system

Abstract

The system according to the embodiment aims to efficiently check vulnerability information and risks from system version information and to understand past cases and their impacts. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a confirmation unit, and a provision unit. The reception unit inputs version information of the target system. The analysis unit analyzes the version information input by the reception unit and checks related vulnerability information and risks. The confirmation unit checks past cases of similar vulnerabilities and their impacts based on the vulnerability information confirmed by the analysis unit. The provision unit compiles an overview explanation to upper levels based on the information confirmed by the confirmation unit.

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] With conventional technology, the process of checking vulnerability information and risks from system version information and understanding past cases and their impact was complex and difficult to carry out efficiently.

[0005] The system according to the embodiment aims to efficiently check vulnerability information and risks from system version information and to understand past cases and their impacts. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a confirmation unit, and a provision unit. The reception unit inputs version information of the target system. The analysis unit analyzes the version information input by the reception unit and checks related vulnerability information and risks. The confirmation unit checks past cases of similar vulnerabilities and their impacts based on the vulnerability information confirmed by the analysis unit. The provision unit compiles an overview explanation to upper layers based on the information confirmed by the confirmation unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently check vulnerability information and risks from system version information, and can grasp past cases and their impacts. [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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[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 system according to an embodiment of the present invention builds a generation AI using Common Vulnerabilities and Exposures (CVE), vulnerability databases, security news, and other sources as learning sources, allowing users responsible for system maintenance and operation to easily perform the following tasks: This system inputs the version information of the target system, and the generation AI analyzes the input version information and checks related vulnerability information and risks. This allows users to quickly grasp vulnerability information for the target system. Furthermore, the generation AI checks past cases of similar vulnerabilities and their impact. For example, if a similar vulnerability has occurred in the past, it presents the scope of impact and countermeasures. This allows users to take appropriate measures by referring to past cases. Finally, the generation AI compiles a summary explanation to higher-level users. For example, it generates a report that concisely summarizes the vulnerability overview, scope of impact, and countermeasures. This allows users to effectively explain the situation to higher-level users. This allows users to quickly check the vulnerability information and risks of the target system, take appropriate measures by referring to past cases, and effectively explain the situation to higher-level users.

[0029] A system according to an embodiment includes a receiving unit, an analysis unit, a verification unit, and a providing unit. The receiving unit inputs version information of a target system. For example, the receiving unit can receive information such as the software version number, release date, and change history entered by a user. The receiving unit can also provide input methods according to the user's preferences, such as voice input, text input, and image input. The analysis unit analyzes the version information entered by the receiving unit and checks related vulnerability information and risks. For example, the analysis unit uses a generation AI to refer to a vulnerability database and security news based on the entered version information and extract related vulnerability information. The analysis unit can also adjust the level of analysis detail based on the importance of the vulnerability information. The verification unit checks past cases of similar vulnerabilities and their impacts based on the vulnerability information confirmed by the analysis unit. For example, the verification unit refers to past vulnerability cases and presents the scope of impact and countermeasures when a similar vulnerability occurs. The verification unit can also improve the accuracy of the verification by taking into account the interrelationships between vulnerability information. The providing unit compiles an overview to higher-level organizations based on the information confirmed by the verification unit. For example, the providing unit generates a report that briefly summarizes an overview of the vulnerability, the scope of the impact, countermeasures, etc. The providing unit can also estimate the user's emotions and adjust the way the report is presented based on the estimated user's emotions. As a result, the system according to the embodiment allows the user to quickly check vulnerability information and risks of the target system, take appropriate countermeasures by referring to past cases, and effectively explain the situation to higher-level personnel.

[0030] The reception unit analyzes the user's past version information input history and selects the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze patterns of version information previously entered by the user and suggest the optimal input method. This improves the efficiency of input work by suggesting the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0031] When the version information is input, the reception unit filters the version information based on the user's current project or area of ​​interest. For example, the reception unit displays only version information related to the project the user is currently working on. The reception unit can also prioritize displaying related version information based on the user's area of ​​interest. The reception unit can also filter related version information by referring to the user's past project history. This supports efficient information gathering by prioritized display of information related to the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project information to the generation AI and cause the generation AI to filter related version information.

[0032] When inputting version information, the reception unit selects the optimal input means according to the user's input method. For example, if the user prefers voice input, the reception unit may provide voice input preferentially. Furthermore, if the user prefers text input, the reception unit may provide text input preferentially. Furthermore, if the user prefers image input, the reception unit may provide image input preferentially. This improves the comfort of input work by providing an input means according to the user's preferences. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may have a generation AI select the user's input method.

[0033] When inputting version information, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information. For example, the reception unit prioritizes displaying version information related to the area where the user is currently located. The reception unit can also filter relevant version information based on the user's geographical location information. The reception unit can also prioritize displaying version information related to areas the user has previously visited. This supports efficient information gathering by providing highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to filter relevant version information.

[0034] When the version information is input, the reception unit analyzes the user's social media activity and inputs related information. The reception unit inputs related version information based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity history and input related version information. The reception unit can also input related version information based on the activity of the user's friends on social media. This supports efficient information gathering by providing related information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related version information.

[0035] The reception unit customizes the input method by reflecting the user's past feedback when entering version information. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input method. The reception unit can also preferentially provide input methods that the user has preferred in the past. This improves the efficiency of input work by customizing the input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data to the generation AI and have the generation AI select the optimal input method.

[0036] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the vulnerability information. For example, the analysis unit performs a detailed analysis on vulnerability information with a high importance. The analysis unit can also perform a concise analysis on vulnerability information with a low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the vulnerability information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the vulnerability information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0037] During analysis, the analysis unit can apply different analysis algorithms depending on the category of vulnerability information. For example, the analysis unit applies a dedicated analysis algorithm to network vulnerabilities. The analysis unit can also apply a dedicated analysis algorithm to application vulnerabilities. The analysis unit can also apply a dedicated analysis algorithm to system vulnerabilities. In this way, the accuracy of the analysis is improved by applying an analysis algorithm depending on the category of vulnerability information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of vulnerability information to the generation AI and have the generation AI select an analysis algorithm.

[0038] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the analysis results previously performed by the user. The analysis unit can also analyze the user's past analysis history to improve the accuracy of the analysis. The analysis unit can also improve the accuracy of the analysis based on feedback previously provided by the user. This improves the accuracy of the analysis based on the user's past analysis results, thereby providing more accurate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0039] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of vulnerability information. For example, the analysis unit prioritizes analysis of recently submitted vulnerability information. The analysis unit can also postpone vulnerability information submitted earlier. The analysis unit can also dynamically adjust the priority of analysis according to the time of submission. This allows for efficient analysis by adjusting the priority of analysis according to the time of submission of vulnerability information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of vulnerability information to the generation AI and have the generation AI determine the priority of analysis.

[0040] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the vulnerability information. For example, the analysis unit prioritizes analysis of highly relevant vulnerability information. The analysis unit can also postpone analysis of less relevant vulnerability information. The analysis unit can also dynamically adjust the order of analysis based on the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the vulnerability information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of vulnerability information to the generation AI and cause the generation AI to adjust the order of analysis.

[0041] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can avoid technical terms and use concise expressions. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terms.

[0042] The verification unit can improve the accuracy of the verification by taking into account the interrelationships between vulnerability information during verification. The verification unit, for example, analyzes the interrelationships between multiple pieces of vulnerability information to improve the accuracy of the verification. The verification unit can also determine the priority of the verification by taking into account the interrelationships between the vulnerability information. The verification unit can also dynamically adjust the accuracy of the verification based on the interrelationships between the vulnerability information. In this way, the accuracy of the verification is improved by taking into account the interrelationships between the vulnerability information. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input interrelation data between vulnerability information into the generation AI and cause the generation AI to improve the accuracy of the verification.

[0043] The verification unit can perform verification taking into account attribute information of the submitter of the vulnerability information. The verification unit can adjust the accuracy of the verification, for example, taking into account the submitter's level of expertise. The verification unit can also improve the accuracy of the verification by referring to the submitter's past submission history. The verification unit can also dynamically adjust the verification criteria based on the submitter's attribute information. This improves the accuracy of the verification by taking into account the submitter's attribute information. Some or all of the above-mentioned processing in the verification unit can be performed, for example, using AI, or can be performed without using AI. For example, the verification unit can input the submitter's attribute information data into the generation AI and cause the generation AI to improve the accuracy of the verification.

[0044] The confirmation unit can weight the confirmation based on the frequency of submission of vulnerability information during confirmation. For example, the confirmation unit prioritizes checking of vulnerability information that is submitted frequently. The confirmation unit can also postpone checking of vulnerability information that is submitted less frequently. The confirmation unit can also dynamically adjust the weighting of the confirmation according to the submission frequency. In this way, efficient confirmation is achieved by weighting the confirmation based on the submission frequency. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input submission frequency data to the generation AI and have the generation AI perform the weighting of the confirmation.

[0045] The confirmation unit can perform confirmation taking into consideration the geographical distribution of vulnerability information. For example, the confirmation unit prioritizes confirmation of geographically close vulnerability information. The confirmation unit can also postpone confirmation of geographically distant vulnerability information. The confirmation unit can also dynamically adjust the confirmation priority according to the geographical distribution. This allows for efficient confirmation by taking the geographical distribution into consideration. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input geographical distribution data of vulnerability information to the generation AI and cause the generation AI to adjust the confirmation priority.

[0046] During verification, the verification unit can improve the accuracy of the verification by referring to related literature for the vulnerability information. The verification unit, for example, improves the accuracy of the verification by referring to related literature. The verification unit can also determine the priority of the verification based on information in the related literature. The verification unit can also dynamically adjust the accuracy of the verification based on the related literature. In this way, the accuracy of the verification is improved by referring to the related literature. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the verification.

[0047] The verification unit can perform verification taking into consideration the market value of the vulnerability information. For example, the verification unit prioritizes verification of vulnerability information with high market value. The verification unit can also postpone verification of vulnerability information with low market value. The verification unit can also dynamically adjust the verification priority according to the market value. This allows for efficient verification by taking market value into consideration. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input market value data into the generation AI and cause the generation AI to adjust the verification priority.

[0048] The providing unit can adjust the level of detail of the report based on the importance of the vulnerability information when providing the report. For example, the providing unit provides a detailed report for vulnerability information with a high importance. The providing unit can also provide a concise report for vulnerability information with a low importance. The providing unit can also dynamically adjust the level of detail of the report according to the importance. This allows for efficient report creation by adjusting the level of detail of the report according to the importance of the vulnerability information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the vulnerability information to the generating AI and cause the generating AI to adjust the level of detail of the report.

[0049] The providing unit can apply different report generation algorithms depending on the category of vulnerability information when providing the information. For example, the providing unit applies a dedicated report generation algorithm to network vulnerabilities. The providing unit can also apply a dedicated report generation algorithm to application vulnerabilities. The providing unit can also apply a dedicated report generation algorithm to system vulnerabilities. In this way, the accuracy of the report is improved by applying a report generation algorithm according to the category of vulnerability information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of vulnerability information to the generation AI and cause the generation AI to select a report generation algorithm.

[0050] When providing a report, the providing unit can improve the accuracy of the report by referring to the user's past report results. The providing unit improves the accuracy of the report, for example, based on the user's past report results. The providing unit can also analyze the user's past report history to improve the accuracy of the report. The providing unit can also improve the accuracy of the report based on feedback provided by the user in the past. In this way, a more accurate report is provided by improving the accuracy of the report based on the user's past report results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past report data into the generation AI and cause the generation AI to improve the accuracy of the report.

[0051] The providing unit can determine the priority of reports based on the time of submission of the vulnerability information when the information is provided. For example, the providing unit can prioritize recently submitted vulnerability information in the report. The providing unit can also postpone vulnerability information that was submitted earlier. The providing unit can also dynamically adjust the priority of reports based on the time of submission. This allows for efficient report creation by adjusting the priority of reports based on the time of submission of the vulnerability information. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the time of submission of vulnerability information to the generation AI and cause the generation AI to determine the priority of reports.

[0052] The providing unit can adjust the order of reports based on the relevance of the vulnerability information when providing the reports. For example, the providing unit prioritizes including highly relevant vulnerability information in the report. The providing unit can also postpone less relevant vulnerability information. The providing unit can also dynamically adjust the order of reports based on the relevance. This allows for efficient report creation by adjusting the order of reports based on the relevance of the vulnerability information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of vulnerability information to a generating AI and cause the generating AI to adjust the order of reports.

[0053] The providing unit can adjust the use of technical terms in the report according to the user's level of expertise when providing the report. For example, if the user has technical expertise, the providing unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the providing unit can avoid technical terms and use concise expressions. The providing unit can also dynamically adjust the use of technical terms in the report according to the user's level of expertise. This allows the report to be easily understood by the user by adjusting the use of technical terms in the report according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The reception unit can predict input errors based on the user's past input history and display a warning in advance. For example, if a user has made an input error in a specific format in the past, a warning will be displayed when the user attempts to input data in a similar format. The reception unit can also analyze patterns in data the user has entered in the past and point out areas where errors are likely to occur in advance. Furthermore, the reception unit can automatically check the input content to check for errors before the user completes the input. This reduces input errors and supports efficient data entry.

[0056] The analysis unit can build a feedback loop to improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit can analyze the user's past analysis results and identify which parts were accurate. The analysis unit can also adjust the analysis algorithm based on feedback provided by the user in the past. Furthermore, the analysis unit can dynamically improve the accuracy of the analysis by referring to the user's past analysis history. This makes it possible to utilize the user's past analysis results to provide more accurate analysis results.

[0057] The verification unit can build a feedback loop to improve the accuracy of the verification based on the user's past verification history. For example, the verification unit can analyze the user's past verification results and identify which parts were accurate. The verification unit can also adjust the verification algorithm based on feedback provided by the user in the past. Furthermore, the verification unit can dynamically improve the accuracy of the verification based on the user's past verification history. This allows the user's past verification results to be utilized to provide more accurate verification results.

[0058] The providing unit can build a feedback loop to improve the accuracy of the report based on the user's past reporting results. For example, the providing unit can analyze the user's past reporting results and identify which parts were accurate. The providing unit can also adjust the report generation algorithm based on feedback provided by the user in the past. Furthermore, the providing unit can dynamically improve the accuracy of the report by referring to the user's past reporting history. In this way, the user's past reporting results are utilized to provide more accurate reports.

[0059] The providing unit can customize the content of the report based on the user's past report results. For example, if the user has preferred reports in a particular format in the past, the providing unit can provide the report in a similar format. The providing unit can also adjust the content of the report based on feedback provided by the user in the past. Furthermore, the providing unit can dynamically customize the content of the report by referring to the user's past report history. In this way, the user's past report results can be utilized to provide a more appropriate report.

[0060] The processing flow of the first embodiment will be briefly explained below.

[0061] Step 1: The reception unit inputs the version information of the target system. For example, the reception unit can receive information such as the software version number, release date, and change history entered by the user. The reception unit can also provide input methods according to the user's preferences, such as voice input, text input, and image input. Step 2: The analysis unit analyzes the version information entered by the reception unit and checks for related vulnerability information and risks. For example, the analysis unit uses the generation AI to refer to vulnerability databases and security news based on the entered version information and extract related vulnerability information. The analysis unit can also adjust the level of detail of the analysis based on the importance of the vulnerability information. Step 3: The verification unit checks past cases of similar vulnerabilities and their impact based on the vulnerability information confirmed by the analysis unit. For example, the verification unit may refer to past vulnerability cases and present the extent of impact and countermeasures in the event of a similar vulnerability occurring. The verification unit may also improve the accuracy of the verification by taking into account the correlation between vulnerability information. Step 4: The provision unit compiles a summary explanation for higher-level users based on the information confirmed by the confirmation unit. For example, the provision unit generates a report that briefly summarizes the vulnerability overview, the scope of impact, and countermeasures. The provision unit can also estimate the user's emotions and adjust the way the report is presented based on the estimated user emotions.

[0062] (Example 2) A system according to an embodiment of the present invention builds a generation AI using Common Vulnerabilities and Exposures (CVE), vulnerability databases, security news, and other sources as learning sources, allowing users responsible for system maintenance and operation to easily perform the following tasks: This system inputs the version information of the target system, and the generation AI analyzes the input version information and checks related vulnerability information and risks. This allows users to quickly grasp vulnerability information for the target system. Furthermore, the generation AI checks past cases of similar vulnerabilities and their impact. For example, if a similar vulnerability has occurred in the past, it presents the scope of impact and countermeasures. This allows users to take appropriate measures by referring to past cases. Finally, the generation AI compiles a summary explanation to higher-level users. For example, it generates a report that concisely summarizes the vulnerability overview, scope of impact, and countermeasures. This allows users to effectively explain the situation to higher-level users. This allows users to quickly check the vulnerability information and risks of the target system, take appropriate measures by referring to past cases, and effectively explain the situation to higher-level users.

[0063] A system according to an embodiment includes a receiving unit, an analysis unit, a verification unit, and a providing unit. The receiving unit inputs version information of a target system. For example, the receiving unit can receive information such as the software version number, release date, and change history entered by a user. The receiving unit can also provide input methods according to the user's preferences, such as voice input, text input, and image input. The analysis unit analyzes the version information entered by the receiving unit and checks related vulnerability information and risks. For example, the analysis unit uses a generation AI to refer to a vulnerability database and security news based on the entered version information and extract related vulnerability information. The analysis unit can also adjust the level of analysis detail based on the importance of the vulnerability information. The verification unit checks past cases of similar vulnerabilities and their impacts based on the vulnerability information confirmed by the analysis unit. For example, the verification unit refers to past vulnerability cases and presents the scope of impact and countermeasures when a similar vulnerability occurs. The verification unit can also improve the accuracy of the verification by taking into account the interrelationships between vulnerability information. The providing unit compiles an overview to higher-level organizations based on the information confirmed by the verification unit. For example, the providing unit generates a report that briefly summarizes an overview of the vulnerability, the scope of the impact, countermeasures, etc. The providing unit can also estimate the user's emotions and adjust the way the report is presented based on the estimated user's emotions. As a result, the system according to the embodiment allows the user to quickly check vulnerability information and risks of the target system, take appropriate countermeasures by referring to past cases, and effectively explain the situation to higher-level personnel.

[0064] The reception unit estimates the user's emotions and adjusts the timing of inputting the version information based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit delays the input timing to provide time for the user to relax. Furthermore, if the user is relaxed, the reception unit can prompt the user to input information immediately to efficiently proceed with work. Furthermore, if the user is in a hurry, the reception unit can accelerate the input timing to quickly collect information. This adjusts the input timing according to the user's emotions, thereby reducing the user's stress and promoting efficient work. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the user's emotions.

[0065] The reception unit analyzes the user's past version information input history and selects the optimal input method. For example, the reception unit preferentially suggests input methods (such as voice and text) that the user has frequently used in the past. The reception unit can also predict and suggest an input method to be used during a specific time period based on the user's past input history. The reception unit can also analyze patterns of version information previously entered by the user and suggest the optimal input method. This improves the efficiency of input work by suggesting the optimal input method based on the user's past input history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past input data into a generation AI and have the generation AI select the optimal input method.

[0066] When the version information is input, the reception unit filters the version information based on the user's current project or area of ​​interest. For example, the reception unit displays only version information related to the project the user is currently working on. The reception unit can also prioritize displaying related version information based on the user's area of ​​interest. The reception unit can also filter related version information by referring to the user's past project history. This supports efficient information gathering by prioritized display of information related to the user's current project or area of ​​interest. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's project information to the generation AI and cause the generation AI to filter related version information.

[0067] When inputting version information, the reception unit selects the optimal input means according to the user's input method. For example, if the user prefers voice input, the reception unit may provide voice input preferentially. Furthermore, if the user prefers text input, the reception unit may provide text input preferentially. Furthermore, if the user prefers image input, the reception unit may provide image input preferentially. This improves the comfort of input work by providing an input means according to the user's preferences. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may have a generation AI select the user's input method.

[0068] The reception unit estimates the user's emotions and determines the priority of the version information to be input based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit postpones input of less important version information. Furthermore, if the user is relaxed, the reception unit can also prioritize input of more important version information. Furthermore, if the user is in a hurry, the reception unit can also input the most important version information first. This supports efficient information collection by adjusting the priority of information to be input according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0069] When inputting version information, the reception unit prioritizes inputting highly relevant information in consideration of the user's geographical location information. For example, the reception unit prioritizes displaying version information related to the area where the user is currently located. The reception unit can also filter relevant version information based on the user's geographical location information. The reception unit can also prioritize displaying version information related to areas the user has previously visited. This supports efficient information gathering by providing highly relevant information based on the user's geographical location information. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information to the generation AI and cause the generation AI to filter relevant version information.

[0070] When the version information is input, the reception unit analyzes the user's social media activity and inputs related information. The reception unit inputs related version information based on, for example, information shared by the user on social media. The reception unit can also analyze the user's social media activity history and input related version information. The reception unit can also input related version information based on the activity of the user's friends on social media. This supports efficient information gathering by providing related information based on the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data to the generation AI and cause the generation AI to select related version information.

[0071] The reception unit customizes the input method by reflecting the user's past feedback when entering version information. The reception unit, for example, suggests the optimal input method based on feedback provided by the user in the past. The reception unit can also analyze the user's past feedback and customize the input method. The reception unit can also preferentially provide input methods that the user has preferred in the past. This improves the efficiency of input work by customizing the input method based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's feedback data to the generation AI and have the generation AI select the optimal input method.

[0072] The analysis unit estimates the user's emotions and adjusts the presentation method of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides simple, highly visible analysis results. Furthermore, if the user is relaxed, the analysis unit can provide detailed analysis results. Furthermore, if the user is in a hurry, the analysis unit can provide analysis results that are easy to understand for the user by adjusting the presentation method of the analysis results according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0073] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the vulnerability information. For example, the analysis unit performs a detailed analysis on vulnerability information with a high importance. The analysis unit can also perform a concise analysis on vulnerability information with a low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance. This allows for efficient analysis by adjusting the level of detail of the analysis according to the importance of the vulnerability information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the vulnerability information to the generation AI and cause the generation AI to adjust the level of detail of the analysis.

[0074] During analysis, the analysis unit can apply different analysis algorithms depending on the category of vulnerability information. For example, the analysis unit applies a dedicated analysis algorithm to network vulnerabilities. The analysis unit can also apply a dedicated analysis algorithm to application vulnerabilities. The analysis unit can also apply a dedicated analysis algorithm to system vulnerabilities. In this way, the accuracy of the analysis is improved by applying an analysis algorithm depending on the category of vulnerability information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of vulnerability information to the generation AI and have the generation AI select an analysis algorithm.

[0075] During analysis, the analysis unit can improve the accuracy of the analysis by referring to the user's past analysis results. The analysis unit improves the accuracy of the analysis, for example, based on the analysis results previously performed by the user. The analysis unit can also analyze the user's past analysis history to improve the accuracy of the analysis. The analysis unit can also improve the accuracy of the analysis based on feedback previously provided by the user. This improves the accuracy of the analysis based on the user's past analysis results, thereby providing more accurate analysis results. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past analysis data into the generation AI and cause the generation AI to improve the accuracy of the analysis.

[0076] The analysis unit estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. For example, if the user is nervous, the analysis unit provides a short and concise analysis result. The analysis unit can also provide a detailed analysis result if the user is relaxed. The analysis unit can also provide a quick analysis result if the user is in a hurry. By adjusting the length of the analysis according to the user's emotions, the analysis unit can provide the optimal analysis result for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0077] During analysis, the analysis unit can determine the priority of analysis based on the time of submission of vulnerability information. For example, the analysis unit prioritizes analysis of recently submitted vulnerability information. The analysis unit can also postpone vulnerability information submitted earlier. The analysis unit can also dynamically adjust the priority of analysis according to the time of submission. This allows for efficient analysis by adjusting the priority of analysis according to the time of submission of vulnerability information. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the time of submission of vulnerability information to the generation AI and have the generation AI determine the priority of analysis.

[0078] During analysis, the analysis unit can adjust the order of analysis based on the relevance of the vulnerability information. For example, the analysis unit prioritizes analysis of highly relevant vulnerability information. The analysis unit can also postpone analysis of less relevant vulnerability information. The analysis unit can also dynamically adjust the order of analysis based on the relevance. This allows for efficient analysis by adjusting the order of analysis based on the relevance of the vulnerability information. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of vulnerability information to the generation AI and cause the generation AI to adjust the order of analysis.

[0079] During analysis, the analysis unit can adjust the use of technical terms in the analysis according to the user's level of expertise. For example, if the user has technical expertise, the analysis unit uses a lot of technical terms. Alternatively, if the user does not have technical expertise, the analysis unit can avoid technical terms and use concise expressions. The analysis unit can also dynamically adjust the use of technical terms in the analysis according to the user's level of expertise. This allows for adjusting the use of technical terms in the analysis according to the user's level of expertise, thereby providing analysis results that are easy for the user to understand. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without AI. For example, the analysis unit can input the user's level of expertise data into a generation AI and have the generation AI use technical terms.

[0080] The confirmation unit estimates the user's emotions and adjusts the confirmation criteria based on the estimated user emotions. For example, the confirmation unit relaxes the confirmation criteria when the user is nervous. The confirmation unit can also tighten the confirmation criteria when the user is relaxed. The confirmation unit can also quickly adjust the confirmation criteria when the user is in a hurry. By adjusting the confirmation criteria according to the user's emotions, the confirmation unit provides the optimal confirmation result for the user. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the confirmation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the confirmation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0081] The verification unit can improve the accuracy of the verification by taking into account the interrelationships between vulnerability information during verification. The verification unit, for example, analyzes the interrelationships between multiple pieces of vulnerability information to improve the accuracy of the verification. The verification unit can also determine the priority of the verification by taking into account the interrelationships between the vulnerability information. The verification unit can also dynamically adjust the accuracy of the verification based on the interrelationships between the vulnerability information. In this way, the accuracy of the verification is improved by taking into account the interrelationships between the vulnerability information. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input interrelation data between vulnerability information into the generation AI and cause the generation AI to improve the accuracy of the verification.

[0082] The verification unit can perform verification taking into account attribute information of the submitter of the vulnerability information. The verification unit can adjust the accuracy of the verification, for example, taking into account the submitter's level of expertise. The verification unit can also improve the accuracy of the verification by referring to the submitter's past submission history. The verification unit can also dynamically adjust the verification criteria based on the submitter's attribute information. This improves the accuracy of the verification by taking into account the submitter's attribute information. Some or all of the above-mentioned processing in the verification unit can be performed, for example, using AI, or can be performed without using AI. For example, the verification unit can input the submitter's attribute information data into the generation AI and cause the generation AI to improve the accuracy of the verification.

[0083] The confirmation unit can weight the confirmation based on the frequency of submission of vulnerability information during confirmation. For example, the confirmation unit prioritizes checking of vulnerability information that is submitted frequently. The confirmation unit can also postpone checking of vulnerability information that is submitted less frequently. The confirmation unit can also dynamically adjust the weighting of the confirmation according to the submission frequency. In this way, efficient confirmation is achieved by weighting the confirmation based on the submission frequency. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input submission frequency data to the generation AI and have the generation AI perform the weighting of the confirmation.

[0084] The confirmation unit estimates the user's emotions and adjusts the order in which the confirmation results are displayed based on the estimated user emotions. For example, if the user is nervous, the confirmation unit may display less important results first to provide a sense of security. Furthermore, if the user is relaxed, the confirmation unit may display more important results first to allow the user to work efficiently. Furthermore, if the user is in a hurry, the confirmation unit may display the most important results first to allow the user to respond quickly. This adjusts the display order of the confirmation results according to the user's emotions, thereby providing the user with optimal confirmation results. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the confirmation unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the confirmation unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0085] The confirmation unit can perform confirmation taking into consideration the geographical distribution of vulnerability information. For example, the confirmation unit prioritizes confirmation of geographically close vulnerability information. The confirmation unit can also postpone confirmation of geographically distant vulnerability information. The confirmation unit can also dynamically adjust the confirmation priority according to the geographical distribution. This allows for efficient confirmation by taking the geographical distribution into consideration. Some or all of the above-mentioned processing in the confirmation unit may be performed using, for example, AI, or may be performed without using AI. For example, the confirmation unit can input geographical distribution data of vulnerability information to the generation AI and cause the generation AI to adjust the confirmation priority.

[0086] During verification, the verification unit can improve the accuracy of the verification by referring to related literature for the vulnerability information. The verification unit, for example, improves the accuracy of the verification by referring to related literature. The verification unit can also determine the priority of the verification based on information in the related literature. The verification unit can also dynamically adjust the accuracy of the verification based on the related literature. In this way, the accuracy of the verification is improved by referring to the related literature. Some or all of the above-described processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input related literature data into the generation AI and cause the generation AI to improve the accuracy of the verification.

[0087] The verification unit can perform verification taking into consideration the market value of the vulnerability information. For example, the verification unit prioritizes verification of vulnerability information with high market value. The verification unit can also postpone verification of vulnerability information with low market value. The verification unit can also dynamically adjust the verification priority according to the market value. This allows for efficient verification by taking market value into consideration. Some or all of the above-mentioned processing in the verification unit may be performed using, for example, AI, or may be performed without using AI. For example, the verification unit can input market value data into the generation AI and cause the generation AI to adjust the verification priority.

[0088] The providing unit estimates the user's emotions and adjusts the presentation method of the report to be provided based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a simple, highly visible report. Furthermore, if the user is relaxed, the providing unit can also provide a report containing detailed information. Furthermore, if the user is in a hurry, the providing unit can also provide a report that focuses on the main points. By adjusting the presentation method of the report according to the user's emotions, the report is provided to the user in an easy-to-understand manner. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0089] The providing unit can adjust the level of detail of the report based on the importance of the vulnerability information when providing the report. For example, the providing unit provides a detailed report for vulnerability information with a high importance. The providing unit can also provide a concise report for vulnerability information with a low importance. The providing unit can also dynamically adjust the level of detail of the report according to the importance. This allows for efficient report creation by adjusting the level of detail of the report according to the importance of the vulnerability information. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit can input importance data of the vulnerability information to the generating AI and cause the generating AI to adjust the level of detail of the report.

[0090] The providing unit can apply different report generation algorithms depending on the category of vulnerability information when providing the information. For example, the providing unit applies a dedicated report generation algorithm to network vulnerabilities. The providing unit can also apply a dedicated report generation algorithm to application vulnerabilities. The providing unit can also apply a dedicated report generation algorithm to system vulnerabilities. In this way, the accuracy of the report is improved by applying a report generation algorithm according to the category of vulnerability information. Some or all of the above-mentioned processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input category data of vulnerability information to the generation AI and cause the generation AI to select a report generation algorithm.

[0091] When providing a report, the providing unit can improve the accuracy of the report by referring to the user's past report results. The providing unit improves the accuracy of the report, for example, based on the user's past report results. The providing unit can also analyze the user's past report history to improve the accuracy of the report. The providing unit can also improve the accuracy of the report based on feedback provided by the user in the past. In this way, a more accurate report is provided by improving the accuracy of the report based on the user's past report results. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's past report data into the generation AI and cause the generation AI to improve the accuracy of the report.

[0092] The providing unit estimates the user's emotions and adjusts the length of the report based on the estimated user emotions. For example, if the user is nervous, the providing unit provides a short, concise report. Furthermore, if the user is relaxed, the providing unit can provide a longer report with detailed explanations. Furthermore, if the user is in a hurry, the providing unit can provide a short report that can be read quickly. By adjusting the length of the report according to the user's emotions, the optimal report for the user is provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the providing unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the providing unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0093] The providing unit can determine the priority of reports based on the time of submission of the vulnerability information when the information is provided. For example, the providing unit can prioritize recently submitted vulnerability information in the report. The providing unit can also postpone vulnerability information that was submitted earlier. The providing unit can also dynamically adjust the priority of reports based on the time of submission. This allows for efficient report creation by adjusting the priority of reports based on the time of submission of the vulnerability information. Some or all of the above-mentioned processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can input data on the time of submission of vulnerability information to the generation AI and cause the generation AI to determine the priority of reports.

[0094] The providing unit can adjust the order of reports based on the relevance of the vulnerability information when providing the reports. For example, the providing unit prioritizes including highly relevant vulnerability information in the report. The providing unit can also postpone less relevant vulnerability information. The providing unit can also dynamically adjust the order of reports based on the relevance. This allows for efficient report creation by adjusting the order of reports based on the relevance of the vulnerability information. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input relevance data of vulnerability information to a generating AI and cause the generating AI to adjust the order of reports.

[0095] The providing unit can adjust the use of technical terms in the report according to the user's level of expertise when providing the report. For example, if the user has technical expertise, the providing unit uses a lot of technical terms. Furthermore, if the user does not have technical expertise, the providing unit can avoid technical terms and use concise expressions. The providing unit can also dynamically adjust the use of technical terms in the report according to the user's level of expertise. This allows the report to be easily understood by the user by adjusting the use of technical terms in the report according to the user's level of expertise. Some or all of the above-described processing in the providing unit may be performed using AI, for example, or may be performed without using AI. For example, the providing unit can input the user's level of expertise data into the generation AI and cause the generation AI to use technical terms. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, confirmation unit, and provision unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and receives version information entered by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the entered version information and checks related vulnerability information and risks. The confirmation unit is realized by the identification processing unit 290 of the data processing device 12 and refers to past vulnerability cases, presenting the scope of impact and countermeasures. The provision unit is realized by the control unit 46A of the smart device 14 and compiles an overview explanation to upper layers. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, confirmation unit, and provision unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and receives version information entered by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the entered version information and checks related vulnerability information and risks. The confirmation unit is realized by the identification processing unit 290 of the data processing device 12 and refers to past vulnerability cases, presenting the scope of impact and countermeasures. The provision unit is realized by the control unit 46A of the smart glasses 214 and compiles an overview explanation to upper layers. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, confirmation unit, and provision unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and receives version information input by the user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input version information and checks related vulnerability information and risks. The confirmation unit is realized by the identification processing unit 290 of the data processing device 12 and refers to past vulnerability cases and presents the scope of impact and countermeasures. The provision unit is realized by the control unit 46A of the headset type terminal 314 and compiles an overview explanation to upper layers. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, confirmation unit, and provision unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and receives version information input by a user. The analysis unit is realized by the identification processing unit 290 of the data processing device 12 and analyzes the input version information and checks related vulnerability information and risks. The confirmation unit is realized by the identification processing unit 290 of the data processing device 12 and refers to past vulnerability cases, presenting the scope of impact and countermeasures. The provision unit is realized by the control unit 46A of the robot 414 and compiles an overview explanation to higher-level layers.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The reception unit can predict input errors based on the user's past input history and display a warning in advance. For example, if a user has made an input error in a specific format in the past, a warning will be displayed when the user attempts to input data in a similar format. The reception unit can also analyze patterns in data the user has entered in the past and point out areas where errors are likely to occur in advance. Furthermore, the reception unit can automatically check the input content to check for errors before the user completes the input. This reduces input errors and supports efficient data entry.

[0098] The reception unit can estimate the user's emotions and dynamically change the design of the input interface based on the estimated user emotions. For example, if the user is feeling stressed, a simple interface with calming colors can be provided. Alternatively, if the user is relaxed, a colorful and interactive interface can be provided. Furthermore, if the user is in a hurry, important information can be highlighted to enable the user to complete input quickly. In this way, providing an interface that corresponds to the user's emotions improves user satisfaction.

[0099] The analysis unit can build a feedback loop to improve the accuracy of the analysis based on the user's past analysis results. For example, the analysis unit can analyze the user's past analysis results and identify which parts were accurate. The analysis unit can also adjust the analysis algorithm based on feedback provided by the user in the past. Furthermore, the analysis unit can dynamically improve the accuracy of the analysis by referring to the user's past analysis history. This makes it possible to utilize the user's past analysis results to provide more accurate analysis results.

[0100] The analysis unit can estimate the user's emotions and adjust the way in which the analysis results are presented based on the estimated user emotions. For example, if the user is nervous, the analysis results can be presented in stages to make them easier to understand. If the user is relaxed, detailed analysis results can be presented all at once. Furthermore, if the user is in a hurry, concise analysis results that focus on the main points can be provided. In this way, by providing a way to present analysis results according to the user's emotions, the system can provide the user with the most appropriate information.

[0101] The verification unit can build a feedback loop to improve the accuracy of the verification based on the user's past verification history. For example, the verification unit can analyze the user's past verification results and identify which parts were accurate. The verification unit can also adjust the verification algorithm based on feedback provided by the user in the past. Furthermore, the verification unit can dynamically improve the accuracy of the verification based on the user's past verification history. This allows the user's past verification results to be utilized to provide more accurate verification results.

[0102] The confirmation unit can estimate the user's emotions and adjust the priority of confirmation based on the estimated user's emotions. For example, if the user is feeling stressed, it can postpone checking items with low importance. Also, if the user is relaxed, it can prioritize checking items with high importance. Furthermore, if the user is in a hurry, it can check the most important items first. In this way, adjusting the priority of confirmation according to the user's emotions supports efficient confirmation work.

[0103] The providing unit can build a feedback loop to improve the accuracy of the report based on the user's past reporting results. For example, the providing unit can analyze the user's past reporting results and identify which parts were accurate. The providing unit can also adjust the report generation algorithm based on feedback provided by the user in the past. Furthermore, the providing unit can dynamically improve the accuracy of the report by referring to the user's past reporting history. In this way, the user's past reporting results are utilized to provide more accurate reports.

[0104] The providing unit can estimate the user's emotions and dynamically change the report format based on the estimated user's emotions. For example, if the user is nervous, a simple, highly visible format can be provided. If the user is relaxed, a format including detailed information can be provided. Furthermore, if the user is in a hurry, a concise format that focuses on the main points can be provided. In this way, by providing a report format according to the user's emotions, a report that is easy for the user to understand can be provided.

[0105] The providing unit can customize the content of the report based on the user's past report results. For example, if the user has preferred reports in a particular format in the past, the providing unit can provide the report in a similar format. The providing unit can also adjust the content of the report based on feedback provided by the user in the past. Furthermore, the providing unit can dynamically customize the content of the report by referring to the user's past report history. In this way, the user's past report results can be utilized to provide a more appropriate report.

[0106] The providing unit can estimate the user's emotions and adjust the timing of report delivery based on the estimated user emotions. For example, if the user is feeling stressed, the delivery of the report can be delayed to provide time for the user to relax. Also, if the user is relaxed, the report can be delivered immediately to allow the user to work efficiently. Furthermore, if the user is in a hurry, the most important report can be delivered first. In this way, adjusting the timing of report delivery according to the user's emotions reduces the user's stress and promotes efficient work.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The reception unit inputs the version information of the target system. For example, the reception unit can receive information such as the software version number, release date, and change history entered by the user. The reception unit can also provide input methods according to the user's preferences, such as voice input, text input, and image input. Step 2: The analysis unit analyzes the version information entered by the reception unit and checks for related vulnerability information and risks. For example, the analysis unit uses the generation AI to refer to vulnerability databases and security news based on the entered version information and extract related vulnerability information. The analysis unit can also adjust the level of detail of the analysis based on the importance of the vulnerability information. Step 3: The verification unit checks past cases of similar vulnerabilities and their impact based on the vulnerability information confirmed by the analysis unit. For example, the verification unit may refer to past vulnerability cases and present the extent of impact and countermeasures in the event of a similar vulnerability occurring. The verification unit may also improve the accuracy of the verification by taking into account the correlation between vulnerability information. Step 4: The provision unit compiles a summary explanation for higher-level users based on the information confirmed by the confirmation unit. For example, the provision unit generates a report that briefly summarizes the vulnerability overview, the scope of impact, and countermeasures. The provision unit can also estimate the user's emotions and adjust the way the report is presented based on the estimated user emotions.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0111] 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.

[0112] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0113] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0123] 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. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0129] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0139] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0145] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0146] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0156] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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).

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] [Explanation of symbols]

[0181] 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 reception unit for inputting version information of the target system; an analysis unit that analyzes the version information input by the reception unit and checks related vulnerability information and risks; a confirmation unit that confirms past cases of similar vulnerabilities and their impacts based on the vulnerability information confirmed by the analysis unit; a providing unit that compiles an overview explanation to a higher layer based on the information confirmed by the confirming unit; Equipped with A system characterized by:

2. The reception unit The system estimates the user's emotions and adjusts the timing of inputting version information based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past version information input history and select the optimal input method 2. The system of claim 1.

4. The reception unit Filter version information based on your current projects and interests 2. The system of claim 1.

5. The reception unit When entering version information, select the most appropriate input method depending on the user's input method.

2. The system of claim 1.

6. The reception unit The user's emotions are estimated, and the priority of the version information to be input is determined based on the estimated user's emotions.

2. The system of claim 1.

7. The reception unit When entering version information, the most relevant information is entered based on the user's geographic location.

2. The system of claim 1.

8. The reception unit When entering version information, analyze the user's social media activity and enter relevant information 2. The system of claim 1.

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