system

Abstract

The system according to this embodiment aims to improve information security by utilizing records of information leakage incidents. [Solution] The system according to the embodiment comprises an anonymization unit, a detection unit, an analysis unit, an education unit, and a training unit. The anonymization unit anonymizes records of information leakage incidents. The detection unit detects information security patterns using the records anonymized by the anonymization unit. The analysis unit analyzes the patterns detected by the detection unit. The education unit provides security education based on the analysis results obtained by the analysis unit. The training unit provides training based on the educational content provided by the education unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, the records of information leakage accidents have not been fully utilized effectively to improve information security, and there is room for improvement.

[0005] The system according to the embodiment aims to utilize the records of information leakage accidents to improve information security.

Means for Solving the Problems

[0006] The system according to the embodiment comprises an anonymization unit, a detection unit, an analysis unit, an education unit, and a training unit. The anonymization unit anonymizes records of information leakage incidents. The detection unit uses the records anonymized by the anonymization unit to detect information security patterns. The analysis unit analyzes the patterns detected by the detection unit. The education unit provides security education based on the analysis results obtained by the analysis unit. The training unit provides training based on the educational content provided by the education unit. [Effects of the Invention]

[0007] The system according to this embodiment can improve information security by utilizing records of information leakage incidents. [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. As an example of the communication standard applied to the communication I / F, wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark) are included.

[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) An information security education system according to an embodiment of the present invention is a system that anonymizes records of information leakage incidents, detects and analyzes information security patterns using an LLM (Large-Scale Language Model), and provides a security education and training platform within an organization. The information security education system anonymizes records of information leakage incidents, detects and analyzes information security patterns using an LLM, and provides a security education and training platform within an organization. For example, records of information leakage incidents are anonymized. In this process, identifiable information such as personal information and company names is deleted to anonymize the data. For example, employee names and department names are deleted from records of information leakage incidents to create anonymized data. Next, the anonymized records are input into an LLM to detect and analyze information security patterns. The LLM can learn from large amounts of data and identify information security patterns. For example, by training the LLM with records of past information leakage incidents, the risk of information leakage in specific industries or office environments can be identified. Furthermore, based on the information security patterns detected and analyzed by the LLM, a security education and training platform is provided within the organization. For example, the system provides information security education to employees by highlighting specific points to consider in particular industries or office environments. Furthermore, employees can learn about information security measures through the training platform. This system allows for the identification of effective measures and the decision to discontinue ineffective ones. For instance, if a particular measure proves effective in a specific industry, that measure can be prioritized for implementation. Ineffective measures can be eliminated without significant effort. In this way, the risk of information leaks can be minimized, and an environment can be created where companies and individuals can work safely and efficiently. For example, in industries with a high risk of information leaks, implementing specific measures can prevent such incidents. Additionally, enhancing information security education for employees can further reduce the risk of information leaks.This allows the information security education system to anonymize records of data breaches, use LLM to detect and analyze information security patterns, and provide a security education and training platform within the organization.

[0029] The information security education system according to this embodiment comprises an anonymization unit, a detection unit, an analysis unit, an education unit, and a training unit. The anonymization unit anonymizes records of information leakage incidents. The anonymization unit anonymizes data by, for example, deleting identifiable information such as personal information and company names. For example, the anonymization unit deletes employee names and department names from records of information leakage incidents to create anonymized data. The detection unit detects information security patterns using the records anonymized by the anonymization unit. The detection unit identifies information security patterns by, for example, learning from a large amount of data using an LLM. For example, the detection unit identifies the risk of information leakage in specific industries or office environments by training the LLM with records of past information leakage incidents. The analysis unit analyzes the patterns detected by the detection unit. The analysis unit analyzes the detected patterns in detail using, for example, an LLM. For example, the analysis unit identifies points of caution in specific industries or office environments using an LLM. The education unit provides security education based on the analysis results obtained by the analysis unit. The Education Department provides information security education to employees, for example, by presenting points of caution in specific industries or office environments. For example, the Education Department educates employees on the effectiveness of specific measures in specific industries. The Training Department provides training based on the educational content provided by the Education Department. For example, the Training Department enables employees to actually learn information security measures through a training platform. For example, the Training Department provides training in which employees actually put information security measures into practice. Thus, the information security education system according to the embodiment can anonymize records of information leakage incidents, use LLM to detect and analyze information security patterns, and provide a security education and training platform within the organization.

[0030] The anonymization unit anonymizes records of data breaches. For example, the anonymization unit removes identifiable information such as personal information and company names to anonymize the data. Specifically, the anonymization unit removes employee names and department names from data breach records to create anonymized data. During data anonymization, the anonymization unit uses specific algorithms to identify and remove or mask personal information. For example, it uses natural language processing (NLP) techniques to automatically detect personal and company names in text data and replace them with random strings or common terms. Furthermore, after data anonymization is complete, the anonymization unit readjusts the data format and structure to maintain data integrity. This ensures that the anonymized data is presented in a format suitable for subsequent processing and analysis. Additionally, the anonymization unit logs the anonymization process for later verification and auditing. This allows the anonymization unit to securely and efficiently anonymize data breach records, ensuring privacy protection while maintaining data usability.

[0031] The detection unit detects information security patterns using records anonymized by the anonymization unit. The detection unit, for example, uses LLM (Learning Language Machine) to learn from large amounts of data and identify information security patterns. Specifically, the detection unit identifies information leakage risks in specific industries or office environments by training the LLM with records of past data breaches. LLM utilizes natural language processing technology to extract patterns and trends within text data, identifying the causes and commonalities of data breaches. For example, LLM can detect specific keywords and phrases from email content and file transfer histories, indicating potential precursors to data breaches. Furthermore, the detection unit can detect new data breach risks in real time based on the LLM's learning results. This allows the detection unit to quickly identify and respond to ongoing risks, not just make predictions based on past data. Additionally, the detection unit can share detected patterns with other systems and departments, strengthening information security measures across the entire organization. This enables the detection unit to achieve early detection and rapid response to data breach risks, improving the overall security level of the organization.

[0032] The analysis department analyzes the patterns detected by the detection department. For example, the analysis department uses LLM (Limited Licensing Modeling) to analyze the detected patterns in detail. Specifically, the analysis department uses LLM to identify points of concern in specific industries or office environments. Based on the detected patterns, LLM analyzes the causes and impacts of data breaches in detail, identifying high-risk behaviors and environmental factors. For example, LLM may indicate that access during specific times or from specific devices increases the risk of data breaches. The analysis department also evaluates and prioritizes risks based on the detected patterns. This allows organizations to quickly take countermeasures in the highest-risk areas. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on data from past data breaches, it can predict fluctuations in risks in specific industries or office environments and formulate future countermeasures. Additionally, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0033] The Education Department conducts security training based on the analysis results obtained by the Analysis Department. For example, the Education Department provides information security training to employees by highlighting points of caution specific to certain industries or office environments. Specifically, based on the analysis results, the Education Department explains the importance of information security and specific countermeasures in a way that is easy for employees to understand. For instance, to educate employees on the effectiveness of specific countermeasures in certain industries, they utilize video materials and interactive e-learning platforms. The Education Department also measures the effectiveness of the training by conducting tests and quizzes to assess employee understanding. Furthermore, the Education Department holds regular seminars and workshops to provide information on the latest information security trends and countermeasures. This ensures that employees always have up-to-date knowledge and raise their awareness of information security. It is also important for the Education Department to collect feedback from employees and use it to improve and update the training content. For example, if an employee finds a particular topic difficult to understand, the Education Department provides additional materials and explanations on that topic. This allows the Education Department to provide flexible training programs tailored to employee needs and improve information security awareness throughout the organization.

[0034] The Training Department provides training based on the educational content provided by the Education Department. For example, the Training Department allows employees to learn information security measures through a training platform. Specifically, the Training Department provides opportunities for employees to experience information security measures firsthand and acquire skills through simulations and practical exercises. For instance, training is conducted using specific scenarios, such as identifying phishing emails and setting secure passwords. The Training Department also monitors employees' training progress and provides customized training programs tailored to each employee's skill level. This allows employees to learn at their own pace and effectively improve their skills. Furthermore, the Training Department implements a feedback system to evaluate the effectiveness of training and collects opinions and feedback from employees. This allows for improvements and updates to the training program, ensuring that the latest information security measures are always provided. The Training Department supports employees in applying the knowledge and skills gained through training to their actual work, thereby improving the overall information security level of the organization.

[0035] The analysis department includes a presentation department that provides points of caution tailored to the industry and office environment. For example, the analysis department uses LLM to identify points of caution in specific industries and office environments. For example, the analysis department uses LLM to identify the risk of information leakage in a specific industry and provides points of caution regarding that risk. The analysis department also uses LLM to identify the risk of information leakage in a specific office environment and provides points of caution regarding that risk. By providing points of caution tailored to the industry and office environment, more effective security measures become possible. For example, if it is found that a particular measure is effective in a specific industry, that measure can be implemented with priority. Similarly, if it is found that a particular measure is effective in a specific office environment, that measure can be implemented with priority. By providing points of caution tailored to the industry and office environment, the risk of information leakage incidents can be minimized.

[0036] The analysis department includes a decision-making department that makes decisions on discontinuing ineffective measures. The analysis department uses, for example, LLM to determine whether a particular measure is effective. For example, if a particular measure is ineffective, the analysis department uses LLM to make a decision to discontinue that measure. Conversely, if a particular measure is effective, the analysis department uses LLM to make a decision to continue that measure. This reduces wasted effort by making decisions on discontinuing ineffective measures. For example, if a particular measure is ineffective, discontinuing it can reduce effort. Also, if a particular measure is effective, continuing it allows for the implementation of effective security measures. This minimizes the risk of information leakage incidents by making decisions on discontinuing ineffective measures.

[0037] The anonymization unit anonymizes data by removing personally identifiable information such as personal information and company names. For example, the anonymization unit removes employee names and department names from records of data breaches to create anonymized data. This ensures data anonymity by removing personally identifiable information such as personal information and company names. For example, records of data breaches can be anonymized by removing personally identifiable information such as personal information and company names. Furthermore, the anonymization unit can ensure data anonymity by removing personally identifiable information such as personal information and company names. This minimizes the risk of data breaches by removing personally identifiable information such as personal information and company names.

[0038] The detection unit learns from records of past data breaches and identifies information security patterns. For example, the detection unit uses LLM to learn from records of past data breaches and identify information security patterns. For example, by using LLM to train the detection unit on records of past data breaches, it can identify the risk of data breaches in specific industries or office environments. This allows for the identification of information security patterns by learning from records of past data breaches. For example, by learning from records of past data breaches, it can identify the risk of data breaches in specific industries or office environments. Furthermore, the detection unit can identify information security patterns by learning from records of past data breaches. This allows for the minimization of the risk of data breaches by learning from records of past data breaches.

[0039] The Ministry of Education provides information security education to employees by highlighting points to note in specific industries and office environments. For example, the Ministry of Education educates employees on the effectiveness of specific measures in specific industries. Furthermore, the Ministry of Education educates employees on the effectiveness of specific measures in specific office environments. This allows for effective information security education for employees by highlighting points to note in specific industries and office environments. For example, educating employees on the effectiveness of specific measures in specific industries can minimize the risk of data breaches. Furthermore, educating employees on the effectiveness of specific measures in specific office environments can minimize the risk of data breaches. This allows for effective information security education for employees by highlighting points to note in specific industries and office environments.

[0040] The training department can enable employees to actually learn information security measures through the training platform. For example, the training department can provide training that allows employees to actually implement information security measures. The training department can also provide simulations through the training platform to help employees learn information security measures. This allows employees to actually learn information security measures through the training platform. For example, by having employees actually implement information security measures, the risk of data breaches can be minimized. Furthermore, by providing simulations through the training platform to help employees learn information security measures, employees can actually learn information security measures. This allows employees to actually learn information security measures through the training platform.

[0041] The anonymization unit applies different anonymization algorithms depending on the type of data during the anonymization process. For example, it applies a strong anonymization algorithm to personal information and a lightweight algorithm to corporate information. Furthermore, it can use natural language processing for anonymization of text data and statistical methods for numerical data. It can also use facial recognition technology to blur identifiable parts of image data and apply a replacement algorithm to text data. This allows for appropriate anonymization by applying different anonymization algorithms depending on the data type. For example, applying a strong anonymization algorithm to personal information can prevent the leakage of personal information. Using a lightweight algorithm for corporate information enables efficient anonymization. This minimizes the risk of data breaches by applying different anonymization algorithms depending on the data type.

[0042] The anonymization unit adjusts the level of anonymization based on the importance of the data during the anonymization process. For example, the anonymization unit can perform detailed anonymization on high-importance data and simplified anonymization on low-importance data. Furthermore, the anonymization unit can perform multi-layered anonymization on confidential information and single-layered anonymization on general information. Additionally, the anonymization unit can perform strict anonymization on important customer information and simplified anonymization on internal memos. This ensures that important data is anonymized by adjusting the level of anonymization based on its importance. For example, detailed anonymization on high-importance data can prevent the leakage of confidential information. Simplifying anonymization on low-importance data enables efficient anonymization. This minimizes the risk of data breaches by adjusting the level of anonymization based on data importance.

[0043] The anonymization unit determines the priority of anonymization based on the data submission date. For example, the anonymization unit prioritizes anonymizing the most recent data and postpones the anonymization of older data. The anonymization unit can also prioritize anonymizing data with an approaching submission deadline. Furthermore, the anonymization unit can process data with an unknown submission date after the anonymization of other data is complete. This allows for anonymization to be performed with priority according to the submission date. For example, prioritizing the anonymization of the most recent data can prevent the leakage of the latest information. Also, prioritizing the anonymization of data with an approaching submission deadline can ensure that anonymization is completed in time for the deadline. In this way, the risk of information leakage incidents can be minimized by prioritizing anonymization based on the data submission date.

[0044] The anonymization unit adjusts the anonymization order based on the relevance of the data during the anonymization process. For example, the anonymization unit can anonymize highly relevant data in bulk for efficient processing. Alternatively, the anonymization unit can anonymize less relevant data individually for more detailed processing. Furthermore, the anonymization unit can optimize the anonymization order based on the relevance of the data. This allows for efficient anonymization by adjusting the anonymization order based on the relevance of the data. For example, highly relevant data can be processed efficiently by anonymizing it in bulk. Less relevant data can be processed individually for more detailed processing. This minimizes the risk of data breaches by adjusting the anonymization order based on the relevance of the data.

[0045] The detection unit improves detection accuracy by considering the interrelationships between data during detection. For example, the detection unit analyzes the correlation between data to detect abnormal patterns. The detection unit can also reduce false positives based on the interrelationships of data. Furthermore, the detection unit can perform more accurate detection by considering the interrelationships of data. As a result, detection accuracy is improved by considering the interrelationships of data. For example, abnormal patterns can be detected by analyzing the correlation between data. Also, by reducing false positives based on the interrelationships of data, more accurate detection becomes possible. As a result, the risk of information leakage incidents can be minimized by considering the interrelationships of data.

[0046] The detection unit performs detection while considering the attribute information of the data submitter. For example, the detection unit can detect abnormal patterns based on the submitter's job title or department. The detection unit can also detect abnormal patterns based on the submitter's past behavioral history. Furthermore, the detection unit can perform more accurate detection by considering the submitter's attribute information. This makes it possible to perform more accurate detection by considering the attribute information of the data submitter. For example, by detecting abnormal patterns based on the submitter's job title or department, the risk of information leakage incidents can be minimized. Also, by detecting abnormal patterns based on the submitter's past behavioral history, the risk of information leakage incidents can be minimized. This makes it possible to minimize the risk of information leakage incidents by considering the attribute information of the data submitter.

[0047] The detection unit performs detection while considering the geographical distribution of the data. For example, the detection unit can detect geographically abnormal patterns. Furthermore, the detection unit can identify abnormal data based on geographical distribution. In addition, the detection unit can perform more accurate detection by considering geographical distribution. This means that by considering the geographical distribution of the data, more accurate detection becomes possible. For example, by detecting geographically abnormal patterns, the risk of information leakage incidents can be minimized. Also, by identifying abnormal data based on geographical distribution, the risk of information leakage incidents can be minimized. This means that by considering the geographical distribution of the data, the risk of information leakage incidents can be minimized.

[0048] The detection unit improves detection accuracy by referring to relevant literature on the data during detection. For example, the detection unit detects abnormal patterns based on relevant literature. The detection unit can also reduce false positives by referring to relevant literature. Furthermore, the detection unit can perform more accurate detection by considering relevant literature. As a result, the detection accuracy is improved by referring to relevant literature on the data. For example, by detecting abnormal patterns based on relevant literature, the risk of information leakage incidents can be minimized. Furthermore, by reducing false positives by referring to relevant literature, the risk of information leakage incidents can be minimized. As a result, the risk of information leakage incidents can be minimized by referring to relevant literature on the data.

[0049] The analysis department predicts current data by referring to past data during analysis. For example, the analysis department predicts trends in current data based on past data. The analysis department can also identify abnormal patterns by referring to past data. Furthermore, the analysis department can predict future risks based on past data. This allows for the prediction of current data trends by referring to past data. For example, by predicting current data trends based on past data, the risk of data breaches can be minimized. Also, by identifying abnormal patterns by referring to past data, the risk of data breaches can be minimized. This allows for the minimization of the risk of data breaches by referring to past data.

[0050] The analysis department applies different analytical methods to each data category during analysis. For example, the analysis department uses natural language processing for text data. It can also use statistical methods for numerical data. Furthermore, it can use image recognition technology for image data. This improves the accuracy of the analysis by applying the appropriate analytical method to each data category. For example, using natural language processing for text data minimizes the risk of data breaches. Similarly, using statistical methods for numerical data minimizes the risk of data breaches. This ensures that the analysis department applies the appropriate analytical method to each data category, thereby minimizing the risk of data breaches.

[0051] The analysis department analyzes changes in analysis based on the data submission date. For example, the analysis department analyzes current trends based on the latest data. It can also analyze long-term changes based on historical data. Furthermore, the analysis department can identify changes in data based on the submission date. This allows for analysis tailored to the submission date by analyzing changes in analysis based on the data submission date. For example, analyzing current trends based on the latest data can minimize the risk of data breaches. Similarly, analyzing long-term changes based on historical data can minimize the risk of data breaches. Therefore, by analyzing changes in analysis based on the data submission date, the risk of data breaches can be minimized.

[0052] The analysis department performs its analysis by referring to relevant market data. For example, the analysis department analyzes current data trends based on relevant market data. The analysis department can also identify anomalous patterns by referring to relevant market data. Furthermore, the analysis department can predict future risks based on relevant market data. This allows for more accurate analysis by referring to relevant market data. For example, by analyzing current data trends based on relevant market data, the risk of data breaches can be minimized. Similarly, by identifying anomalous patterns by referring to relevant market data, the risk of data breaches can be minimized. This demonstrates that by referring to relevant market data, the risk of data breaches can be minimized.

[0053] The Ministry of Education selects the optimal teaching method by referring to past teaching history during training. For example, the Ministry of Education proposes the optimal teaching method based on the user's past teaching history. Furthermore, the Ministry of Education can select effective teaching methods by referring to past teaching history. In addition, the Ministry of Education can provide customized teaching methods based on the user's past teaching history. This allows for the selection of the optimal teaching method by referring to past teaching history. For example, by proposing the optimal teaching method based on the user's past teaching history, the risk of information leakage incidents can be minimized. Similarly, by selecting effective teaching methods by referring to past teaching history, the risk of information leakage incidents can be minimized. This allows for the minimization of the risk of information leakage incidents by referring to past teaching history.

[0054] The Ministry of Education applies different educational methods depending on the specific industry and office environment. For example, the Ministry of Education provides specialized educational content for specific industries. It can also apply educational methods tailored to the office environment. Furthermore, the Ministry of Education can provide customized educational methods based on the industry and office environment. This allows for effective education by applying educational methods tailored to specific industries and office environments. For example, providing specialized educational content for specific industries can minimize the risk of information leaks. Similarly, applying educational methods tailored to the office environment can minimize the risk of information leaks. This allows for effective education by applying educational methods tailored to specific industries and office environments.

[0055] The Ministry of Education adjusts educational content based on the timing of data submission. For example, the Ministry of Education provides educational content that is aligned with current trends based on the latest data. It can also provide educational content that is adapted to long-term changes based on historical data. Furthermore, the Ministry of Education can provide educational content that is adapted to changes in data based on the submission timing. This allows for education tailored to the submission timing by adjusting the educational content accordingly. For example, providing educational content that is aligned with current trends based on the latest data minimizes the risk of data breaches. Similarly, providing educational content that is adapted to long-term changes based on historical data minimizes the risk of data breaches. This allows for minimizing the risk of data breaches by adjusting the educational content based on the data submission timing.

[0056] The Ministry of Education improves the accuracy of education by referring to relevant literature on data during training. For example, the Ministry of Education provides educational content that reflects the latest knowledge based on relevant literature. The Ministry of Education can also provide educational content that avoids misunderstandings by referring to relevant literature. Furthermore, the Ministry of Education can provide educational content that promotes a deeper understanding based on relevant literature. In this way, the accuracy of education is improved by referring to relevant literature on data. For example, by providing educational content that reflects the latest knowledge based on relevant literature, the risk of information leakage incidents can be minimized. Furthermore, by providing educational content that avoids misunderstandings by referring to relevant literature, the risk of information leakage incidents can be minimized. In this way, the risk of information leakage incidents can be minimized by referring to relevant literature on data.

[0057] The training department selects the optimal training method by referring to past training history during training. For example, the training department proposes the optimal training method based on the user's past training history. The training department can also select an effective training method by referring to past training history. Furthermore, the training department can provide a customized training method based on the user's past training history. This allows for the selection of the optimal training method by referring to past training history. For example, by proposing the optimal training method based on the user's past training history, the risk of information leakage incidents can be minimized. Similarly, by selecting an effective training method by referring to past training history, the risk of information leakage incidents can be minimized. This allows for the minimization of the risk of information leakage incidents by referring to past training history.

[0058] The training department applies different training methods depending on the specific industry and office environment. For example, the training department provides training content tailored to specific industries. It can also apply training methods appropriate to the office environment. Furthermore, the training department can provide customized training methods based on the industry and office environment. This allows for effective training by applying training methods tailored to specific industries and office environments. For example, providing training content tailored to specific industries minimizes the risk of information leaks. Similarly, applying training methods appropriate to the office environment minimizes the risk of information leaks. This allows for effective training by applying training methods tailored to specific industries and office environments.

[0059] The training department adjusts the training content based on the data submission timing. For example, the training department provides training content that is aligned with current trends based on the latest data. It can also provide training content that is aligned with long-term changes based on historical data. Furthermore, the training department can provide training content that is aligned with data changes based on the submission timing. This allows for training tailored to the submission timing by adjusting the training content accordingly. For example, providing training content aligned with current trends based on the latest data minimizes the risk of data breaches. Similarly, providing training content that is aligned with long-term changes based on historical data minimizes the risk of data breaches. This allows for training content that is aligned with data submission timing, minimizing the risk of data breaches.

[0060] The training department improves the accuracy of training by referring to relevant literature on the data during training. For example, the training department provides training content that reflects the latest knowledge based on relevant literature. The training department can also provide training content that avoids misunderstandings by referring to relevant literature. Furthermore, the training department can provide training content that promotes a deeper understanding based on relevant literature. In this way, the accuracy of training is improved by referring to relevant literature on the data. For example, by providing training content that reflects the latest knowledge based on relevant literature, the risk of information leakage incidents can be minimized. Furthermore, by providing training content that avoids misunderstandings by referring to relevant literature, the risk of information leakage incidents can be minimized. In this way, by referring to relevant literature on the data, the risk of information leakage incidents can be minimized.

[0061] The training department selects the optimal training method during training sessions, taking into account the user's health condition. For example, if a user is tired, the training department will provide a lighter training session. Conversely, if a user is seeking healthy exercise, the training department can also provide a slightly more intense training session. Furthermore, if a user is feeling unwell, the training department can provide a training session that includes rest periods. This allows the selection of the optimal training method by considering the user's health condition. For instance, providing a lighter training session when a user is tired minimizes the risk of data breaches. Similarly, providing a slightly more intense training session when a user is seeking healthy exercise minimizes the risk of data breaches. This demonstrates how considering the user's health condition minimizes the risk of data breaches.

[0062] The training department adjusts the training progress by referring to the user's past training history. For example, the training department adjusts the pace of training based on the user's past training history. The training department can also select an effective training method by referring to past training history. Furthermore, the training department can provide a customized training method based on the user's past training history. This allows training to be conducted at an optimal pace by referring to the user's past training history. For example, by adjusting the pace of training based on the user's past training history, the risk of information leakage incidents can be minimized. Furthermore, by selecting an effective training method by referring to past training history, the risk of information leakage incidents can be minimized. This allows training to be conducted at an optimal pace by referring to the user's past training history.

[0063] The training department adjusts training content based on user feedback during training sessions. For example, the training department improves training content based on user feedback. The training department can also select effective training methods based on feedback. Furthermore, the training department can provide customized training content based on user feedback. This allows for more effective training by adjusting training content based on user feedback. For example, improving training content based on user feedback minimizes the risk of data breaches. Similarly, selecting effective training methods based on feedback minimizes the risk of data breaches. This allows for minimizing the risk of data breaches by adjusting training content based on user feedback.

[0064] The training department adjusts the training method according to the user's learning style. For example, if the user is a visual learner, the training department will provide training that makes extensive use of visual content. If the user is an auditory learner, the training department can also provide training that makes extensive use of audio guides. Furthermore, if the user is an experiential learner, the training department can provide practical training. By adjusting the training method according to the user's learning style, more effective training becomes possible. For example, if the user is a visual learner, providing training that makes extensive use of visual content can minimize the risk of information leakage. Similarly, if the user is an auditory learner, providing training that makes extensive use of audio guides can minimize the risk of information leakage. This allows for more effective training by adjusting the training method according to the user's learning style.

[0065] The training department adjusts the training content according to the user's skill level. For example, the training department can provide training content for beginners, intermediate users, and advanced users. By adjusting the training content according to the user's skill level, more effective training becomes possible. For example, providing training content for beginners minimizes the risk of data breaches. Similarly, providing training content for intermediate users minimizes the risk of data breaches. By adjusting the training content according to the user's skill level, the risk of data breaches can be minimized.

[0066] The training department adjusts the training content according to the user's goals. For example, the training department can provide training content tailored to the user's short-term goals. It can also provide training content tailored to the user's medium-term goals. Furthermore, it can provide training content tailored to the user's long-term goals. By adjusting the training content according to the user's goals, more effective training becomes possible. For example, by providing training content tailored to the user's short-term goals, the risk of information leakage incidents can be minimized. Similarly, by providing training content tailored to the user's medium-term goals, the risk of information leakage incidents can be minimized. Therefore, by adjusting the training content according to the user's goals, the risk of information leakage incidents can be minimized.

[0067] The training department adjusts the training content according to the user's progress during training. For example, the training department improves the training content based on the user's progress. The training department can also select effective training methods by referring to the progress. Furthermore, the training department can provide customized training content based on the user's progress. This makes it possible to conduct more effective training by adjusting the training content according to the user's progress. For example, improving the training content based on the user's progress can minimize the risk of information leakage incidents. Also, selecting effective training methods by referring to the progress can minimize the risk of information leakage incidents. This makes it possible to minimize the risk of information leakage incidents by adjusting the training content according to the user's progress.

[0068] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0069] The anonymization unit can apply different anonymization algorithms depending on the type of data during the anonymization process. For example, a robust anonymization algorithm can be applied to personal information, while a lightweight algorithm can be used for corporate information. Furthermore, natural language processing can be used for anonymization of text data, and statistical methods can be applied to numerical data. Additionally, facial recognition technology can be used to blur identifiable parts of image data, and a replacement algorithm can be applied to text data. This allows for appropriate anonymization by applying different anonymization algorithms depending on the data type.

[0070] The detection unit can improve detection accuracy by considering the interrelationships between data during detection. For example, it can analyze the correlation between data and detect abnormal patterns. It can also reduce false positives based on the interrelationships of the data. Furthermore, it can perform detection with higher accuracy by considering the interrelationships of the data. In short, considering the interrelationships of the data improves detection accuracy.

[0071] The analysis unit can apply different analytical methods to each data category during analysis. For example, natural language processing can be used for text data, statistical methods for numerical data, and image recognition technology for image data. By applying the appropriate analytical method to each data category, the accuracy of the analysis is improved.

[0072] The Ministry of Education can apply different teaching methods to specific industries and office environments during training. For example, it can provide specialized training content tailored to specific industries. It can also apply teaching methods appropriate to the office environment. Furthermore, it can provide customized teaching methods based on the industry and office environment. This allows for more effective training by applying teaching methods tailored to specific industries and office environments.

[0073] The training unit can adjust the training method according to the user's learning style. For example, if the user is a visual learner, training can be provided that makes extensive use of visual content. If the user is an auditory learner, training can be provided that makes extensive use of audio guidance. Furthermore, if the user is an experiential learner, practical training can be provided. By adjusting the training method according to the user's learning style, more effective training becomes possible.

[0074] The following briefly describes the processing flow for example form 1.

[0075] Step 1: The anonymization department anonymizes the records of the data breach. For example, it removes identifiable information such as personal information and company names to anonymize the data. Specifically, it removes employee names and department names to create anonymized data. Step 2: The detection unit detects information security patterns using the anonymized records from the anonymization unit. For example, an LLM is used to learn from a large amount of data and identify information security patterns. By training the LLM with records of past data breaches, the risk of data breaches in specific industries or office environments can be identified. Step 3: The analysis unit analyzes the patterns detected by the detection unit. For example, LLM is used to analyze the detected patterns in detail and identify points of concern in specific industries or office environments. Step 4: The Education Department conducts security training based on the analysis results obtained by the Analysis Department. For example, it provides information security training to employees by highlighting points to be aware of in specific industries or office environments. It also educates employees on the effectiveness of specific measures in specific industries. Step 5: The training department provides training based on the educational content provided by the education department. For example, employees can learn about information security measures through the training platform. The training provides employees with practical training in implementing information security measures.

[0076] (Example of form 2) An information security education system according to an embodiment of the present invention is a system that anonymizes records of information leakage incidents, detects and analyzes information security patterns using an LLM (Large-Scale Language Model), and provides a security education and training platform within an organization. The information security education system anonymizes records of information leakage incidents, detects and analyzes information security patterns using an LLM, and provides a security education and training platform within an organization. For example, records of information leakage incidents are anonymized. In this process, identifiable information such as personal information and company names is deleted to anonymize the data. For example, employee names and department names are deleted from records of information leakage incidents to create anonymized data. Next, the anonymized records are input into an LLM to detect and analyze information security patterns. The LLM can learn from large amounts of data and identify information security patterns. For example, by training the LLM with records of past information leakage incidents, the risk of information leakage in specific industries or office environments can be identified. Furthermore, based on the information security patterns detected and analyzed by the LLM, a security education and training platform is provided within the organization. For example, the system provides information security education to employees by highlighting specific points to consider in particular industries or office environments. Furthermore, employees can learn about information security measures through the training platform. This system allows for the identification of effective measures and the decision to discontinue ineffective ones. For instance, if a particular measure proves effective in a specific industry, that measure can be prioritized for implementation. Ineffective measures can be eliminated without significant effort. In this way, the risk of information leaks can be minimized, and an environment can be created where companies and individuals can work safely and efficiently. For example, in industries with a high risk of information leaks, implementing specific measures can prevent such incidents. Additionally, enhancing information security education for employees can further reduce the risk of information leaks.This allows the information security education system to anonymize records of data breaches, use LLM to detect and analyze information security patterns, and provide a security education and training platform within the organization.

[0077] The information security education system according to this embodiment comprises an anonymization unit, a detection unit, an analysis unit, an education unit, and a training unit. The anonymization unit anonymizes records of information leakage incidents. The anonymization unit anonymizes data by, for example, deleting identifiable information such as personal information and company names. For example, the anonymization unit deletes employee names and department names from records of information leakage incidents to create anonymized data. The detection unit detects information security patterns using the records anonymized by the anonymization unit. The detection unit identifies information security patterns by, for example, learning from a large amount of data using an LLM. For example, the detection unit identifies the risk of information leakage in specific industries or office environments by training the LLM with records of past information leakage incidents. The analysis unit analyzes the patterns detected by the detection unit. The analysis unit analyzes the detected patterns in detail using, for example, an LLM. For example, the analysis unit identifies points of caution in specific industries or office environments using an LLM. The education unit provides security education based on the analysis results obtained by the analysis unit. The Education Department provides information security education to employees, for example, by presenting points of caution in specific industries or office environments. For example, the Education Department educates employees on the effectiveness of specific measures in specific industries. The Training Department provides training based on the educational content provided by the Education Department. For example, the Training Department enables employees to actually learn information security measures through a training platform. For example, the Training Department provides training in which employees actually put information security measures into practice. Thus, the information security education system according to the embodiment can anonymize records of information leakage incidents, use LLM to detect and analyze information security patterns, and provide a security education and training platform within the organization.

[0078] The anonymization unit anonymizes records of data breaches. For example, the anonymization unit removes identifiable information such as personal information and company names to anonymize the data. Specifically, the anonymization unit removes employee names and department names from data breach records to create anonymized data. During data anonymization, the anonymization unit uses specific algorithms to identify and remove or mask personal information. For example, it uses natural language processing (NLP) techniques to automatically detect personal and company names in text data and replace them with random strings or common terms. Furthermore, after data anonymization is complete, the anonymization unit readjusts the data format and structure to maintain data integrity. This ensures that the anonymized data is presented in a format suitable for subsequent processing and analysis. Additionally, the anonymization unit logs the anonymization process for later verification and auditing. This allows the anonymization unit to securely and efficiently anonymize data breach records, ensuring privacy protection while maintaining data usability.

[0079] The detection unit detects information security patterns using records anonymized by the anonymization unit. The detection unit, for example, uses LLM (Learning Language Machine) to learn from large amounts of data and identify information security patterns. Specifically, the detection unit identifies information leakage risks in specific industries or office environments by training the LLM with records of past data breaches. LLM utilizes natural language processing technology to extract patterns and trends within text data, identifying the causes and commonalities of data breaches. For example, LLM can detect specific keywords and phrases from email content and file transfer histories, indicating potential precursors to data breaches. Furthermore, the detection unit can detect new data breach risks in real time based on the LLM's learning results. This allows the detection unit to quickly identify and respond to ongoing risks, not just make predictions based on past data. Additionally, the detection unit can share detected patterns with other systems and departments, strengthening information security measures across the entire organization. This enables the detection unit to achieve early detection and rapid response to data breach risks, improving the overall security level of the organization.

[0080] The analysis department analyzes the patterns detected by the detection department. For example, the analysis department uses LLM (Limited Licensing Modeling) to analyze the detected patterns in detail. Specifically, the analysis department uses LLM to identify points of concern in specific industries or office environments. Based on the detected patterns, LLM analyzes the causes and impacts of data breaches in detail, identifying high-risk behaviors and environmental factors. For example, LLM may indicate that access during specific times or from specific devices increases the risk of data breaches. The analysis department also evaluates and prioritizes risks based on the detected patterns. This allows organizations to quickly take countermeasures in the highest-risk areas. Furthermore, the analysis department can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on data from past data breaches, it can predict fluctuations in risks in specific industries or office environments and formulate future countermeasures. Additionally, the analysis department can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the analysis department to not only grasp the situation in real time, but also to handle long-term risk management and anomaly detection, thereby improving the reliability and security of the entire system.

[0081] The Education Department conducts security training based on the analysis results obtained by the Analysis Department. For example, the Education Department provides information security training to employees by highlighting points of caution specific to certain industries or office environments. Specifically, based on the analysis results, the Education Department explains the importance of information security and specific countermeasures in a way that is easy for employees to understand. For instance, to educate employees on the effectiveness of specific countermeasures in certain industries, they utilize video materials and interactive e-learning platforms. The Education Department also measures the effectiveness of the training by conducting tests and quizzes to assess employee understanding. Furthermore, the Education Department holds regular seminars and workshops to provide information on the latest information security trends and countermeasures. This ensures that employees always have up-to-date knowledge and raise their awareness of information security. It is also important for the Education Department to collect feedback from employees and use it to improve and update the training content. For example, if an employee finds a particular topic difficult to understand, the Education Department provides additional materials and explanations on that topic. This allows the Education Department to provide flexible training programs tailored to employee needs and improve information security awareness throughout the organization.

[0082] The Training Department provides training based on the educational content provided by the Education Department. For example, the Training Department allows employees to learn information security measures through a training platform. Specifically, the Training Department provides opportunities for employees to experience information security measures firsthand and acquire skills through simulations and practical exercises. For instance, training is conducted using specific scenarios, such as identifying phishing emails and setting secure passwords. The Training Department also monitors employees' training progress and provides customized training programs tailored to each employee's skill level. This allows employees to learn at their own pace and effectively improve their skills. Furthermore, the Training Department implements a feedback system to evaluate the effectiveness of training and collects opinions and feedback from employees. This allows for improvements and updates to the training program, ensuring that the latest information security measures are always provided. The Training Department supports employees in applying the knowledge and skills gained through training to their actual work, thereby improving the overall information security level of the organization.

[0083] The analysis department includes a presentation department that provides points of caution tailored to the industry and office environment. For example, the analysis department uses LLM to identify points of caution in specific industries and office environments. For example, the analysis department uses LLM to identify the risk of information leakage in a specific industry and provides points of caution regarding that risk. The analysis department also uses LLM to identify the risk of information leakage in a specific office environment and provides points of caution regarding that risk. By providing points of caution tailored to the industry and office environment, more effective security measures become possible. For example, if it is found that a particular measure is effective in a specific industry, that measure can be implemented with priority. Similarly, if it is found that a particular measure is effective in a specific office environment, that measure can be implemented with priority. By providing points of caution tailored to the industry and office environment, the risk of information leakage incidents can be minimized.

[0084] The analysis department includes a decision-making department that makes decisions on discontinuing ineffective measures. The analysis department uses, for example, LLM to determine whether a particular measure is effective. For example, if a particular measure is ineffective, the analysis department uses LLM to make a decision to discontinue that measure. Conversely, if a particular measure is effective, the analysis department uses LLM to make a decision to continue that measure. This reduces wasted effort by making decisions on discontinuing ineffective measures. For example, if a particular measure is ineffective, discontinuing it can reduce effort. Also, if a particular measure is effective, continuing it allows for the implementation of effective security measures. This minimizes the risk of information leakage incidents by making decisions on discontinuing ineffective measures.

[0085] The anonymization unit anonymizes data by removing personally identifiable information such as personal information and company names. For example, the anonymization unit removes employee names and department names from records of data breaches to create anonymized data. This ensures data anonymity by removing personally identifiable information such as personal information and company names. For example, records of data breaches can be anonymized by removing personally identifiable information such as personal information and company names. Furthermore, the anonymization unit can ensure data anonymity by removing personally identifiable information such as personal information and company names. This minimizes the risk of data breaches by removing personally identifiable information such as personal information and company names.

[0086] The detection unit learns from records of past data breaches and identifies information security patterns. For example, the detection unit uses LLM to learn from records of past data breaches and identify information security patterns. For example, by using LLM to train the detection unit on records of past data breaches, it can identify the risk of data breaches in specific industries or office environments. This allows for the identification of information security patterns by learning from records of past data breaches. For example, by learning from records of past data breaches, it can identify the risk of data breaches in specific industries or office environments. Furthermore, the detection unit can identify information security patterns by learning from records of past data breaches. This allows for the minimization of the risk of data breaches by learning from records of past data breaches.

[0087] The Ministry of Education provides information security education to employees by highlighting points to note in specific industries and office environments. For example, the Ministry of Education educates employees on the effectiveness of specific measures in specific industries. Furthermore, the Ministry of Education educates employees on the effectiveness of specific measures in specific office environments. This allows for effective information security education for employees by highlighting points to note in specific industries and office environments. For example, educating employees on the effectiveness of specific measures in specific industries can minimize the risk of data breaches. Furthermore, educating employees on the effectiveness of specific measures in specific office environments can minimize the risk of data breaches. This allows for effective information security education for employees by highlighting points to note in specific industries and office environments.

[0088] The training department can enable employees to actually learn information security measures through the training platform. For example, the training department can provide training that allows employees to actually implement information security measures. The training department can also provide simulations through the training platform to help employees learn information security measures. This allows employees to actually learn information security measures through the training platform. For example, by having employees actually implement information security measures, the risk of data breaches can be minimized. Furthermore, by providing simulations through the training platform to help employees learn information security measures, employees can actually learn information security measures. This allows employees to actually learn information security measures through the training platform.

[0089] The anonymization unit estimates the user's emotions and adjusts the anonymization method based on the estimated emotions. For example, if the user is stressed, the anonymization unit simplifies the anonymization procedure and processes it quickly. If the user is relaxed, the anonymization unit can also provide detailed anonymization options and suggest a customizable method. Furthermore, if the user is in a hurry, the anonymization unit can anonymize only the most important information and complete the process quickly. This allows for situation-appropriate anonymization by adjusting the anonymization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows for situation-appropriate anonymization by adjusting the anonymization method based on the user's emotions.

[0090] The anonymization unit applies different anonymization algorithms depending on the type of data during the anonymization process. For example, it applies a strong anonymization algorithm to personal information and a lightweight algorithm to corporate information. Furthermore, it can use natural language processing for anonymization of text data and statistical methods for numerical data. It can also use facial recognition technology to blur identifiable parts of image data and apply a replacement algorithm to text data. This allows for appropriate anonymization by applying different anonymization algorithms depending on the data type. For example, applying a strong anonymization algorithm to personal information can prevent the leakage of personal information. Using a lightweight algorithm for corporate information enables efficient anonymization. This minimizes the risk of data breaches by applying different anonymization algorithms depending on the data type.

[0091] The anonymization unit adjusts the level of anonymization based on the importance of the data during the anonymization process. For example, the anonymization unit can perform detailed anonymization on high-importance data and simplified anonymization on low-importance data. Furthermore, the anonymization unit can perform multi-layered anonymization on confidential information and single-layered anonymization on general information. Additionally, the anonymization unit can perform strict anonymization on important customer information and simplified anonymization on internal memos. This ensures that important data is anonymized by adjusting the level of anonymization based on its importance. For example, detailed anonymization on high-importance data can prevent the leakage of confidential information. Simplifying anonymization on low-importance data enables efficient anonymization. This minimizes the risk of data breaches by adjusting the level of anonymization based on data importance.

[0092] The anonymization unit estimates the user's emotions and determines the anonymization priority based on the estimated emotions. For example, if the user is stressed, the anonymization unit prioritizes anonymizing the most important data. If the user is relaxed, the anonymization unit can also anonymize all data equally. Furthermore, if the user is in a hurry, the anonymization unit can prioritize anonymizing the data that can be processed quickly. This allows for anonymization with a priority tailored to the user's situation by determining the anonymization priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the anonymization unit may be performed using AI or not. For example, the anonymization unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows for anonymization with a priority tailored to the user's situation by determining the anonymization priority based on the user's emotions.

[0093] The anonymization unit determines the priority of anonymization based on the data submission date. For example, the anonymization unit prioritizes anonymizing the most recent data and postpones the anonymization of older data. The anonymization unit can also prioritize anonymizing data with an approaching submission deadline. Furthermore, the anonymization unit can process data with an unknown submission date after the anonymization of other data is complete. This allows for anonymization to be performed with priority according to the submission date. For example, prioritizing the anonymization of the most recent data can prevent the leakage of the latest information. Also, prioritizing the anonymization of data with an approaching submission deadline can ensure that anonymization is completed in time for the deadline. In this way, the risk of information leakage incidents can be minimized by prioritizing anonymization based on the data submission date.

[0094] The anonymization unit adjusts the anonymization order based on the relevance of the data during the anonymization process. For example, the anonymization unit can anonymize highly relevant data in bulk for efficient processing. Alternatively, the anonymization unit can anonymize less relevant data individually for more detailed processing. Furthermore, the anonymization unit can optimize the anonymization order based on the relevance of the data. This allows for efficient anonymization by adjusting the anonymization order based on the relevance of the data. For example, highly relevant data can be processed efficiently by anonymizing it in bulk. Less relevant data can be processed individually for more detailed processing. This minimizes the risk of data breaches by adjusting the anonymization order based on the relevance of the data.

[0095] The detection unit estimates the user's emotions and adjusts the detection criteria based on the estimated emotions. For example, if the user is tense, the detection unit will use strict criteria for detection. If the user is relaxed, the detection unit can also use flexible criteria for detection. Furthermore, if the user is in a hurry, the detection unit can set criteria for rapid detection. By adjusting the detection criteria based on the user's emotions, detection can be adapted to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI, or not using AI. For example, the detection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. By adjusting the detection criteria based on the user's emotions, detection can be adapted to the user's situation.

[0096] The detection unit improves detection accuracy by considering the interrelationships between data during detection. For example, the detection unit analyzes the correlation between data to detect abnormal patterns. The detection unit can also reduce false positives based on the interrelationships of data. Furthermore, the detection unit can perform more accurate detection by considering the interrelationships of data. As a result, detection accuracy is improved by considering the interrelationships of data. For example, abnormal patterns can be detected by analyzing the correlation between data. Also, by reducing false positives based on the interrelationships of data, more accurate detection becomes possible. As a result, the risk of information leakage incidents can be minimized by considering the interrelationships of data.

[0097] The detection unit performs detection while considering the attribute information of the data submitter. For example, the detection unit can detect abnormal patterns based on the submitter's job title or department. The detection unit can also detect abnormal patterns based on the submitter's past behavioral history. Furthermore, the detection unit can perform more accurate detection by considering the submitter's attribute information. This makes it possible to perform more accurate detection by considering the attribute information of the data submitter. For example, by detecting abnormal patterns based on the submitter's job title or department, the risk of information leakage incidents can be minimized. Also, by detecting abnormal patterns based on the submitter's past behavioral history, the risk of information leakage incidents can be minimized. This makes it possible to minimize the risk of information leakage incidents by considering the attribute information of the data submitter.

[0098] The detection unit estimates the user's emotions and adjusts the order in which the detection results are displayed based on the estimated emotions. For example, if the user is tense, the detection unit will prioritize displaying the most important results. If the user is relaxed, the detection unit can also display all results equally. Furthermore, if the user is in a hurry, the detection unit can prioritize displaying results that can be quickly reviewed. This allows for result display tailored to the user's situation by adjusting the order in which the detection results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the detection unit may be performed using AI or not. For example, the detection unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows for result display tailored to the user's situation by adjusting the order in which the detection results are displayed based on the user's emotions.

[0099] The detection unit performs detection while considering the geographical distribution of the data. For example, the detection unit can detect geographically abnormal patterns. Furthermore, the detection unit can identify abnormal data based on geographical distribution. In addition, the detection unit can perform more accurate detection by considering geographical distribution. This means that by considering the geographical distribution of the data, more accurate detection becomes possible. For example, by detecting geographically abnormal patterns, the risk of information leakage incidents can be minimized. Also, by identifying abnormal data based on geographical distribution, the risk of information leakage incidents can be minimized. This means that by considering the geographical distribution of the data, the risk of information leakage incidents can be minimized.

[0100] The detection unit improves detection accuracy by referring to relevant literature on the data during detection. For example, the detection unit detects abnormal patterns based on relevant literature. The detection unit can also reduce false positives by referring to relevant literature. Furthermore, the detection unit can perform more accurate detection by considering relevant literature. As a result, the detection accuracy is improved by referring to relevant literature on the data. For example, by detecting abnormal patterns based on relevant literature, the risk of information leakage incidents can be minimized. Furthermore, by reducing false positives by referring to relevant literature, the risk of information leakage incidents can be minimized. As a result, the risk of information leakage incidents can be minimized by referring to relevant literature on the data.

[0101] The analysis unit estimates the user's emotions and adjusts the analysis method based on the estimated emotions. For example, if the user is tense, the analysis unit provides a simple analysis method. It can also provide a more detailed analysis method if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can provide a method for rapid analysis. This allows for situation-appropriate analysis by adjusting the analysis method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows for situation-appropriate analysis by adjusting the analysis method based on the user's emotions.

[0102] The analysis department predicts current data by referring to past data during analysis. For example, the analysis department predicts trends in current data based on past data. The analysis department can also identify abnormal patterns by referring to past data. Furthermore, the analysis department can predict future risks based on past data. This allows for the prediction of current data trends by referring to past data. For example, by predicting current data trends based on past data, the risk of data breaches can be minimized. Also, by identifying abnormal patterns by referring to past data, the risk of data breaches can be minimized. This allows for the minimization of the risk of data breaches by referring to past data.

[0103] The analysis department applies different analytical methods to each data category during analysis. For example, the analysis department uses natural language processing for text data. It can also use statistical methods for numerical data. Furthermore, it can use image recognition technology for image data. This improves the accuracy of the analysis by applying the appropriate analytical method to each data category. For example, using natural language processing for text data minimizes the risk of data breaches. Similarly, using statistical methods for numerical data minimizes the risk of data breaches. This ensures that the analysis department applies the appropriate analytical method to each data category, thereby minimizing the risk of data breaches.

[0104] The analysis unit estimates the user's emotions and adjusts the importance of the analysis based on the estimated emotions. For example, if the user is stressed, the analysis unit prioritizes analyzing important data. If the user is relaxed, the analysis unit can also analyze all data equally. Furthermore, if the user is in a hurry, the analysis unit can prioritize analyzing data that can be analyzed quickly. This allows the analysis to be performed with importance appropriate to the user's situation by adjusting the importance of the analysis based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows the analysis to be performed with importance appropriate to the user's situation by adjusting the importance of the analysis based on the user's emotions.

[0105] The analysis department analyzes changes in analysis based on the data submission date. For example, the analysis department analyzes current trends based on the latest data. It can also analyze long-term changes based on historical data. Furthermore, the analysis department can identify changes in data based on the submission date. This allows for analysis tailored to the submission date by analyzing changes in analysis based on the data submission date. For example, analyzing current trends based on the latest data can minimize the risk of data breaches. Similarly, analyzing long-term changes based on historical data can minimize the risk of data breaches. Therefore, by analyzing changes in analysis based on the data submission date, the risk of data breaches can be minimized.

[0106] The analysis department performs its analysis by referring to relevant market data. For example, the analysis department analyzes current data trends based on relevant market data. The analysis department can also identify anomalous patterns by referring to relevant market data. Furthermore, the analysis department can predict future risks based on relevant market data. This allows for more accurate analysis by referring to relevant market data. For example, by analyzing current data trends based on relevant market data, the risk of data breaches can be minimized. Similarly, by identifying anomalous patterns by referring to relevant market data, the risk of data breaches can be minimized. This demonstrates that by referring to relevant market data, the risk of data breaches can be minimized.

[0107] The Ministry of Education estimates the user's emotions and adjusts the educational content based on those emotions. For example, if the user is stressed, the Ministry of Education provides simple and highly visual educational content. If the user is relaxed, the Ministry of Education can also provide detailed educational content. Furthermore, if the user is in a hurry, the Ministry of Education can provide educational content that can be learned quickly. By adjusting the educational content based on the user's emotions, education tailored to the user's situation becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input user emotion data into a generative AI and have the generative AI perform emotion estimation. By adjusting the educational content based on the user's emotions, education tailored to the user's situation becomes possible.

[0108] The Ministry of Education selects the optimal teaching method by referring to past teaching history during training. For example, the Ministry of Education proposes the optimal teaching method based on the user's past teaching history. Furthermore, the Ministry of Education can select effective teaching methods by referring to past teaching history. In addition, the Ministry of Education can provide customized teaching methods based on the user's past teaching history. This allows for the selection of the optimal teaching method by referring to past teaching history. For example, by proposing the optimal teaching method based on the user's past teaching history, the risk of information leakage incidents can be minimized. Similarly, by selecting effective teaching methods by referring to past teaching history, the risk of information leakage incidents can be minimized. This allows for the minimization of the risk of information leakage incidents by referring to past teaching history.

[0109] The Ministry of Education applies different educational methods depending on the specific industry and office environment. For example, the Ministry of Education provides specialized educational content for specific industries. It can also apply educational methods tailored to the office environment. Furthermore, the Ministry of Education can provide customized educational methods based on the industry and office environment. This allows for effective education by applying educational methods tailored to specific industries and office environments. For example, providing specialized educational content for specific industries can minimize the risk of information leaks. Similarly, applying educational methods tailored to the office environment can minimize the risk of information leaks. This allows for effective education by applying educational methods tailored to specific industries and office environments.

[0110] The Ministry of Education estimates the user's emotions and determines educational priorities based on those emotions. For example, if the user is stressed, the Ministry of Education prioritizes providing the most important educational content. If the user is relaxed, the Ministry of Education can also provide all educational content equally. Furthermore, if the user is in a hurry, the Ministry of Education can prioritize providing educational content that can be learned quickly. This allows for education to be prioritized according to the user's situation by determining educational priorities based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing by the Ministry of Education may be performed using AI or not. For example, the Ministry of Education can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows for education to be prioritized according to the user's situation by determining educational priorities based on the user's emotions.

[0111] The Ministry of Education adjusts educational content based on the timing of data submission. For example, the Ministry of Education provides educational content that is aligned with current trends based on the latest data. It can also provide educational content that is adapted to long-term changes based on historical data. Furthermore, the Ministry of Education can provide educational content that is adapted to changes in data based on the submission timing. This allows for education tailored to the submission timing by adjusting the educational content accordingly. For example, providing educational content that is aligned with current trends based on the latest data minimizes the risk of data breaches. Similarly, providing educational content that is adapted to long-term changes based on historical data minimizes the risk of data breaches. This allows for minimizing the risk of data breaches by adjusting the educational content based on the data submission timing.

[0112] The Ministry of Education improves the accuracy of education by referring to relevant literature on data during training. For example, the Ministry of Education provides educational content that reflects the latest knowledge based on relevant literature. The Ministry of Education can also provide educational content that avoids misunderstandings by referring to relevant literature. Furthermore, the Ministry of Education can provide educational content that promotes a deeper understanding based on relevant literature. In this way, the accuracy of education is improved by referring to relevant literature on data. For example, by providing educational content that reflects the latest knowledge based on relevant literature, the risk of information leakage incidents can be minimized. Furthermore, by providing educational content that avoids misunderstandings by referring to relevant literature, the risk of information leakage incidents can be minimized. In this way, the risk of information leakage incidents can be minimized by referring to relevant literature on data.

[0113] The training unit estimates the user's emotions and adjusts the training content based on the estimated emotions. For example, if the user is nervous, the training unit provides simple and highly visual training content. If the user is relaxed, the training unit can also provide detailed training content. Furthermore, if the user is in a hurry, the training unit can provide training content that can be learned quickly. This allows for training tailored to the user's situation by adjusting the training content based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI or not using AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. This allows for training tailored to the user's situation by adjusting the training content based on the user's emotions.

[0114] The training department selects the optimal training method by referring to past training history during training. For example, the training department proposes the optimal training method based on the user's past training history. The training department can also select an effective training method by referring to past training history. Furthermore, the training department can provide a customized training method based on the user's past training history. This allows for the selection of the optimal training method by referring to past training history. For example, by proposing the optimal training method based on the user's past training history, the risk of information leakage incidents can be minimized. Similarly, by selecting an effective training method by referring to past training history, the risk of information leakage incidents can be minimized. This allows for the minimization of the risk of information leakage incidents by referring to past training history.

[0115] The training department applies different training methods depending on the specific industry and office environment. For example, the training department provides training content tailored to specific industries. It can also apply training methods appropriate to the office environment. Furthermore, the training department can provide customized training methods based on the industry and office environment. This allows for effective training by applying training methods tailored to specific industries and office environments. For example, providing training content tailored to specific industries minimizes the risk of information leaks. Similarly, applying training methods appropriate to the office environment minimizes the risk of information leaks. This allows for effective training by applying training methods tailored to specific industries and office environments.

[0116] The training unit estimates the user's emotions and determines training priorities based on the estimated emotions. For example, if the user is nervous, the training unit will prioritize providing the most important training content. If the user is relaxed, the training unit can also provide all training content equally. Furthermore, if the user is in a hurry, the training unit can prioritize providing training content that can be learned quickly. In this way, by determining training priorities based on the user's emotions, training can be conducted with priorities appropriate to the user's situation. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the training unit may be performed using AI, or not using AI. For example, the training unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation. In this way, by determining training priorities based on the user's emotions, training can be conducted with priorities appropriate to the user's situation.

[0117] The training department adjusts the training content based on the data submission timing. For example, the training department provides training content that is aligned with current trends based on the latest data. It can also provide training content that is aligned with long-term changes based on historical data. Furthermore, the training department can provide training content that is aligned with data changes based on the submission timing. This allows for training tailored to the submission timing by adjusting the training content accordingly. For example, providing training content aligned with current trends based on the latest data minimizes the risk of data breaches. Similarly, providing training content that is aligned with long-term changes based on historical data minimizes the risk of data breaches. This allows for training content that is aligned with data submission timing, minimizing the risk of data breaches.

[0118] The training department improves the accuracy of training by referring to relevant literature on the data during training. For example, the training department provides training content that reflects the latest knowledge based on relevant literature. The training department can also provide training content that avoids misunderstandings by referring to relevant literature. Furthermore, the training department can provide training content that promotes a deeper understanding based on relevant literature. In this way, the accuracy of training is improved by referring to relevant literature on the data. For example, by providing training content that reflects the latest knowledge based on relevant literature, the risk of information leakage incidents can be minimized. Furthermore, by providing training content that avoids misunderstandings by referring to relevant literature, the risk of information leakage incidents can be minimized. In this way, by referring to relevant literature on the data, the risk of information leakage incidents can be minimized.

[0119] The training department selects the optimal training method during training sessions, taking into account the user's health condition. For example, if a user is tired, the training department will provide a lighter training session. Conversely, if a user is seeking healthy exercise, the training department can also provide a slightly more intense training session. Furthermore, if a user is feeling unwell, the training department can provide a training session that includes rest periods. This allows the selection of the optimal training method by considering the user's health condition. For instance, providing a lighter training session when a user is tired minimizes the risk of data breaches. Similarly, providing a slightly more intense training session when a user is seeking healthy exercise minimizes the risk of data breaches. This demonstrates how considering the user's health condition minimizes the risk of data breaches.

[0120] The training department adjusts the training progress by referring to the user's past training history. For example, the training department adjusts the pace of training based on the user's past training history. The training department can also select an effective training method by referring to past training history. Furthermore, the training department can provide a customized training method based on the user's past training history. This allows training to be conducted at an optimal pace by referring to the user's past training history. For example, by adjusting the pace of training based on the user's past training history, the risk of information leakage incidents can be minimized. Furthermore, by selecting an effective training method by referring to past training history, the risk of information leakage incidents can be minimized. This allows training to be conducted at an optimal pace by referring to the user's past training history.

[0121] The training department adjusts training content based on user feedback during training sessions. For example, the training department improves training content based on user feedback. The training department can also select effective training methods based on feedback. Furthermore, the training department can provide customized training content based on user feedback. This allows for more effective training by adjusting training content based on user feedback. For example, improving training content based on user feedback minimizes the risk of data breaches. Similarly, selecting effective training methods based on feedback minimizes the risk of data breaches. This allows for minimizing the risk of data breaches by adjusting training content based on user feedback.

[0122] The training department adjusts the training method according to the user's learning style. For example, if the user is a visual learner, the training department will provide training that makes extensive use of visual content. If the user is an auditory learner, the training department can also provide training that makes extensive use of audio guides. Furthermore, if the user is an experiential learner, the training department can provide practical training. By adjusting the training method according to the user's learning style, more effective training becomes possible. For example, if the user is a visual learner, providing training that makes extensive use of visual content can minimize the risk of information leakage. Similarly, if the user is an auditory learner, providing training that makes extensive use of audio guides can minimize the risk of information leakage. This allows for more effective training by adjusting the training method according to the user's learning style.

[0123] The training department adjusts the training content according to the user's skill level. For example, the training department can provide training content for beginners, intermediate users, and advanced users. By adjusting the training content according to the user's skill level, more effective training becomes possible. For example, providing training content for beginners minimizes the risk of data breaches. Similarly, providing training content for intermediate users minimizes the risk of data breaches. By adjusting the training content according to the user's skill level, the risk of data breaches can be minimized.

[0124] The training department adjusts the training content according to the user's goals. For example, the training department can provide training content tailored to the user's short-term goals. It can also provide training content tailored to the user's medium-term goals. Furthermore, it can provide training content tailored to the user's long-term goals. By adjusting the training content according to the user's goals, more effective training becomes possible. For example, by providing training content tailored to the user's short-term goals, the risk of information leakage incidents can be minimized. Similarly, by providing training content tailored to the user's medium-term goals, the risk of information leakage incidents can be minimized. Therefore, by adjusting the training content according to the user's goals, the risk of information leakage incidents can be minimized.

[0125] The training department adjusts the training content according to the user's progress during training. For example, the training department improves the training content based on the user's progress. The training department can also select effective training methods by referring to the progress. Furthermore, the training department can provide customized training content based on the user's progress. This makes it possible to conduct more effective training by adjusting the training content according to the user's progress. For example, improving the training content based on the user's progress can minimize the risk of information leakage incidents. Also, selecting effective training methods by referring to the progress can minimize the risk of information leakage incidents. This makes it possible to minimize the risk of information leakage incidents by adjusting the training content according to the user's progress.

[0126] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0127] The anonymization unit can estimate the user's emotions and adjust the anonymization method based on those emotions. For example, if the user is stressed, the anonymization process can be simplified and processed quickly. If the user is relaxed, detailed anonymization options can be provided, and a customizable method can be suggested. Furthermore, if the user is in a hurry, only the most important information can be anonymized, and processing can be completed quickly. In this way, by adjusting the anonymization method based on the user's emotions, it becomes possible to perform anonymization that is tailored to the user's situation.

[0128] The detection unit can estimate the user's emotions and adjust the detection criteria based on those emotions. For example, if the user is tense, strict detection criteria can be used. Conversely, if the user is relaxed, flexible detection criteria can be used. Furthermore, if the user is in a hurry, criteria for rapid detection can be set. By adjusting the detection criteria based on the user's emotions, detection can be adapted to the user's situation.

[0129] The analysis unit can estimate the user's emotions and adjust the analysis method based on those emotions. For example, if the user is nervous, a simple analysis method can be provided. If the user is relaxed, a more detailed analysis method can be provided. Furthermore, if the user is in a hurry, a method for rapid analysis can be provided. By adjusting the analysis method based on the user's emotions, it becomes possible to perform analysis tailored to the user's situation.

[0130] The education department can estimate the user's emotions and adjust the educational content based on those emotions. For example, if the user is stressed, it can provide simple and highly visual educational content. If the user is relaxed, it can provide more detailed content. Furthermore, if the user is in a hurry, it can provide educational content that can be learned quickly. In this way, by adjusting the educational content based on the user's emotions, education tailored to the user's situation becomes possible.

[0131] The training unit can estimate the user's emotions and adjust the training content based on those emotions. For example, if the user is nervous, it can provide simple and highly visual training content. If the user is relaxed, it can provide more detailed training content. Furthermore, if the user is in a hurry, it can provide training content that can be learned quickly. In this way, by adjusting the training content based on the user's emotions, it becomes possible to provide training tailored to the user's situation.

[0132] The anonymization unit can apply different anonymization algorithms depending on the type of data during the anonymization process. For example, a robust anonymization algorithm can be applied to personal information, while a lightweight algorithm can be used for corporate information. Furthermore, natural language processing can be used for anonymization of text data, and statistical methods can be applied to numerical data. Additionally, facial recognition technology can be used to blur identifiable parts of image data, and a replacement algorithm can be applied to text data. This allows for appropriate anonymization by applying different anonymization algorithms depending on the data type.

[0133] The detection unit can improve detection accuracy by considering the interrelationships between data during detection. For example, it can analyze the correlation between data and detect abnormal patterns. It can also reduce false positives based on the interrelationships of the data. Furthermore, it can perform detection with higher accuracy by considering the interrelationships of the data. In short, considering the interrelationships of the data improves detection accuracy.

[0134] The analysis unit can apply different analytical methods to each data category during analysis. For example, natural language processing can be used for text data, statistical methods for numerical data, and image recognition technology for image data. By applying the appropriate analytical method to each data category, the accuracy of the analysis is improved.

[0135] The Ministry of Education can apply different teaching methods to specific industries and office environments during training. For example, it can provide specialized training content tailored to specific industries. It can also apply teaching methods appropriate to the office environment. Furthermore, it can provide customized teaching methods based on the industry and office environment. This allows for more effective training by applying teaching methods tailored to specific industries and office environments.

[0136] The training unit can adjust the training method according to the user's learning style. For example, if the user is a visual learner, training can be provided that makes extensive use of visual content. If the user is an auditory learner, training can be provided that makes extensive use of audio guidance. Furthermore, if the user is an experiential learner, practical training can be provided. By adjusting the training method according to the user's learning style, more effective training becomes possible.

[0137] The following briefly describes the processing flow for example form 2.

[0138] Step 1: The anonymization department anonymizes the records of the data breach. For example, it removes identifiable information such as personal information and company names to anonymize the data. Specifically, it removes employee names and department names to create anonymized data. Step 2: The detection unit detects information security patterns using the anonymized records from the anonymization unit. For example, an LLM is used to learn from a large amount of data and identify information security patterns. By training the LLM with records of past data breaches, the risk of data breaches in specific industries or office environments can be identified. Step 3: The analysis unit analyzes the patterns detected by the detection unit. For example, LLM is used to analyze the detected patterns in detail and identify points of concern in specific industries or office environments. Step 4: The Education Department conducts security training based on the analysis results obtained by the Analysis Department. For example, it provides information security training to employees by highlighting points to be aware of in specific industries or office environments. It also educates employees on the effectiveness of specific measures in specific industries. Step 5: The training department provides training based on the educational content provided by the education department. For example, employees can learn about information security measures through the training platform. The training provides employees with practical training in implementing information security measures.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the anonymization unit, detection unit, analysis unit, education unit, and training unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the smart device 14 and removes personal information and company names from records of information leakage incidents. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects information security patterns using LLM. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the detected patterns in detail. The education unit is implemented by the control unit 46A of the smart device 14 and provides information security education to employees. The training unit is implemented by the control unit 46A of the smart device 14 and provides training in which employees actually learn information security measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0143] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the anonymization unit, detection unit, analysis unit, education unit, and training unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the smart glasses 214 and removes personal information and company names from records of information leakage incidents. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects information security patterns using LLM. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the detected patterns in detail. The education unit is implemented by the control unit 46A of the smart glasses 214 and provides information security education to employees. The training unit is implemented by the control unit 46A of the smart glasses 214 and provides training in which employees actually learn information security measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0159] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the anonymization unit, detection unit, analysis unit, education unit, and training unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the headset terminal 314 and removes personal information and company names from records of information leakage incidents. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects information security patterns using LLM. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the detected patterns in detail. The education unit is implemented by the control unit 46A of the headset terminal 314 and provides information security education to employees. The training unit is implemented by the control unit 46A of the headset terminal 314 and provides training in which employees actually learn information security measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0175] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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).

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.).

[0188] 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.

[0189] 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.

[0190] 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.

[0191] Each of the multiple elements described above, including the anonymization unit, detection unit, analysis unit, education unit, and training unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the anonymization unit is implemented by the control unit 46A of the robot 414 and removes personal information and company names from records of information leakage incidents. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects information security patterns using LLM. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the detected patterns in detail. The education unit is implemented by the control unit 46A of the robot 414 and provides information security education to employees. The training unit is implemented by the control unit 46A of the robot 414 and provides training in which employees actually learn information security measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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."

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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.

[0210] (Note 1) An anonymization unit that anonymizes records of data breach incidents, A detection unit that detects information security patterns using records anonymized by the anonymization unit, An analysis unit analyzes the pattern detected by the detection unit, Based on the analysis results obtained by the aforementioned analysis department, the education department conducts security education, The system comprises a training department that provides training based on the educational content provided by the aforementioned education department. A system characterized by the following features. (Note 2) The aforementioned analysis unit is It features a display section that provides points to consider tailored to the industry and office environment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is It includes a decision-making unit that determines when to discontinue ineffective measures. The system described in Appendix 1, characterized by the features described herein. (Note 4) The anonymization unit is, Personal information and identifiable information such as company names will be removed, and the data will be anonymized. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit is Learn from records of past data breaches and identify information security patterns. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned Ministry of Education, We provide information security training to employees, highlighting points to be aware of in specific industries and office environments. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned training department Through the training platform, employees can actually learn about information security measures. The system described in Appendix 1, characterized by the features described herein. (Note 8) The anonymization unit is, The system estimates the user's emotions and adjusts the anonymization method based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The anonymization unit is, When anonymizing data, different anonymization algorithms are applied depending on the type of data. The system described in Appendix 1, characterized by the features described herein. (Note 10) The anonymization unit is, During anonymization, adjust the level of anonymization based on the importance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 11) The anonymization unit is, The system estimates the user's emotions and determines the priority of anonymization based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The anonymization unit is, When anonymizing data, the priority of anonymization is determined based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 13) The anonymization unit is, During anonymization, the order of anonymization is adjusted based on the relevance of the data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is It estimates the user's emotions and adjusts the detection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is When detection occurs, the accuracy of the detection is improved by considering the interrelationships between the data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is When detection occurs, the data submitter's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is It estimates the user's emotions and adjusts the order in which detection results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is When detection is performed, the geographical distribution of the data is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is During detection, the accuracy of the detection is improved by referring to relevant literature for the data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned analysis unit is During analysis, we refer to past data to predict current data. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned analysis unit is During analysis, different analytical methods are applied to each data category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is We estimate the user's emotions and adjust the importance of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During the analysis, we analyze changes in the analysis based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned analysis unit is During the analysis, the analysis is performed by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned Ministry of Education, The system estimates the user's emotions and adjusts the educational content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned Ministry of Education, During education, the optimal teaching method is selected by referring to past educational records. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned Ministry of Education, During training, different training methods are applied depending on the specific industry or office environment. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned Ministry of Education, It estimates user emotions and determines educational priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Ministry of Education, During education, the content of the curriculum will be adjusted based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Ministry of Education, During education, we improve the accuracy of teaching by referring to relevant literature on data. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned training department It estimates the user's emotions and adjusts the training content based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned training department During training, the optimal training method is selected by referring to past training history. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned training department During training, different training methods are applied depending on the specific industry or office environment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned training department It estimates the user's emotions and determines training priorities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned training department During training, the training content will be adjusted based on when the data was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned training department During training, refer to relevant literature to improve training accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned training department During training, the optimal training method is selected considering the user's health condition. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned training department During training, the system adjusts the training progress by referring to the user's past training history. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned training department During training, the training content is adjusted based on user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned training department During training, the training method is adjusted according to the user's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned training department During training, the training content is adjusted according to the user's skill level. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned training department During training, the training content is adjusted according to the user's goals. The system described in Appendix 1, characterized by the features described herein. (Note 44) The aforementioned training department During training, the training content is adjusted according to the user's progress. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0211] 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. An anonymization unit that anonymizes records of data breach incidents, A detection unit that detects information security patterns using records anonymized by the anonymization unit, An analysis unit analyzes the pattern detected by the detection unit, Based on the analysis results obtained by the aforementioned analysis department, the education department conducts security education, The system comprises a training department that provides training based on the educational content provided by the aforementioned education department. A system characterized by the following features.

2. The aforementioned analysis unit is It features a display section that provides points to consider tailored to the industry and office environment. The system according to feature 1.

3. The aforementioned analysis unit is It includes a decision-making unit that determines when to discontinue ineffective measures. The system according to feature 1.

4. The anonymization unit is, Personal information and identifiable information such as company names will be removed, and the data will be anonymized. The system according to feature 1.

5. The detection unit is Learn from records of past data breaches and identify information security patterns. The system according to feature 1.

6. The aforementioned Ministry of Education, We provide information security training to employees, highlighting points to be aware of in specific industries and office environments. The system according to feature 1.

7. The aforementioned training department Through the training platform, employees can actually learn about information security measures. The system according to feature 1.

8. The anonymization unit is, The system estimates the user's emotions and adjusts the anonymization method based on those estimated emotions. The system according to feature 1.

9. The anonymization unit is, When anonymizing data, different anonymization algorithms are applied depending on the type of data. The system according to feature 1.

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