system

Abstract

The system according to this embodiment aims to automate the analysis of financial statement data and financial forecasting. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a creation unit, a provision unit, and a forecasting unit. The collection unit collects financial statement data. The analysis unit analyzes the data collected by the collection unit. The creation unit creates an analysis report based on the results of the analysis by the analysis unit. The provisioning unit provides the report created by the creation unit. The forecasting unit constructs a financial forecasting model based on historical data collected by the collection unit.

Description

Technical Field

[0006] , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it takes time and labor to manually analyze financial statement data and make financial forecasts.

[0005] The system according to the embodiment aims to automate the analysis of financial statement data and financial forecasting.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a data collection unit, an analysis unit, a data creation unit, a data provision unit, and a forecasting unit. The data collection unit collects financial statement data. The analysis unit analyzes the data collected by the data collection unit. The data creation unit creates an analysis report based on the results of the analysis performed by the analysis unit. The data provision unit provides the report created by the data creation unit. The forecasting unit constructs a financial forecasting model based on historical data collected by the data collection unit. [Effects of the Invention]

[0007] The system according to this embodiment can automate the analysis of financial statement data and financial forecasting. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The financial analysis system according to an embodiment of the present invention is a tool that automatically generates analysis reports from a company's financial statements using a generative AI. This financial analysis system takes a company's financial statement data as input, and the generative AI automatically analyzes it to identify important figures and trends and detect anomalies. Next, the generative AI automatically generates an analysis report based on the analysis results. Furthermore, the generative AI constructs a financial forecasting model based on past financial statement data. This model is used to predict future growth and profitability. Users can also compare forecast results for different companies and industries. This tool is designed to reduce the time spent inputting and verifying financial statement data and can be used even without financial knowledge. Because the generative AI automatically analyzes the data and generates reports, users can free up time for decision-making and client interactions. This is expected to improve operational efficiency and productivity. As a result, the financial analysis system can quickly and accurately analyze a company's financial statement data and automatically generate analysis reports.

[0029] The financial analysis system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, a provision unit, and a forecasting unit. The collection unit collects financial statement data of companies. The collection unit can, for example, automatically collect financial statement data from a database. The collection unit can also collect financial statement data that has been manually entered. For example, the collection unit can analyze financial statement files uploaded by users and store them in a database. Furthermore, the collection unit can also obtain financial statement data from external systems via an API. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can identify important figures and trends from the financial statement data. For example, the analysis unit extracts important figures such as sales and profit margins and analyzes trends. The analysis unit can also detect outliers from the financial statement data. For example, the analysis unit identifies outliers using a statistical outlier detection algorithm. The creation unit creates an analysis report based on the results analyzed by the analysis unit. The creation unit creates the report using, for example, graphs, text, tables, etc. The creation unit can automatically generate reports using a generation AI. For example, the creation unit inputs analysis results into the generation AI and automatically creates a report based on a report template. The delivery unit provides the reports created by the creation unit. The delivery unit can provide reports, for example, via email or a web portal. The delivery unit can also provide reports as printed materials. For example, the delivery unit generates a report in PDF format and sends it to the user. The forecasting unit builds a financial forecasting model based on historical data collected by the collection unit. The forecasting unit builds a forecasting model, for example, using regression analysis or time series analysis. The forecasting unit is used to predict future growth and profitability. For example, the forecasting unit predicts future sales based on historical sales data. Thus, the financial analysis system according to this embodiment can automate the collection, analysis, report creation, delivery, and construction of forecasting models of a company's financial statement data, supporting rapid decision-making.

[0030] The data collection unit collects financial statement data from companies. For example, the data collection unit can automatically collect financial statement data from databases. Specifically, it can access companies' financial databases and set schedules to periodically retrieve the latest financial statement data. This allows the data collection unit to obtain the latest data immediately after a company's financial results are announced and reflect it in the system. The data collection unit can also collect manually entered financial statement data. For example, it can analyze financial statement files uploaded by users and save them to the database. The data collection unit can use OCR (Optical Character Recognition) technology to extract text data from scanned financial statement images and save it as structured data. Furthermore, the data collection unit can retrieve financial statement data from external systems via APIs. For example, it can use APIs from financial information services to retrieve financial statement data from multiple companies at once and import it into the system. This allows the data collection unit to collect financial statement data in diverse ways and integrate it into the system. The data collection unit also includes functions to ensure data integrity and eliminate duplicate data to guarantee data quality. This enables the data collection unit to provide accurate and reliable data, improving the overall system performance.

[0031] The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, statistical analysis and machine learning algorithms. Specifically, the analysis unit can identify important figures and trends from financial statement data. For example, it can extract important figures such as sales and profit margins and analyze trends. As part of data preprocessing, the analysis unit improves the accuracy of the analysis by imputing missing values ​​and removing outliers. The analysis unit can also detect outliers from financial statement data. For example, it can identify outliers using statistical outlier detection algorithms. This allows the analysis unit to detect abnormal fluctuations in a company's financial situation early, which is useful for risk management. Furthermore, the analysis unit can learn data patterns using machine learning algorithms and predict future trends. For example, it can predict future increases or decreases in sales based on past sales data, supporting a company's growth strategy. The analysis unit can also analyze text data from financial statements using natural language processing technology and extract important information. This allows the analysis unit to perform comprehensive financial analysis that includes not only quantitative data but also qualitative information.

[0032] The creation unit creates analysis reports based on the results analyzed by the analysis unit. The creation unit uses graphs, text, tables, and other elements to create the reports. Specifically, it generates various graphs such as bar graphs, line graphs, and pie charts to visually display the analysis results in an easy-to-understand manner. Furthermore, the creation unit can automatically generate reports using generative AI. For example, the creation unit inputs the analysis results into the generative AI, which then automatically creates a report based on a report template. The generative AI uses natural language generation technology to convert the analysis results into easily understandable text and fill in each section of the report. This allows the creation unit to produce high-quality reports quickly and efficiently. Additionally, the creation unit can create customized reports according to user requests. For example, it can create reports focusing on specific metrics or time periods to provide information tailored to user needs. The creation unit can adjust the report layout and design to provide visually appealing and easy-to-read reports. This allows the creation unit to produce reports that effectively communicate analysis results and support user decision-making.

[0033] The delivery department provides reports created by the creation department. The delivery department can provide reports, for example, via email or a web portal. Specifically, the delivery department generates reports in PDF format and sends them to users. When providing reports via email, the delivery department sends them as attachments to allow users easy access. When providing reports via a web portal, the delivery department operates a dedicated portal site where users can log in and download reports. Furthermore, the delivery department can also provide reports in printed form. For example, the delivery department can generate reports as high-quality printed materials and deliver them to users by mail. This allows the delivery department to provide reports in diverse ways to meet user needs, improving the ease of information delivery. The delivery department also has a function to manage the status of report delivery and confirm whether users have received the reports. This allows the delivery department to efficiently manage the report delivery process and ensure that information is delivered to users.

[0034] The forecasting unit constructs financial forecasting models based on historical data collected by the data collection unit. The forecasting unit constructs these models using methods such as regression analysis and time series analysis. Specifically, it creates models to predict future sales and profits based on historical sales and profit data. When using regression analysis, the forecasting unit considers multiple factors influencing sales and profits and models their relationships. When using time series analysis, the forecasting unit analyzes temporal patterns in historical data and predicts future trends. Using these models, the forecasting unit can predict future growth and profitability. For example, it can predict future sales based on historical sales data, supporting a company's growth strategy. Furthermore, the forecasting unit can periodically update its models and incorporate new data to improve the accuracy of its forecasting models. This allows the forecasting unit to always provide highly accurate forecasts based on the latest information. In addition, the forecasting unit can simulate multiple scenarios and identify the most likely risks and opportunities. This allows the forecasting unit to support corporate decision-making and contribute to future risk management and strategic planning.

[0035] The analysis unit can identify key figures and trends from financial statement data. For example, the analysis unit can extract key figures such as sales revenue and profit margins. For example, the analysis unit can analyze annual fluctuations in sales revenue and identify growth trends. It can also analyze fluctuations in profit margins and identify profitability trends. Furthermore, the analysis unit can analyze fluctuations in cash flow and identify cash flow trends. This improves the accuracy of the analysis report by identifying key figures and trends. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input financial statement data into a generative AI and have the generative AI perform the identification of key figures and trends.

[0036] The analysis unit can detect outliers from financial statement data. The analysis unit identifies outliers using, for example, a statistical outlier detection algorithm. For example, the analysis unit can detect sudden fluctuations in sales as outliers. The analysis unit can also detect abnormal fluctuations in profit margins. Furthermore, the analysis unit can detect abnormal fluctuations in cash flow. By detecting outliers, abnormal data can be discovered at an early stage. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input financial statement data into a generation AI and have the generation AI perform the detection of outliers.

[0037] The forecasting unit can construct a financial forecasting model that predicts future growth and profitability based on past financial statement data. For example, the forecasting unit can predict sales growth rates using regression analysis. For example, the forecasting unit can predict future sales based on past sales data. The forecasting unit can also predict fluctuations in profit margins using time series analysis. Furthermore, the forecasting unit can also forecast cash flows. This allows for an understanding of a company's future trends by predicting future growth and profitability. Some or all of the above-described processes in the forecasting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the forecasting unit can input past financial statement data into a generative AI and have the generative AI construct a financial forecasting model.

[0038] The data provider can compare forecast results for different companies and industries. For example, it can display the financial forecast results for multiple companies side by side. For example, it can compare growth forecasts for different industries. It can also compare the profitability of companies. Furthermore, it can compare growth forecasts by region. This allows for analysis from a broader perspective by comparing forecast results for different companies and industries. Some or all of the above processing in the data provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the data provider can input the financial forecast results for multiple companies into a generative AI and have the generative AI generate the comparison results.

[0039] The service provider can provide users with analytical reports. For example, the service provider can provide reports via email. For example, the service provider can provide reports via a web portal. Furthermore, the service provider can provide reports as printed materials. In addition, the service provider can generate reports in PDF format and send them to users. This allows for faster decision-making by providing users with analytical reports. Some or all of the above processes in the service provider may be performed, for example, using generative AI, or without generative AI. For example, the service provider can have the generative AI select the method of providing the report.

[0040] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has frequently used in the past. For example, the data collection unit can select the most efficient collection method from the user's past data collection history. The data collection unit can also analyze the user's past data collection history and suggest improvements to the collection method. This enables efficient data collection by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI and have the generative AI select the optimal collection method.

[0041] The data collection unit can filter financial statement data based on the user's current work situation and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. The data collection unit can also collect only the necessary data according to the user's work situation. This allows for the collection of highly relevant data by filtering the data based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's work situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial statement data. For example, the data collection unit can prioritize the collection of financial statement data of companies in the region where the user is located. For example, the data collection unit can collect highly relevant industry data based on the user's geographical location. The data collection unit can also collect region-specific economic data by considering the user's geographical location. This allows for the collection of region-specific and highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0043] The data collection unit can analyze the user's social media activity and collect relevant data when collecting financial statement data. For example, the data collection unit can collect financial statement data of companies that the user has shown interest in on social media. For example, the data collection unit can collect relevant industry data from the user's social media activity. The data collection unit can also analyze the user's social media activity and collect trend-based data. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI perform the collection of relevant data.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can apply a trend analysis algorithm to trend data. Furthermore, the analysis unit can apply an anomaly detection algorithm to anomaly data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of data with an approaching submission deadline. The analysis unit can also adjust the priority of analysis in stages according to the data submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data submission date into the generative AI and have the generative AI determine the priority of analysis.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis in stages according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0048] The report creation unit can adjust the level of detail in the report based on the importance of the analysis results. For example, the unit can create a detailed report for analysis results of high importance, and a simplified report for analysis results of low importance. The unit can also adjust the level of detail in the report in stages according to the importance of the analysis results. This allows for efficient report creation by adjusting the level of detail in the report based on the importance of the analysis results. Some or all of the above processing in the report creation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the report creation unit can input the importance of the analysis results into the generation AI and have the generation AI adjust the level of detail in the report.

[0049] The report generation unit can apply different report generation algorithms depending on the category of the analysis results when generating reports. For example, the unit can apply a specific financial report generation algorithm to financial analysis results. For example, the unit can apply a trend report generation algorithm to trend analysis results. Furthermore, the unit can apply an anomaly report generation algorithm to anomaly analysis results. This enables the creation of optimal reports according to the category of the analysis results. Some or all of the above processing in the report generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the report generation unit can input the category of the analysis results into the generation AI and have the generation AI execute the application of the report generation algorithm.

[0050] The report creation unit can determine the priority of reports based on the submission timing of the analysis results when creating reports. For example, the creation unit can prioritize the inclusion of the most recent analysis results in the report. For example, the creation unit can prioritize the inclusion of analysis results with approaching submission deadlines in the report. Furthermore, the creation unit can adjust the priority of reports in stages according to the submission timing of the analysis results. This enables efficient report creation by determining the priority of reports based on the submission timing of the analysis results. Some or all of the above processing in the creation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the creation unit can input the submission timing of the analysis results into the generation AI and have the generation AI determine the priority of the reports.

[0051] The report creation unit can adjust the order of the report based on the relevance of the analysis results. For example, the unit can prioritize reflecting highly relevant analysis results in the report. For example, it can postpone less relevant analysis results. The unit can also adjust the order of the report in stages according to the relevance of the analysis results. This allows for efficient report creation by adjusting the order of the report based on the relevance of the analysis results. Some or all of the above processing in the report creation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the report creation unit can input the relevance of the analysis results into a generation AI and have the generation AI perform the adjustment of the report order.

[0052] The delivery unit can select the optimal delivery method by referring to the user's past report viewing history when providing reports. For example, the delivery unit may prioritize suggesting delivery methods that the user has preferred in the past. For example, the delivery unit can select the most efficient delivery method from the user's past report viewing history. The delivery unit can also analyze the user's past report viewing history and suggest improvements to the delivery method. This makes it possible to provide reports efficiently by referring to the user's past report viewing history. Some or all of the above processing in the delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the delivery unit can input the user's past report viewing history into a generation AI and have the generation AI select the optimal delivery method.

[0053] The delivery unit can select the optimal delivery method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can select a delivery method optimized for a larger screen. Furthermore, if the user is using a desktop computer, the delivery unit can select a delivery method that includes detailed information. This makes it possible to provide optimal reports by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the delivery unit can input the user's device information into a generation AI and have the generation AI select the optimal delivery method.

[0054] The prediction unit can adjust the level of detail of the model based on the importance of past data when building a prediction model. For example, the prediction unit can build a detailed prediction model based on historical data of high importance. For example, the prediction unit can build a simplified prediction model based on historical data of low importance. The prediction unit can also adjust the level of detail of the prediction model in stages according to the importance of the historical data. This makes it possible to build an efficient prediction model by adjusting the level of detail of the model based on the importance of the historical data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the importance of the historical data into the generative AI and have the generative AI perform the adjustment of the level of detail of the model.

[0055] The prediction unit can apply different prediction algorithms depending on the data category when building a prediction model. For example, the prediction unit can apply a specific financial prediction algorithm to financial data. For example, the prediction unit can apply a trend prediction algorithm to trend data. Furthermore, the prediction unit can apply an outlier prediction algorithm to outlier data. This improves prediction accuracy by applying the most appropriate prediction algorithm for each data category. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the data category into a generative AI and have the generative AI execute the application of the prediction algorithm.

[0056] The prediction unit can determine the priority of prediction models based on the data submission timing when building prediction models. For example, the prediction unit can prioritize the incorporation of the most recent data into the prediction model. For example, it can prioritize the incorporation of data with an approaching submission deadline into the prediction model. The prediction unit can also adjust the priority of prediction models in stages according to the data submission timing. This enables efficient prediction model construction by determining the priority of models based on the data submission timing. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input the data submission timing into a generative AI and have the generative AI perform the determination of model priorities.

[0057] The prediction unit can adjust the order of models based on the relevance of the data when building a prediction model. For example, the prediction unit can prioritize reflecting highly relevant data in the prediction model. For example, it can postpone processing less relevant data. The prediction unit can also adjust the order of models incrementally according to the relevance of the data. This allows for efficient prediction model construction by adjusting the order of models based on the relevance of the data. Some or all of the above processing in the prediction unit may be performed using, for example, generative AI, or without using generative AI. For example, the prediction unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the order of the models.

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

[0059] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has frequently used in the past. For example, the data collection unit can select the most efficient collection method from the user's past data collection history. The data collection unit can also analyze the user's past data collection history and suggest improvements to the collection method. This enables efficient data collection by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI and have the generative AI select the optimal collection method.

[0060] The data collection unit can filter financial statement data based on the user's current work situation and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. The data collection unit can also collect only the necessary data according to the user's work situation. This allows for the collection of highly relevant data by filtering the data based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's work situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0061] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial statement data. For example, the data collection unit can prioritize the collection of financial statement data of companies in the region where the user is located. For example, the data collection unit can collect highly relevant industry data based on the user's geographical location. The data collection unit can also collect region-specific economic data by considering the user's geographical location. This allows for the collection of region-specific and highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0062] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0063] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can apply a trend analysis algorithm to trend data. Furthermore, the analysis unit can apply an anomaly detection algorithm to anomaly data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0064] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of data with an approaching submission deadline. The analysis unit can also adjust the priority of analysis in stages according to the data submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data submission date into the generative AI and have the generative AI determine the priority of analysis.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The collection unit collects financial statement data from companies. The collection unit can automatically collect financial statement data from the database. It can also analyze manually entered financial statement data and financial statement files uploaded by users and save them to the database. Furthermore, it can obtain financial statement data from external systems via APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to analyze the data and identify important figures and trends from the financial statement data. For example, it extracts important figures such as sales and profit margins and analyzes trends. It can also identify outliers using statistical anomaly detection algorithms. Step 3: The creation unit creates an analysis report based on the results analyzed by the analysis unit. The creation unit can create reports using graphs, text, tables, etc., and can also automatically generate reports using generation AI. For example, it can input analysis results and automatically create a report based on a report template. Step 4: The delivery department provides the report created by the production department. The delivery department can provide the report via email or a web portal. It can also provide the report as a printed document. For example, the report can be generated in PDF format and sent to the user. Step 5: The forecasting unit builds a financial forecasting model based on historical data collected by the data collection unit. The forecasting unit uses regression analysis and time series analysis to build the forecasting model and predict future growth and profitability. For example, it forecasts future sales based on historical sales data.

[0067] (Example of form 2) The financial analysis system according to an embodiment of the present invention is a tool that automatically generates analysis reports from a company's financial statements using a generative AI. This financial analysis system takes a company's financial statement data as input, and the generative AI automatically analyzes it to identify important figures and trends and detect anomalies. Next, the generative AI automatically generates an analysis report based on the analysis results. Furthermore, the generative AI constructs a financial forecasting model based on past financial statement data. This model is used to predict future growth and profitability. Users can also compare forecast results for different companies and industries. This tool is designed to reduce the time spent inputting and verifying financial statement data and can be used even without financial knowledge. Because the generative AI automatically analyzes the data and generates reports, users can free up time for decision-making and client interactions. This is expected to improve operational efficiency and productivity. As a result, the financial analysis system can quickly and accurately analyze a company's financial statement data and automatically generate analysis reports.

[0068] The financial analysis system according to this embodiment comprises a collection unit, an analysis unit, a creation unit, a provision unit, and a forecasting unit. The collection unit collects financial statement data of companies. The collection unit can, for example, automatically collect financial statement data from a database. The collection unit can also collect financial statement data that has been manually entered. For example, the collection unit can analyze financial statement files uploaded by users and store them in a database. Furthermore, the collection unit can also obtain financial statement data from external systems via an API. The analysis unit analyzes the data collected by the collection unit. The analysis unit analyzes the data using, for example, statistical analysis or machine learning algorithms. The analysis unit can identify important figures and trends from the financial statement data. For example, the analysis unit extracts important figures such as sales and profit margins and analyzes trends. The analysis unit can also detect outliers from the financial statement data. For example, the analysis unit identifies outliers using a statistical outlier detection algorithm. The creation unit creates an analysis report based on the results analyzed by the analysis unit. The creation unit creates the report using, for example, graphs, text, tables, etc. The creation unit can automatically generate reports using a generation AI. For example, the creation unit inputs analysis results into the generation AI and automatically creates a report based on a report template. The delivery unit provides the reports created by the creation unit. The delivery unit can provide reports, for example, via email or a web portal. The delivery unit can also provide reports as printed materials. For example, the delivery unit generates a report in PDF format and sends it to the user. The forecasting unit builds a financial forecasting model based on historical data collected by the collection unit. The forecasting unit builds a forecasting model, for example, using regression analysis or time series analysis. The forecasting unit is used to predict future growth and profitability. For example, the forecasting unit predicts future sales based on historical sales data. Thus, the financial analysis system according to this embodiment can automate the collection, analysis, report creation, delivery, and construction of forecasting models of a company's financial statement data, supporting rapid decision-making.

[0069] The data collection unit collects financial statement data from companies. For example, the data collection unit can automatically collect financial statement data from databases. Specifically, it can access companies' financial databases and set schedules to periodically retrieve the latest financial statement data. This allows the data collection unit to obtain the latest data immediately after a company's financial results are announced and reflect it in the system. The data collection unit can also collect manually entered financial statement data. For example, it can analyze financial statement files uploaded by users and save them to the database. The data collection unit can use OCR (Optical Character Recognition) technology to extract text data from scanned financial statement images and save it as structured data. Furthermore, the data collection unit can retrieve financial statement data from external systems via APIs. For example, it can use APIs from financial information services to retrieve financial statement data from multiple companies at once and import it into the system. This allows the data collection unit to collect financial statement data in diverse ways and integrate it into the system. The data collection unit also includes functions to ensure data integrity and eliminate duplicate data to guarantee data quality. This enables the data collection unit to provide accurate and reliable data, improving the overall system performance.

[0070] The analysis unit analyzes the data collected by the data collection unit. The analysis unit analyzes the data using, for example, statistical analysis and machine learning algorithms. Specifically, the analysis unit can identify important figures and trends from financial statement data. For example, it can extract important figures such as sales and profit margins and analyze trends. As part of data preprocessing, the analysis unit improves the accuracy of the analysis by imputing missing values ​​and removing outliers. The analysis unit can also detect outliers from financial statement data. For example, it can identify outliers using statistical outlier detection algorithms. This allows the analysis unit to detect abnormal fluctuations in a company's financial situation early, which is useful for risk management. Furthermore, the analysis unit can learn data patterns using machine learning algorithms and predict future trends. For example, it can predict future increases or decreases in sales based on past sales data, supporting a company's growth strategy. The analysis unit can also analyze text data from financial statements using natural language processing technology and extract important information. This allows the analysis unit to perform comprehensive financial analysis that includes not only quantitative data but also qualitative information.

[0071] The creation unit creates analysis reports based on the results analyzed by the analysis unit. The creation unit uses graphs, text, tables, and other elements to create the reports. Specifically, it generates various graphs such as bar graphs, line graphs, and pie charts to visually display the analysis results in an easy-to-understand manner. Furthermore, the creation unit can automatically generate reports using generative AI. For example, the creation unit inputs the analysis results into the generative AI, which then automatically creates a report based on a report template. The generative AI uses natural language generation technology to convert the analysis results into easily understandable text and fill in each section of the report. This allows the creation unit to produce high-quality reports quickly and efficiently. Additionally, the creation unit can create customized reports according to user requests. For example, it can create reports focusing on specific metrics or time periods to provide information tailored to user needs. The creation unit can adjust the report layout and design to provide visually appealing and easy-to-read reports. This allows the creation unit to produce reports that effectively communicate analysis results and support user decision-making.

[0072] The delivery department provides reports created by the creation department. The delivery department can provide reports, for example, via email or a web portal. Specifically, the delivery department generates reports in PDF format and sends them to users. When providing reports via email, the delivery department sends them as attachments to allow users easy access. When providing reports via a web portal, the delivery department operates a dedicated portal site where users can log in and download reports. Furthermore, the delivery department can also provide reports in printed form. For example, the delivery department can generate reports as high-quality printed materials and deliver them to users by mail. This allows the delivery department to provide reports in diverse ways to meet user needs, improving the ease of information delivery. The delivery department also has a function to manage the status of report delivery and confirm whether users have received the reports. This allows the delivery department to efficiently manage the report delivery process and ensure that information is delivered to users.

[0073] The forecasting unit constructs financial forecasting models based on historical data collected by the data collection unit. The forecasting unit constructs these models using methods such as regression analysis and time series analysis. Specifically, it creates models to predict future sales and profits based on historical sales and profit data. When using regression analysis, the forecasting unit considers multiple factors influencing sales and profits and models their relationships. When using time series analysis, the forecasting unit analyzes temporal patterns in historical data and predicts future trends. Using these models, the forecasting unit can predict future growth and profitability. For example, it can predict future sales based on historical sales data, supporting a company's growth strategy. Furthermore, the forecasting unit can periodically update its models and incorporate new data to improve the accuracy of its forecasting models. This allows the forecasting unit to always provide highly accurate forecasts based on the latest information. In addition, the forecasting unit can simulate multiple scenarios and identify the most likely risks and opportunities. This allows the forecasting unit to support corporate decision-making and contribute to future risk management and strategic planning.

[0074] The analysis unit can identify key figures and trends from financial statement data. For example, the analysis unit can extract key figures such as sales revenue and profit margins. For example, the analysis unit can analyze annual fluctuations in sales revenue and identify growth trends. It can also analyze fluctuations in profit margins and identify profitability trends. Furthermore, the analysis unit can analyze fluctuations in cash flow and identify cash flow trends. This improves the accuracy of the analysis report by identifying key figures and trends. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input financial statement data into a generative AI and have the generative AI perform the identification of key figures and trends.

[0075] The analysis unit can detect outliers from financial statement data. The analysis unit identifies outliers using, for example, a statistical outlier detection algorithm. For example, the analysis unit can detect sudden fluctuations in sales as outliers. The analysis unit can also detect abnormal fluctuations in profit margins. Furthermore, the analysis unit can detect abnormal fluctuations in cash flow. By detecting outliers, abnormal data can be discovered at an early stage. Some or all of the above processing in the analysis unit may be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input financial statement data into a generation AI and have the generation AI perform the detection of outliers.

[0076] The forecasting unit can construct a financial forecasting model that predicts future growth and profitability based on past financial statement data. For example, the forecasting unit can predict sales growth rates using regression analysis. For example, the forecasting unit can predict future sales based on past sales data. The forecasting unit can also predict fluctuations in profit margins using time series analysis. Furthermore, the forecasting unit can also forecast cash flows. This allows for an understanding of a company's future trends by predicting future growth and profitability. Some or all of the above-described processes in the forecasting unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the forecasting unit can input past financial statement data into a generative AI and have the generative AI construct a financial forecasting model.

[0077] The data provider can compare forecast results for different companies and industries. For example, it can display the financial forecast results for multiple companies side by side. For example, it can compare growth forecasts for different industries. It can also compare the profitability of companies. Furthermore, it can compare growth forecasts by region. This allows for analysis from a broader perspective by comparing forecast results for different companies and industries. Some or all of the above processing in the data provider may be performed using, for example, a generative AI, or not using a generative AI. For example, the data provider can input the financial forecast results for multiple companies into a generative AI and have the generative AI generate the comparison results.

[0078] The service provider can provide users with analytical reports. For example, the service provider can provide reports via email. For example, the service provider can provide reports via a web portal. Furthermore, the service provider can provide reports as printed materials. In addition, the service provider can generate reports in PDF format and send them to users. This allows for faster decision-making by providing users with analytical reports. Some or all of the above processes in the service provider may be performed, for example, using generative AI, or without generative AI. For example, the service provider can have the generative AI select the method of providing the report.

[0079] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. For example, if the user is in a hurry, the data collection unit can advance the collection timing to collect data quickly. The data collection unit can also adjust the collection timing if the user is concentrating to avoid interrupting the user's concentration. In this way, the burden on the user can be reduced by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0080] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has frequently used in the past. For example, the data collection unit can select the most efficient collection method from the user's past data collection history. The data collection unit can also analyze the user's past data collection history and suggest improvements to the collection method. This enables efficient data collection by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI and have the generative AI select the optimal collection method.

[0081] The data collection unit can filter financial statement data based on the user's current work situation and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. The data collection unit can also collect only the necessary data according to the user's work situation. This allows for the collection of highly relevant data by filtering the data based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's work situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0082] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone collecting less important data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. Also, if the user is in a hurry, the data collection unit may prioritize collecting high-priority data. In this way, important data can be collected preferentially by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0083] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial statement data. For example, the data collection unit can prioritize the collection of financial statement data of companies in the region where the user is located. For example, the data collection unit can collect highly relevant industry data based on the user's geographical location. The data collection unit can also collect region-specific economic data by considering the user's geographical location. This allows for the collection of region-specific and highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0084] The data collection unit can analyze the user's social media activity and collect relevant data when collecting financial statement data. For example, the data collection unit can collect financial statement data of companies that the user has shown interest in on social media. For example, the data collection unit can collect relevant industry data from the user's social media activity. The data collection unit can also analyze the user's social media activity and collect trend-based data. This allows for the collection of highly relevant data by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's social media activity data into a generative AI and have the generative AI perform the collection of relevant data.

[0085] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0086] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0087] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can apply a trend analysis algorithm to trend data. Furthermore, the analysis unit can apply an anomaly detection algorithm to anomaly data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0088] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0089] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of data with an approaching submission deadline. The analysis unit can also adjust the priority of analysis in stages according to the data submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data submission date into the generative AI and have the generative AI determine the priority of analysis.

[0090] The analysis unit can adjust the order of analysis based on the relevance of the data during the analysis. For example, the analysis unit can prioritize the analysis of highly relevant data. For example, the analysis unit can postpone the analysis of less relevant data. The analysis unit can also adjust the order of analysis in stages according to the relevance of the data. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the relevance of the data into a generative AI and have the generative AI perform the adjustment of the order of analysis.

[0091] The report generation unit can estimate the user's emotions and adjust the report's presentation based on those emotions. For example, if the user is nervous, the unit can provide a simple and easy-to-read report. If the user is relaxed, the unit can provide a detailed report. If the user is in a hurry, the unit can provide a concise report. By adjusting the report's presentation according to the user's emotions, the system can provide a report that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the report generation unit may be performed using or without a generative AI. For example, the report generation unit can input user emotion data into a generative AI and have the generative AI adjust the report's presentation.

[0092] The report creation unit can adjust the level of detail in the report based on the importance of the analysis results. For example, the unit can create a detailed report for analysis results of high importance, and a simplified report for analysis results of low importance. The unit can also adjust the level of detail in the report in stages according to the importance of the analysis results. This allows for efficient report creation by adjusting the level of detail in the report based on the importance of the analysis results. Some or all of the above processing in the report creation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the report creation unit can input the importance of the analysis results into the generation AI and have the generation AI adjust the level of detail in the report.

[0093] The report generation unit can apply different report generation algorithms depending on the category of the analysis results when generating reports. For example, the unit can apply a specific financial report generation algorithm to financial analysis results. For example, the unit can apply a trend report generation algorithm to trend analysis results. Furthermore, the unit can apply an anomaly report generation algorithm to anomaly analysis results. This enables the creation of optimal reports according to the category of the analysis results. Some or all of the above processing in the report generation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the report generation unit can input the category of the analysis results into the generation AI and have the generation AI execute the application of the report generation algorithm.

[0094] The creation unit can estimate the user's emotions and adjust the length of the report based on the estimated emotions. For example, if the user is in a hurry, the creation unit can create a short, concise report. If the user is relaxed, the creation unit can create a detailed report. Furthermore, if the user is excited, the creation unit can create a visually stimulating report. This allows the user to receive the most suitable report by adjusting the report length according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the creation unit may be performed using or without a generative AI. For example, the creation unit can input user emotion data into a generative AI and have the generative AI adjust the report length.

[0095] The report creation unit can determine the priority of reports based on the submission timing of the analysis results when creating reports. For example, the creation unit can prioritize the inclusion of the most recent analysis results in the report. For example, the creation unit can prioritize the inclusion of analysis results with approaching submission deadlines in the report. Furthermore, the creation unit can adjust the priority of reports in stages according to the submission timing of the analysis results. This enables efficient report creation by determining the priority of reports based on the submission timing of the analysis results. Some or all of the above processing in the creation unit may be performed using, for example, a generation AI, or without using a generation AI. For example, the creation unit can input the submission timing of the analysis results into the generation AI and have the generation AI determine the priority of the reports.

[0096] The report creation unit can adjust the order of the report based on the relevance of the analysis results. For example, the unit can prioritize reflecting highly relevant analysis results in the report. For example, it can postpone less relevant analysis results. The unit can also adjust the order of the report in stages according to the relevance of the analysis results. This allows for efficient report creation by adjusting the order of the report based on the relevance of the analysis results. Some or all of the above processing in the report creation unit may be performed using, for example, a generation AI, or without a generation AI. For example, the report creation unit can input the relevance of the analysis results into a generation AI and have the generation AI perform the adjustment of the report order.

[0097] The delivery unit can estimate the user's emotions and adjust the report delivery method based on the estimated emotions. For example, if the user is nervous, the delivery unit may select a simple and highly visible delivery method. If the user is relaxed, the delivery unit may select a delivery method that includes detailed information. If the user is in a hurry, the delivery unit may select a delivery method that gets straight to the point. In this way, by adjusting the report delivery method according to the user's emotions, the optimal delivery method for the user can be selected. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using a generative AI, or not using a generative AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the report delivery method.

[0098] The delivery unit can select the optimal delivery method by referring to the user's past report viewing history when providing reports. For example, the delivery unit may prioritize suggesting delivery methods that the user has preferred in the past. For example, the delivery unit can select the most efficient delivery method from the user's past report viewing history. The delivery unit can also analyze the user's past report viewing history and suggest improvements to the delivery method. This makes it possible to provide reports efficiently by referring to the user's past report viewing history. Some or all of the above processing in the delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the delivery unit can input the user's past report viewing history into a generation AI and have the generation AI select the optimal delivery method.

[0099] The delivery unit can estimate the user's emotions and adjust the order in which reports are delivered based on the estimated emotions. For example, if the user is stressed, the delivery unit may postpone less important reports. For example, if the user is relaxed, the delivery unit may prioritize detailed reports. Also, if the user is in a hurry, the delivery unit may prioritize highly important reports. In this way, important reports can be prioritized by adjusting the order in which reports are delivered according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the delivery unit may be performed using a generative AI, or not using a generative AI. For example, the delivery unit can input user emotion data into a generative AI and have the generative AI adjust the order in which reports are delivered.

[0100] The delivery unit can select the optimal delivery method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can select a delivery method optimized for a larger screen. Furthermore, if the user is using a desktop computer, the delivery unit can select a delivery method that includes detailed information. This makes it possible to provide optimal reports by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using, for example, a generation AI, or without a generation AI. For example, the delivery unit can input the user's device information into a generation AI and have the generation AI select the optimal delivery method.

[0101] The prediction unit can estimate the user's emotions and adjust how the prediction model is constructed based on the estimated emotions. For example, if the user is relaxed, the prediction unit can construct a detailed prediction model. If the user is in a hurry, for example, the prediction unit can construct a simplified prediction model. If the user is excited, the prediction unit can also construct a visually stimulating prediction model. In this way, by adjusting how the prediction model is constructed according to the user's emotions, the optimal prediction model for the user can be constructed. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using a generative AI, or not using a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust how the prediction model is constructed.

[0102] The prediction unit can adjust the level of detail of the model based on the importance of past data when building a prediction model. For example, the prediction unit can build a detailed prediction model based on historical data of high importance. For example, the prediction unit can build a simplified prediction model based on historical data of low importance. The prediction unit can also adjust the level of detail of the prediction model in stages according to the importance of the historical data. This makes it possible to build an efficient prediction model by adjusting the level of detail of the model based on the importance of the historical data. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the importance of the historical data into the generative AI and have the generative AI perform the adjustment of the level of detail of the model.

[0103] The prediction unit can apply different prediction algorithms depending on the data category when building a prediction model. For example, the prediction unit can apply a specific financial prediction algorithm to financial data. For example, the prediction unit can apply a trend prediction algorithm to trend data. Furthermore, the prediction unit can apply an outlier prediction algorithm to outlier data. This improves prediction accuracy by applying the most appropriate prediction algorithm for each data category. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without a generative AI. For example, the prediction unit can input the data category into a generative AI and have the generative AI execute the application of the prediction algorithm.

[0104] The prediction unit can estimate the user's emotions and adjust the display method of the prediction results based on the estimated emotions. For example, if the user is nervous, the prediction unit can provide a simple and highly visible display method. For example, if the user is relaxed, the prediction unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the prediction unit can provide a concise display method. In this way, by adjusting the display method of the prediction results according to the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the prediction unit may be performed using a generative AI, or not using a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust the display method of the prediction results.

[0105] The prediction unit can determine the priority of prediction models based on the data submission timing when building prediction models. For example, the prediction unit can prioritize the incorporation of the most recent data into the prediction model. For example, it can prioritize the incorporation of data with an approaching submission deadline into the prediction model. The prediction unit can also adjust the priority of prediction models in stages according to the data submission timing. This enables efficient prediction model construction by determining the priority of models based on the data submission timing. Some or all of the above processing in the prediction unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the prediction unit can input the data submission timing into a generative AI and have the generative AI perform the determination of model priorities.

[0106] The prediction unit can adjust the order of models based on the relevance of the data when building a prediction model. For example, the prediction unit can prioritize reflecting highly relevant data in the prediction model. For example, it can postpone processing less relevant data. The prediction unit can also adjust the order of models incrementally according to the relevance of the data. This allows for efficient prediction model construction by adjusting the order of models based on the relevance of the data. Some or all of the above processing in the prediction unit may be performed using, for example, generative AI, or without using generative AI. For example, the prediction unit can input the relevance of the data into the generative AI and have the generative AI perform the adjustment of the order of the models.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The data collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing and collect data when the user is relaxed. For example, if the user is in a hurry, the data collection unit can advance the collection timing to collect data quickly. The data collection unit can also adjust the collection timing if the user is concentrating to avoid interrupting the user's concentration. In this way, the burden on the user can be reduced by adjusting the collection timing according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI adjust the collection timing.

[0109] The data collection unit can analyze the user's past data collection history and select the optimal collection method. For example, the data collection unit may prioritize suggesting data collection methods that the user has frequently used in the past. For example, the data collection unit can select the most efficient collection method from the user's past data collection history. The data collection unit can also analyze the user's past data collection history and suggest improvements to the collection method. This enables efficient data collection by analyzing past data collection history. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's past data collection history into a generative AI and have the generative AI select the optimal collection method.

[0110] The data collection unit can filter financial statement data based on the user's current work situation and areas of interest. For example, the data collection unit can prioritize collecting data related to projects the user is currently working on. For example, the data collection unit can filter and collect highly relevant data based on the user's areas of interest. The data collection unit can also collect only the necessary data according to the user's work situation. This allows for the collection of highly relevant data by filtering the data based on the user's work situation and areas of interest. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's work situation and areas of interest into a generative AI and have the generative AI perform the filtering.

[0111] The data collection unit can estimate the user's emotions and determine the priority of data to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit may postpone collecting less important data. For example, if the user is relaxed, the data collection unit may prioritize collecting detailed data. Also, if the user is in a hurry, the data collection unit may prioritize collecting high-priority data. In this way, important data can be collected preferentially by prioritizing data according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using a generative AI, or not using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the data.

[0112] The data collection unit can prioritize the collection of highly relevant data by considering the user's geographical location when collecting financial statement data. For example, the data collection unit can prioritize the collection of financial statement data of companies in the region where the user is located. For example, the data collection unit can collect highly relevant industry data based on the user's geographical location. The data collection unit can also collect region-specific economic data by considering the user's geographical location. This allows for the collection of region-specific and highly relevant data by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the data collection unit can input the user's geographical location information into a generative AI and have the generative AI perform the collection of highly relevant data.

[0113] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide simple and easy-to-understand analysis results. If the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide concise analysis results. In this way, by adjusting the presentation of the analysis according to the user's emotions, the analysis results can be provided that are easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using a generative AI, or not using a generative AI. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the presentation of the analysis.

[0114] The analysis unit can adjust the level of detail of the analysis based on the importance of the data during the analysis. For example, the analysis unit can perform a detailed analysis on data with high importance, and a simplified analysis on data with low importance. The analysis unit can also adjust the level of detail of the analysis in stages according to the importance of the data. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the data. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the importance of the data into the generative AI and have the generative AI perform the adjustment of the level of detail of the analysis.

[0115] The analysis unit can apply different analysis algorithms depending on the data category during analysis. For example, the analysis unit can apply a specific financial analysis algorithm to financial data. For example, the analysis unit can apply a trend analysis algorithm to trend data. Furthermore, the analysis unit can apply an anomaly detection algorithm to anomaly data. By applying the appropriate analysis algorithm according to the data category, the accuracy of the analysis is improved. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the data category into the generative AI and have the generative AI execute the application of the analysis algorithm.

[0116] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis. If the user is relaxed, the analysis unit can provide a detailed analysis. Furthermore, if the user is excited, the analysis unit can provide a visually stimulating analysis. By adjusting the length of the analysis according to the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the length of the analysis.

[0117] The analysis unit can determine the priority of analysis based on the data submission date during the analysis process. For example, the analysis unit may prioritize the analysis of the most recent data. For example, the analysis unit may prioritize the analysis of data with an approaching submission deadline. The analysis unit can also adjust the priority of analysis in stages according to the data submission date. This enables efficient analysis by determining the priority of analysis based on the data submission date. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the analysis unit can input the data submission date into the generative AI and have the generative AI determine the priority of analysis.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The collection unit collects financial statement data from companies. The collection unit can automatically collect financial statement data from the database. It can also analyze manually entered financial statement data and financial statement files uploaded by users and save them to the database. Furthermore, it can obtain financial statement data from external systems via APIs. Step 2: The analysis unit analyzes the data collected by the collection unit. The analysis unit uses statistical analysis and machine learning algorithms to analyze the data and identify important figures and trends from the financial statement data. For example, it extracts important figures such as sales and profit margins and analyzes trends. It can also identify outliers using statistical anomaly detection algorithms. Step 3: The creation unit creates an analysis report based on the results analyzed by the analysis unit. The creation unit can create reports using graphs, text, tables, etc., and can also automatically generate reports using generation AI. For example, it can input analysis results and automatically create a report based on a report template. Step 4: The delivery department provides the report created by the production department. The delivery department can provide the report via email or a web portal. It can also provide the report as a printed document. For example, the report can be generated in PDF format and sent to the user. Step 5: The forecasting unit builds a financial forecasting model based on historical data collected by the data collection unit. The forecasting unit uses regression analysis and time series analysis to build the forecasting model and predict future growth and profitability. For example, it forecasts future sales based on historical sales data.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0123] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, provision unit, and forecasting unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and collects financial statement data of a company. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The creation unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and creates a report based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing unit 12, and provides the created report. The forecasting unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and constructs a financial forecasting model based on historical data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0129] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, provision unit, and prediction unit, is implemented, for example, by at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and collects financial statement data of a company. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The creation unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and creates a report based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing unit 12, and provides the created report. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and constructs a financial forecast model based on historical data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0144] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, provision unit, and prediction unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and collects financial statement data of a company. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The creation unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and creates a report based on the analysis results. The provision unit is implemented by, for example, the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing unit 12, and provides the created report. The prediction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, and constructs a financial forecast model based on historical data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0160] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, creation unit, provision unit, and prediction unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and collects financial statement data of a company. The analysis unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and analyzes the collected data. The creation unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and creates a report based on the analysis results. The provision unit is implemented, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing unit 12, and provides the created report. The prediction unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12, and constructs a financial forecast model based on historical data. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0173] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] 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.

[0183] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) The data collection department collects financial statement data, An analysis unit analyzes the data collected by the aforementioned collection unit, A creation unit that creates an analysis report based on the results of the analysis performed by the aforementioned analysis unit, A provisioning unit that provides the report created by the creation unit, The system includes a forecasting unit that constructs a financial forecasting model based on historical data collected by the aforementioned collection unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Identifying key figures and trends from financial statement data The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Detecting anomalies from financial statement data The system described in Appendix 1, characterized by the features described herein. (Note 4) The prediction unit, We build financial forecasting models that predict future growth and profitability based on past financial statement data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, Compare forecast results from different companies and industries. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned supply unit is, Provide users with analytical reports. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial statement data collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting financial statement data, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting financial statement data, the system prioritizes collecting highly relevant data by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is When collecting financial statement data, analyze users' social media activity and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, adjust the level of detail based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the timing of data submission. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, adjust the order of analysis based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned creation unit, It estimates user sentiment and adjusts the way reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned creation unit, When creating a report, adjust the level of detail in the report based on the importance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned creation unit, When creating a report, different report generation algorithms are applied depending on the category of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned creation unit, It estimates the user's sentiment and adjusts the length of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned creation unit, When preparing reports, prioritize reports based on the submission deadline for the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned creation unit, When creating a report, adjust the order of the report based on the relevance of the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, We estimate user sentiment and adjust how reports are delivered based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, When providing reports, the system selects the most suitable delivery method by referring to the user's past report viewing history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates user sentiment and adjusts the order in which reports are presented based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, When providing reports, the optimal delivery method will be selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The prediction unit, We estimate the user's emotions and adjust how the predictive model is built based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, When building a predictive model, adjust the level of detail of the model based on the importance of historical data. The system described in Appendix 1, characterized by the features described herein. (Note 31) The prediction unit, When building a predictive model, different predictive algorithms are applied depending on the data category. The system described in Appendix 1, characterized by the features described herein. (Note 32) The prediction unit, It estimates the user's emotions and adjusts how the prediction results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The prediction unit, When building a predictive model, prioritize the model based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 34) The prediction unit, When building a predictive model, adjust the order of the models based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The data collection department collects financial statement data, An analysis unit analyzes the data collected by the aforementioned collection unit, A creation unit that creates an analysis report based on the results of the analysis performed by the aforementioned analysis unit, A provisioning unit that provides the report created by the creation unit, The system includes a forecasting unit that constructs a financial forecasting model based on historical data collected by the aforementioned collection unit. A system characterized by the following features.

2. The aforementioned analysis unit, Identifying key figures and trends from financial statement data The system according to feature 1.

3. The aforementioned analysis unit, Detecting anomalies from financial statement data The system according to feature 1.

4. The prediction unit, We build financial forecasting models that predict future growth and profitability based on past financial statement data. The system according to feature 1.

5. The aforementioned supply unit is, Compare forecast results from different companies and industries. The system according to feature 1.

6. The aforementioned supply unit is, Provide users with analytical reports. The system according to feature 1.

7. The aforementioned collection unit is We estimate user sentiment and adjust the timing of financial statement data collection based on the estimated user sentiment. The system according to feature 1.

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

9. The aforementioned collection unit is When collecting financial statement data, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

10. The aforementioned collection unit is It estimates the user's emotions and prioritizes the data to collect based on those estimated emotions. The system according to feature 1.

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