system

Abstract

The system according to this embodiment aims to effectively audit the control status of group companies and reduce risks. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a checking unit, a countermeasures unit, and a coordination unit. The reception unit receives answers to questionnaires and data submissions. The analysis unit describes specific methods for conducting internal control audits and data analysis based on the information received by the reception unit. The checking unit self-checks the effectiveness of the control status based on the results obtained by the analysis unit. The countermeasures unit takes necessary countermeasures based on the results obtained by the checking unit. The coordination unit implements the countermeasures taken by the countermeasures unit in cooperation with the internal audit office and second-line departments.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the process for effectively auditing the control status of a group company and reducing risks is complex and there is room for improvement.

[0005] The system according to the embodiment aims to effectively audit the control status of a group company and reduce risks.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a checking unit, a countermeasures unit, and a coordination unit. The reception unit receives responses to questionnaires and data submissions. The analysis unit describes specific methods for conducting internal control audits and data analysis based on the information received by the reception unit. The checking unit self-checks the effectiveness of the control status based on the results obtained by the analysis unit. The countermeasures unit takes necessary countermeasures based on the results obtained by the checking unit. The coordination unit implements the countermeasures taken by the countermeasures unit in cooperation with the internal audit office and second-line departments. [Effects of the Invention]

[0007] The system according to this embodiment can effectively audit the control status of group companies and reduce 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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The risk management system according to an embodiment of the present invention is a system for checking risks and control status that affect the parent company and other group companies, and for reducing damage to the company and reputational risk. This risk management system allows group company personnel to self-check the effectiveness of their current control status based on the results of internal control audits and data analysis conducted by the internal audit department, by answering a questionnaire and submitting data. For example, a group company personnel answers a questionnaire and submits data. At this time, the questionnaire includes detailed information on the control status and risks of each group company. For example, it includes the status of internal control implementation and risk management methods. This information is sent to the internal audit department. Next, the internal audit department self-checks the effectiveness of their current control status based on the results of internal control audits and data analysis conducted by the internal audit department. The internal audit department evaluates the control status based on the submitted data and confirms its effectiveness. For example, it checks whether internal controls are being implemented appropriately and whether risk management is being carried out effectively. Through this self-check, it is possible to identify trends in deficiencies in the control status and formulate countermeasures. Furthermore, it is possible to judge the status of rule development such as regulations and trends in data analysis results from the accumulated information and take necessary measures. For example, if deficiencies are found in the control structure of a particular group company, the cause can be identified and corrective measures can be proposed. Furthermore, common problems can be identified from data analysis results, and overall improvement measures can be implemented. Finally, necessary support for group companies can be provided in collaboration with the internal audit department and second-line departments. For instance, specific improvement measures can be provided to group companies with deficiencies in their control structure, and their implementation can be supported. Training and educational programs can also be provided to strengthen overall risk management. In this way, risks and control structures affecting the parent company and other group companies can be identified, reducing corporate damage and reputational risk. Thus, the risk management system can reduce corporate damage and reputational risk.

[0029] The risk management system according to this embodiment comprises a reception unit, an analysis unit, a checking unit, a countermeasures unit, and a coordination unit. The reception unit receives responses to a questionnaire and data submissions from representatives of group companies. The questionnaire includes detailed information on the control status and risks of each group company. For example, it includes the status of internal control implementation and risk management methods. The reception unit accepts responses to the questionnaire, for example, through an online form. The reception unit also has a function to securely store the submitted data and transmit it to the internal audit office. The analysis unit performs internal control audits and data analysis based on the information received by the reception unit. The analysis unit analyzes the submitted data using, for example, data mining techniques and evaluates the control status. The analysis unit also analyzes data trends using statistical methods and identifies risks. The checking unit self-checks the effectiveness of the control status based on the results obtained by the analysis unit. The checking unit verifies, for example, whether internal controls are being implemented appropriately using a checklist. The checking unit also evaluates whether risk management is being carried out effectively. The Countermeasures Department takes necessary measures based on the results obtained by the Checking Department. For example, the Countermeasures Department identifies trends in deficiencies in control status and develops specific improvement measures. The Countermeasures Department also formulates procedures for implementing improvement measures and supports their execution. The Liaison Department implements the measures taken by the Countermeasures Department in cooperation with the Internal Audit Office and second-line departments. For example, the Liaison Department provides specific improvement measures to group companies that show deficiencies in control status and supports their implementation. The Liaison Department also provides training and educational programs to strengthen overall risk management. As a result, the risk management system according to this embodiment can reduce damage to the company and reputational risk.

[0030] The reception department accepts responses to questionnaires and data submissions from group company representatives. The questionnaires include detailed information about each group company's control status and risks, such as the implementation status of internal controls and risk management methods. The reception department accepts responses to the questionnaires, for example, through an online form. The online form is securely implemented, using encryption technology to prevent data leaks and unauthorized access. Representatives access the form using dedicated login information and enter the required information. The questionnaires include multiple-choice and open-ended questions, requiring representatives to provide detailed information. The reception department has the capability to securely store the submitted data and transmit it to the internal audit department. The data is stored in a dedicated database and managed so that only authorized personnel can view it. Furthermore, secure communication protocols are used for data transmission, ensuring data integrity and confidentiality. The reception department can efficiently and securely carry out the entire process from data receipt to storage and transmission. This allows the reception department to smoothly collect information from group companies and play a crucial role in supporting the foundation of the risk management system.

[0031] The Analysis Department conducts internal control audits and data analysis based on information received by the Reception Department. For example, the Analysis Department analyzes submitted data using data mining techniques to evaluate the control status. Data mining techniques include methods such as clustering, association analysis, and decision trees, which are used to extract data patterns and trends. The Analysis Department also analyzes data trends using statistical methods to identify risks. For example, regression analysis and time series analysis are used to evaluate the extent to which specific risk factors have an impact. Furthermore, the Analysis Department can utilize AI technology to automate data analysis. AI uses machine learning algorithms to rapidly analyze large amounts of data and perform anomaly detection and predictive analysis. For example, anomaly detection algorithms are used to detect unusual data patterns early and identify potential risks. This allows the Analysis Department to analyze collected data from multiple perspectives and evaluate control status and risks with high accuracy. Furthermore, the Analysis Department visualizes the analysis results and provides them in an easy-to-understand report. Graphs and charts are used to show data trends and risk distributions, making them easily understandable to stakeholders. This allows the analytics department to provide data-driven, objective assessments and support risk management decision-making.

[0032] The Check Department self-checks the effectiveness of the control system based on the results obtained by the Analysis Department. For example, the Check Department uses a checklist to verify whether internal controls are being implemented appropriately. The checklist lists specific items, and the Check Department is required to evaluate the implementation status of each item. Examples include the frequency of internal audits, the methods of risk assessment, and the implementation status of risk countermeasures. The Check Department also evaluates whether risk management is being carried out effectively. Specifically, it verifies whether risk countermeasures are being implemented appropriately and whether risk reduction effects are being achieved. Based on the results of the self-check, the Check Department identifies areas for improvement in the control system and areas for strengthening risk management. Furthermore, the Check Department periodically reviews the results of the self-check to promote continuous improvement. Personnel from the Internal Audit Office and related departments participate in the review, sharing the results of the self-check and considering improvement measures. The Check Department also standardizes the self-check process to ensure consistent evaluation across all group companies. This allows the Check Department to objectively evaluate the effectiveness of the control system and promote improvements in risk management.

[0033] The countermeasures department takes necessary measures based on the results obtained by the checking department. For example, the countermeasures department identifies trends in deficiencies in the control status and develops specific improvement measures. Improvement measures include strengthening internal controls and reviewing risk response measures. For example, this could include increasing the frequency of internal audits, improving risk assessment methods, and clarifying the procedures for implementing risk response measures. The countermeasures department also formulates the procedures for implementing the improvement measures and supports their execution. Specifically, it develops implementation plans for the improvement measures and provides specific instructions to those in charge. Furthermore, the countermeasures department monitors the implementation status of the improvement measures and checks the progress. Monitoring includes periodic reports and on-site inspections to confirm that the improvement measures are being implemented appropriately. The countermeasures department evaluates the effectiveness of the improvement measures and takes additional measures as needed. This allows the countermeasures department to continuously promote improvements in the control status and strengthen risk management. Furthermore, the countermeasures department optimizes the costs and resources associated with implementing the improvement measures. For example, it sets priorities for improvement measures and effectively utilizes limited resources. The countermeasures department also evaluates the risks associated with implementing the improvement measures and takes measures to minimize those risks. This allows the countermeasures department to implement improvement measures efficiently and effectively, thereby improving the reliability of the risk management system.

[0034] The Liaison Department implements the measures taken by the Countermeasures Department in cooperation with the Internal Audit Office and second-line departments. For example, the Liaison Department provides specific improvement measures to group companies that show deficiencies in their control systems and supports their implementation. Specifically, it shares the implementation plan for the improvement measures and provides specific instructions to the person in charge. The Liaison Department also provides training and education programs to strengthen overall risk management. These programs include the importance of internal controls, methods of risk management, and procedures for implementing specific improvement measures. This allows the person in charge at the group company to improve their knowledge and skills in risk management. Furthermore, the Liaison Department regularly shares information with the Internal Audit Office and second-line departments to understand the status of risk management. This information sharing includes regular meetings and the submission of reports to share the current status and challenges of risk management. The Liaison Department also collects best practices for risk management and shares them throughout the group. This allows each group company to refer to the success stories of other companies and improve its own risk management. The Liaison Department formulates and promotes the implementation of an overall strategy to strengthen risk management. This will enable the collaboration department to uniformly strengthen risk management across the entire group and reduce overall corporate risk.

[0035] The reception department can accept responses to questionnaires and data submissions containing specific information about the control status and risks of each group company. The reception department accepts responses to the questionnaires, for example, through an online form. The questionnaires include detailed information about the control status and risks of each group company, such as the implementation status of internal controls and risk management methods. The reception department has the capability to securely store the submitted data and transmit it to the internal audit department. This allows for the collection of detailed information about each group company, thereby improving the accuracy of control status assessments.

[0036] The analysis department can evaluate the control status and confirm its effectiveness based on the submitted data. For example, the analysis department can analyze the submitted data using data mining techniques to evaluate the control status. Furthermore, the analysis department can analyze data trends using statistical methods to identify risks. This allows for a proper evaluation by confirming the effectiveness of the control status based on the submitted data.

[0037] The checking section can describe specific criteria for verifying whether internal controls are being implemented appropriately and whether risk management is being carried out properly. For example, the checking section can use a checklist to verify whether internal controls are being implemented appropriately. Furthermore, the checking section can evaluate whether risk management is being carried out effectively. By verifying the appropriateness of internal controls and risk management, the effectiveness of the control system can be assessed.

[0038] The countermeasures department can identify trends in deficiencies in control systems and formulate specific countermeasures. For example, the countermeasures department can identify trends in deficiencies in control systems and formulate specific improvement measures. Furthermore, the countermeasures department can formulate implementation procedures for improvement measures and support their execution. This allows for the identification of trends in deficiencies in control systems and the formulation of appropriate countermeasures.

[0039] The Liaison Department can describe specific methods for providing corrective measures to group companies with deficiencies in their control systems and supporting their implementation. For example, the Liaison Department can provide specific corrective measures to group companies with deficiencies in their control systems and support their implementation. Furthermore, the Liaison Department can provide training and educational programs to strengthen overall risk management. This allows for the provision of specific corrective measures to group companies with deficiencies in their control systems and support for their implementation, thereby improving the overall control system.

[0040] The Liaison Department can provide training and educational programs aimed at strengthening overall risk management. For example, the Liaison Department can provide training and educational programs aimed at strengthening overall risk management. By providing training and educational programs aimed at strengthening overall risk management, improvements in risk management can be expected.

[0041] The reception system can provide an auto-completion function by referring to past answer history when users respond to questionnaires. For example, the reception system can automatically complete answers to similar questions based on what the user has answered in the past. It can also refer to data previously entered by the user and suggest candidates for related questions. Furthermore, the reception system provides a completion function to maintain consistency in answers based on the user's past responses. By providing an auto-completion function that refers to past answer history, the system streamlines the user's response process.

[0042] The reception desk can automatically verify the format and content of submitted data and notify of errors in real time. For example, if the format of the submitted data is inappropriate, the reception desk will display an error message in real time and prompt correction. Furthermore, if there are deficiencies in the content of the submitted data, the reception desk can point out the specific error and suggest how to correct it. In addition, if the submitted data is incomplete, the reception desk will request any necessary additional information in real time. This ensures data accuracy by automatically verifying the format and content of submitted data and notifying errors in real time.

[0043] The reception desk can add region-specific questions when users answer questionnaires, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can add questions about region-specific risks. It can also add questions about regional regulations and laws based on the user's location. Furthermore, if a user is in a different region, the reception desk can dynamically change the questions to reflect the characteristics of that region. This allows for region-specific risk management by adding region-specific questions that take the user's geographical location into account.

[0044] The reception desk can analyze users' social media activity and automatically supplement relevant data upon data submission. For example, it can analyze users' social media posts and automatically supplement relevant risk information. It can also extract data related to specific events or situations from users' social media activity and add it to the submitted data. Furthermore, the reception desk verifies the consistency of the submitted data based on users' social media activity and makes necessary supplements. This improves data consistency and accuracy by analyzing users' social media activity and supplementing relevant data.

[0045] The analysis department can analyze submitted data in real time and provide immediate feedback. For example, it can analyze submitted data in real time and provide immediate results of the control status evaluation. Furthermore, the analysis department can detect anomalies in submitted data in real time and provide immediate notification. In addition, the analysis department can analyze trends in submitted data in real time and provide immediate feedback. This real-time analysis of submitted data and immediate feedback enable rapid response.

[0046] The analysis unit can automatically detect anomalies by comparing current data with historical data during data analysis. For example, the analysis unit can automatically detect anomalies by comparing current data with historical data. Furthermore, if anomalies are detected, the analysis unit can identify their causes and generate detailed reports. In addition, if anomalies are detected, the analysis unit will immediately notify the user and propose necessary countermeasures. This enables early detection of anomalies by automatically detecting them by comparing them with historical data.

[0047] The analytics department can improve the accuracy of its analysis by referencing data specific to the user's industry. For example, the analytics department can improve the accuracy of its analysis by referencing data specific to the user's industry. Furthermore, the analytics department can perform data analysis while considering industry-specific risk factors. In addition, the analytics department can perform data analysis while considering industry-specific regulations and laws. By improving the accuracy of the analysis by referencing data specific to the user's industry, more accurate analysis results can be obtained.

[0048] The analysis unit can supplement its analysis results by referencing relevant external databases during data analysis. For example, the analysis unit can retrieve relevant data from external databases and supplement the analysis results. Furthermore, the analysis unit can improve the accuracy of data analysis based on information from external databases. In addition, the analysis unit can access information from external databases in real time and update the analysis results. This improves the accuracy of the analysis by supplementing the results with information from relevant external databases.

[0049] The checking unit can automatically suggest areas for improvement during self-checks by referring to past check results. For example, the checking unit can automatically suggest areas for improvement based on past check results. Furthermore, the checking unit can extract common problems from past check results and suggest areas for improvement. In addition, the checking unit analyzes past check results and suggests the most effective areas for improvement. This enables efficient improvement by automatically suggesting areas for improvement based on past check results.

[0050] The checking unit can provide real-time feedback during self-checks and prompt immediate corrections. For example, the checking unit can provide real-time feedback during self-checks and prompt immediate corrections. Furthermore, the checking unit can also suggest areas for improvement in real time based on the self-check results. In addition, if an anomaly is detected during a self-check, the checking unit will immediately notify and prompt correction. This allows for a rapid response by providing real-time feedback and prompting immediate corrections.

[0051] The checking unit can customize check items during self-checks by referring to industry-specific standards. For example, the checking unit can customize check items by referring to industry-specific standards. It can also add check items considering industry-specific risk factors. Furthermore, the checking unit adjusts check items considering industry-specific regulations and laws. This allows for appropriate checks tailored to the industry by customizing check items based on industry-specific standards.

[0052] The checking unit can automatically refer to relevant laws and regulations and supplement the checklist during self-checks. For example, the checking unit automatically refers to relevant laws and regulations and supplements the checklist items. Furthermore, the checking unit can automatically update the checklist items in response to changes in laws and regulations. In addition, the checking unit adjusts the priority of the checklist items based on the laws and regulations. This enables compliance with laws and regulations by automatically referring to relevant laws and regulations and supplementing the checklist items.

[0053] The countermeasures department can automatically propose the optimal countermeasure by referring to past countermeasure history when proposing countermeasures. For example, the countermeasures department can automatically propose the optimal countermeasure based on past countermeasure history. Furthermore, the countermeasures department can extract the optimal countermeasures for common problems from past countermeasure history. In addition, the countermeasures department analyzes past countermeasure history and proposes the most effective countermeasure. This enables efficient countermeasures by automatically proposing the optimal countermeasure by referring to past countermeasure history.

[0054] The countermeasures department can provide real-time feedback when proposing countermeasures and prompt immediate corrections. For example, the department can provide real-time feedback and prompt immediate corrections during the countermeasures proposal process. Furthermore, the department can also suggest improvements in real time based on the results of the countermeasures proposal. In addition, if an anomaly is detected during the countermeasures proposal process, the department will immediately notify and prompt corrections. This real-time feedback and prompt immediate corrections enable a rapid response.

[0055] The countermeasures department can improve the accuracy of countermeasures by referring to industry-specific data of the user when proposing countermeasures. For example, the countermeasures department can improve the accuracy of countermeasures by referring to industry-specific data of the user. Furthermore, the countermeasures department can propose countermeasures considering industry-specific risk factors. In addition, the countermeasures department adjusts countermeasures considering industry-specific regulations and laws. This allows for the proposal of more accurate countermeasures by improving the accuracy of countermeasures through the referencing of industry-specific data of the user.

[0056] The countermeasures department can supplement its proposed countermeasures by referring to relevant external databases. For example, the department can retrieve relevant data from external databases to supplement its countermeasures. Furthermore, the department can improve the accuracy of its countermeasures based on the information in external databases. In addition, the department can update its countermeasures by referring to external databases in real time. This improves the accuracy of countermeasures by supplementing them with information from relevant external databases.

[0057] The integration unit can automatically propose the optimal integration method by referring to past integration history during integration. For example, the integration unit automatically proposes the optimal integration method based on past integration history. Furthermore, the integration unit can extract the optimal integration method for common problems from past integration history. In addition, the integration unit analyzes past integration history and proposes the most effective integration method. This enables efficient integration by automatically proposing the optimal integration method by referring to past integration history.

[0058] The integration unit can provide real-time feedback during integration and prompt immediate corrections. For example, the integration unit can provide real-time feedback during integration and prompt immediate corrections. Furthermore, the integration unit can also suggest improvements in real time based on the integration results. In addition, if an anomaly is detected during integration, the integration unit will immediately notify and prompt corrections. This enables a rapid response by providing real-time feedback and prompting immediate corrections.

[0059] The integration unit can improve the accuracy of integrations by referencing the user's industry-specific data during the integration process. For example, the integration unit can improve the accuracy of integrations by referencing the user's industry-specific data. Furthermore, the integration unit can propose integrations while considering industry-specific risk factors. In addition, the integration unit adjusts integrations while considering industry-specific regulations and laws. This allows for more accurate integrations by improving the accuracy of integrations by referencing the user's industry-specific data.

[0060] The integration unit can supplement the integration process by referencing relevant external databases. For example, the integration unit can retrieve relevant data from external databases to supplement the integration. Furthermore, the integration unit can improve the accuracy of the integration based on the information in the external databases. In addition, the integration unit updates the integration by referencing information in external databases in real time. This improves the accuracy of the integration by supplementing it with information from relevant external databases.

[0061] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0062] The risk management system can also include a forecasting unit. This unit can predict future risks based on historical and current data. For example, it can analyze the tendency for specific risks to occur based on historical data and predict the likelihood of future risk occurrences. Furthermore, the forecasting unit can incorporate information from external databases and make predictions considering industry-wide risk trends. In addition, based on the prediction results, the forecasting unit can propose specific measures to avoid risks. This allows companies to identify future risks in advance and take appropriate measures, thereby reducing damage to their business and reputational risk.

[0063] The analysis department can evaluate the control status based on the submitted data and confirm its effectiveness. Furthermore, the analysis department can visualize the results of the data analysis and display them clearly using graphs and charts. For example, the evaluation results of the control status can be displayed using bar graphs or pie charts to make them easy to understand visually. The analysis department can also use line graphs that show data trends to visually grasp fluctuations in risk. In this way, by visualizing the results of the data analysis, the evaluation results of the control status become easier to understand intuitively.

[0064] The reception desk can provide an auto-completion function by referring to past answer history when users respond to questionnaires. Furthermore, the reception desk can add region-specific questions based on the user's geographical location. For example, if a user is in a specific region, questions about region-specific risks can be added. It can also add questions about local regulations and laws based on the user's location. This allows for region-specific risk management by adding region-specific questions based on the user's geographical location.

[0065] The analysis department can evaluate the control status based on the submitted data and confirm its effectiveness. Furthermore, the analysis department can automatically detect outliers by comparing current data with historical data during data analysis. For example, it can automatically detect outliers by comparing historical data with current data. If an outlier is detected, it can also identify the cause and generate a detailed report. This enables early detection of anomalies by automatically detecting outliers by comparing them with historical data.

[0066] The checking section can specify concrete criteria for verifying whether internal controls are being implemented appropriately and whether risk management is being carried out properly. Furthermore, the checking section can automatically suggest areas for improvement by referring to past check results during self-checks. For example, it can automatically suggest areas for improvement based on past check results. It can also extract common problems from past check results and suggest areas for improvement. This enables efficient improvement by automatically suggesting areas for improvement by referring to past check results.

[0067] The countermeasures department can identify trends in deficiencies in control systems and develop specific countermeasures. Furthermore, when proposing countermeasures, the department can automatically suggest the optimal solution by referring to past countermeasure history. For example, it can automatically suggest the optimal solution based on past countermeasure history. It can also extract the optimal solution for common problems from past countermeasure history. This enables efficient countermeasures by automatically suggesting the optimal solution by referring to past countermeasure history.

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

[0069] Step 1: The reception department accepts responses from group company representatives to a questionnaire and submits the data. The questionnaire includes detailed information about each group company's control status and risks, such as the implementation status of internal controls and risk management methods. The reception department accepts responses to the questionnaire, for example, through an online form. The reception department also has the capability to securely store the submitted data and transmit it to the internal audit department. Step 2: The Analysis Department conducts internal control audits and data analysis based on the information received by the Reception Department. The Analysis Department analyzes the submitted data using, for example, data mining techniques to assess the control status. The Analysis Department also analyzes data trends using statistical methods to identify risks. Step 3: The checking unit self-checks the effectiveness of the control system based on the results obtained by the analysis unit. The checking unit verifies, for example, whether internal controls are being properly implemented using a checklist. The checking unit also evaluates whether risk management is being carried out effectively. Step 4: The countermeasures department takes necessary measures based on the results obtained by the checking department. For example, the countermeasures department identifies trends in deficiencies in the control status and develops specific improvement measures. The countermeasures department also formulates procedures for implementing the improvement measures and supports their execution. Step 5: The Liaison Department implements the measures taken by the Countermeasures Department in cooperation with the Internal Audit Office and other second-line departments. For example, the Liaison Department provides specific improvement measures to group companies that show deficiencies in their control systems and supports their implementation. The Liaison Department also provides training and educational programs to strengthen overall risk management.

[0070] (Example of form 2) The risk management system according to an embodiment of the present invention is a system for checking risks and control status that affect the parent company and other group companies, and for reducing damage to the company and reputational risk. This risk management system allows group company personnel to self-check the effectiveness of their current control status based on the results of internal control audits and data analysis conducted by the internal audit department, by answering a questionnaire and submitting data. For example, a group company personnel answers a questionnaire and submits data. At this time, the questionnaire includes detailed information on the control status and risks of each group company. For example, it includes the status of internal control implementation and risk management methods. This information is sent to the internal audit department. Next, the internal audit department self-checks the effectiveness of their current control status based on the results of internal control audits and data analysis conducted by the internal audit department. The internal audit department evaluates the control status based on the submitted data and confirms its effectiveness. For example, it checks whether internal controls are being implemented appropriately and whether risk management is being carried out effectively. Through this self-check, it is possible to identify trends in deficiencies in the control status and formulate countermeasures. Furthermore, it is possible to judge the status of rule development such as regulations and trends in data analysis results from the accumulated information and take necessary measures. For example, if deficiencies are found in the control structure of a particular group company, the cause can be identified and corrective measures can be proposed. Furthermore, common problems can be identified from data analysis results, and overall improvement measures can be implemented. Finally, necessary support for group companies can be provided in collaboration with the internal audit department and second-line departments. For instance, specific improvement measures can be provided to group companies with deficiencies in their control structure, and their implementation can be supported. Training and educational programs can also be provided to strengthen overall risk management. In this way, risks and control structures affecting the parent company and other group companies can be identified, reducing corporate damage and reputational risk. Thus, the risk management system can reduce corporate damage and reputational risk.

[0071] The risk management system according to this embodiment comprises a reception unit, an analysis unit, a checking unit, a countermeasures unit, and a coordination unit. The reception unit receives responses to a questionnaire and data submissions from representatives of group companies. The questionnaire includes detailed information on the control status and risks of each group company. For example, it includes the status of internal control implementation and risk management methods. The reception unit accepts responses to the questionnaire, for example, through an online form. The reception unit also has a function to securely store the submitted data and transmit it to the internal audit office. The analysis unit performs internal control audits and data analysis based on the information received by the reception unit. The analysis unit analyzes the submitted data using, for example, data mining techniques and evaluates the control status. The analysis unit also analyzes data trends using statistical methods and identifies risks. The checking unit self-checks the effectiveness of the control status based on the results obtained by the analysis unit. The checking unit verifies, for example, whether internal controls are being implemented appropriately using a checklist. The checking unit also evaluates whether risk management is being carried out effectively. The Countermeasures Department takes necessary measures based on the results obtained by the Checking Department. For example, the Countermeasures Department identifies trends in deficiencies in control status and develops specific improvement measures. The Countermeasures Department also formulates procedures for implementing improvement measures and supports their execution. The Liaison Department implements the measures taken by the Countermeasures Department in cooperation with the Internal Audit Office and second-line departments. For example, the Liaison Department provides specific improvement measures to group companies that show deficiencies in control status and supports their implementation. The Liaison Department also provides training and educational programs to strengthen overall risk management. As a result, the risk management system according to this embodiment can reduce damage to the company and reputational risk.

[0072] The reception department accepts responses to questionnaires and data submissions from group company representatives. The questionnaires include detailed information about each group company's control status and risks, such as the implementation status of internal controls and risk management methods. The reception department accepts responses to the questionnaires, for example, through an online form. The online form is securely implemented, using encryption technology to prevent data leaks and unauthorized access. Representatives access the form using dedicated login information and enter the required information. The questionnaires include multiple-choice and open-ended questions, requiring representatives to provide detailed information. The reception department has the capability to securely store the submitted data and transmit it to the internal audit department. The data is stored in a dedicated database and managed so that only authorized personnel can view it. Furthermore, secure communication protocols are used for data transmission, ensuring data integrity and confidentiality. The reception department can efficiently and securely carry out the entire process from data receipt to storage and transmission. This allows the reception department to smoothly collect information from group companies and play a crucial role in supporting the foundation of the risk management system.

[0073] The Analysis Department conducts internal control audits and data analysis based on information received by the Reception Department. For example, the Analysis Department analyzes submitted data using data mining techniques to evaluate the control status. Data mining techniques include methods such as clustering, association analysis, and decision trees, which are used to extract data patterns and trends. The Analysis Department also analyzes data trends using statistical methods to identify risks. For example, regression analysis and time series analysis are used to evaluate the extent to which specific risk factors have an impact. Furthermore, the Analysis Department can utilize AI technology to automate data analysis. AI uses machine learning algorithms to rapidly analyze large amounts of data and perform anomaly detection and predictive analysis. For example, anomaly detection algorithms are used to detect unusual data patterns early and identify potential risks. This allows the Analysis Department to analyze collected data from multiple perspectives and evaluate control status and risks with high accuracy. Furthermore, the Analysis Department visualizes the analysis results and provides them in an easy-to-understand report. Graphs and charts are used to show data trends and risk distributions, making them easily understandable to stakeholders. This allows the analytics department to provide data-driven, objective assessments and support risk management decision-making.

[0074] The Check Department self-checks the effectiveness of the control system based on the results obtained by the Analysis Department. For example, the Check Department uses a checklist to verify whether internal controls are being implemented appropriately. The checklist lists specific items, and the Check Department is required to evaluate the implementation status of each item. Examples include the frequency of internal audits, the methods of risk assessment, and the implementation status of risk countermeasures. The Check Department also evaluates whether risk management is being carried out effectively. Specifically, it verifies whether risk countermeasures are being implemented appropriately and whether risk reduction effects are being achieved. Based on the results of the self-check, the Check Department identifies areas for improvement in the control system and areas for strengthening risk management. Furthermore, the Check Department periodically reviews the results of the self-check to promote continuous improvement. Personnel from the Internal Audit Office and related departments participate in the review, sharing the results of the self-check and considering improvement measures. The Check Department also standardizes the self-check process to ensure consistent evaluation across all group companies. This allows the Check Department to objectively evaluate the effectiveness of the control system and promote improvements in risk management.

[0075] The countermeasures department takes necessary measures based on the results obtained by the checking department. For example, the countermeasures department identifies trends in deficiencies in the control status and develops specific improvement measures. Improvement measures include strengthening internal controls and reviewing risk response measures. For example, this could include increasing the frequency of internal audits, improving risk assessment methods, and clarifying the procedures for implementing risk response measures. The countermeasures department also formulates the procedures for implementing the improvement measures and supports their execution. Specifically, it develops implementation plans for the improvement measures and provides specific instructions to those in charge. Furthermore, the countermeasures department monitors the implementation status of the improvement measures and checks the progress. Monitoring includes periodic reports and on-site inspections to confirm that the improvement measures are being implemented appropriately. The countermeasures department evaluates the effectiveness of the improvement measures and takes additional measures as needed. This allows the countermeasures department to continuously promote improvements in the control status and strengthen risk management. Furthermore, the countermeasures department optimizes the costs and resources associated with implementing the improvement measures. For example, it sets priorities for improvement measures and effectively utilizes limited resources. The countermeasures department also evaluates the risks associated with implementing the improvement measures and takes measures to minimize those risks. This allows the countermeasures department to implement improvement measures efficiently and effectively, thereby improving the reliability of the risk management system.

[0076] The Liaison Department implements the measures taken by the Countermeasures Department in cooperation with the Internal Audit Office and second-line departments. For example, the Liaison Department provides specific improvement measures to group companies that show deficiencies in their control systems and supports their implementation. Specifically, it shares the implementation plan for the improvement measures and provides specific instructions to the person in charge. The Liaison Department also provides training and education programs to strengthen overall risk management. These programs include the importance of internal controls, methods of risk management, and procedures for implementing specific improvement measures. This allows the person in charge at the group company to improve their knowledge and skills in risk management. Furthermore, the Liaison Department regularly shares information with the Internal Audit Office and second-line departments to understand the status of risk management. This information sharing includes regular meetings and the submission of reports to share the current status and challenges of risk management. The Liaison Department also collects best practices for risk management and shares them throughout the group. This allows each group company to refer to the success stories of other companies and improve its own risk management. The Liaison Department formulates and promotes the implementation of an overall strategy to strengthen risk management. This will enable the collaboration department to uniformly strengthen risk management across the entire group and reduce overall corporate risk.

[0077] The reception department can accept responses to questionnaires and data submissions containing specific information about the control status and risks of each group company. The reception department accepts responses to the questionnaires, for example, through an online form. The questionnaires include detailed information about the control status and risks of each group company, such as the implementation status of internal controls and risk management methods. The reception department has the capability to securely store the submitted data and transmit it to the internal audit department. This allows for the collection of detailed information about each group company, thereby improving the accuracy of control status assessments.

[0078] The analysis department can evaluate the control status and confirm its effectiveness based on the submitted data. For example, the analysis department can analyze the submitted data using data mining techniques to evaluate the control status. Furthermore, the analysis department can analyze data trends using statistical methods to identify risks. This allows for a proper evaluation by confirming the effectiveness of the control status based on the submitted data.

[0079] The checking section can describe specific criteria for verifying whether internal controls are being implemented appropriately and whether risk management is being carried out properly. For example, the checking section can use a checklist to verify whether internal controls are being implemented appropriately. Furthermore, the checking section can evaluate whether risk management is being carried out effectively. By verifying the appropriateness of internal controls and risk management, the effectiveness of the control system can be assessed.

[0080] The countermeasures department can identify trends in deficiencies in control systems and formulate specific countermeasures. For example, the countermeasures department can identify trends in deficiencies in control systems and formulate specific improvement measures. Furthermore, the countermeasures department can formulate implementation procedures for improvement measures and support their execution. This allows for the identification of trends in deficiencies in control systems and the formulation of appropriate countermeasures.

[0081] The Liaison Department can describe specific methods for providing corrective measures to group companies with deficiencies in their control systems and supporting their implementation. For example, the Liaison Department can provide specific corrective measures to group companies with deficiencies in their control systems and support their implementation. Furthermore, the Liaison Department can provide training and educational programs to strengthen overall risk management. This allows for the provision of specific corrective measures to group companies with deficiencies in their control systems and support for their implementation, thereby improving the overall control system.

[0082] The Liaison Department can provide training and educational programs aimed at strengthening overall risk management. For example, the Liaison Department can provide training and educational programs aimed at strengthening overall risk management. By providing training and educational programs aimed at strengthening overall risk management, improvements in risk management can be expected.

[0083] The reception desk can estimate the user's emotions and dynamically adjust the content of the questionnaire based on the estimated emotions. For example, if the user is stressed, the reception desk will reduce the number of items in the questionnaire and prioritize concise questions. If the user is relaxed, the reception desk can add more detailed questions and gather deeper information. Furthermore, if the user is in a hurry, the reception desk will prioritize displaying important questions to allow for quick responses. In this way, by adjusting the content of the questionnaire according to the user's emotions, the burden on the user is reduced and the quality of responses is improved. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The reception system can provide an auto-completion function by referring to past answer history when users respond to questionnaires. For example, the reception system can automatically complete answers to similar questions based on what the user has answered in the past. It can also refer to data previously entered by the user and suggest candidates for related questions. Furthermore, the reception system provides a completion function to maintain consistency in answers based on the user's past responses. By providing an auto-completion function that refers to past answer history, the system streamlines the user's response process.

[0085] The reception desk can automatically verify the format and content of submitted data and notify of errors in real time. For example, if the format of the submitted data is inappropriate, the reception desk will display an error message in real time and prompt correction. Furthermore, if there are deficiencies in the content of the submitted data, the reception desk can point out the specific error and suggest how to correct it. In addition, if the submitted data is incomplete, the reception desk will request any necessary additional information in real time. This ensures data accuracy by automatically verifying the format and content of submitted data and notifying errors in real time.

[0086] The reception desk can estimate the user's emotions and dynamically change the priority of the questionnaire based on the estimated emotions. For example, if the user is stressed, the reception desk can prioritize important questions to allow for quick answers. Conversely, if the user is relaxed, the reception desk can postpone detailed questions and start with basic ones. Furthermore, if the user is in a hurry, the reception desk can display the most important questions first to save time. This reduces the user's burden and improves the quality of answers by changing the priority of the questionnaire according to the user's emotions. Emotion estimation is achieved using emotion estimation functions, such as emotion engines or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The reception desk can add region-specific questions when users answer questionnaires, taking into account their geographical location. For example, if a user is in a specific region, the reception desk can add questions about region-specific risks. It can also add questions about regional regulations and laws based on the user's location. Furthermore, if a user is in a different region, the reception desk can dynamically change the questions to reflect the characteristics of that region. This allows for region-specific risk management by adding region-specific questions that take the user's geographical location into account.

[0088] The reception desk can analyze users' social media activity and automatically supplement relevant data upon data submission. For example, it can analyze users' social media posts and automatically supplement relevant risk information. It can also extract data related to specific events or situations from users' social media activity and add it to the submitted data. Furthermore, the reception desk verifies the consistency of the submitted data based on users' social media activity and makes necessary supplements. This improves data consistency and accuracy by analyzing users' social media activity and supplementing relevant data.

[0089] The analysis unit can estimate the user's emotions and adjust the data analysis algorithm based on the estimated emotions. For example, if the user is stressed, the analysis unit may use an algorithm that performs concise and rapid data analysis. If the user is relaxed, the analysis unit may also use an algorithm that performs detailed data analysis. Furthermore, if the user is in a hurry, the analysis unit may use an algorithm that prioritizes analyzing the most important data. This improves the accuracy of the analysis results by adjusting the data analysis algorithm according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The analysis department can analyze submitted data in real time and provide immediate feedback. For example, it can analyze submitted data in real time and provide immediate results of the control status evaluation. Furthermore, the analysis department can detect anomalies in submitted data in real time and provide immediate notification. In addition, the analysis department can analyze trends in submitted data in real time and provide immediate feedback. This real-time analysis of submitted data and immediate feedback enable rapid response.

[0091] The analysis unit can automatically detect anomalies by comparing current data with historical data during data analysis. For example, the analysis unit can automatically detect anomalies by comparing current data with historical data. Furthermore, if anomalies are detected, the analysis unit can identify their causes and generate detailed reports. In addition, if anomalies are detected, the analysis unit will immediately notify the user and propose necessary countermeasures. This enables early detection of anomalies by automatically detecting them by comparing them with historical data.

[0092] 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 nervous, the analysis unit provides a simple and highly visible display method. If the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit provides a concise display method. By adjusting the display method of the analysis results according to the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0093] The analytics department can improve the accuracy of its analysis by referencing data specific to the user's industry. For example, the analytics department can improve the accuracy of its analysis by referencing data specific to the user's industry. Furthermore, the analytics department can perform data analysis while considering industry-specific risk factors. In addition, the analytics department can perform data analysis while considering industry-specific regulations and laws. By improving the accuracy of the analysis by referencing data specific to the user's industry, more accurate analysis results can be obtained.

[0094] The analysis unit can supplement its analysis results by referencing relevant external databases during data analysis. For example, the analysis unit can retrieve relevant data from external databases and supplement the analysis results. Furthermore, the analysis unit can improve the accuracy of data analysis based on information from external databases. In addition, the analysis unit can access information from external databases in real time and update the analysis results. This improves the accuracy of the analysis by supplementing the results with information from relevant external databases.

[0095] The self-assessment unit can estimate the user's emotions and adjust the self-assessment criteria based on those emotions. For example, if the user is feeling stressed, the unit provides concise self-assessment criteria. If the user is relaxed, the unit can also provide detailed self-assessment criteria. Furthermore, if the user is in a hurry, the unit prioritizes displaying important check items. This allows for an appropriate self-assessment by adjusting the criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The checking unit can automatically suggest areas for improvement during self-checks by referring to past check results. For example, the checking unit can automatically suggest areas for improvement based on past check results. Furthermore, the checking unit can extract common problems from past check results and suggest areas for improvement. In addition, the checking unit analyzes past check results and suggests the most effective areas for improvement. This enables efficient improvement by automatically suggesting areas for improvement based on past check results.

[0097] The checking unit can provide real-time feedback during self-checks and prompt immediate corrections. For example, the checking unit can provide real-time feedback during self-checks and prompt immediate corrections. Furthermore, the checking unit can also suggest areas for improvement in real time based on the self-check results. In addition, if an anomaly is detected during a self-check, the checking unit will immediately notify and prompt correction. This allows for a rapid response by providing real-time feedback and prompting immediate corrections.

[0098] The checking unit can estimate the user's emotions and change the priority of self-checks based on the estimated emotions. For example, if the user is feeling stressed, the checking unit will prioritize displaying important check items. Conversely, if the user is relaxed, the checking unit can postpone detailed check items. Furthermore, if the user is in a hurry, the checking unit will display the most important check items first. This reduces the user's burden and improves the quality of the check by changing the priority of self-checks according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The checking unit can customize check items during self-checks by referring to industry-specific standards. For example, the checking unit can customize check items by referring to industry-specific standards. It can also add check items considering industry-specific risk factors. Furthermore, the checking unit adjusts check items considering industry-specific regulations and laws. This allows for appropriate checks tailored to the industry by customizing check items based on industry-specific standards.

[0100] The checking unit can automatically refer to relevant laws and regulations and supplement the checklist during self-checks. For example, the checking unit automatically refers to relevant laws and regulations and supplements the checklist items. Furthermore, the checking unit can automatically update the checklist items in response to changes in laws and regulations. In addition, the checking unit adjusts the priority of the checklist items based on the laws and regulations. This enables compliance with laws and regulations by automatically referring to relevant laws and regulations and supplementing the checklist items.

[0101] The response unit can estimate the user's emotions and adjust the method of suggesting solutions based on the estimated emotions. For example, if the user is stressed, the response unit will suggest simple and easy-to-implement solutions. If the user is relaxed, the response unit can also suggest detailed solutions. Furthermore, if the user is in a hurry, the response unit will prioritize suggesting the most effective solutions. In this way, by adjusting the method of suggesting solutions according to the user's emotions, solutions that are easy for the user to implement are suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, for example, text generation AI (e.g., LLM) or multimodal generation AI, but is not limited to these examples.

[0102] The countermeasures department can automatically propose the optimal countermeasure by referring to past countermeasure history when proposing countermeasures. For example, the countermeasures department can automatically propose the optimal countermeasure based on past countermeasure history. Furthermore, the countermeasures department can extract the optimal countermeasures for common problems from past countermeasure history. In addition, the countermeasures department analyzes past countermeasure history and proposes the most effective countermeasure. This enables efficient countermeasures by automatically proposing the optimal countermeasure by referring to past countermeasure history.

[0103] The countermeasures department can provide real-time feedback when proposing countermeasures and prompt immediate corrections. For example, the department can provide real-time feedback and prompt immediate corrections during the countermeasures proposal process. Furthermore, the department can also suggest improvements in real time based on the results of the countermeasures proposal. In addition, if an anomaly is detected during the countermeasures proposal process, the department will immediately notify and prompt corrections. This real-time feedback and prompt immediate corrections enable a rapid response.

[0104] The response system can estimate the user's emotions and change the priority of responses based on those emotions. For example, if the user is stressed, the response system will prioritize suggesting important responses. Conversely, if the user is relaxed, the response system can postpone suggesting detailed responses. Furthermore, if the user is in a hurry, the response system will suggest the most effective response first. In this way, by changing the priority of responses according to the user's emotions, the optimal response for the user is suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The countermeasures department can improve the accuracy of countermeasures by referring to industry-specific data of the user when proposing countermeasures. For example, the countermeasures department can improve the accuracy of countermeasures by referring to industry-specific data of the user. Furthermore, the countermeasures department can propose countermeasures considering industry-specific risk factors. In addition, the countermeasures department adjusts countermeasures considering industry-specific regulations and laws. This allows for the proposal of more accurate countermeasures by improving the accuracy of countermeasures through the referencing of industry-specific data of the user.

[0106] The countermeasures department can supplement its proposed countermeasures by referring to relevant external databases. For example, the department can retrieve relevant data from external databases to supplement its countermeasures. Furthermore, the department can improve the accuracy of its countermeasures based on the information in external databases. In addition, the department can update its countermeasures by referring to external databases in real time. This improves the accuracy of countermeasures by supplementing them with information from relevant external databases.

[0107] The interaction unit can estimate the user's emotions and adjust the interaction method based on the estimated emotions. For example, if the user is stressed, the interaction unit will suggest a concise and quick interaction method. If the user is relaxed, the interaction unit can also suggest a detailed interaction method. Furthermore, if the user is in a hurry, the interaction unit will prioritize suggesting the most effective interaction method. This allows for optimal interaction for the user by adjusting the interaction method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The integration unit can automatically propose the optimal integration method by referring to past integration history during integration. For example, the integration unit automatically proposes the optimal integration method based on past integration history. Furthermore, the integration unit can extract the optimal integration method for common problems from past integration history. In addition, the integration unit analyzes past integration history and proposes the most effective integration method. This enables efficient integration by automatically proposing the optimal integration method by referring to past integration history.

[0109] The integration unit can provide real-time feedback during integration and prompt immediate corrections. For example, the integration unit can provide real-time feedback during integration and prompt immediate corrections. Furthermore, the integration unit can also suggest improvements in real time based on the integration results. In addition, if an anomaly is detected during integration, the integration unit will immediately notify and prompt corrections. This enables a rapid response by providing real-time feedback and prompting immediate corrections.

[0110] The collaboration unit can estimate the user's emotions and change the priority of collaborations based on the estimated emotions. For example, if the user is feeling stressed, the collaboration unit will prioritize suggesting important collaborations. Conversely, if the user is relaxed, the collaboration unit can postpone detailed collaborations. Furthermore, if the user is in a hurry, the collaboration unit will suggest the most effective collaborations first. This allows for optimal collaborations for the user by changing the priority of collaborations according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0111] The integration unit can improve the accuracy of integrations by referencing the user's industry-specific data during the integration process. For example, the integration unit can improve the accuracy of integrations by referencing the user's industry-specific data. Furthermore, the integration unit can propose integrations while considering industry-specific risk factors. In addition, the integration unit adjusts integrations while considering industry-specific regulations and laws. This allows for more accurate integrations by improving the accuracy of integrations by referencing the user's industry-specific data.

[0112] The integration unit can supplement the integration process by referencing relevant external databases. For example, the integration unit can retrieve relevant data from external databases to supplement the integration. Furthermore, the integration unit can improve the accuracy of the integration based on the information in the external databases. In addition, the integration unit updates the integration by referencing information in external databases in real time. This improves the accuracy of the integration by supplementing it with information from relevant external databases.

[0113] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0114] The risk management system can also include a forecasting unit. This unit can predict future risks based on historical and current data. For example, it can analyze the tendency for specific risks to occur based on historical data and predict the likelihood of future risk occurrences. Furthermore, the forecasting unit can incorporate information from external databases and make predictions considering industry-wide risk trends. In addition, based on the prediction results, the forecasting unit can propose specific measures to avoid risks. This allows companies to identify future risks in advance and take appropriate measures, thereby reducing damage to their business and reputational risk.

[0115] The reception desk can estimate the user's emotions and dynamically adjust the content of the questionnaire based on that estimation. For example, if the user is stressed, the number of questions in the questionnaire will be reduced and concise questions will be prioritized. If the user is relaxed, more detailed questions can be added to gather deeper information. Furthermore, if the user is in a hurry, important questions will be displayed preferentially to allow for quick responses. In this way, by adjusting the content of the questionnaire according to the user's emotions, the burden on the user is reduced and the quality of responses is improved.

[0116] The analysis department can evaluate the control status based on the submitted data and confirm its effectiveness. Furthermore, the analysis department can visualize the results of the data analysis and display them clearly using graphs and charts. For example, the evaluation results of the control status can be displayed using bar graphs or pie charts to make them easy to understand visually. The analysis department can also use line graphs that show data trends to visually grasp fluctuations in risk. In this way, by visualizing the results of the data analysis, the evaluation results of the control status become easier to understand intuitively.

[0117] The checking section can include specific criteria for verifying whether internal controls are being properly implemented and whether risk management is being carried out appropriately. Furthermore, the checking section can estimate the user's emotions and adjust the self-check criteria based on the estimated user emotions. For example, if the user is feeling stressed, it can provide concise self-check criteria. Conversely, if the user is relaxed, it can provide detailed self-check criteria. This allows for appropriate self-checking for the user by adjusting the criteria according to their emotions.

[0118] The countermeasures department can identify trends in deficiencies in control systems and develop specific countermeasures. Furthermore, the countermeasures department can estimate user emotions and adjust the method of proposing countermeasures based on those emotions. For example, if a user is feeling stressed, it can propose simple and easy-to-implement countermeasures. Conversely, if a user is relaxed, it can propose more detailed countermeasures. In this way, by adjusting the method of proposing countermeasures according to the user's emotions, countermeasures that are easy for the user to implement are proposed.

[0119] The Collaboration Department can provide improvement measures to group companies with deficiencies in their control systems and describe specific methods for supporting their implementation. Furthermore, the Collaboration Department can estimate user emotions and adjust collaboration methods based on those estimates. For example, if a user is stressed, it can suggest a concise and quick collaboration method. If a user is relaxed, it can suggest a more detailed collaboration method. By adjusting collaboration methods according to user emotions, optimal collaboration for the user becomes possible.

[0120] The reception desk can provide an auto-completion function by referring to past answer history when users respond to questionnaires. Furthermore, the reception desk can add region-specific questions based on the user's geographical location. For example, if a user is in a specific region, questions about region-specific risks can be added. It can also add questions about local regulations and laws based on the user's location. This allows for region-specific risk management by adding region-specific questions based on the user's geographical location.

[0121] The analysis department can evaluate the control status based on the submitted data and confirm its effectiveness. Furthermore, the analysis department can automatically detect outliers by comparing current data with historical data during data analysis. For example, it can automatically detect outliers by comparing historical data with current data. If an outlier is detected, it can also identify the cause and generate a detailed report. This enables early detection of anomalies by automatically detecting outliers by comparing them with historical data.

[0122] The checking section can specify concrete criteria for verifying whether internal controls are being implemented appropriately and whether risk management is being carried out properly. Furthermore, the checking section can automatically suggest areas for improvement by referring to past check results during self-checks. For example, it can automatically suggest areas for improvement based on past check results. It can also extract common problems from past check results and suggest areas for improvement. This enables efficient improvement by automatically suggesting areas for improvement by referring to past check results.

[0123] The countermeasures department can identify trends in deficiencies in control systems and develop specific countermeasures. Furthermore, when proposing countermeasures, the department can automatically suggest the optimal solution by referring to past countermeasure history. For example, it can automatically suggest the optimal solution based on past countermeasure history. It can also extract the optimal solution for common problems from past countermeasure history. This enables efficient countermeasures by automatically suggesting the optimal solution by referring to past countermeasure history.

[0124] The following briefly describes the processing flow for example form 2.

[0125] Step 1: The reception department accepts responses from group company representatives to a questionnaire and submits the data. The questionnaire includes detailed information about each group company's control status and risks, such as the implementation status of internal controls and risk management methods. The reception department accepts responses to the questionnaire, for example, through an online form. The reception department also has the capability to securely store the submitted data and transmit it to the internal audit department. Step 2: The Analysis Department conducts internal control audits and data analysis based on the information received by the Reception Department. The Analysis Department analyzes the submitted data using, for example, data mining techniques to assess the control status. The Analysis Department also analyzes data trends using statistical methods to identify risks. Step 3: The checking unit self-checks the effectiveness of the control system based on the results obtained by the analysis unit. The checking unit verifies, for example, whether internal controls are being properly implemented using a checklist. The checking unit also evaluates whether risk management is being carried out effectively. Step 4: The countermeasures department takes necessary measures based on the results obtained by the checking department. For example, the countermeasures department identifies trends in deficiencies in the control status and develops specific improvement measures. The countermeasures department also formulates procedures for implementing the improvement measures and supports their execution. Step 5: The Liaison Department implements the measures taken by the Countermeasures Department in cooperation with the Internal Audit Office and other second-line departments. For example, the Liaison Department provides specific improvement measures to group companies that show deficiencies in their control systems and supports their implementation. The Liaison Department also provides training and educational programs to strengthen overall risk management.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] For example, the reception unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, it receives answers to a questionnaire using the touch panel 38A or microphone 38B of the smart device 14. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the submitted data. The checking unit is implemented by the specific processing unit 290 of the data processing device 12 and self-checks the effectiveness of the control status. The countermeasures unit is implemented by the specific processing unit 290 of the data processing device 12 and takes necessary countermeasures. The coordination unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12 and implements countermeasures in cooperation with the internal audit office or second-line departments. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0130] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, it receives answers to the questionnaire using the microphone 238 of the smart glasses 214. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the submitted data. The checking unit is implemented by the specific processing unit 290 of the data processing device 12 and self-checks the effectiveness of the control status. The countermeasures unit is implemented by the specific processing unit 290 of the data processing device 12 and takes necessary countermeasures. The coordination unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12 and implements countermeasures in cooperation with the internal audit office or second-line departments. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0146] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, it receives answers to the questionnaire using the microphone 238 of the headset terminal 314. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the submitted data. The checking unit is implemented by the specific processing unit 290 of the data processing device 12 and self-checks the effectiveness of the control status. The countermeasures unit is implemented by the specific processing unit 290 of the data processing device 12 and takes necessary countermeasures. The coordination unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12 and implements countermeasures in cooperation with the internal audit office or second-line departments. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0162] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.).

[0175] 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.

[0176] 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.

[0177] 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.

[0178] For example, the reception unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, it receives answers to a questionnaire using the microphone 238 of the robot 414. The analysis unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the submitted data. The checking unit is implemented by the specific processing unit 290 of the data processing device 12 and self-checks the effectiveness of the control status. The countermeasures unit is implemented by the specific processing unit 290 of the data processing device 12 and takes necessary countermeasures. The coordination unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12 and implements countermeasures in cooperation with the internal audit office or second-line departments. The correspondence between each unit and the devices and control units is not limited to the examples described above and can be changed in various ways.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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."

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] (Note 1) The reception desk accepts responses to questionnaires and data submissions, The Analysis Department describes the specific methods for conducting internal control audits and data analysis based on the information received by the aforementioned Reception Department, A checking unit that self-checks the effectiveness of the control status based on the results obtained by the aforementioned analysis unit, A countermeasures unit takes necessary measures based on the results obtained by the aforementioned checking unit, The system includes a coordination department that implements the measures taken by the aforementioned countermeasures department in cooperation with the internal audit department and second-line departments. A system characterized by the following features. (Note 2) The aforementioned reception unit is We accept responses and data submissions to a questionnaire that includes specific information regarding the control status and risks of each group company. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is Based on the submitted data, we will evaluate the control status and confirm its effectiveness. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned checking unit is This document outlines specific criteria for verifying whether internal controls are being implemented appropriately and whether risk management is being carried out properly. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned countermeasures unit, Identify trends in deficiencies in control systems and develop specific countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned linkage unit is, This document describes specific methods for providing improvement measures to group companies that have deficiencies in their control systems and for supporting their implementation. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned linkage unit is, We provide training and educational programs to strengthen overall risk management. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is It estimates the user's emotions and dynamically adjusts the content of the questionnaire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When answering questionnaires, the system provides an auto-completion function that references past answer history. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When data is submitted, the system automatically verifies the format and content of the submitted data and notifies of any errors in real time. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is It estimates the user's emotions and dynamically changes the priority of the questionnaire based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When users answer questionnaires, region-specific questions will be added, taking into account their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned reception unit is When data is submitted, the system analyzes the user's social media activity and automatically supplements it with relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit is It estimates user sentiment and adjusts data analysis algorithms 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 We analyze submitted data in real time and provide immediate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit is During data analysis, anomalies are automatically detected by comparing them with historical data. 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 performing data analysis, referencing industry-specific data of the user improves the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit is During data analysis, refer to relevant external databases to supplement the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned checking unit is The system estimates the user's emotions and adjusts the self-assessment criteria based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned checking unit is During self-checks, the system automatically suggests areas for improvement by referencing past check results. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned checking unit is During self-checks, real-time feedback is provided to encourage immediate corrections. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned checking unit is The system estimates the user's emotions and changes the priority of self-checks based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned checking unit is During self-checks, customize the checklist items by referring to the user's industry-specific standards. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned checking unit is During self-checks, the system automatically references relevant laws and regulations to complete the checklist. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned countermeasures unit, It estimates the user's emotions and adjusts the suggested solutions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned countermeasures unit, When proposing countermeasures, the system automatically suggests the most suitable measures by referring to past countermeasure history. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned countermeasures unit, When proposing countermeasures, provide real-time feedback and encourage immediate correction. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned countermeasures unit, We estimate user sentiment and change the priority of countermeasures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned countermeasures unit, When proposing countermeasures, we improve the accuracy of those countermeasures by referring to data specific to the user's industry. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned countermeasures unit, When proposing countermeasures, supplement them by referring to relevant external databases. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned linkage unit is, It estimates the user's emotions and adjusts the interaction method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned linkage unit is, During integration, the system automatically suggests the optimal integration method by referring to past integration history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned linkage unit is, During integration, we provide real-time feedback and prompt immediate corrections. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned linkage unit is, It estimates the user's emotions and changes the priority of collaborations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned linkage unit is, When integrating, we improve the accuracy of the integration by referencing the user's industry-specific data. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned linkage unit is, During integration, the system complements the integration by referencing relevant external databases. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0198] 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 reception desk accepts responses to questionnaires and data submissions, The Analysis Department describes the specific methods for conducting internal control audits and data analysis based on the information received by the aforementioned Reception Department, A checking unit that self-checks the effectiveness of the control status based on the results obtained by the aforementioned analysis unit, A countermeasures unit takes necessary measures based on the results obtained by the aforementioned checking unit, The system includes a coordination department that implements the measures taken by the aforementioned countermeasures department in cooperation with the internal audit department and second-line departments. A system characterized by the following features.

2. The aforementioned reception unit is We accept responses and data submissions to a questionnaire that includes specific information regarding the control status and risks of each group company. The system according to feature 1.

3. The aforementioned analysis unit is Based on the submitted data, we will evaluate the control status and confirm its effectiveness. The system according to feature 1.

4. The aforementioned checking unit is This document outlines specific criteria for verifying whether internal controls are being implemented appropriately and whether risk management is being carried out properly. The system according to feature 1.

5. The aforementioned countermeasures unit, Identify trends in deficiencies in control systems and develop specific countermeasures. The system according to feature 1.

6. The aforementioned linkage unit is, This document describes specific methods for providing improvement measures to group companies that have deficiencies in their control systems and for supporting their implementation. The system according to feature 1.

7. The aforementioned linkage unit is, We provide training and educational programs to strengthen overall risk management. The system according to feature 1.

8. The aforementioned reception unit is It estimates the user's emotions and dynamically adjusts the content of the questionnaire based on the estimated user emotions. The system according to feature 1.

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