvement. The financial data analysis department also identifies profitable departments and departments that need improvement, and creates reports that include proposals based on that. For example, the financial data analysis department analyzes sales and profit margins by department and identifies profitable departments and departments that need improvement. In this way, by identifying profitable departments and departments that need improvement and creating reports that include proposals based on that, management can make quick and accurate decisions.

[0048] The financial data analysis department can graph trends in sales and profit margins over the past five years and make future predictions. For example, the financial data analysis department can display trends in sales over the past five years in a line graph and predict future sales. The financial data analysis department can also display trends in profit margins over the past five years in a bar graph and predict future profit margins. The financial data analysis department can also graph trends in sales and profit margins over the past five years and make future predictions. For example, the financial data analysis department can perform regression analysis based on sales and profit margin data over the past five years to predict future sales and profit margins. In this way, graphing trends in past sales and profit margins and making future predictions makes it easier for managers to understand the future financial situation.

[0049] The financial data analysis unit can detect a sudden decrease in sales or an increase in costs and issue an alert. For example, the financial data analysis unit monitors sales data in real time and issues an alert if the sales data falls below a certain threshold. The financial data analysis unit can also detect a sudden increase in costs and issue an alert. For example, the financial data analysis unit monitors cost data in real time and issues an alert if the data exceeds a certain threshold. The financial data analysis unit can also detect a sudden decrease in sales or an increase in costs and issue an alert. For example, the financial data analysis unit monitors sales data and cost data in real time and issues an alert if an abnormal value is detected. In this way, sudden decrease in sales or increase in costs can be detected and an alert issued, allowing management to respond quickly.

[0050] The financial data collection unit can use the generation AI to evaluate the reliability of data and automatically exclude unreliable data. For example, the generation AI can evaluate the reliability of each data point. For example, the generation AI can check the origin and consistency of the data and automatically exclude unreliable data. The financial data collection unit can also evaluate the reliability of financial data collected using the generation AI in real time and filter out unreliable data. For example, the generation AI can detect outliers and inconsistent data and exclude them. The financial data collection unit can also use the generation AI to score the reliability of data and automatically exclude data below a certain score. For example, the generation AI can calculate a score based on the origin and past reliability history of the data. This allows the reliability of data to be evaluated and unreliable data to be automatically excluded, thereby improving the accuracy of the analysis results.

[0051] The financial data collection unit updates the collected data in real time, allowing it to always reflect the latest financial situation. For example, the financial data collection unit automatically updates the database whenever new data is added. The financial data collection unit also analyzes the collected financial data in real time using the generation AI to reflect the latest financial situation. For example, the generation AI periodically crawls the data to obtain the latest information. The financial data collection unit also develops an API for updating financial data in real time, instantly reflecting data from external systems. For example, it obtains transaction data and market data in real time and reflects it in the database. This allows the collected data to be updated in real time and always reflect the latest financial situation, allowing management to make decisions based on the latest information.

[0052] The financial data collection department can expand the scope of financial data collection to include other departments within the company, enabling comprehensive corporate analysis. For example, the financial data collection department collects marketing data and human resources data and integrates it with financial data. The financial data collection department also uses generative AI to collect data from different departments within the company and conduct comprehensive corporate analysis. For example, it analyzes the effectiveness of marketing campaigns and the impact of personnel changes in relation to financial data. The financial data collection department also expands the scope of financial data collection and develops a system for integrated analysis of data from the entire company. For example, it collects data from each department in real time and evaluates overall corporate performance. This allows the financial data collection department to expand to other departments within the company, enabling comprehensive corporate analysis, and enabling the evaluation of overall corporate performance.

[0053] The financial data collection unit collects financial data from different industries and can perform comparative analysis between industries. For example, the financial data collection unit compares financial data from the manufacturing and service industries and analyzes industry-specific trends. The financial data collection unit also uses generative AI to collect financial data from different industries and perform comparative analysis between industries. For example, it compares the profitability and efficiency of each industry and sets benchmarks. The financial data collection unit also collects financial data from different industries and builds a database for comparative analysis between industries. For example, it collects financial indicators for each industry in a unified format and performs comparative analysis. This makes it possible to understand industry-specific trends and benchmarks by collecting financial data from different industries and performing comparative analysis between industries.

[0054] The financial data analysis department can use generative AI to perform risk assessments based on the analysis results and identify high-risk areas. For example, a system can be built that analyzes collected financial data using generative AI and performs risk assessments. For example, areas of low profitability and liquidity can be identified and high-risk areas can be clearly indicated. The financial data analysis department can also perform risk assessments based on the results of financial data analysis and identify high-risk areas. For example, it can detect outliers and trend changes and issue risk alerts. The financial data analysis department can also develop a system that uses generative AI to perform risk assessments based on the results of financial data analysis and identify high-risk areas. For example, it can calculate a risk score and visualize high-risk areas. This allows management to effectively manage risk by performing risk assessments based on the analysis results and identifying high-risk areas.

[0055] The Financial Data Analysis Department can automatically detect abnormal values ​​and outliers in financial data analysis and reflect them in the analysis results. For example, it uses generative AI to build a system that automatically detects abnormal values ​​and outliers in financial data analysis. For example, it analyzes the consistency and patterns of data to identify outliers. The Financial Data Analysis Department also automatically detects abnormal values ​​and outliers in financial data analysis and reflects them in the analysis results. For example, it excludes or corrects outliers. The Financial Data Analysis Department also develops a system that uses generative AI to automatically detect abnormal values ​​and outliers in financial data analysis and reflects them in the analysis results. For example, it introduces an algorithm to minimize the impact of outliers. This makes it possible to automatically detect abnormal values ​​and outliers and reflect them in the analysis results, thereby improving the accuracy of the analysis results.

[0056] The financial data analysis department can compare the results of financial data analysis with data from other companies and perform benchmark analysis. For example, a system can be built to compare the results of collected financial data analysis with data from other companies and perform benchmark analysis. For example, the system can compare the results with financial indicators from competitors to evaluate a company's performance. The financial data analysis department can also use generative AI to compare the results of financial data analysis with data from other companies and perform benchmark analysis. For example, the system can compare with industry averages or data from top companies to identify areas for improvement. The financial data analysis department can also develop a system to compare the results of financial data analysis with data from other companies and perform benchmark analysis. For example, the system can collect financial data from competitors and perform comparative analysis. This allows the system to compare the results of financial data analysis with data from other companies and perform benchmark analysis to evaluate a company's performance and identify areas for improvement.

[0057] The financial data analysis department can predict future financial conditions based on the analysis results and perform scenario analysis. For example, a system can be built that predicts future financial conditions based on the analysis results of financial data collected using generative AI. For example, future forecasts of sales and profit margins can be made. The financial data analysis department can also predict future financial conditions based on the analysis results of financial data and perform scenario analysis. For example, multiple scenarios can be set and financial forecasts can be made based on each scenario. The financial data analysis department can also develop a system that uses generative AI to predict future financial conditions based on the analysis results of financial data and perform scenario analysis. For example, scenario analysis can be performed that takes into account different economic situations and market conditions. In this way, by predicting future financial conditions based on the analysis results and performing scenario analysis, management can understand future risks and opportunities and make strategic decisions.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The financial data collection unit automatically collects the company's financial data. For example, it uses APIs to obtain the company's financial statements and transaction data. It can also use scraping technology to collect financial data from websites. It can also automatically extract data from the company's internal systems, using APIs and database queries. Step 2: The Financial Data Analysis Department performs analysis based on the collected financial data. For example, it performs a profitability analysis to evaluate the company's profit margin. It can also perform a liquidity analysis to evaluate the company's short-term solvency. It also performs an efficiency analysis to evaluate the efficiency of the company's asset utilization. Specifically, it calculates the return on sales, net profit margin, current ratio, quick ratio, total asset turnover, and inventory turnover.

[0060] (Example 2) The financial analysis AI system according to the embodiment of the present invention is a system that automatically collects corporate financial data and analyzes it using a generation AI. This allows the financial analysis AI system to automate the collection and analysis of financial data, thereby improving business efficiency.

[0061] A financial analysis AI system according to an embodiment includes a financial data collection unit and a financial data analysis unit. The financial data collection unit automatically collects financial data from a company. For example, the financial data collection unit uses an API to obtain financial statements and transaction data from the company. The financial data collection unit can also collect financial data from websites using scraping technology. The financial data collection unit can also automatically extract data from a company's internal systems. For example, the financial data collection unit uses an API to obtain financial data from a company's accounting system. Scraping technology analyzes the HTML structure of a website to extract the necessary data. Data extraction from a company's internal systems can be performed using database queries. The financial data analysis unit performs analysis based on the collected financial data. For example, the financial data analysis unit performs a profitability analysis to evaluate a company's profit margin. The financial data analysis unit can also perform a liquidity analysis to evaluate a company's short-term solvency. The financial data analysis unit can also perform an efficiency analysis to evaluate the efficiency of a company's asset utilization. For example, the profitability analysis calculates the return on sales and net profit margin. The liquidity analysis calculates the current ratio and quick ratio. Efficiency analysis calculates total asset turnover and inventory turnover. As a result, the financial analysis AI system according to the embodiment automates the collection and analysis of financial data, improving business efficiency. For example, managers can make quick and accurate decisions based on reports generated by the financial analysis AI system. Furthermore, finance department personnel are freed from manual data collection and analysis tasks, allowing them to focus on more strategic business operations.

[0062] The financial data collection unit can collect financial statements and transaction data from the past few years in bulk and store it in a database. For example, the financial data collection unit collects financial statements and transaction data from the past few years in bulk and stores it in a database. For example, the financial data collection unit uses an API to obtain financial statements from the past five years and stores it in a database. The financial data collection unit can also use scraping technology to collect past transaction data and store it in a database. The financial data collection unit can also extract past data from a company's internal system and store it in a database. For example, an API is used to obtain financial data from a company's accounting system for the past five years. Scraping technology analyzes the HTML structure of a website and extracts past transaction data. Data extraction from a company's internal system can be performed using a database query. This allows past financial data to be efficiently collected and stored in a database, eliminating the need for manual data collection.

[0063] The financial data analysis unit can calculate various financial indicators, such as profitability analysis, liquidity analysis, and efficiency analysis, to evaluate the financial situation of a company. The financial data analysis unit, for example, performs profitability analysis to evaluate the profit margin of a company. For example, the financial data analysis unit calculates the profit margin on sales and the net profit margin. The financial data analysis unit also performs liquidity analysis to evaluate the short-term solvency of a company. For example, the financial data analysis unit calculates the current ratio and the quick ratio. The financial data analysis unit also performs efficiency analysis to evaluate the efficiency of asset utilization of a company. For example, the financial data analysis unit calculates the total asset turnover rate and the inventory turnover rate. This enables detailed financial analysis by calculating various financial indicators and evaluating the financial situation of a company.

[0064] The financial data analysis department can identify profitable departments and departments that need improvement, and create a report that includes proposals based on the identified departments. The financial data analysis department, for example, identifies profitable departments and creates a report that includes proposals based on the identified departments. For example, the financial data analysis department calculates profit margins by department and identifies profitable departments. The financial data analysis department also identifies departments that need improvement and creates a report that includes proposals based on the identified departments. For example, the financial data analysis department analyzes costs by department and identifies departments that need improvement. The financial data analysis department also identifies profitable departments and departments that need improvement and creates a report that includes proposals based on the identified departments. For example, the financial data analysis department analyzes sales and profit margins by department and identifies profitable departments and departments that need improvement. This allows management to make quick and accurate decisions by identifying profitable departments and departments that need improvement and creating a report that includes proposals based on the identified departments.

[0065] The financial data analysis unit can graph trends in sales and profit margins over the past five years and make future predictions. For example, the financial data analysis unit graphs trends in sales and profit margins over the past five years and makes future predictions. For example, the financial data analysis unit displays trends in sales over the past five years in a line graph and predicts future sales. The financial data analysis unit also displays trends in profit margins over the past five years in a bar graph and predicts future profit margins. The financial data analysis unit also graphs trends in sales and profit margins over the past five years and makes future predictions. For example, the financial data analysis unit performs a regression analysis based on sales and profit margin data over the past five years to predict future sales and profit margins. In this way, graphing trends in past sales and profit margins and making future predictions makes it easier for managers to grasp the future financial situation.

[0066] The financial data analysis unit can detect a sudden decrease in sales or an increase in costs and issue an alert. For example, the financial data analysis unit detects a sudden decrease in sales and issues an alert. For example, the financial data analysis unit monitors sales data in real time and issues an alert when the sales data falls below a certain threshold. The financial data analysis unit also detects a sudden increase in costs and issues an alert. For example, the financial data analysis unit monitors cost data in real time and issues an alert when the cost data exceeds a certain threshold. The financial data analysis unit also detects a sudden decrease in sales or an increase in costs and issues an alert. For example, the financial data analysis unit monitors sales data and cost data in real time and issues an alert when an abnormal value is detected. In this way, by detecting a sudden decrease in sales or an increase in costs and issuing an alert, management can respond quickly.

[0067] The financial data collection unit can use the generation AI to evaluate the reliability of data and automatically exclude unreliable data. The financial data collection unit, for example, uses the generation AI to evaluate the reliability of each data point. For example, the generation AI checks the origin and consistency of the data and automatically excludes unreliable data. The financial data collection unit also evaluates the reliability of financial data collected using the generation AI in real time and filters out unreliable data. For example, the generation AI detects and excludes outliers and inconsistent data. The financial data collection unit also uses the generation AI to score the reliability of data and automatically excludes data below a certain score. For example, the generation AI calculates a score based on the origin and past reliability history of the data. This evaluates the reliability of the data and automatically excludes unreliable data, thereby improving the accuracy of the analysis results.

[0068] The financial data collection unit updates the collected data in real time, allowing it to constantly reflect the latest financial situation. For example, the financial data collection unit automatically updates the collected financial data each time new data is added to the database. The financial data collection unit also analyzes the collected financial data in real time using a generation AI to reflect the latest financial situation. For example, the generation AI periodically crawls the data to obtain the latest information. The financial data collection unit also develops an API for updating financial data in real time, instantly reflecting data from external systems. For example, it obtains transaction data and market data in real time and reflects it in the database. This allows the collected data to be updated in real time and constantly reflect the latest financial situation, allowing management to make decisions based on the latest information.

[0069] The financial data collection unit can use an emotion estimation function to monitor a user's stress level during data collection and adjust the collection method if stress increases. The financial data collection unit, for example, uses the emotion estimation function to monitor a user's stress level during data collection. For example, the emotion estimation function analyzes the user's facial expressions and voice to evaluate the stress level in real time. The financial data collection unit also automatically adjusts the data collection method if the user's stress level increases. For example, it takes measures such as reducing the collection frequency or changing the data to be collected. The financial data collection unit also develops a system that uses the emotion estimation function to monitor a user's stress level and adjusts the collection method if stress increases. For example, it dynamically changes the collection method based on user feedback. This makes it possible to reduce the burden on the user by monitoring the user's stress level during data collection and adjusting the collection method if stress increases.

[0070] The financial data collection department can expand the scope of financial data collection to include other departments within the company, enabling comprehensive corporate analysis. For example, the financial data collection department expands the scope of financial data collection to include other departments within the company and performs comprehensive corporate analysis. For example, the financial data collection department collects marketing data and human resources data and integrates it with financial data. The financial data collection department also uses generative AI to collect data from different departments within the company and performs comprehensive corporate analysis. For example, it analyzes the effectiveness of marketing campaigns and the impact of personnel changes in relation to financial data. The financial data collection department also expands the scope of financial data collection and develops a system for integrated analysis of data from the entire company. For example, it collects data from each department in real time and evaluates overall corporate performance. This allows the scope of financial data collection to be expanded to include other departments within the company and enables comprehensive corporate analysis, thereby enabling the evaluation of overall corporate performance.

[0071] The financial data collection unit can collect financial data from different industries and perform comparative analysis between industries. For example, the financial data collection unit collects financial data from different industries and performs comparative analysis between industries. For example, the financial data collection unit compares financial data from the manufacturing industry and the service industry and analyzes industry-specific trends. The financial data collection unit also uses generative AI to collect financial data from different industries and perform comparative analysis between industries. For example, it compares the profitability and efficiency of each industry and sets benchmarks. The financial data collection unit also collects financial data from different industries and builds a database for comparative analysis between industries. For example, it collects financial indicators for each industry in a unified format and performs comparative analysis. This makes it possible to understand industry-specific trends and benchmarks by collecting financial data from different industries and performing comparative analysis between industries.

[0072] The financial data collection unit can use the emotion estimation function to analyze a user's emotions during data collection and provide an interface for eliciting positive emotions. The financial data collection unit, for example, uses the emotion estimation function to analyze a user's emotions in real time during data collection and provide an interface for eliciting positive emotions. For example, the financial data collection unit analyzes a user's facial expressions and voice and displays positive feedback. The financial data collection unit also develops an interface for analyzing a user's emotions and eliciting positive emotions. For example, the financial data collection unit increases the user's motivation by displaying encouraging messages and success stories. The financial data collection unit also builds a system that uses the emotion estimation function to analyze a user's emotions during data collection and provides an interface for eliciting positive emotions. For example, the interface is dynamically changed depending on the user's emotion score. This makes it possible to increase the user's motivation by analyzing a user's emotions during data collection and providing an interface for eliciting positive emotions.

[0073] The financial data analysis department can use generative AI to perform risk assessments based on the analysis results and identify high-risk areas. The financial data analysis department, for example, analyzes financial data collected using generative AI and builds a system for risk assessment. For example, it identifies areas of low profitability and liquidity and clearly indicates high-risk areas. The financial data analysis department also performs risk assessments based on the results of financial data analysis and identifies high-risk areas. For example, it detects outliers and trend changes and issues risk alerts. The financial data analysis department also develops a system that uses generative AI to perform risk assessments based on the results of financial data analysis and identifies high-risk areas. For example, it calculates a risk score and visualizes high-risk areas. This allows management to effectively manage risk by performing risk assessments based on the analysis results and identifying high-risk areas.

[0074] The financial data analysis department can automatically detect abnormal values ​​and outliers in financial data analysis and reflect them in the analysis results. For example, the financial data analysis department uses generative AI to build a system that automatically detects abnormal values ​​and outliers in financial data analysis. For example, it analyzes the consistency and patterns of data and identifies outliers. The financial data analysis department also automatically detects abnormal values ​​and outliers in financial data analysis and reflects them in the analysis results. For example, it excludes or corrects outliers. The financial data analysis department also develops a system that automatically detects abnormal values ​​and outliers in financial data analysis and reflects them in the analysis results using generative AI. For example, it introduces an algorithm to minimize the impact of outliers. This makes it possible to automatically detect abnormal values ​​and outliers and reflect them in the analysis results, thereby improving the accuracy of the analysis results.

[0075] The financial data analysis unit uses the emotion estimation function to collect users' emotional reactions to the analysis results, and can improve the analysis method if there are many negative reactions. The financial data analysis unit, for example, uses the emotion estimation function to build a system that collects users' emotional reactions to the analysis results in real time. For example, it analyzes the user's facial expressions and voice and calculates an emotion score. The financial data analysis unit also identifies areas for improvement in the analysis results based on the user's emotional reaction data. For example, it adjusts the analysis method if there are many negative reactions. The financial data analysis unit also uses the emotion estimation function to collect users' emotional reactions to the analysis results, and develops a system that improves the analysis method if there are many negative reactions. For example, it adjusts the analysis algorithm based on user feedback. In this way, it is possible to collect users' emotional reactions to the analysis results and improve the analysis method if there are many negative reactions, thereby improving user satisfaction.

[0076] The financial data analysis department can compare the results of financial data analysis with data from other companies and perform benchmark analysis. For example, the financial data analysis department builds a system that compares the results of collected financial data analysis with data from other companies and performs benchmark analysis. For example, it compares the results with financial indicators from competitors to evaluate the company's performance. The financial data analysis department also uses generative AI to compare the results of financial data analysis with data from other companies and performs benchmark analysis. For example, it compares the results with industry averages or data from top companies to identify areas for improvement. The financial data analysis department also develops a system that compares the results of financial data analysis with data from other companies and performs benchmark analysis. For example, it collects financial data from competitors and performs comparative analysis. This allows the company to compare the results of financial data analysis with data from other companies and perform benchmark analysis to evaluate the company's performance and identify areas for improvement.

[0077] The financial data analysis department can predict future financial conditions based on the analysis results and perform scenario analysis. For example, the financial data analysis department uses generative AI to build a system that predicts future financial conditions based on the analysis results of financial data collected. For example, it makes future predictions of sales and profit margins. The financial data analysis department also predicts future financial conditions based on the analysis results of financial data and performs scenario analysis. For example, it sets multiple scenarios and makes financial predictions based on each scenario. The financial data analysis department also develops a system that uses generative AI to predict future financial conditions based on the analysis results of financial data and performs scenario analysis. For example, it performs scenario analysis that takes into account different economic situations and market conditions. In this way, by predicting future financial conditions based on the analysis results and performing scenario analysis, management can understand future risks and opportunities and make strategic decisions.

[0078] The financial data analysis unit can use the emotion estimation function to analyze the user's emotions regarding the analysis results and make suggestions to elicit positive emotions. The financial data analysis unit, for example, uses the emotion estimation function to analyze the user's emotions regarding the analysis results in real time and builds a system that makes suggestions to elicit positive emotions. For example, the financial data analysis unit analyzes the user's facial expressions and voice and displays positive feedback. The financial data analysis unit also analyzes the user's emotions and makes suggestions to elicit positive emotions. For example, the financial data analysis unit increases the user's motivation by displaying encouraging messages and success stories. The financial data analysis unit also develops a system that uses the emotion estimation function to analyze the user's emotions regarding the analysis results and makes suggestions to elicit positive emotions. For example, the content of the suggestions is dynamically changed depending on the user's emotion score. This makes it possible to analyze the user's emotions regarding the analysis results and make suggestions to elicit positive emotions, thereby increasing the user's motivation.

[0079] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0080] The financial data collection unit automatically collects a company's financial data. For example, the financial data collection unit may use an API to obtain a company's financial statements and transaction data. The financial data collection unit may also use scraping technology to collect financial data from websites. The financial data collection unit may also automatically extract data from a company's internal systems. For example, an API may be used to obtain financial data from a company's accounting system. Scraping technology analyzes a website's HTML structure and extracts the necessary data. Data extraction from a company's internal systems can be performed using database queries. The financial data analysis unit performs analysis based on the collected financial data. For example, the financial data analysis unit may perform a profitability analysis to evaluate a company's profit margin. The financial data analysis unit may also perform a liquidity analysis to evaluate a company's short-term solvency. The financial data analysis unit may also perform an efficiency analysis to evaluate a company's asset utilization efficiency. For example, a profitability analysis may calculate the return on sales and net profit margin. A liquidity analysis may calculate the current ratio and quick ratio. An efficiency analysis may calculate the total asset turnover and inventory turnover. As a result, the financial analysis AI system according to the embodiment automates the collection and analysis of financial data, thereby improving business efficiency. For example, managers can make quick and accurate decisions based on reports generated by the financial analysis AI system. Furthermore, financial department personnel are freed from manual data collection and analysis tasks, allowing them to focus on more strategic work.

[0081] The financial data collection department can collect financial statements and transaction data from the past few years in bulk and store it in a database. For example, the financial data collection department can use an API to obtain financial statements from the past five years and store it in a database. The financial data collection department can also use scraping technology to collect past transaction data and store it in a database. The financial data collection department can also extract past data from a company's internal system and store it in a database. For example, an API can be used to obtain financial data from a company's accounting system from the past five years. Scraping technology analyzes the HTML structure of a website and extracts past transaction data. Data extraction from a company's internal system can be performed using a database query. This allows past financial data to be collected efficiently and stored in a database, eliminating the need for manual data collection.

[0082] The financial data analysis section calculates various financial indicators, such as profitability analysis, liquidity analysis, and efficiency analysis, to evaluate a company's financial situation. For example, the financial data analysis section performs profitability analysis to evaluate a company's profit margin. For example, the financial data analysis section calculates the return on sales and net profit margin. The financial data analysis section also performs liquidity analysis to evaluate a company's short-term solvency. For example, the financial data analysis section calculates the current ratio and quick ratio. The financial data analysis section also performs efficiency analysis to evaluate the efficiency of a company's asset utilization. For example, the financial data analysis section calculates the total asset turnover rate and inventory turnover rate. This enables detailed financial analysis by calculating various financial indicators and evaluating a company's financial situation.

[0083] The financial data analysis department can identify profitable departments and departments that need improvement, and create reports that include proposals based on that. For example, the financial data analysis department calculates profit margins by department and identifies profitable departments. The financial data analysis department also identifies departments that need improvement, and creates reports that include proposals based on that. For example, the financial data analysis department analyzes costs by department and identifies departments that need improvement. The financial data analysis department also identifies profitable departments and departments that need improvement, and creates reports that include proposals based on that. For example, the financial data analysis department analyzes sales and profit margins by department and identifies profitable departments and departments that need improvement. In this way, by identifying profitable departments and departments that need improvement and creating reports that include proposals based on that, management can make quick and accurate decisions.

[0084] The financial data analysis department can graph trends in sales and profit margins over the past five years and make future predictions. For example, the financial data analysis department can display trends in sales over the past five years in a line graph and predict future sales. The financial data analysis department can also display trends in profit margins over the past five years in a bar graph and predict future profit margins. The financial data analysis department can also graph trends in sales and profit margins over the past five years and make future predictions. For example, the financial data analysis department can perform regression analysis based on sales and profit margin data over the past five years to predict future sales and profit margins. In this way, graphing trends in past sales and profit margins and making future predictions makes it easier for managers to understand the future financial situation.

[0085] The financial data analysis unit can detect a sudden decrease in sales or an increase in costs and issue an alert. For example, the financial data analysis unit monitors sales data in real time and issues an alert if the sales data falls below a certain threshold. The financial data analysis unit can also detect a sudden increase in costs and issue an alert. For example, the financial data analysis unit monitors cost data in real time and issues an alert if the data exceeds a certain threshold. The financial data analysis unit can also detect a sudden decrease in sales or an increase in costs and issue an alert. For example, the financial data analysis unit monitors sales data and cost data in real time and issues an alert if an abnormal value is detected. In this way, sudden decrease in sales or increase in costs can be detected and an alert issued, allowing management to respond quickly.

[0086] The financial data collection unit can use the generation AI to evaluate the reliability of data and automatically exclude unreliable data. For example, the generation AI can evaluate the reliability of each data point. For example, the generation AI can check the origin and consistency of the data and automatically exclude unreliable data. The financial data collection unit can also evaluate the reliability of financial data collected using the generation AI in real time and filter out unreliable data. For example, the generation AI can detect outliers and inconsistent data and exclude them. The financial data collection unit can also use the generation AI to score the reliability of data and automatically exclude data below a certain score. For example, the generation AI can calculate a score based on the origin and past reliability history of the data. This allows the reliability of data to be evaluated and unreliable data to be automatically excluded, thereby improving the accuracy of the analysis results.

[0087] The financial data collection unit updates the collected data in real time, allowing it to always reflect the latest financial situation. For example, the financial data collection unit automatically updates the database whenever new data is added. The financial data collection unit also analyzes the collected financial data in real time using the generation AI to reflect the latest financial situation. For example, the generation AI periodically crawls the data to obtain the latest information. The financial data collection unit also develops an API for updating financial data in real time, instantly reflecting data from external systems. For example, it obtains transaction data and market data in real time and reflects it in the database. This allows the collected data to be updated in real time and always reflect the latest financial situation, allowing management to make decisions based on the latest information.

[0088] The financial data collection unit can use an emotion estimation function to monitor a user's stress level during data collection and adjust the collection method if stress increases. For example, the emotion estimation function is used to monitor a user's stress level during data collection. For example, the emotion estimation function analyzes the user's facial expressions and voice to evaluate the stress level in real time. Furthermore, the financial data collection unit automatically adjusts the data collection method if the user's stress level increases. For example, it takes measures such as reducing the collection frequency or changing the data to be collected. Furthermore, the financial data collection unit develops a system that uses the emotion estimation function to monitor a user's stress level and adjusts the collection method if stress increases. For example, it dynamically changes the collection method based on user feedback. In this way, the user's stress level can be monitored during data collection and the collection method can be adjusted if stress increases, thereby reducing the burden on the user.

[0089] The financial data collection department can expand the scope of financial data collection to include other departments within the company, enabling comprehensive corporate analysis. For example, the financial data collection department collects marketing data and human resources data and integrates it with financial data. The financial data collection department also uses generative AI to collect data from different departments within the company and conduct comprehensive corporate analysis. For example, it analyzes the effectiveness of marketing campaigns and the impact of personnel changes in relation to financial data. The financial data collection department also expands the scope of financial data collection and develops a system for integrated analysis of data from the entire company. For example, it collects data from each department in real time and evaluates overall corporate performance. This allows the financial data collection department to expand to other departments within the company, enabling comprehensive corporate analysis, and enabling the evaluation of overall corporate performance.

[0090] The financial data collection unit collects financial data from different industries and can perform comparative analysis between industries. For example, the financial data collection unit compares financial data from the manufacturing and service industries and analyzes industry-specific trends. The financial data collection unit also uses generative AI to collect financial data from different industries and perform comparative analysis between industries. For example, it compares the profitability and efficiency of each industry and sets benchmarks. The financial data collection unit also collects financial data from different industries and builds a database for comparative analysis between industries. For example, it collects financial indicators for each industry in a unified format and performs comparative analysis. This makes it possible to understand industry-specific trends and benchmarks by collecting financial data from different industries and performing comparative analysis between industries.

[0091] The financial data collection unit can use the emotion estimation function to analyze a user's emotions during data collection and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze a user's emotions in real time during data collection and provide an interface for eliciting positive emotions. For example, the emotion estimation function can be used to analyze a user's emotions in real time during data collection and provide an interface for eliciting positive emotions. For example, the user's facial expressions and voice can be analyzed and positive feedback can be displayed. The financial data collection unit can also develop an interface for analyzing a user's emotions and eliciting positive emotions. For example, encouraging messages and success stories can be displayed to increase user motivation. The financial data collection unit can also build a system that uses the emotion estimation function to analyze a user's emotions during data collection and provide an interface for eliciting positive emotions. For example, the interface can be dynamically changed depending on the user's emotion score. This makes it possible to analyze a user's emotions during data collection and provide an interface for eliciting positive emotions, thereby increasing user motivation.

[0092] The financial data analysis department can use generative AI to perform risk assessments based on the analysis results and identify high-risk areas. For example, a system can be built that analyzes collected financial data using generative AI and performs risk assessments. For example, areas of low profitability and liquidity can be identified and high-risk areas can be clearly indicated. The financial data analysis department can also perform risk assessments based on the results of financial data analysis and identify high-risk areas. For example, it can detect outliers and trend changes and issue risk alerts. The financial data analysis department can also develop a system that uses generative AI to perform risk assessments based on the results of financial data analysis and identify high-risk areas. For example, it can calculate a risk score and visualize high-risk areas. This allows management to effectively manage risk by performing risk assessments based on the analysis results and identifying high-risk areas.

[0093] The Financial Data Analysis Department can automatically detect abnormal values ​​and outliers in financial data analysis and reflect them in the analysis results. For example, it uses generative AI to build a system that automatically detects abnormal values ​​and outliers in financial data analysis. For example, it analyzes the consistency and patterns of data to identify outliers. The Financial Data Analysis Department also automatically detects abnormal values ​​and outliers in financial data analysis and reflects them in the analysis results. For example, it excludes or corrects outliers. The Financial Data Analysis Department also develops a system that uses generative AI to automatically detect abnormal values ​​and outliers in financial data analysis and reflects them in the analysis results. For example, it introduces an algorithm to minimize the impact of outliers. This makes it possible to automatically detect abnormal values ​​and outliers and reflect them in the analysis results, thereby improving the accuracy of the analysis results.

[0094] The financial data analysis unit uses the emotion estimation function to collect users' emotional reactions to the analysis results, and can improve the analysis method if there are many negative reactions. For example, a system is built that uses the emotion estimation function to collect users' emotional reactions to the analysis results in real time. For example, the emotion estimation function is used to analyze the user's facial expressions and voice and calculate an emotion score. The financial data analysis unit also identifies areas for improvement in the analysis results based on the user's emotional reaction data. For example, the analysis method is adjusted if there are many negative reactions. The financial data analysis unit also uses the emotion estimation function to collect users' emotional reactions to the analysis results, and develops a system that improves the analysis method if there are many negative reactions. For example, the analysis algorithm is adjusted based on user feedback. In this way, user satisfaction can be improved by collecting users' emotional reactions to the analysis results and improving the analysis method if there are many negative reactions.

[0095] The financial data analysis department can compare the results of financial data analysis with data from other companies and perform benchmark analysis. For example, a system can be built to compare the results of collected financial data analysis with data from other companies and perform benchmark analysis. For example, the system can compare the results with financial indicators from competitors to evaluate a company's performance. The financial data analysis department can also use generative AI to compare the results of financial data analysis with data from other companies and perform benchmark analysis. For example, the system can compare with industry averages or data from top companies to identify areas for improvement. The financial data analysis department can also develop a system to compare the results of financial data analysis with data from other companies and perform benchmark analysis. For example, the system can collect financial data from competitors and perform comparative analysis. This allows the system to compare the results of financial data analysis with data from other companies and perform benchmark analysis to evaluate a company's performance and identify areas for improvement.

[0096] The financial data analysis department can predict future financial conditions based on the analysis results and perform scenario analysis. For example, a system can be built that predicts future financial conditions based on the analysis results of financial data collected using generative AI. For example, future forecasts of sales and profit margins can be made. The financial data analysis department can also predict future financial conditions based on the analysis results of financial data and perform scenario analysis. For example, multiple scenarios can be set and financial forecasts can be made based on each scenario. The financial data analysis department can also develop a system that uses generative AI to predict future financial conditions based on the analysis results of financial data and perform scenario analysis. For example, scenario analysis can be performed that takes into account different economic situations and market conditions. In this way, by predicting future financial conditions based on the analysis results and performing scenario analysis, management can understand future risks and opportunities and make strategic decisions.

[0097] The financial data analysis unit can use the emotion estimation function to analyze the user's emotions regarding the analysis results and make suggestions to elicit positive emotions. For example, a system is constructed that uses the emotion estimation function to analyze the user's emotions regarding the analysis results in real time and make suggestions to elicit positive emotions. For example, the system analyzes the user's facial expressions and voice and displays positive feedback. The financial data analysis unit also analyzes the user's emotions and makes suggestions to elicit positive emotions. For example, the system displays encouraging messages and success stories to increase the user's motivation. The financial data analysis unit also develops a system that uses the emotion estimation function to analyze the user's emotions regarding the analysis results and makes suggestions to elicit positive emotions. For example, the system dynamically changes the content of suggestions depending on the user's emotion score. This allows the user's emotions regarding the analysis results to be analyzed and suggestions to elicit positive emotions to increase the user's motivation.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The financial data collection unit automatically collects the company's financial data. For example, it uses APIs to obtain the company's financial statements and transaction data. It can also use scraping technology to collect financial data from websites. It can also automatically extract data from the company's internal systems, using APIs and database queries. Step 2: The Financial Data Analysis Department performs analysis based on the collected financial data. For example, it performs a profitability analysis to evaluate the company's profit margin. It can also perform a liquidity analysis to evaluate the company's short-term solvency. It also performs an efficiency analysis to evaluate the efficiency of the company's asset utilization. Specifically, it calculates the return on sales, net profit margin, current ratio, quick ratio, total asset turnover, and inventory turnover.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0117] 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.

[0118] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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).

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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]

[0167] 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. a financial data collection unit that automatically collects financial data of a company; a financial data analysis unit that performs analysis based on the financial data collected by the financial data collection unit. A system characterized by:

2. The financial data collection unit Collect financial statements and transaction data from the past few years in one place and store them in a database 2. The system of claim 1.

3. The financial data analysis unit Calculate various financial indicators such as profitability analysis, liquidity analysis, efficiency analysis, etc. to assess the financial condition of the company 2. The system of claim 1.

4. The financial data analysis unit Identify profitable departments and those needing improvement, and create reports with recommendations based on that.

2. The system of claim 1.

5. The financial data analysis unit Graph the trends in sales and profit margins over the past five years and make future predictions 2. The system of claim 1.

6. The financial data analysis unit Detect and alert on sudden sales declines or cost increases 2. The system of claim 1.

7. The financial data collection unit Generative AI is used to evaluate the reliability of data and automatically exclude the unreliable data.

2. The system of claim 1.

8. The financial data collection unit The collected data is updated in real time to always reflect the latest financial situation.

2. The system of claim 1.

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