System

Abstract

An object of a system according to an embodiment is to collect vulnerability information from version information of a system, evaluate a risk, check a past similar case, and efficiently generate a summary description to an upper layer.SOLUTION: A system according to an embodiment includes a version information acquisition unit, a vulnerability information collection unit, a risk evaluation unit, a past case confirmation unit, and a summary explanation generation unit. The version information acquisition unit acquires version information of the target system. The vulnerability information collection unit collects vulnerability information based on the version information acquired by the version information acquisition unit. The risk evaluation unit evaluates a risk on the basis of the vulnerability information collected by the vulnerability information collection unit. The past case confirmation unit confirms a past similar case on the basis of the risk evaluated by the risk evaluation unit. The outline explanation generation unit generates an outline explanation to an upper layer based on the past case confirmed by the past case confirmation unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technology has the drawback of being complex and difficult to carry out efficiently, as it involves collecting vulnerability information from system version information, assessing risk, checking similar past cases, and generating a summary explanation for higher-level systems.

[0005] The system according to the embodiment aims to collect vulnerability information from system version information, evaluate risks, check similar cases from the past, and efficiently generate an overview explanation to upper layers. [Means for solving the problem]

[0006] The system according to the embodiment includes a version information acquisition unit, a vulnerability information collection unit, a risk assessment unit, a past case checking unit, and a summary generation unit. The version information acquisition unit acquires version information of the target system. The vulnerability information collection unit collects vulnerability information based on the version information acquired by the version information acquisition unit. The risk assessment unit evaluates risk based on the vulnerability information collected by the vulnerability information collection unit. The past case checking unit checks similar past cases based on the risk evaluated by the risk assessment unit. The summary generation unit generates a summary to a higher level based on the past cases checked by the past case checking unit. [Effects of the Invention]

[0007] The system according to the embodiment can collect vulnerability information from system version information, assess risks, check similar cases from the past, and efficiently generate an overview explanation to upper layers. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8]FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

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

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

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

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

[0028] (Example 1) A vulnerability management system according to an embodiment of the present invention is a system that builds a generative AI that uses Common Vulnerabilities and Exposures (CVE), vulnerability databases (such as JVN), and security news as learning sources, allowing users in charge of system maintenance and operation to check vulnerability information and risks from the version information of the target system, check past cases of similar vulnerabilities and their impact, and compile an overview explanation to higher levels. As a result, the vulnerability management system allows users to quickly check vulnerability information and risks of the target system, understand past cases and their impact, and efficiently report to higher levels.

[0029] A vulnerability management system according to an embodiment includes a version information acquisition unit, a vulnerability information collection unit, a risk assessment unit, a past case checking unit, and a summary description generation unit. The version information acquisition unit acquires version information of a target system. For example, it acquires a system version number entered by a user. The version information acquisition unit can also acquire the system's release date and update history. For example, the version information acquisition unit acquires the system's version number from a database and checks the release date and update history. The vulnerability information collection unit collects vulnerability information based on the version information acquired by the version information acquisition unit. For example, the vulnerability information collection unit searches for related vulnerability information from Common Vulnerabilities and Exposures (CVE) or a vulnerability database (such as JVN). The vulnerability information collection unit can also collect vulnerability information from security news and advisories. For example, the vulnerability information collection unit acquires vulnerability information related to a specific version from a CVE database and collects the latest vulnerability information from security news. The risk assessment unit assesses risk based on the vulnerability information collected by the vulnerability information collection unit. For example, the risk assessment unit assesses the severity and scope of impact of vulnerabilities. The risk assessment unit can also assess risk by taking into account vulnerability fix history and patch information. For example, the risk assessment unit evaluates the severity of a vulnerability using a CVSS score and identifies the scope of impact. The past case review unit checks similar past cases based on the risk assessed by the risk assessment unit. For example, the past case review unit investigates the impact of similar vulnerabilities in the past. The past case review unit can also check past security incidents and countermeasures. For example, the past case review unit searches a database for past vulnerability cases and checks the scope of impact and countermeasures. The summary generation unit generates a summary for higher-level personnel based on the past cases confirmed by the past case review unit. For example, the summary generation unit generates a report that concisely summarizes the impact of the vulnerability and countermeasures. The summary generation unit can also customize the report by taking into account the interests and priorities of higher-level personnel. For example, the summary generation unit visually displays the impact of the vulnerability using graphs and charts and specifically explains countermeasures.As a result, the vulnerability management system according to the embodiment allows users to quickly check vulnerability information and risks of a target system, understand past cases and their impacts, and efficiently report to higher levels. For example, by simply inputting system version information, users can check related vulnerability information and risks and take appropriate measures by referring to past cases. Furthermore, by automatically generating reports to higher levels, the burden of reporting work is reduced and quick decision-making is supported.

[0030] The vulnerability information collection unit can also simultaneously collect vulnerability fix history or patch information and provide it to the user. For example, when the generation AI receives version information for a target system, the vulnerability information collection unit not only collects related vulnerability information, but also simultaneously collects fix history and patch information for that vulnerability and provides it to the user. For example, it displays the release date and application method of the fix patch for CVE-2023-12345. Furthermore, when the generation AI collects vulnerability information, the vulnerability information collection unit provides detailed information, including the vulnerability fix history. For example, it provides the user with information on how a specific vulnerability was fixed, as well as the time and resources required for the fix. Furthermore, when the generation AI collects vulnerability information, the vulnerability information collection unit also provides patch information for the vulnerability. For example, it provides the user with patch application procedures and precautions for a specific vulnerability, supporting rapid response. This allows the user to quickly understand the vulnerability fix history and patch information and take appropriate measures.

[0031] The risk assessment unit can take into account the reliability of the vulnerability discoverer or reporter and reflect this in the risk assessment. For example, when the generation AI collects vulnerability information, the risk assessment unit evaluates the reliability of the vulnerability discoverer or reporter and reflects this information in the risk assessment. For example, it may prioritize vulnerability information reported by highly reliable security researchers or organizations. In addition, when the generation AI evaluates vulnerability information, the risk assessment unit considers the past track record and reliability of the discoverer or reporter and reflects this in the risk assessment. For example, it may highly evaluate information from discoverers who have provided a lot of accurate vulnerability information in the past. In addition, when the generation AI collects vulnerability information, the risk assessment unit builds a system that performs risk assessment based on the reliability of the reporter. For example, it may set a low risk assessment for information from unreliable reporters and warn the user to be careful. This enables more accurate risk assessment by performing risk assessment based on highly reliable information.

[0032] The vulnerability information collection unit can simultaneously provide information on related security tools or solutions, allowing users to take countermeasures immediately. For example, when the generation AI collects vulnerability information, the vulnerability information collection unit simultaneously provides information on related security tools and solutions, allowing users to take countermeasures immediately. For example, it displays recommended tools and solutions for specific vulnerabilities. The vulnerability information collection unit also builds a system that provides information on related security tools and solutions when the generation AI collects vulnerability information. For example, it displays information on recommended software and hardware for vulnerabilities. The vulnerability information collection unit also builds a system that provides information on related security tools and solutions when the generation AI collects vulnerability information, allowing users to take countermeasures immediately. For example, it displays information on recommended settings and configurations for vulnerabilities. This allows users to quickly take appropriate security measures.

[0033] The past case checking unit simultaneously provides detailed technical backgrounds or countermeasures for past cases, making it easier for users to take specific countermeasures. For example, when the generation AI investigates past cases, the past case checking unit simultaneously provides detailed technical backgrounds and countermeasures for the cases, making it easier for users to take specific countermeasures. For example, it displays fix methods and technical details for past vulnerability cases. The past case checking unit also builds a system that provides detailed information, including the technical backgrounds and countermeasures for past vulnerability cases, when the generation AI investigates past cases. For example, it displays fix procedures and technical explanations for past vulnerability cases. The past case checking unit also simultaneously provides the technical backgrounds and countermeasures for past vulnerability cases when the generation AI investigates past cases, making it easier for users to take specific countermeasures. For example, it displays how to apply fix patches for past vulnerability cases and technical details. This allows users to understand the detailed technical backgrounds and countermeasures for past cases, making it easier for them to take specific countermeasures.

[0034] The summary generation unit can generate customized reports taking into account the interests and priorities of upper-level executives. For example, when the generation AI generates a summary, the summary generation unit generates a customized report taking into account the interests and priorities of upper-level executives. For example, it emphasizes risk factors and countermeasures that are of particular interest to management. In addition, the summary generation unit builds a system that generates customized reports based on the interests and priorities of upper-level executives when the generation AI generates a summary. For example, it prioritizes displaying information related to specific projects or departments. In addition, when the generation AI generates a summary, the summary generation unit generates customized reports taking into account the interests and priorities of upper-level executives. For example, it emphasizes KPIs and goal achievement status that are important to management. In this way, by generating a customized report based on the interests and priorities of upper-level executives, upper-level executives can quickly understand and respond.

[0035] The summary generation unit makes extensive use of visual elements and can provide a report that is visually easy to understand. For example, when the generation AI generates a summary, the summary generation unit makes extensive use of visual elements (graphs, charts, infographics) and provides a report that is visually easy to understand. For example, it displays a risk assessment in a graph. The summary generation unit also builds a system that makes extensive use of visual elements when the generation AI generates a summary and provides a report that is visually easy to understand. For example, it displays the scope of impact of a vulnerability in a chart. The summary generation unit also makes extensive use of visual elements when the generation AI generates a summary and provides a report that is visually easy to understand. For example, it displays countermeasures in an infographic. This provides a report that is visually easy to understand, allowing higher-level users to quickly understand.

[0036] The summary generation unit can automatically generate reports customized for different departments and positions, providing information tailored to the needs of each department. For example, the generation AI in the summary generation unit automatically generates reports customized for different departments and positions, providing information tailored to the needs of each department. For example, it could emphasize technical details for the technical department and risk assessments for management. The summary generation unit also builds a system in which the generation AI automatically generates reports customized for different departments and positions, providing information tailored to the needs of each department. For example, it could emphasize vulnerability details for the security department and cost impacts for the finance department. The summary generation unit also builds a system in which the generation AI automatically generates reports customized for different departments and positions, providing information tailored to the needs of each department. For example, it could emphasize customer impacts for the marketing department and legal risks for the legal department. This makes it possible to provide information tailored to the needs of each department by providing reports customized for each department and position.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The vulnerability management system can further include a behavior analysis unit that analyzes the user's behavior history and suggests priorities for vulnerability response. For example, the behavior analysis unit analyzes what responses the user has taken to what vulnerabilities in the past and suggests the optimal response method for the current vulnerability. The behavior analysis unit can also automatically set priorities based on the user's response speed and success rate. For example, a high priority is assigned to vulnerabilities that have been addressed quickly and effectively in the past, and the user is notified of this. The behavior analysis unit can also learn the user's behavior patterns and improve the efficiency of future vulnerability responses. This allows the user to utilize past experience to respond to vulnerabilities quickly and effectively.

[0039] The vulnerability management system can further include a skill assessment unit that evaluates a user's skill level and provides appropriate training content. For example, the skill assessment unit evaluates the user's skill level based on what vulnerabilities the user has dealt with in the past and how they have responded. The skill assessment unit can also automatically select and provide training content according to the user's skill level. For example, it can provide basic vulnerability response methods for beginners and advanced security measures for advanced users. The skill assessment unit can also provide regular assessments and feedback to help users improve their skills. This allows users to receive appropriate training according to their skill level and improve their vulnerability response skills.

[0040] The vulnerability management system may further include a prediction unit that predicts future vulnerabilities based on the user's behavior history. For example, the prediction unit may analyze what kind of responses the user has taken to vulnerabilities in the past and predict vulnerabilities that may occur in the future. The prediction unit may also calculate the probability of a vulnerability occurring based on the user's behavior patterns and system usage. For example, the prediction unit may evaluate the possibility that a specific operation or setting change will lead to the occurrence of a vulnerability and notify the user. The prediction unit may also suggest countermeasures to the user based on the prediction results. This allows the user to be aware of future vulnerabilities in advance and take appropriate countermeasures.

[0041] The vulnerability management system can further include an effectiveness evaluation unit that evaluates the effectiveness of vulnerability responses based on the user's behavioral history. For example, the effectiveness evaluation unit analyzes the results of vulnerability responses that the user has performed in the past and evaluates their effectiveness. The effectiveness evaluation unit can also quantify the effectiveness of the response based on the user's response speed and success rate. For example, the effectiveness evaluation unit can evaluate how quickly and effectively the response to a specific vulnerability was and provide feedback to the user. The effectiveness evaluation unit can also suggest areas for improvement to the user based on the evaluation results. This allows the user to understand the effectiveness of their vulnerability responses and use this information in future responses.

[0042] The vulnerability management system can further include a timing suggestion unit that suggests the optimal timing for vulnerability response based on the user's behavior history. For example, the timing suggestion unit analyzes when the user has previously responded to vulnerabilities and suggests the optimal response timing. The timing suggestion unit can also set the optimal response timing taking into account system usage and busy periods. For example, it can suggest a time period or period when work is relatively quiet and notify the user. The timing suggestion unit can also send the user a reminder to take measures based on the suggestion result. This allows the user to respond to vulnerabilities at the optimal timing, thereby improving work efficiency.

[0043] The processing flow of the first embodiment will be briefly explained below.

[0044] Step 1: The version information acquisition unit acquires the version information of the target system. For example, it acquires the system version number entered by the user. The version information acquisition unit can also acquire the system release date and update history. For example, the version information acquisition unit acquires the system version number from a database and checks the release date and update history. Step 2: The vulnerability information collection unit collects vulnerability information based on the version information acquired by the version information acquisition unit. For example, the vulnerability information collection unit searches for related vulnerability information from the Common Vulnerabilities and Exposures (CVE) or a vulnerability database (such as JVN). The vulnerability information collection unit can also collect vulnerability information from security news and advisories. For example, the vulnerability information collection unit acquires vulnerability information related to a specific version from the CVE database and collects the latest vulnerability information from security news. Step 3: The risk assessment unit assesses risk based on the vulnerability information collected by the vulnerability information collection unit. For example, the risk assessment unit assesses the severity and scope of impact of the vulnerability. The risk assessment unit can also assess risk by taking into account the vulnerability's remediation history and patch information. For example, the risk assessment unit assesses the severity of the vulnerability using a CVSS score and identifies the scope of impact. Step 4: The past case review unit checks for similar past cases based on the risks assessed by the risk assessment unit. For example, the past case review unit investigates the impact of similar vulnerabilities in the past. The past case review unit can also check past security incidents and countermeasures. For example, the past case review unit searches a database for past vulnerability cases and checks the scope of impact and countermeasures. Step 5: The summary generator generates a summary for higher-level users based on the past cases confirmed by the past case checker. For example, the summary generator generates a report that briefly summarizes the impact of the vulnerability and countermeasures. The summary generator can also customize the report taking into account the interests and priorities of higher-level users. For example, the summary generator visually displays the impact of the vulnerability in graphs and charts and provides specific explanations of countermeasures.

[0045] (Example 2) A vulnerability management system according to an embodiment of the present invention is a system that builds a generative AI that uses Common Vulnerabilities and Exposures (CVE), vulnerability databases (such as JVN), and security news as learning sources, allowing users in charge of system maintenance and operation to check vulnerability information and risks from the version information of the target system, check past cases of similar vulnerabilities and their impact, and compile an overview explanation to higher levels. As a result, the vulnerability management system allows users to quickly check vulnerability information and risks of the target system, understand past cases and their impact, and efficiently report to higher levels.

[0046] A vulnerability management system according to an embodiment includes a version information acquisition unit, a vulnerability information collection unit, a risk assessment unit, a past case checking unit, and a summary description generation unit. The version information acquisition unit acquires version information of a target system. For example, it acquires a system version number entered by a user. The version information acquisition unit can also acquire the system's release date and update history. For example, the version information acquisition unit acquires the system's version number from a database and checks the release date and update history. The vulnerability information collection unit collects vulnerability information based on the version information acquired by the version information acquisition unit. For example, the vulnerability information collection unit searches for related vulnerability information from Common Vulnerabilities and Exposures (CVE) or a vulnerability database (such as JVN). The vulnerability information collection unit can also collect vulnerability information from security news and advisories. For example, the vulnerability information collection unit acquires vulnerability information related to a specific version from a CVE database and collects the latest vulnerability information from security news. The risk assessment unit assesses risk based on the vulnerability information collected by the vulnerability information collection unit. For example, the risk assessment unit assesses the severity and scope of impact of vulnerabilities. The risk assessment unit can also assess risk by taking into account vulnerability fix history and patch information. For example, the risk assessment unit evaluates the severity of a vulnerability using a CVSS score and identifies the scope of impact. The past case review unit checks similar past cases based on the risk assessed by the risk assessment unit. For example, the past case review unit investigates the impact of similar vulnerabilities in the past. The past case review unit can also check past security incidents and countermeasures. For example, the past case review unit searches a database for past vulnerability cases and checks the scope of impact and countermeasures. The summary generation unit generates a summary for higher-level personnel based on the past cases confirmed by the past case review unit. For example, the summary generation unit generates a report that concisely summarizes the impact of the vulnerability and countermeasures. The summary generation unit can also customize the report by taking into account the interests and priorities of higher-level personnel. For example, the summary generation unit visually displays the impact of the vulnerability using graphs and charts and specifically explains countermeasures.As a result, the vulnerability management system according to the embodiment allows users to quickly check vulnerability information and risks of a target system, understand past cases and their impacts, and efficiently report to higher levels. For example, by simply inputting system version information, users can check related vulnerability information and risks and take appropriate measures by referring to past cases. Furthermore, by automatically generating reports to higher levels, the burden of reporting work is reduced and quick decision-making is supported.

[0047] The vulnerability information collection unit can also simultaneously collect vulnerability fix history or patch information and provide it to the user. For example, when the generation AI receives version information for a target system, the vulnerability information collection unit not only collects related vulnerability information, but also simultaneously collects fix history and patch information for that vulnerability and provides it to the user. For example, it displays the release date and application method of the fix patch for CVE-2023-12345. Furthermore, when the generation AI collects vulnerability information, the vulnerability information collection unit provides detailed information, including the vulnerability fix history. For example, it provides the user with information on how a specific vulnerability was fixed, as well as the time and resources required for the fix. Furthermore, when the generation AI collects vulnerability information, the vulnerability information collection unit also provides patch information for the vulnerability. For example, it provides the user with patch application procedures and precautions for a specific vulnerability, supporting rapid response. This allows the user to quickly understand the vulnerability fix history and patch information and take appropriate measures.

[0048] The risk assessment unit can take into account the reliability of the vulnerability discoverer or reporter and reflect this in the risk assessment. For example, when the generation AI collects vulnerability information, the risk assessment unit evaluates the reliability of the vulnerability discoverer or reporter and reflects this information in the risk assessment. For example, it may prioritize vulnerability information reported by highly reliable security researchers or organizations. In addition, when the generation AI evaluates vulnerability information, the risk assessment unit considers the past track record and reliability of the discoverer or reporter and reflects this in the risk assessment. For example, it may highly evaluate information from discoverers who have provided a lot of accurate vulnerability information in the past. In addition, when the generation AI collects vulnerability information, the risk assessment unit builds a system that performs risk assessment based on the reliability of the reporter. For example, it may set a low risk assessment for information from unreliable reporters and warn the user to be careful. This enables more accurate risk assessment by performing risk assessment based on highly reliable information.

[0049] The vulnerability information collection unit can simultaneously provide information on related security tools or solutions, allowing users to take countermeasures immediately. For example, when the generation AI collects vulnerability information, the vulnerability information collection unit simultaneously provides information on related security tools and solutions, allowing users to take countermeasures immediately. For example, it displays recommended tools and solutions for specific vulnerabilities. The vulnerability information collection unit also builds a system that provides information on related security tools and solutions when the generation AI collects vulnerability information. For example, it displays information on recommended software and hardware for vulnerabilities. The vulnerability information collection unit also builds a system that provides information on related security tools and solutions when the generation AI collects vulnerability information, allowing users to take countermeasures immediately. For example, it displays information on recommended settings and configurations for vulnerabilities. This allows users to quickly take appropriate security measures.

[0050] The past case checking unit simultaneously provides detailed technical backgrounds or countermeasures for past cases, making it easier for users to take specific countermeasures. For example, when the generation AI investigates past cases, the past case checking unit simultaneously provides detailed technical backgrounds and countermeasures for the cases, making it easier for users to take specific countermeasures. For example, it displays fix methods and technical details for past vulnerability cases. The past case checking unit also builds a system that provides detailed information, including the technical backgrounds and countermeasures for past vulnerability cases, when the generation AI investigates past cases. For example, it displays fix procedures and technical explanations for past vulnerability cases. The past case checking unit also simultaneously provides the technical backgrounds and countermeasures for past vulnerability cases when the generation AI investigates past cases, making it easier for users to take specific countermeasures. For example, it displays how to apply fix patches for past vulnerability cases and technical details. This allows users to understand the detailed technical backgrounds and countermeasures for past cases, making it easier for them to take specific countermeasures.

[0051] The summary generation unit can generate customized reports taking into account the interests and priorities of upper-level executives. For example, when the generation AI generates a summary, the summary generation unit generates a customized report taking into account the interests and priorities of upper-level executives. For example, it emphasizes risk factors and countermeasures that are of particular interest to management. In addition, the summary generation unit builds a system that generates customized reports based on the interests and priorities of upper-level executives when the generation AI generates a summary. For example, it prioritizes displaying information related to specific projects or departments. In addition, when the generation AI generates a summary, the summary generation unit generates customized reports taking into account the interests and priorities of upper-level executives. For example, it emphasizes KPIs and goal achievement status that are important to management. In this way, by generating a customized report based on the interests and priorities of upper-level executives, upper-level executives can quickly understand and respond.

[0052] The summary generation unit makes extensive use of visual elements and can provide a report that is visually easy to understand. For example, when the generation AI generates a summary, the summary generation unit makes extensive use of visual elements (graphs, charts, infographics) and provides a report that is visually easy to understand. For example, it displays a risk assessment in a graph. The summary generation unit also builds a system that makes extensive use of visual elements when the generation AI generates a summary and provides a report that is visually easy to understand. For example, it displays the scope of impact of a vulnerability in a chart. The summary generation unit also makes extensive use of visual elements when the generation AI generates a summary and provides a report that is visually easy to understand. For example, it displays countermeasures in an infographic. This provides a report that is visually easy to understand, allowing higher-level users to quickly understand.

[0053] The summary generation unit can automatically generate reports customized for different departments and positions, providing information tailored to the needs of each department. For example, the generation AI in the summary generation unit automatically generates reports customized for different departments and positions, providing information tailored to the needs of each department. For example, it could emphasize technical details for the technical department and risk assessments for management. The summary generation unit also builds a system in which the generation AI automatically generates reports customized for different departments and positions, providing information tailored to the needs of each department. For example, it could emphasize vulnerability details for the security department and cost impacts for the finance department. The summary generation unit also builds a system in which the generation AI automatically generates reports customized for different departments and positions, providing information tailored to the needs of each department. For example, it could emphasize customer impacts for the marketing department and legal risks for the legal department. This makes it possible to provide information tailored to the needs of each department by providing reports customized for each department and position.

[0054] The summary generation unit can use the emotion estimation function to monitor the emotions of the upper layer when viewing the report in real time and adjust the report content as needed. The summary generation unit, for example, uses the emotion estimation function to monitor the emotions of the upper layer when viewing the report in real time and adjust the report content as needed. For example, if the upper layer feels anxious, information that gives a sense of security is added. The summary generation unit also uses the emotion estimation function to build a system that monitors the emotions of the upper layer when viewing the report in real time and adjusts the report content as needed. For example, if the upper layer feels surprised, a detailed explanation is added. The summary generation unit also uses the emotion estimation function to monitor the emotions of the upper layer when viewing the report in real time and adjust the report content as needed. For example, if the upper layer feels satisfied, success stories are emphasized. In this way, by adjusting the report content according to the emotions of the upper layer, the upper layer can quickly understand and respond.

[0055] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0056] The vulnerability management system can further include a behavior analysis unit that analyzes the user's behavior history and suggests priorities for vulnerability response. For example, the behavior analysis unit analyzes what responses the user has taken to what vulnerabilities in the past and suggests the optimal response method for the current vulnerability. The behavior analysis unit can also automatically set priorities based on the user's response speed and success rate. For example, a high priority is assigned to vulnerabilities that have been addressed quickly and effectively in the past, and the user is notified of this. The behavior analysis unit can also learn the user's behavior patterns and improve the efficiency of future vulnerability responses. This allows the user to utilize past experience to respond to vulnerabilities quickly and effectively.

[0057] The vulnerability management system may further include an emotion adaptation unit that estimates the user's emotion and adjusts the way vulnerability information is presented based on the estimated emotion. For example, if the emotion adaptation unit is feeling anxious, it may prioritize displaying information that provides a sense of security. In addition, if the user is feeling stressed, the emotion adaptation unit may display information in a concise manner, for example, emphasizing only the important points and allowing the user to check detailed information later. In addition, if the user is feeling satisfied, the emotion adaptation unit may display success stories or positive feedback. This makes it possible to provide optimal information according to the user's emotion, reducing user stress and supporting efficient vulnerability response.

[0058] The vulnerability management system can further include a skill assessment unit that evaluates a user's skill level and provides appropriate training content. For example, the skill assessment unit evaluates the user's skill level based on what vulnerabilities the user has dealt with in the past and how they have responded. The skill assessment unit can also automatically select and provide training content according to the user's skill level. For example, it can provide basic vulnerability response methods for beginners and advanced security measures for advanced users. The skill assessment unit can also provide regular assessments and feedback to help users improve their skills. This allows users to receive appropriate training according to their skill level and improve their vulnerability response skills.

[0059] The vulnerability management system may further include an emotion-adaptive training unit that estimates the user's emotions and adjusts training content based on the estimated emotions. For example, if the emotion-adaptive training unit is feeling anxious, it may provide training content that gives the user a sense of security. Furthermore, if the user is feeling stressed, the emotion-adaptive training unit may provide a concise summary of the training content. For example, it may highlight only the important points and allow the user to review the details later. Furthermore, if the user is feeling satisfied, the emotion-adaptive training unit may provide training content that includes success stories and positive feedback. This enables optimal training according to the user's emotions, reduces the user's stress, and supports efficient skill improvement.

[0060] The vulnerability management system may further include a prediction unit that predicts future vulnerabilities based on the user's behavior history. For example, the prediction unit may analyze what kind of responses the user has taken to vulnerabilities in the past and predict vulnerabilities that may occur in the future. The prediction unit may also calculate the probability of a vulnerability occurring based on the user's behavior patterns and system usage. For example, the prediction unit may evaluate the possibility that a specific operation or setting change will lead to the occurrence of a vulnerability and notify the user. The prediction unit may also suggest countermeasures to the user based on the prediction results. This allows the user to be aware of future vulnerabilities in advance and take appropriate countermeasures.

[0061] The vulnerability management system may further include an emotion-adaptive prediction unit that estimates the user's emotion and adjusts the presentation method of the prediction results based on the estimated emotion. For example, if the emotion-adaptive prediction unit is feeling anxious, it may prioritize displaying prediction results that provide a sense of security. Furthermore, if the user is feeling stressed, the emotion-adaptive prediction unit may display a concise summary of the prediction results, for example, highlighting only the important points and allowing the user to check detailed information later. Furthermore, if the user is feeling satisfied, the emotion-adaptive prediction unit may display prediction results that include success stories and positive feedback. This makes it possible to provide optimal prediction results according to the user's emotion, reducing user stress and supporting efficient vulnerability response.

[0062] The vulnerability management system can further include an effectiveness evaluation unit that evaluates the effectiveness of vulnerability responses based on the user's behavioral history. For example, the effectiveness evaluation unit analyzes the results of vulnerability responses that the user has performed in the past and evaluates their effectiveness. The effectiveness evaluation unit can also quantify the effectiveness of the response based on the user's response speed and success rate. For example, the effectiveness evaluation unit can evaluate how quickly and effectively the response to a specific vulnerability was and provide feedback to the user. The effectiveness evaluation unit can also suggest areas for improvement to the user based on the evaluation results. This allows the user to understand the effectiveness of their vulnerability responses and use this information in future responses.

[0063] The vulnerability management system may further include an emotion adaptation effect evaluation unit that estimates the user's emotion and adjusts the presentation method of the effectiveness evaluation results based on the estimated emotion. For example, if the user is feeling anxious, the emotion adaptation effect evaluation unit may prioritize displaying effectiveness evaluation results that provide a sense of security. Furthermore, if the user is feeling stressed, the emotion adaptation effect evaluation unit may also display a concise summary of the effectiveness evaluation results. For example, it may highlight only the important points, allowing the user to check detailed information later. Furthermore, if the user is feeling satisfied, the emotion adaptation effect evaluation unit may display effectiveness evaluation results that include success stories and positive feedback. This makes it possible to provide optimal effectiveness evaluation results according to the user's emotion, thereby reducing the user's stress and supporting efficient vulnerability response.

[0064] The vulnerability management system can further include a timing suggestion unit that suggests the optimal timing for vulnerability response based on the user's behavior history. For example, the timing suggestion unit analyzes when the user has previously responded to vulnerabilities and suggests the optimal response timing. The timing suggestion unit can also set the optimal response timing taking into account system usage and busy periods. For example, it can suggest a time period or period when work is relatively quiet and notify the user. The timing suggestion unit can also send the user a reminder to take measures based on the suggestion result. This allows the user to respond to vulnerabilities at the optimal timing, thereby improving work efficiency.

[0065] The vulnerability management system can further include an emotion adaptation timing suggestion unit that estimates the user's emotions and suggests optimal timing based on the estimated emotions. For example, if the user is feeling anxious, the emotion adaptation timing suggestion unit suggests a timing that will give the user a sense of security. Also, if the user is feeling stressed, the emotion adaptation timing suggestion unit can suggest a timing that will make it easier to respond. For example, the emotion adaptation timing suggestion unit can suggest a time period or period when work is relatively calm and notify the user. Also, if the user is feeling satisfied, the emotion adaptation timing suggestion unit can suggest a timing that includes success stories and positive feedback. This makes it possible to suggest optimal timing based on the user's emotions, reducing user stress and supporting efficient vulnerability response.

[0066] The processing flow of the second embodiment will be briefly explained below.

[0067] Step 1: The version information acquisition unit acquires the version information of the target system. For example, it acquires the system version number entered by the user. The version information acquisition unit can also acquire the system release date and update history. For example, the version information acquisition unit acquires the system version number from a database and checks the release date and update history. Step 2: The vulnerability information collection unit collects vulnerability information based on the version information acquired by the version information acquisition unit. For example, the vulnerability information collection unit searches for related vulnerability information from the Common Vulnerabilities and Exposures (CVE) or a vulnerability database (such as JVN). The vulnerability information collection unit can also collect vulnerability information from security news and advisories. For example, the vulnerability information collection unit acquires vulnerability information related to a specific version from the CVE database and collects the latest vulnerability information from security news. Step 3: The risk assessment unit assesses risk based on the vulnerability information collected by the vulnerability information collection unit. For example, the risk assessment unit assesses the severity and scope of impact of the vulnerability. The risk assessment unit can also assess risk by taking into account the vulnerability's remediation history and patch information. For example, the risk assessment unit assesses the severity of the vulnerability using a CVSS score and identifies the scope of impact. Step 4: The past case review unit checks for similar past cases based on the risks assessed by the risk assessment unit. For example, the past case review unit investigates the impact of similar vulnerabilities in the past. The past case review unit can also check past security incidents and countermeasures. For example, the past case review unit searches a database for past vulnerability cases and checks the scope of impact and countermeasures. Step 5: The summary generator generates a summary for higher-level users based on the past cases confirmed by the past case checker. For example, the summary generator generates a report that briefly summarizes the impact of the vulnerability and countermeasures. The summary generator can also customize the report taking into account the interests and priorities of higher-level users. For example, the summary generator visually displays the impact of the vulnerability in graphs and charts and provides specific explanations of countermeasures.

[0068] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0069] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0070] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0071] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0072] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0073] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0074] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0075] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0076] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0077] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0078] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0079] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0080] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0081] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0082] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0086] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0087] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0102] 7, a 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.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0109] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0112] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0113] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0114] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0118] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0119] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0120] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0121] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0122] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0123] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0124] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0125] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0126] 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.

[0127] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0128] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0129] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0130] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0131] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0132] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0133] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0134] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0135] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a version information acquisition unit that acquires version information of the target system; a vulnerability information collection unit that collects vulnerability information based on the version information acquired by the version information acquisition unit; a risk assessment unit that assesses risks based on the vulnerability information collected by the vulnerability information collection unit; a past case checking unit that checks past similar cases based on the risk assessed by the risk assessment unit; a summary explanation generation unit that generates a summary explanation to a higher level based on the past cases confirmed by the past case confirmation unit; A system characterized by:

2. The vulnerability information collection unit Vulnerability fix history or patch information is also collected and provided to users.

2. The system of claim 1.

3. The risk assessment unit Consider the credibility of the person who discovered or reported the vulnerability and reflect this in your risk assessment 2. The system of claim 1.

4. The past case checking unit Detailed technical background and countermeasures for the past cases will also be provided to make it easier for users to take specific countermeasures.

2. The system of claim 1.

5. The summary description generation unit Monitor the sentiment of the senior executives when they view the report in real time and adjust the report content as needed.

2. The system of claim 1.

Images