system

Abstract

The system according to this embodiment aims to efficiently collect and analyze data from each group company and to appropriately understand the governance situation and risks. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a prediction unit, and an alert unit. The collection unit collects data from each group company. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a report based on the analysis results obtained by the analysis unit. The provision unit provides the report generated by the generation unit. The prediction unit predicts future risks based on the data analyzed by the analysis unit. The alert unit sends an alert when the risks predicted by the prediction unit increase.

Description

Technical Field

[0003]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot's character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance 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 is difficult to efficiently collect and analyze the data of each group company and appropriately grasp the governance situation and risks.

[0005] The system according to the embodiment aims to efficiently collect and analyze the data of each group company and appropriately grasp the governance situation and risks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a prediction unit, and an alert unit. The collection unit collects data from each group company. The analysis unit analyzes the data collected by the collection unit. The generation unit generates a report based on the analysis results obtained by the analysis unit. The provision unit provides the report generated by the generation unit. The prediction unit predicts future risks based on the data analyzed by the analysis unit. The alert unit sends an alert when the risks predicted by the prediction unit increase. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and analyze data from each group company, and appropriately grasp the governance status and risks. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 governance status report generation system according to an embodiment of the present invention is a system that automatically collects and analyzes data from each group company and automatically generates a periodic report on the governance status. This system is provided to the human resources department and management in a visually easy-to-understand format, allowing for instant grasp of risk and compliance status. Since the report is compiled in natural language by the generating AI, it is easy to modify and re-edit. Specifically, it consists of the following steps: First, data from each group company is automatically collected. Next, the AI ​​analyzes the collected data and evaluates the governance status, risk status, and compliance status. The evaluation results are compiled into a report in natural language by the generating AI. Because this report is provided in a visually easy-to-understand format, the human resources department and management can instantly grasp the situation. Furthermore, a function has been added in which the generating AI learns past trends and patterns from the data of each group company and predicts the occurrence of future governance risks and compliance violations. When the risk increases, an alert is automatically sent to management. This function allows management to take countermeasures quickly. For example, financial data, human resources data, and operational data from each group company are collected, and the AI ​​analyzes this data. Based on the analysis results, the AI ​​evaluates the governance status, risk status, and compliance status, and generates a report in natural language. This report includes visual elements such as graphs and charts, allowing management to intuitively grasp the situation. Furthermore, the AI ​​learns from past data and predicts future risks. For example, it identifies specific patterns from past data, and if there is a high probability of that pattern recurring, it sends an alert to management. This alert allows management to take proactive measures, preventing risks before they occur. Thus, the AI ​​tool of this invention not only automatically collects and analyzes data from each group company and automatically generates regular reports on governance status, but also has the function of predicting future risks and sending alerts to management. This allows management to take swift countermeasures, leading to improved governance and risk management.This allows the governance status report generation system to automatically collect and analyze data from each group company and automatically generate regular reports on the governance status.

[0029] The governance status report generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a forecasting unit, and an alert unit. The collection unit collects data from each group company. For example, the collection unit collects financial data, personnel data, and operational data from each group company. For example, the collection unit can collect financial data from each group company and obtain information such as revenue, expenses, and profits. The collection unit can also collect personnel data from each group company and obtain information such as the number of employees, job titles, and salaries. Furthermore, the collection unit can collect operational data from each group company and obtain information such as project progress and operational efficiency. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to evaluate the governance status, risk status, and compliance status. For example, to evaluate the governance status, the analysis unit can analyze the evaluation of internal controls and the compliance status. Furthermore, to evaluate the risk status, the analysis unit can analyze the types of risks and the impact of risks. Furthermore, to evaluate the compliance status, the analysis unit can analyze the status of legal compliance and compliance with internal regulations. The generation unit generates reports based on the analysis results obtained by the analysis unit. For example, the generation unit can generate reports based on evaluation results and create reports that include evaluation scores and evaluation criteria. The generation unit can use generation AI to summarize evaluation results as reports in natural language. The delivery unit provides the reports generated by the generation unit. For example, the delivery unit can provide the generated reports in a visually easy-to-understand format. For example, the delivery unit can provide reports that include visual elements such as graphs, charts, and dashboards. The prediction unit predicts future risks based on the data analyzed by the analysis unit. For example, the prediction unit can learn past trends and patterns to predict the occurrence of future governance risks and compliance violations. The prediction unit can use generation AI to find specific patterns from past data and predict risks if there is a high probability that those patterns will occur again. The alert unit sends alerts when the risks predicted by the prediction unit increase.The alert unit can, for example, automatically send alerts to management when risks increase. The alert unit can also send alerts based on, for example, a risk score threshold or the occurrence of a specific event. As a result, the governance status report generation system according to the embodiment can automatically collect and analyze data from each group company and automatically generate reports on governance status on a regular basis.

[0030] The Data Collection Department collects data from each group company. Specifically, it collects financial data, human resources data, and operational data from each group company. Financial data includes detailed information such as revenue, expenses, profits, assets, and liabilities. This data is automatically obtained from each group company's accounting system and ERP system. Human resources data includes the number of employees, job titles, salaries, attendance information, and performance evaluations, and this is obtained from the human resources management system. Operational data includes project progress, operational efficiency, working hours, and quality control data, and this is obtained from the project management system and operational management system. The Data Collection Department centrally manages this data and performs data cleansing to ensure data integrity and accuracy. Furthermore, the Data Collection Department adjusts the frequency and timing of data collection to enable real-time data updates. This allows the Data Collection Department to always be aware of the latest situation of each group company and respond quickly. In addition, the Data Collection Department implements encryption technology and access control to ensure data security and prevent data leaks and unauthorized access. This enables the Data Collection Department to achieve highly reliable data collection and improve the reliability of the entire system.

[0031] The Analysis Department analyzes the data collected by the Data Collection Department. Specifically, it analyzes the collected data from multiple perspectives to evaluate the governance status, risk status, and compliance status. The evaluation of the governance status includes the evaluation of internal controls and the analysis of compliance status. For example, in the evaluation of internal controls, the effectiveness of controls is evaluated based on the business processes and internal audit results of each group company. In the analysis of compliance status, the status of legal compliance and adherence to internal regulations is evaluated to check for any violations. The evaluation of the risk status includes the analysis of the types of risks and the impact of risks. For example, various risks such as financial risks, operational risks, and legal risks are identified and their impact is evaluated. The evaluation of compliance status includes the analysis of legal compliance and adherence to internal regulations. As a result, the Analysis Department can comprehensively evaluate the governance status of each group company and support the early detection of risks and the planning of countermeasures. Furthermore, the Analysis Department can use AI to perform data pattern recognition and anomaly detection, enabling it to detect unusual data movements early. As a result, the Analysis Department can not only grasp the situation in real time but also respond to long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The generation unit generates reports based on the analysis results obtained by the analysis unit. Specifically, it creates reports detailing governance status, risk status, and compliance status based on the evaluation results. The generation unit uses generation AI to summarize the evaluation results as reports in natural language. Based on the input evaluation results, the generation AI selects appropriate context and expression to generate reports that are easy to read and understand. For example, based on the evaluation results of governance status, it details the evaluation of internal controls and compliance status, and based on the evaluation results of risk status, it specifically explains the types and impacts of risks. Furthermore, the generation unit adds visual elements to the reports, visualizing data using graphs, charts, dashboards, etc. This makes the report content easier to understand intuitively. The generation unit can also customize the format and content of reports, generating reports that meet specific needs and requirements. This allows the generation unit to report the governance status of each group company in detail and provide useful information to management and stakeholders.

[0033] The Delivery Department provides reports generated by the Generation Department. Specifically, it provides generated reports in a visually easy-to-understand format. For example, the Delivery Department provides reports that include visual elements such as graphs, charts, and dashboards. This makes the report content easier to understand intuitively and supports rapid decision-making. The Delivery Department provides reports through a web portal and mobile app, allowing stakeholders to access them anytime, anywhere. Furthermore, the Delivery Department can set a report distribution schedule and automatically distribute the latest reports regularly. This ensures that stakeholders are always up-to-date and can respond quickly. The Delivery Department also provides a function to customize the content of reports, allowing it to generate reports tailored to specific needs and requirements. For example, it can generate detailed reports on specific departments or projects and provide them to stakeholders. This allows the Delivery Department to report in detail on the governance status of each group company and provide valuable information to management and stakeholders.

[0034] The prediction unit predicts future risks based on data analyzed by the analysis unit. Specifically, it learns past trends and patterns to predict future governance risks and compliance violations. Using generative AI, the prediction unit identifies specific patterns from historical data and predicts risks if those patterns are likely to recur. For example, it identifies specific risk factors and anomalous patterns based on historical financial, human resources, and operational data, and calculates their probability of occurrence. Furthermore, the prediction unit conducts simulations and considers multiple scenarios to identify the most likely risks. This allows the prediction unit to predict future risks with high accuracy and provide information for taking appropriate countermeasures. The prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest situations. For example, if new data is provided by the data collection unit, the prediction unit immediately incorporates that data and updates the prediction results. In addition, the prediction unit can perform more accurate risk assessments by considering regional characteristics and past disaster history. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information and support quick and appropriate responses.

[0035] The alert unit sends alerts when the risks predicted by the forecast unit increase. Specifically, it automatically sends alerts to management when risks rise. The alert unit can send alerts based, for example, on risk score thresholds or the occurrence of specific events. Risk score thresholds are set based on the impact and probability of occurrence of risks calculated by the forecast unit. The occurrence of specific events includes actions that may violate laws and regulations or abnormal fluctuations in financial data. When these conditions are met, the alert unit immediately notifies management and stakeholders. Notification methods include email, SMS, and push notifications, enabling the rapid transmission of important information. Furthermore, the alert unit provides a function to customize the content and priority of alerts, supporting appropriate responses tailored to specific risks and situations. For example, it can emphasize the need for immediate action for high-risk events and encourage regular monitoring for low-risk events. This allows the alert unit to provide management and stakeholders with quick and appropriate information, supporting early detection and rapid response to risks.

[0036] The data collection unit can collect financial data, personnel data, and operational data from each group company. For example, the data collection unit can collect financial data from each group company and obtain information such as revenue, expenses, and profits. For example, the data collection unit can also collect personnel data from each group company and obtain information such as the number of employees, job titles, and salaries. For example, the data collection unit can collect operational data from each group company and obtain information such as project progress and operational efficiency. This enables comprehensive data collection by collecting financial data, personnel data, and operational data from each group company. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from each group company into the AI ​​and have the AI ​​perform the data collection.

[0037] The analysis department can analyze the data collected by the collection department to evaluate governance, risk, and compliance status. For example, the analysis department can analyze the collected data to evaluate governance status. For example, the analysis department can analyze internal controls and compliance status. For example, the analysis department can analyze the types and impacts of risks to evaluate risk status. For example, the analysis department can analyze compliance status, such as compliance with laws and regulations and compliance with internal regulations to evaluate compliance status. This allows for detailed analysis by analyzing the collected data and evaluating governance, risk, and compliance status. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the collected data into AI and have the AI ​​perform the data analysis.

[0038] The generation unit can generate a report based on the evaluation results obtained by the analysis unit. For example, the generation unit can generate a report based on the evaluation results, creating a report that includes evaluation scores and evaluation criteria. The generation unit can use a generation AI to summarize the evaluation results as a report in natural language. This makes it possible to create an accurate report by generating a report based on the evaluation results. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the evaluation results into a generation AI and have the generation AI execute the report generation.

[0039] The delivery unit can provide the reports generated by the generation unit in a visually easy-to-understand format. For example, the delivery unit can provide the generated reports in a format that includes visual elements such as graphs, charts, and dashboards. For example, the delivery unit can visually show data fluctuations using graphs. For example, the delivery unit can visually show data comparisons using charts. For example, the delivery unit can display multiple data sets in a unified manner using dashboards. This allows users to intuitively grasp the situation by providing reports in a visually easy-to-understand format. Some or all of the above-described processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the generated reports into AI and have the AI ​​perform the task of providing them in a format that includes visual elements.

[0040] The prediction unit can learn past trends and patterns from data analyzed by the analysis unit and predict future governance risks and compliance violations. For example, the prediction unit learns past trends and patterns and predicts future risks. The prediction unit can use generative AI to find specific patterns from past data and predict risks if there is a high probability that the pattern will occur again. For example, the prediction unit learns past trends and patterns using time series data analysis and pattern recognition algorithms. The prediction unit can also predict future risks by calculating the probability of risk occurrence based on past data. This allows for proactive measures to be taken by learning past trends and patterns and predicting future risks. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, the prediction unit can input past data into the generative AI and have the generative AI perform risk prediction.

[0041] The alert unit can send alerts when the risk predicted by the prediction unit increases. For example, the alert unit can automatically send alerts to management when the risk increases. The alert unit can also send alerts based on, for example, a risk score threshold or the occurrence of a specific event. For example, the alert unit can send an alert when the risk score exceeds a certain threshold. The alert unit can also send an alert when a specific event occurs. This allows for quick countermeasures to be taken by sending an alert when the risk increases. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input predicted risk data into AI and have the AI ​​execute the sending of alerts.

[0042] The data collection unit can evaluate the reliability of data when collecting data from each group company and prioritize the collection of reliable data. For example, the data collection unit can verify the source of the data and prioritize the collection of data from reliable data sources. For example, the data collection unit can also check the consistency of the data and prioritize the collection of consistent data. For example, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. This improves data quality by prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data reliability evaluation into AI and have the AI ​​perform the collection of reliable data.

[0043] The data collection unit can adjust its data collection method to account for changes in the business processes of each group company during data collection. For example, the data collection unit can adjust the timing of data collection in response to changes in business processes. The data collection unit can also adjust the types of data to be collected in response to changes in business processes. The data collection unit can also change the data collection method in response to changes in business processes. By adjusting the data collection method in response to changes in business processes, efficient data collection becomes possible. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input business process change data into the AI ​​and have the AI ​​perform the adjustment of the data collection method.

[0044] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each group company during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the location of each group company. The data collection unit can also collect data by considering the characteristics of each region based on geographical location information. For example, the data collection unit can evaluate the risks of each region based on geographical location information and collect highly relevant data. In this way, highly relevant data can be efficiently collected by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0045] The data collection unit can analyze the social media activities of each group company and collect relevant data during data collection. For example, the data collection unit can monitor the social media activities of each group company and collect relevant data. The data collection unit can also analyze trends on social media and collect relevant data. The data collection unit can also analyze user reactions on social media and collect relevant data. This allows for more multifaceted data collection by analyzing social media activities and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0046] The analysis department can improve the accuracy of its analysis by considering the performance indicators of each group company during data analysis. For example, the analysis department can improve the accuracy of its analysis by considering the sales data of each group company. The analysis department can also improve the accuracy of its analysis by considering the profit data of each group company. The analysis department can also improve the accuracy of its analysis by considering the growth rate data of each group company. By improving the accuracy of the analysis by considering performance indicators, more accurate analysis results can be obtained. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input performance indicator data into AI and have the AI ​​perform the analysis accuracy improvement.

[0047] The analysis department can adjust its analysis methods when analyzing data, taking into account changes in the business processes of each group company. For example, the analysis department can change its analysis methods in response to changes in business processes. The analysis department can also adjust the timing of the analysis in response to changes in business processes. The analysis department can also change the data being analyzed in response to changes in business processes. By adjusting the analysis methods in response to changes in business processes, efficient data analysis becomes possible. Some or all of the above processes in the analysis department may be performed using AI, or they may not be performed using AI. For example, the analysis department can input business process change data into AI and have the AI ​​perform the adjustment of the analysis methods.

[0048] The analysis department can perform data analysis while considering the geographical location information of each group company. For example, the analysis department can perform analysis considering the characteristics of each region based on the location of each group company. For example, the analysis department can also evaluate and analyze the risks of each region based on geographical location information. For example, the analysis department can compare and analyze the performance of each region based on geographical location information. By performing analysis while considering geographical location information, it is possible to obtain analysis results that reflect the characteristics of each region. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input geographical location information into AI and have the AI ​​perform the analysis.

[0049] The analysis department can improve the accuracy of its analysis by referring to relevant literature from each group company during data analysis. For example, the analysis department can improve the accuracy of its analysis by referring to past reports from each group company. The analysis department can also improve the accuracy of its analysis by referring to industry reports from each group company. The analysis department can also improve the accuracy of its analysis by referring to academic papers from each group company. By improving the accuracy of the analysis by referring to relevant literature, more accurate analysis results can be obtained. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input relevant literature data into AI and have the AI ​​perform the analysis accuracy improvement.

[0050] The generation unit can adjust the level of detail in the report based on the performance indicators of each group company when generating the report. For example, the generation unit can generate a detailed report based on the sales data of each group company. The generation unit can also generate a detailed report based on the profit data of each group company. The generation unit can also generate a detailed report based on the growth rate data of each group company. This allows for the generation of an appropriate report by adjusting the level of detail based on performance indicators. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input performance indicator data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0051] The generation unit can adjust the content of reports when generating them, taking into account changes in the business processes of each group company. For example, the generation unit can change the content of reports in response to changes in business processes. The generation unit can also adjust the timing of reports in response to changes in business processes. For example, the generation unit can change the data targeted by reports in response to changes in business processes. By adjusting the content of reports in response to changes in business processes, appropriate reports can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input business process change data into a generation AI and have the generation AI perform the adjustment of the report content.

[0052] The generation unit can determine the priority of reports based on the submission dates of each group company when generating reports. For example, the generation unit can prioritize the generation of reports with approaching submission deadlines. The generation unit can also, for example, postpone the generation of reports with distant submission deadlines. The generation unit can also, for example, adjust the order of report generation based on submission deadlines. This enables efficient report generation by determining the priority of reports based on submission dates. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input submission date data into a generation AI and have the generation AI perform the determination of report priorities.

[0053] The generation unit can adjust the order of reports based on the relationships between group companies when generating reports. For example, the generation unit can prioritize generating reports for highly relevant group companies. For example, the generation unit can postpone generating reports for less relevant group companies. The generation unit can also adjust the report generation order based on relevance. This allows for efficient report generation by adjusting the report order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevance data into a generation AI and have the generation AI perform the adjustment of the report order.

[0054] The service provider can select the optimal display method by referring to the user's past operation history when providing reports. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. For example, the service provider can also suggest the most efficient display method based on the user's past operation history. For example, the service provider can provide a customized display method based on display methods that the user has used in the past. In this way, by selecting the optimal display method by referring to past operation history, a display method suitable for the user can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past operation history data into AI and have the AI ​​perform the selection of the optimal display method.

[0055] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a desktop, the service provider can also provide a display method that includes detailed information. In this way, by selecting the optimal display method considering device information, the service provider can provide a display method that is suitable for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input device information into AI and have the AI ​​select the optimal display method.

[0056] The prediction unit can improve the accuracy of its predictions by referring to the historical risk data of each group company when making risk predictions. For example, the prediction unit improves the accuracy of its predictions based on the historical risk data of each group company. The prediction unit can also, for example, analyze historical risk data and identify risk occurrence patterns. The prediction unit can also, for example, calculate the probability of risk occurrence based on historical risk data. By improving the accuracy of predictions by referring to historical risk data, it is possible to provide more accurate risk predictions. Some or all of the above processing in the prediction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the prediction unit can input historical risk data into a generation AI and have the generation AI perform the improvement of prediction accuracy.

[0057] The prediction unit can adjust its prediction method when predicting risks, taking into account fluctuations in the business processes of each group company. For example, the prediction unit can change the prediction method in response to fluctuations in business processes. The prediction unit can also adjust the timing of the prediction in response to fluctuations in business processes. For example, the prediction unit can change the data to be predicted in response to fluctuations in business processes. This allows for efficient risk prediction by adjusting the prediction method in response to fluctuations in business processes. Some or all of the above-described processes in the prediction unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the prediction unit can input business process fluctuation data into a generation AI and have the generation AI perform the adjustment of the prediction method.

[0058] The prediction unit can make predictions by taking into account the geographical location information of each group company when predicting risks. For example, the prediction unit predicts risks for each region based on the location of each group company. The prediction unit can also calculate the probability of risk occurrence for each region based on geographical location information. The prediction unit can also identify risk factors for each region based on geographical location information. As a result, by making predictions while taking geographical location information into account, risks for each region can be accurately predicted. Some or all of the above processing in the prediction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the prediction unit can input geographical location information into a generation AI and have the generation AI perform risk prediction.

[0059] The prediction unit can improve the accuracy of its predictions by referring to relevant literature for each group company when making risk predictions. For example, the prediction unit can improve the accuracy of its predictions by referring to past reports of each group company. The prediction unit can also improve the accuracy of its predictions by referring to industry reports of each group company. The prediction unit can also improve the accuracy of its predictions by referring to academic papers of each group company. By improving the accuracy of predictions by referring to relevant literature, it is possible to provide more accurate risk predictions. Some or all of the above processing in the prediction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the prediction unit can input relevant literature data into a generation AI and have the generation AI perform the improvement of prediction accuracy.

[0060] The alert unit can improve the accuracy of alerts by referring to the historical risk data of each group company when sending an alert. For example, the alert unit can improve the accuracy of alerts based on the historical risk data of each group company. The alert unit can also, for example, analyze historical risk data to identify risk occurrence patterns. The alert unit can also, for example, calculate the probability of risk occurrence based on historical risk data. By improving the accuracy of alerts by referring to historical risk data, it is possible to provide more accurate alerts. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input historical risk data into AI and have the AI ​​perform the task of improving the accuracy of alerts.

[0061] The alert unit can send alerts while considering the geographical location information of each group company. For example, the alert unit can evaluate the risks for each region based on the location of each group company and send alerts. For example, the alert unit can calculate the probability of risk occurrence for each region based on geographical location information and send alerts. For example, the alert unit can identify risk factors for each region based on geographical location information and send alerts. In this way, by sending alerts while considering geographical location information, it is possible to accurately notify of risks for each region. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input geographical location information into AI and have the AI ​​execute the sending of alerts.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The governance status report generation system can also be equipped with a function to evaluate the reliability of data and prioritize the collection of reliable data when collecting data from each group company. For example, it can verify the source of the data and prioritize the collection of data from reliable data sources. It can also check the consistency of the data and prioritize the collection of consistent data. Furthermore, it can evaluate the timeliness of the data and prioritize the collection of the most recent data. This improves data quality by prioritizing the collection of reliable data. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the data reliability evaluation into the AI ​​and have the AI ​​perform the collection of reliable data.

[0064] The governance status report generation system can also include a function to adjust the data collection method in consideration of changes in the business processes of each group company. For example, the timing of data collection can be adjusted in accordance with changes in business processes. It is also possible to adjust the type of data collected in accordance with changes in business processes. Furthermore, the method of data collection can be changed in accordance with changes in business processes. This allows for efficient data collection by adjusting the data collection method in consideration of changes in business processes. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input business process change data into the AI ​​and have the AI ​​perform the adjustment of the collection method.

[0065] The governance status report generation system can also be equipped with a function to prioritize the collection of highly relevant data, taking into account the geographical location information of each group company. For example, it can prioritize the collection of highly relevant data based on the location of each group company. It is also possible to collect data considering the characteristics of each region based on geographical location information. Furthermore, it is possible to evaluate the risks of each region based on geographical location information and collect highly relevant data. In this way, highly relevant data can be efficiently collected by considering geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input geographical location information into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0066] The governance status report generation system can also include a function to improve the accuracy of the analysis by considering the performance indicators of each group company. For example, it can improve the accuracy of the analysis by considering the sales data of each group company. It can also improve the accuracy of the analysis by considering the profit data of each group company. Furthermore, it can improve the accuracy of the analysis by considering the growth rate data of each group company. By improving the accuracy of the analysis by considering performance indicators, more accurate analysis results can be obtained. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input performance indicator data into the AI ​​and have the AI ​​perform the analysis accuracy improvement.

[0067] The governance status report generation system may also include a function to improve the accuracy of the analysis by referring to relevant literature for each group company. For example, it may improve the accuracy of the analysis by referring to past reports of each group company. It may also improve the accuracy of the analysis by referring to industry reports of each group company. Furthermore, it may improve the accuracy of the analysis by referring to academic papers of each group company. By improving the accuracy of the analysis by referring to relevant literature, more accurate analysis results can be obtained. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department may input relevant literature data into the AI ​​and have the AI ​​perform the analysis accuracy improvement.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The data collection unit collects data from each group company. For example, the data collection unit collects financial data, personnel data, and operational data from each group company. The data collection unit can collect financial data from each group company and obtain information such as revenue, expenses, and profits. The data collection unit can also collect personnel data from each group company and obtain information such as the number of employees, job titles, and salaries. Furthermore, the data collection unit can collect operational data from each group company and obtain information such as project progress and operational efficiency. Step 2: The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes the collected data to evaluate the governance situation, risk situation, and compliance situation. To evaluate the governance situation, the analysis department can analyze the evaluation of internal controls and the compliance status. To evaluate the risk situation, it can also analyze the types of risks and the impact of those risks. Furthermore, to evaluate the compliance situation, it can also analyze the compliance status with laws and regulations and the compliance status with internal regulations. Step 3: The generation unit generates a report based on the analysis results obtained by the analysis unit. For example, the generation unit can generate a report based on evaluation results and create a report that includes evaluation scores and evaluation criteria. The generation unit can use generation AI to summarize the evaluation results as a report in natural language. Step 4: The provider unit provides the report generated by the generator unit. The provider unit can, for example, provide the generated report in a visually easy-to-understand format. The provider unit can provide a report that includes visual elements such as graphs, charts, and dashboards. Step 5: The prediction unit predicts future risks based on the data analyzed by the analysis unit. For example, the prediction unit can learn from past trends and patterns to predict future governance risks and compliance breaches. Using generative AI, the prediction unit can identify specific patterns from past data and predict risks if those patterns are likely to occur again. Step 6: The alerting unit sends an alert when the risk predicted by the forecasting unit increases. For example, the alerting unit can automatically send an alert to management when the risk increases. The alerting unit can send alerts based on risk score thresholds or the occurrence of specific events.

[0070] (Example of form 2) The governance status report generation system according to an embodiment of the present invention is a system that automatically collects and analyzes data from each group company and automatically generates a periodic report on the governance status. This system is provided to the human resources department and management in a visually easy-to-understand format, allowing for instant grasp of risk and compliance status. Since the report is compiled in natural language by the generating AI, it is easy to modify and re-edit. Specifically, it consists of the following steps: First, data from each group company is automatically collected. Next, the AI ​​analyzes the collected data and evaluates the governance status, risk status, and compliance status. The evaluation results are compiled into a report in natural language by the generating AI. Because this report is provided in a visually easy-to-understand format, the human resources department and management can instantly grasp the situation. Furthermore, a function has been added in which the generating AI learns past trends and patterns from the data of each group company and predicts the occurrence of future governance risks and compliance violations. When the risk increases, an alert is automatically sent to management. This function allows management to take countermeasures quickly. For example, financial data, human resources data, and operational data from each group company are collected, and the AI ​​analyzes this data. Based on the analysis results, the AI ​​evaluates the governance status, risk status, and compliance status, and generates a report in natural language. This report includes visual elements such as graphs and charts, allowing management to intuitively grasp the situation. Furthermore, the AI ​​learns from past data and predicts future risks. For example, it identifies specific patterns from past data, and if there is a high probability of that pattern recurring, it sends an alert to management. This alert allows management to take proactive measures, preventing risks before they occur. Thus, the AI ​​tool of this invention not only automatically collects and analyzes data from each group company and automatically generates regular reports on governance status, but also has the function of predicting future risks and sending alerts to management. This allows management to take swift countermeasures, leading to improved governance and risk management.This allows the governance status report generation system to automatically collect and analyze data from each group company and automatically generate regular reports on the governance status.

[0071] The governance status report generation system according to this embodiment comprises a collection unit, an analysis unit, a generation unit, a provision unit, a forecasting unit, and an alert unit. The collection unit collects data from each group company. For example, the collection unit collects financial data, personnel data, and operational data from each group company. For example, the collection unit can collect financial data from each group company and obtain information such as revenue, expenses, and profits. The collection unit can also collect personnel data from each group company and obtain information such as the number of employees, job titles, and salaries. Furthermore, the collection unit can collect operational data from each group company and obtain information such as project progress and operational efficiency. The analysis unit analyzes the data collected by the collection unit. For example, the analysis unit analyzes the collected data to evaluate the governance status, risk status, and compliance status. For example, to evaluate the governance status, the analysis unit can analyze the evaluation of internal controls and the compliance status. Furthermore, to evaluate the risk status, the analysis unit can analyze the types of risks and the impact of risks. Furthermore, to evaluate the compliance status, the analysis unit can analyze the status of legal compliance and compliance with internal regulations. The generation unit generates reports based on the analysis results obtained by the analysis unit. For example, the generation unit can generate reports based on evaluation results and create reports that include evaluation scores and evaluation criteria. The generation unit can use generation AI to summarize evaluation results as reports in natural language. The delivery unit provides the reports generated by the generation unit. For example, the delivery unit can provide the generated reports in a visually easy-to-understand format. For example, the delivery unit can provide reports that include visual elements such as graphs, charts, and dashboards. The prediction unit predicts future risks based on the data analyzed by the analysis unit. For example, the prediction unit can learn past trends and patterns to predict the occurrence of future governance risks and compliance violations. The prediction unit can use generation AI to find specific patterns from past data and predict risks if there is a high probability that those patterns will occur again. The alert unit sends alerts when the risks predicted by the prediction unit increase.The alert unit can, for example, automatically send alerts to management when risks increase. The alert unit can also send alerts based on, for example, a risk score threshold or the occurrence of a specific event. As a result, the governance status report generation system according to the embodiment can automatically collect and analyze data from each group company and automatically generate reports on governance status on a regular basis.

[0072] The Data Collection Department collects data from each group company. Specifically, it collects financial data, human resources data, and operational data from each group company. Financial data includes detailed information such as revenue, expenses, profits, assets, and liabilities. This data is automatically obtained from each group company's accounting system and ERP system. Human resources data includes the number of employees, job titles, salaries, attendance information, and performance evaluations, and this is obtained from the human resources management system. Operational data includes project progress, operational efficiency, working hours, and quality control data, and this is obtained from the project management system and operational management system. The Data Collection Department centrally manages this data and performs data cleansing to ensure data integrity and accuracy. Furthermore, the Data Collection Department adjusts the frequency and timing of data collection to enable real-time data updates. This allows the Data Collection Department to always be aware of the latest situation of each group company and respond quickly. In addition, the Data Collection Department implements encryption technology and access control to ensure data security and prevent data leaks and unauthorized access. This enables the Data Collection Department to achieve highly reliable data collection and improve the reliability of the entire system.

[0073] The Analysis Department analyzes the data collected by the Data Collection Department. Specifically, it analyzes the collected data from multiple perspectives to evaluate the governance status, risk status, and compliance status. The evaluation of the governance status includes the evaluation of internal controls and the analysis of compliance status. For example, in the evaluation of internal controls, the effectiveness of controls is evaluated based on the business processes and internal audit results of each group company. In the analysis of compliance status, the status of legal compliance and adherence to internal regulations is evaluated to check for any violations. The evaluation of the risk status includes the analysis of the types of risks and the impact of risks. For example, various risks such as financial risks, operational risks, and legal risks are identified and their impact is evaluated. The evaluation of compliance status includes the analysis of legal compliance and adherence to internal regulations. As a result, the Analysis Department can comprehensively evaluate the governance status of each group company and support the early detection of risks and the planning of countermeasures. Furthermore, the Analysis Department can use AI to perform data pattern recognition and anomaly detection, enabling it to detect unusual data movements early. As a result, the Analysis Department can not only grasp the situation in real time but also respond to long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0074] The generation unit generates reports based on the analysis results obtained by the analysis unit. Specifically, it creates reports detailing governance status, risk status, and compliance status based on the evaluation results. The generation unit uses generation AI to summarize the evaluation results as reports in natural language. Based on the input evaluation results, the generation AI selects appropriate context and expression to generate reports that are easy to read and understand. For example, based on the evaluation results of governance status, it details the evaluation of internal controls and compliance status, and based on the evaluation results of risk status, it specifically explains the types and impacts of risks. Furthermore, the generation unit adds visual elements to the reports, visualizing data using graphs, charts, dashboards, etc. This makes the report content easier to understand intuitively. The generation unit can also customize the format and content of reports, generating reports that meet specific needs and requirements. This allows the generation unit to report the governance status of each group company in detail and provide useful information to management and stakeholders.

[0075] The Delivery Department provides reports generated by the Generation Department. Specifically, it provides generated reports in a visually easy-to-understand format. For example, the Delivery Department provides reports that include visual elements such as graphs, charts, and dashboards. This makes the report content easier to understand intuitively and supports rapid decision-making. The Delivery Department provides reports through a web portal and mobile app, allowing stakeholders to access them anytime, anywhere. Furthermore, the Delivery Department can set a report distribution schedule and automatically distribute the latest reports regularly. This ensures that stakeholders are always up-to-date and can respond quickly. The Delivery Department also provides a function to customize the content of reports, allowing it to generate reports tailored to specific needs and requirements. For example, it can generate detailed reports on specific departments or projects and provide them to stakeholders. This allows the Delivery Department to report in detail on the governance status of each group company and provide valuable information to management and stakeholders.

[0076] The prediction unit predicts future risks based on data analyzed by the analysis unit. Specifically, it learns past trends and patterns to predict future governance risks and compliance violations. Using generative AI, the prediction unit identifies specific patterns from historical data and predicts risks if those patterns are likely to recur. For example, it identifies specific risk factors and anomalous patterns based on historical financial, human resources, and operational data, and calculates their probability of occurrence. Furthermore, the prediction unit conducts simulations and considers multiple scenarios to identify the most likely risks. This allows the prediction unit to predict future risks with high accuracy and provide information for taking appropriate countermeasures. The prediction unit can continuously revise its prediction results based on real-time updated data to respond to the latest situations. For example, if new data is provided by the data collection unit, the prediction unit immediately incorporates that data and updates the prediction results. In addition, the prediction unit can perform more accurate risk assessments by considering regional characteristics and past disaster history. This allows the prediction unit to always provide highly accurate risk predictions based on the latest information and support quick and appropriate responses.

[0077] The alert unit sends alerts when the risks predicted by the forecast unit increase. Specifically, it automatically sends alerts to management when risks rise. The alert unit can send alerts based, for example, on risk score thresholds or the occurrence of specific events. Risk score thresholds are set based on the impact and probability of occurrence of risks calculated by the forecast unit. The occurrence of specific events includes actions that may violate laws and regulations or abnormal fluctuations in financial data. When these conditions are met, the alert unit immediately notifies management and stakeholders. Notification methods include email, SMS, and push notifications, enabling the rapid transmission of important information. Furthermore, the alert unit provides a function to customize the content and priority of alerts, supporting appropriate responses tailored to specific risks and situations. For example, it can emphasize the need for immediate action for high-risk events and encourage regular monitoring for low-risk events. This allows the alert unit to provide management and stakeholders with quick and appropriate information, supporting early detection and rapid response to risks.

[0078] The data collection unit can collect financial data, personnel data, and operational data from each group company. For example, the data collection unit can collect financial data from each group company and obtain information such as revenue, expenses, and profits. For example, the data collection unit can also collect personnel data from each group company and obtain information such as the number of employees, job titles, and salaries. For example, the data collection unit can collect operational data from each group company and obtain information such as project progress and operational efficiency. This enables comprehensive data collection by collecting financial data, personnel data, and operational data from each group company. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input data from each group company into the AI ​​and have the AI ​​perform the data collection.

[0079] The analysis department can analyze the data collected by the collection department to evaluate governance, risk, and compliance status. For example, the analysis department can analyze the collected data to evaluate governance status. For example, the analysis department can analyze internal controls and compliance status. For example, the analysis department can analyze the types and impacts of risks to evaluate risk status. For example, the analysis department can analyze compliance status, such as compliance with laws and regulations and compliance with internal regulations to evaluate compliance status. This allows for detailed analysis by analyzing the collected data and evaluating governance, risk, and compliance status. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input the collected data into AI and have the AI ​​perform the data analysis.

[0080] The generation unit can generate a report based on the evaluation results obtained by the analysis unit. For example, the generation unit can generate a report based on the evaluation results, creating a report that includes evaluation scores and evaluation criteria. The generation unit can use a generation AI to summarize the evaluation results as a report in natural language. This makes it possible to create an accurate report by generating a report based on the evaluation results. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input the evaluation results into a generation AI and have the generation AI execute the report generation.

[0081] The delivery unit can provide the reports generated by the generation unit in a visually easy-to-understand format. For example, the delivery unit can provide the generated reports in a format that includes visual elements such as graphs, charts, and dashboards. For example, the delivery unit can visually show data fluctuations using graphs. For example, the delivery unit can visually show data comparisons using charts. For example, the delivery unit can display multiple data sets in a unified manner using dashboards. This allows users to intuitively grasp the situation by providing reports in a visually easy-to-understand format. Some or all of the above-described processes in the delivery unit may be performed using AI or not. For example, the delivery unit can input the generated reports into AI and have the AI ​​perform the task of providing them in a format that includes visual elements.

[0082] The prediction unit can learn past trends and patterns from data analyzed by the analysis unit and predict future governance risks and compliance violations. For example, the prediction unit learns past trends and patterns and predicts future risks. The prediction unit can use generative AI to find specific patterns from past data and predict risks if there is a high probability that the pattern will occur again. For example, the prediction unit learns past trends and patterns using time series data analysis and pattern recognition algorithms. The prediction unit can also predict future risks by calculating the probability of risk occurrence based on past data. This allows for proactive measures to be taken by learning past trends and patterns and predicting future risks. Some or all of the above processing in the prediction unit may be performed using generative AI or not. For example, the prediction unit can input past data into the generative AI and have the generative AI perform risk prediction.

[0083] The alert unit can send alerts when the risk predicted by the prediction unit increases. For example, the alert unit can automatically send alerts to management when the risk increases. The alert unit can also send alerts based on, for example, a risk score threshold or the occurrence of a specific event. For example, the alert unit can send an alert when the risk score exceeds a certain threshold. The alert unit can also send an alert when a specific event occurs. This allows for quick countermeasures to be taken by sending an alert when the risk increases. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input predicted risk data into AI and have the AI ​​execute the sending of alerts.

[0084] 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 reduce the frequency of data collection to alleviate the burden. For example, if the user is relaxed, the data collection unit can increase the frequency of data collection to collect more detailed data. For example, if the user is in a hurry, the data collection unit can prioritize collecting only important data. This reduces the burden on the user by adjusting the timing of data collection based on 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0085] The data collection unit can evaluate the reliability of data when collecting data from each group company and prioritize the collection of reliable data. For example, the data collection unit can verify the source of the data and prioritize the collection of data from reliable data sources. For example, the data collection unit can also check the consistency of the data and prioritize the collection of consistent data. For example, the data collection unit can evaluate the timeliness of the data and prioritize the collection of the most recent data. This improves data quality by prioritizing the collection of reliable data. Some or all of the above processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input data reliability evaluation into AI and have the AI ​​perform the collection of reliable data.

[0086] The data collection unit can adjust its data collection method to account for changes in the business processes of each group company during data collection. For example, the data collection unit can adjust the timing of data collection in response to changes in business processes. The data collection unit can also adjust the types of data to be collected in response to changes in business processes. The data collection unit can also change the data collection method in response to changes in business processes. By adjusting the data collection method in response to changes in business processes, efficient data collection becomes possible. Some or all of the above-described processes in the data collection unit may be performed using AI or not. For example, the data collection unit can input business process change data into the AI ​​and have the AI ​​perform the adjustment of the data collection method.

[0087] 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 prioritize collecting only important data. If the user is relaxed, the data collection unit may prioritize collecting detailed data. If the user is in a hurry, the data collection unit may prioritize collecting data that can be collected quickly. This allows for the priority collection of important data by prioritizing data based on the user's 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 processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input user emotion data into an AI and have the AI ​​determine the data priority.

[0088] The data collection unit can prioritize the collection of highly relevant data by considering the geographical location information of each group company during data collection. For example, the data collection unit can prioritize the collection of highly relevant data based on the location of each group company. The data collection unit can also collect data by considering the characteristics of each region based on geographical location information. For example, the data collection unit can evaluate the risks of each region based on geographical location information and collect highly relevant data. In this way, highly relevant data can be efficiently collected by considering geographical location information. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input geographical location information into AI and have the AI ​​perform the collection of highly relevant data.

[0089] The data collection unit can analyze the social media activities of each group company and collect relevant data during data collection. For example, the data collection unit can monitor the social media activities of each group company and collect relevant data. The data collection unit can also analyze trends on social media and collect relevant data. The data collection unit can also analyze user reactions on social media and collect relevant data. This allows for more multifaceted data collection by analyzing social media activities and collecting relevant data. Some or all of the above processing in the data collection unit may be performed using AI or not. For example, the data collection unit can input social media activity data into AI and have the AI ​​perform the collection of relevant data.

[0090] The analysis unit can estimate the user's emotions and adjust the data analysis method based on the estimated user emotions. For example, if the user is stressed, the analysis unit may use a simple analysis method. For example, if the user is relaxed, the analysis unit may use a detailed analysis method. For example, if the user is in a hurry, the analysis unit may use an analysis method that provides results quickly. This allows the analysis unit to provide analysis results that are appropriate for the user by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into AI and have the AI ​​adjust the data analysis method.

[0091] The analysis department can improve the accuracy of its analysis by considering the performance indicators of each group company during data analysis. For example, the analysis department can improve the accuracy of its analysis by considering the sales data of each group company. The analysis department can also improve the accuracy of its analysis by considering the profit data of each group company. The analysis department can also improve the accuracy of its analysis by considering the growth rate data of each group company. By improving the accuracy of the analysis by considering performance indicators, more accurate analysis results can be obtained. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input performance indicator data into AI and have the AI ​​perform the analysis accuracy improvement.

[0092] The analysis department can adjust its analysis methods when analyzing data, taking into account changes in the business processes of each group company. For example, the analysis department can change its analysis methods in response to changes in business processes. The analysis department can also adjust the timing of the analysis in response to changes in business processes. The analysis department can also change the data being analyzed in response to changes in business processes. By adjusting the analysis methods in response to changes in business processes, efficient data analysis becomes possible. Some or all of the above processes in the analysis department may be performed using AI, or they may not be performed using AI. For example, the analysis department can input business process change data into AI and have the AI ​​perform the adjustment of the analysis methods.

[0093] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit can provide a simple and highly visible display method. For example, if the user is relaxed, the analysis unit can also provide a display method that includes detailed information. For example, if the user is in a hurry, the analysis unit can also provide a display method that gets straight to the point. In this way, by adjusting the display method of the analysis results based on the user's emotions, a display method suitable for the user can be provided. 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 processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into an AI and have the AI ​​adjust the display method of the analysis results.

[0094] The analysis department can perform data analysis while considering the geographical location information of each group company. For example, the analysis department can perform analysis considering the characteristics of each region based on the location of each group company. For example, the analysis department can also evaluate and analyze the risks of each region based on geographical location information. For example, the analysis department can compare and analyze the performance of each region based on geographical location information. By performing analysis while considering geographical location information, it is possible to obtain analysis results that reflect the characteristics of each region. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input geographical location information into AI and have the AI ​​perform the analysis.

[0095] The analysis department can improve the accuracy of its analysis by referring to relevant literature from each group company during data analysis. For example, the analysis department can improve the accuracy of its analysis by referring to past reports from each group company. The analysis department can also improve the accuracy of its analysis by referring to industry reports from each group company. The analysis department can also improve the accuracy of its analysis by referring to academic papers from each group company. By improving the accuracy of the analysis by referring to relevant literature, more accurate analysis results can be obtained. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input relevant literature data into AI and have the AI ​​perform the analysis accuracy improvement.

[0096] The generation unit can estimate the user's emotions and adjust the presentation of the report based on the estimated emotions. For example, if the user is stressed, the generation unit can generate a simple and easy-to-read report. For example, if the user is relaxed, the generation unit can also generate a report with detailed information. For example, if the user is in a hurry, the generation unit can generate a report that gets straight to the point. In this way, by adjusting the presentation of the report based on the user's emotions, it is possible to provide a report that is appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using the generative AI or not. For example, the generation unit can input user emotion data into the generative AI and have the generative AI adjust the presentation of the report.

[0097] The generation unit can adjust the level of detail in the report based on the performance indicators of each group company when generating the report. For example, the generation unit can generate a detailed report based on the sales data of each group company. The generation unit can also generate a detailed report based on the profit data of each group company. The generation unit can also generate a detailed report based on the growth rate data of each group company. This allows for the generation of an appropriate report by adjusting the level of detail based on performance indicators. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input performance indicator data into a generation AI and have the generation AI perform the adjustment of the level of detail in the report.

[0098] The generation unit can adjust the content of reports when generating them, taking into account changes in the business processes of each group company. For example, the generation unit can change the content of reports in response to changes in business processes. The generation unit can also adjust the timing of reports in response to changes in business processes. For example, the generation unit can change the data targeted by reports in response to changes in business processes. By adjusting the content of reports in response to changes in business processes, appropriate reports can be provided. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the generation unit can input business process change data into a generation AI and have the generation AI perform the adjustment of the report content.

[0099] The generation 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 stressed, the generation unit can generate a short, concise report. If the user is relaxed, for example, the generation unit can generate a longer report with detailed explanations. If the user is in a hurry, for example, the generation unit can generate a short report that can be read quickly. This allows for the provision of reports tailored to the user by adjusting the report length based on the user's emotions. 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 processing in the generation unit may be performed using or without a generative AI. For example, the generation unit can input user emotion data into a generative AI and have the generative AI adjust the length of the report.

[0100] The generation unit can determine the priority of reports based on the submission dates of each group company when generating reports. For example, the generation unit can prioritize the generation of reports with approaching submission deadlines. The generation unit can also, for example, postpone the generation of reports with distant submission deadlines. The generation unit can also, for example, adjust the order of report generation based on submission deadlines. This enables efficient report generation by determining the priority of reports based on submission dates. Some or all of the above-described processes in the generation unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the generation unit can input submission date data into a generation AI and have the generation AI perform the determination of report priorities.

[0101] The generation unit can adjust the order of reports based on the relationships between group companies when generating reports. For example, the generation unit can prioritize generating reports for highly relevant group companies. For example, the generation unit can postpone generating reports for less relevant group companies. The generation unit can also adjust the report generation order based on relevance. This allows for efficient report generation by adjusting the report order based on relevance. Some or all of the above processing in the generation unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the generation unit can input relevance data into a generation AI and have the generation AI perform the adjustment of the report order.

[0102] The service provider can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is stressed, the service provider can provide a simple and highly visible display. For example, if the user is relaxed, the service provider can also provide a display that includes detailed information. For example, if the user is in a hurry, the service provider can provide a display that gets straight to the point. In this way, by adjusting how the report is displayed based on the user's emotions, a display method suitable for the user can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 service provider may be performed using AI or not. For example, the service provider can input user emotion data into AI and have the AI ​​adjust how the report is displayed.

[0103] The service provider can select the optimal display method by referring to the user's past operation history when providing reports. For example, the service provider can prioritize providing display methods that the user has preferred to use in the past. For example, the service provider can also suggest the most efficient display method based on the user's past operation history. For example, the service provider can provide a customized display method based on display methods that the user has used in the past. In this way, by selecting the optimal display method by referring to past operation history, a display method suitable for the user can be provided. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input past operation history data into AI and have the AI ​​perform the selection of the optimal display method.

[0104] The service provider can estimate the user's emotions and adjust the report's operating procedures based on the estimated emotions. For example, if the user is stressed, the service provider may simplify the operating procedures. For example, if the user is relaxed, the service provider may provide detailed operating procedures. For example, if the user is in a hurry, the service provider may provide procedures that allow for quick operation. In this way, by adjusting the operating procedures based on the user's emotions, it is possible to provide operating procedures that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, such as 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 processing described above in the service provider may be performed using AI or not. For example, the service provider can input user emotion data into AI and have the AI ​​perform the adjustment of operating procedures.

[0105] The service provider can select the optimal display method when providing reports, taking into account the user's device information. For example, if the user is using a smartphone, the service provider can provide a display method that matches the screen size. For example, if the user is using a tablet, the service provider can also provide a display method optimized for a larger screen. For example, if the user is using a desktop, the service provider can also provide a display method that includes detailed information. In this way, by selecting the optimal display method considering device information, the service provider can provide a display method that is suitable for the user. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input device information into AI and have the AI ​​select the optimal display method.

[0106] The prediction unit can estimate the user's emotions and adjust the risk prediction method based on the estimated user emotions. For example, if the user is stressed, the prediction unit may use a simple risk prediction method. For example, if the user is relaxed, the prediction unit may also use a detailed risk prediction method. For example, if the user is in a hurry, the prediction unit may also use a risk prediction method that provides quick results. This allows the system to provide a risk prediction that is appropriate for the user by adjusting the risk prediction method based on the user's 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 processing in the prediction unit may be performed using or without a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI adjust the risk prediction method.

[0107] The prediction unit can improve the accuracy of its predictions by referring to the historical risk data of each group company when making risk predictions. For example, the prediction unit improves the accuracy of its predictions based on the historical risk data of each group company. The prediction unit can also, for example, analyze historical risk data and identify risk occurrence patterns. The prediction unit can also, for example, calculate the probability of risk occurrence based on historical risk data. By improving the accuracy of predictions by referring to historical risk data, it is possible to provide more accurate risk predictions. Some or all of the above processing in the prediction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the prediction unit can input historical risk data into a generation AI and have the generation AI perform the improvement of prediction accuracy.

[0108] The prediction unit can adjust its prediction method when predicting risks, taking into account fluctuations in the business processes of each group company. For example, the prediction unit can change the prediction method in response to fluctuations in business processes. The prediction unit can also adjust the timing of the prediction in response to fluctuations in business processes. For example, the prediction unit can change the data to be predicted in response to fluctuations in business processes. This allows for efficient risk prediction by adjusting the prediction method in response to fluctuations in business processes. Some or all of the above-described processes in the prediction unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the prediction unit can input business process fluctuation data into a generation AI and have the generation AI perform the adjustment of the prediction method.

[0109] The prediction unit can estimate the user's emotions and determine the priority of risk predictions based on the estimated user emotions. For example, if the user is stressed, the prediction unit will prioritize predicting only important risks. For example, if the user is relaxed, the prediction unit can also perform detailed risk predictions. For example, if the user is in a hurry, the prediction unit can also prioritize risks that can be predicted quickly. In this way, by determining the priority of risk predictions based on the user's emotions, important risks can be predicted preferentially. Emotion estimation is achieved using an emotion estimation function, such as 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 or without a generative AI. For example, the prediction unit can input user emotion data into a generative AI and have the generative AI perform the determination of risk prediction priorities.

[0110] The prediction unit can make predictions by taking into account the geographical location information of each group company when predicting risks. For example, the prediction unit predicts risks for each region based on the location of each group company. The prediction unit can also calculate the probability of risk occurrence for each region based on geographical location information. The prediction unit can also identify risk factors for each region based on geographical location information. As a result, by making predictions while taking geographical location information into account, risks for each region can be accurately predicted. Some or all of the above processing in the prediction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the prediction unit can input geographical location information into a generation AI and have the generation AI perform risk prediction.

[0111] The prediction unit can improve the accuracy of its predictions by referring to relevant literature for each group company when making risk predictions. For example, the prediction unit can improve the accuracy of its predictions by referring to past reports of each group company. The prediction unit can also improve the accuracy of its predictions by referring to industry reports of each group company. The prediction unit can also improve the accuracy of its predictions by referring to academic papers of each group company. By improving the accuracy of predictions by referring to relevant literature, it is possible to provide more accurate risk predictions. Some or all of the above processing in the prediction unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the prediction unit can input relevant literature data into a generation AI and have the generation AI perform the improvement of prediction accuracy.

[0112] The alert unit can estimate the user's emotions and adjust how alerts are sent based on the estimated emotions. For example, if the user is stressed, the alert unit can send a simple, highly visible alert. If the user is relaxed, the alert unit can also send an alert with more detailed information. If the user is in a hurry, the alert unit can send a concise alert. This allows the system to provide alerts tailored to the user by adjusting how alerts are sent based on 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 alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into an AI and have the AI ​​adjust how alerts are sent.

[0113] The alert unit can improve the accuracy of alerts by referring to the historical risk data of each group company when sending an alert. For example, the alert unit can improve the accuracy of alerts based on the historical risk data of each group company. The alert unit can also, for example, analyze historical risk data to identify risk occurrence patterns. The alert unit can also, for example, calculate the probability of risk occurrence based on historical risk data. By improving the accuracy of alerts by referring to historical risk data, it is possible to provide more accurate alerts. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input historical risk data into AI and have the AI ​​perform the task of improving the accuracy of alerts.

[0114] The alert unit can estimate the user's emotions and prioritize alerts based on those emotions. For example, if the user is stressed, the alert unit will prioritize sending only important alerts. For example, if the user is relaxed, the alert unit may also send detailed alerts. For example, if the user is in a hurry, the alert unit may prioritize alerts that can be addressed quickly. This allows for the priority sending of important alerts by prioritizing them based on the user's 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 processing in the alert unit may be performed using AI or not. For example, the alert unit can input user emotion data into an AI and have the AI ​​determine the priority of alerts.

[0115] The alert unit can send alerts while considering the geographical location information of each group company. For example, the alert unit can evaluate the risks for each region based on the location of each group company and send alerts. For example, the alert unit can calculate the probability of risk occurrence for each region based on geographical location information and send alerts. For example, the alert unit can identify risk factors for each region based on geographical location information and send alerts. In this way, by sending alerts while considering geographical location information, it is possible to accurately notify of risks for each region. Some or all of the above processing in the alert unit may be performed using AI or not. For example, the alert unit can input geographical location information into AI and have the AI ​​execute the sending of alerts.

[0116] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0117] The governance status report generation system can further include the ability to estimate the user's emotions and customize the report content based on the estimated emotions. For example, if the user is stressed, the report content can be made concise and only the important points can be highlighted. If the user is relaxed, the report can be provided with detailed data and analysis results. Furthermore, if the user is in a hurry, it is possible to generate a report that summarizes the key points for quick understanding. This reduces the burden on the user and enables efficient information delivery by providing reports tailored to the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the generation unit may be performed using or without generative AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI customize the report content.

[0118] The governance status report generation system can also be equipped with a function to evaluate the reliability of data and prioritize the collection of reliable data when collecting data from each group company. For example, it can verify the source of the data and prioritize the collection of data from reliable data sources. It can also check the consistency of the data and prioritize the collection of consistent data. Furthermore, it can evaluate the timeliness of the data and prioritize the collection of the most recent data. This improves data quality by prioritizing the collection of reliable data. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input the data reliability evaluation into the AI ​​and have the AI ​​perform the collection of reliable data.

[0119] The governance status report generation system can also be equipped with the ability to 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 frequency of data collection can be reduced to alleviate the burden. Conversely, if the user is relaxed, the frequency of data collection can be increased to collect more detailed data. Furthermore, if the user is in a hurry, only important data can be prioritized for collection. This reduces the burden on the user by adjusting the timing of data collection based on their emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into an AI and have the AI ​​adjust the timing of data collection.

[0120] The governance status report generation system can also include a function to adjust the data collection method in consideration of changes in the business processes of each group company. For example, the timing of data collection can be adjusted in accordance with changes in business processes. It is also possible to adjust the type of data collected in accordance with changes in business processes. Furthermore, the method of data collection can be changed in accordance with changes in business processes. This allows for efficient data collection by adjusting the data collection method in consideration of changes in business processes. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input business process change data into the AI ​​and have the AI ​​perform the adjustment of the collection method.

[0121] The governance status report generation system can further include the ability to estimate the user's emotions and prioritize data based on those emotions. For example, if the user is stressed, only important data can be collected preferentially. If the user is relaxed, detailed data can be collected preferentially. Furthermore, if the user is in a hurry, data that can be collected quickly can be collected preferentially. This allows for the priority collection of important data by prioritizing data based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input user emotion data into an AI and have the AI ​​determine the data prioritization.

[0122] The governance status report generation system can also be equipped with a function to prioritize the collection of highly relevant data, taking into account the geographical location information of each group company. For example, it can prioritize the collection of highly relevant data based on the location of each group company. It is also possible to collect data considering the characteristics of each region based on geographical location information. Furthermore, it is possible to evaluate the risks of each region based on geographical location information and collect highly relevant data. In this way, highly relevant data can be efficiently collected by considering geographical location information. Some or all of the above processing in the collection unit may be performed using AI or not. For example, the collection unit can input geographical location information into the AI ​​and have the AI ​​perform the collection of highly relevant data.

[0123] The governance status report generation system can further include the ability to estimate the user's emotions and adjust the data analysis method based on the estimated emotions. For example, if the user is stressed, a simple analysis method can be used. If the user is relaxed, a more detailed analysis method can be used. Furthermore, if the user is in a hurry, an analysis method that provides results quickly can be used. This allows the system to provide analysis results that are appropriate for the user by adjusting the data analysis method based on the user's emotions. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the AI ​​and have the AI ​​adjust the data analysis method.

[0124] The governance status report generation system can also include a function to improve the accuracy of the analysis by considering the performance indicators of each group company. For example, it can improve the accuracy of the analysis by considering the sales data of each group company. It can also improve the accuracy of the analysis by considering the profit data of each group company. Furthermore, it can improve the accuracy of the analysis by considering the growth rate data of each group company. By improving the accuracy of the analysis by considering performance indicators, more accurate analysis results can be obtained. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input performance indicator data into the AI ​​and have the AI ​​perform the analysis accuracy improvement.

[0125] The governance status report generation system can further include a function to estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is stressed, a simple and highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is in a hurry, a display method that gets straight to the point can be provided. In this way, by adjusting the display method of the analysis results based on the user's emotions, a display method suitable for the user can be provided. Emotion estimation is achieved using an emotion engine or generative AI, etc. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into AI and have the AI ​​perform the adjustment of the display method of the analysis results.

[0126] The governance status report generation system may also include a function to improve the accuracy of the analysis by referring to relevant literature for each group company. For example, it may improve the accuracy of the analysis by referring to past reports of each group company. It may also improve the accuracy of the analysis by referring to industry reports of each group company. Furthermore, it may improve the accuracy of the analysis by referring to academic papers of each group company. By improving the accuracy of the analysis by referring to relevant literature, more accurate analysis results can be obtained. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department may input relevant literature data into the AI ​​and have the AI ​​perform the analysis accuracy improvement.

[0127] The following briefly describes the processing flow for example form 2.

[0128] Step 1: The data collection unit collects data from each group company. For example, the data collection unit collects financial data, personnel data, and operational data from each group company. The data collection unit can collect financial data from each group company and obtain information such as revenue, expenses, and profits. The data collection unit can also collect personnel data from each group company and obtain information such as the number of employees, job titles, and salaries. Furthermore, the data collection unit can collect operational data from each group company and obtain information such as project progress and operational efficiency. Step 2: The analysis department analyzes the data collected by the data collection department. For example, the analysis department analyzes the collected data to evaluate the governance situation, risk situation, and compliance situation. To evaluate the governance situation, the analysis department can analyze the evaluation of internal controls and the compliance status. To evaluate the risk situation, it can also analyze the types of risks and the impact of those risks. Furthermore, to evaluate the compliance situation, it can also analyze the compliance status with laws and regulations and the compliance status with internal regulations. Step 3: The generation unit generates a report based on the analysis results obtained by the analysis unit. For example, the generation unit can generate a report based on evaluation results and create a report that includes evaluation scores and evaluation criteria. The generation unit can use generation AI to summarize the evaluation results as a report in natural language. Step 4: The provider unit provides the report generated by the generator unit. The provider unit can, for example, provide the generated report in a visually easy-to-understand format. The provider unit can provide a report that includes visual elements such as graphs, charts, and dashboards. Step 5: The prediction unit predicts future risks based on the data analyzed by the analysis unit. For example, the prediction unit can learn from past trends and patterns to predict future governance risks and compliance breaches. Using generative AI, the prediction unit can identify specific patterns from past data and predict risks if those patterns are likely to occur again. Step 6: The alerting unit sends an alert when the risk predicted by the forecasting unit increases. For example, the alerting unit can automatically send an alert to management when the risk increases. The alerting unit can send alerts based on risk score thresholds or the occurrence of specific events.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, prediction unit, and alert 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 collects data from each group company using the camera 42 and microphone 38B of the smart device 14 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and evaluates the governance status, risk status, and compliance status. The generation unit generates a report based on the evaluation results using the specific processing unit 290 of the data processing unit 12 and summarizes it in natural language using a generation AI. The provision unit provides the report in a visually easy-to-understand format using, for example, the display 40A and speaker 40B of the smart device 14. The prediction unit learns from past data using the specific processing unit 290 of the data processing unit 12 and predicts future risks. The alert unit automatically sends an alert to management when the risk increases using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0133] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, prediction unit, and alert unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects data from each group company using the camera 42 and microphone 238 of the smart glasses 214 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and evaluates the governance status, risk status, and compliance status. The generation unit generates a report based on the evaluation results using the specific processing unit 290 of the data processing unit 12 and summarizes it in natural language using generation AI. The provision unit provides the report in a visually easy-to-understand format using, for example, the display and speaker 240 of the smart glasses 214. The prediction unit learns from past data using the specific processing unit 290 of the data processing unit 12 and predicts future risks. The alert unit automatically sends an alert to management when the risk increases using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0149] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, prediction unit, and alert unit, is implemented, for example, in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects data from each group company using the camera 42 and microphone 238 of the headset terminal 314 and transmits it to the data processing unit 12 via the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 to evaluate governance status, risk status, and compliance status. The generation unit generates a report based on the evaluation results using the specific processing unit 290 of the data processing unit 12 and summarizes it in natural language using a generation AI. The provision unit provides the report in a visually easy-to-understand format using the display 343 and speaker 240 of the headset terminal 314. The prediction unit learns from past data using the specific processing unit 290 of the data processing unit 12 and predicts future risks. The alert unit, for example, automatically sends an alert to management when the risk increases due to the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.).

[0178] 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.

[0179] 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.

[0180] 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.

[0181] Each of the multiple elements described above, including the collection unit, analysis unit, generation unit, provision unit, prediction unit, and alert unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects data from each group company using the camera 42 and microphone 238 of the robot 414 and transmits it to the data processing unit 12 by the control unit 46A. The analysis unit analyzes the collected data using the specific processing unit 290 of the data processing unit 12 and evaluates the governance status, risk status, and compliance status. The generation unit generates a report based on the evaluation results using the specific processing unit 290 of the data processing unit 12 and summarizes it in natural language using a generation AI. The provision unit provides the report in a visually easy-to-understand format using, for example, the display and speaker 240 of the robot 414. The prediction unit learns from past data using the specific processing unit 290 of the data processing unit 12 and predicts future risks. The alert unit automatically sends an alert to management when the risk increases using the specific processing unit 290 of the data processing unit 12. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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."

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] (Note 1) The data collection department collects data from each group company, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a report based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides the report generated by the generation unit, A prediction unit that predicts future risks based on the data analyzed by the aforementioned analysis unit, The system includes an alert unit that sends an alert when the risk predicted by the prediction unit increases. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect financial, human resources, and operational data from each group company. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is The data collected by the aforementioned data collection unit is analyzed to evaluate the governance status, risk status, and compliance status. The system described in Appendix 1, characterized by the features described herein. (Note 4) The generating unit is The report is generated based on the evaluation results obtained by the aforementioned analysis unit. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, The report generated by the aforementioned generation unit is provided in a visually easy-to-understand format. The system described in Appendix 1, characterized by the features described herein. (Note 6) The prediction unit, The aforementioned analysis unit learns past trends and patterns from the analyzed data and predicts future governance risks and compliance violations. The system described in Appendix 1, characterized by the features described herein. (Note 7) The alert unit is, When the risk predicted by the aforementioned forecasting unit increases, an alert is sent to management. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is We estimate the user's emotions and adjust the timing of data collection based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is When collecting data from each group company, the reliability of the data is evaluated, and reliable data is prioritized for collection. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting data, we adjust the collection method to take into account variations in the business processes of each group company. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned collection unit is When collecting data, the geographical location information of each group company is taken into consideration to prioritize the collection of highly relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, we analyze the social media activities of each group company and collect relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is We estimate user sentiment and adjust the data analysis method based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit is When analyzing data, we improve the accuracy of the analysis by considering the performance indicators of each group company. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is When analyzing data, adjust the analysis methodology to take into account variations in the business processes of each group company. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit is When analyzing data, the geographical location information of each group company will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is When analyzing data, we refer to relevant literature from each group company to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The generating unit is 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 21) The generating unit is When generating reports, adjust the level of detail in the reports based on the performance indicators of each group company. The system described in Appendix 1, characterized by the features described herein. (Note 22) The generating unit is When generating reports, the content of the reports is adjusted to take into account changes in the business processes of each group company. The system described in Appendix 1, characterized by the features described herein. (Note 23) The generating unit is 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 24) The generating unit is When generating reports, the priority of reports is determined based on the submission schedule of each group company. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is When generating reports, the order of reports is adjusted based on the relationships between each group company. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, When providing reports, the system selects the optimal display method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, It estimates the user's emotions and adjusts the report's operation steps based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, When providing reports, the optimal display method is selected considering the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 30) The prediction unit, We estimate user sentiment and adjust risk prediction methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 31) The prediction unit, When predicting risks, we improve the accuracy of our predictions by referring to historical risk data from each group company. The system described in Appendix 1, characterized by the features described herein. (Note 32) The prediction unit, When predicting risks, the prediction method is adjusted to take into account the changes in the business processes of each group company. The system described in Appendix 1, characterized by the features described herein. (Note 33) The prediction unit, It estimates user sentiment and prioritizes risk predictions based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 34) The prediction unit, When predicting risks, the geographical location information of each group company is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 35) The prediction unit, When predicting risks, we improve the accuracy of our predictions by referring to relevant literature from each group company. The system described in Appendix 1, characterized by the features described herein. (Note 36) The alert unit is, It estimates the user's emotions and adjusts how alerts are sent based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The alert unit is, When sending alerts, we improve the accuracy of the alerts by referring to historical risk data from each group company. The system described in Appendix 1, characterized by the features described herein. (Note 38) The alert unit is, It estimates the user's emotions and determines the priority of alerts based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The alert unit is, When sending an alert, the alert will be sent taking into account the geographical location information of each group company. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0201] 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 data from each group company, An analysis unit analyzes the data collected by the aforementioned collection unit, A generation unit that generates a report based on the analysis results obtained by the aforementioned analysis unit, A providing unit that provides the report generated by the generation unit, A prediction unit that predicts future risks based on the data analyzed by the aforementioned analysis unit, The system includes an alert unit that sends an alert when the risk predicted by the prediction unit increases. A system characterized by the following features.

2. The aforementioned collection unit is Collect financial, human resources, and operational data from each group company. The system according to feature 1.

3. The aforementioned analysis unit is The data collected by the aforementioned data collection unit is analyzed to evaluate the governance status, risk status, and compliance status. The system according to feature 1.

4. The generating unit is The report is generated based on the evaluation results obtained by the aforementioned analysis unit. The system according to feature 1.

5. The aforementioned supply unit is, The report generated by the aforementioned generation unit is provided in a visually easy-to-understand format. The system according to feature 1.

6. The prediction unit, The aforementioned analysis unit learns past trends and patterns from the analyzed data and predicts future governance risks and compliance violations. The system according to feature 1.

7. The alert unit is, When the risk predicted by the aforementioned forecasting unit increases, an alert is sent to management. The system according to feature 1.

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

Images