System

Abstract

A system is provided.SOLUTION: The system includes a means for monitoring a vulnerability database and detecting new vulnerability information, a means for confirming the existence of a corresponding version to the detected vulnerability, a means for automatically updating target open source software when the corresponding version exists, and a means for executing a system test after the update and evaluating a test result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Currently, many systems use open source software (OSS), but new vulnerabilities are discovered every day. This increases the workload required to address vulnerabilities, putting the security of the system at risk. In particular, if a vulnerability is not addressed promptly and appropriately when it is detected, serious security risks may arise. The purpose of this invention is to solve these problems by automatically detecting and correcting OSS vulnerabilities and ensuring the security of the system at all times. [Means for solving the problem]

[0005] The present invention solves the above problems by the following means: It provides a means for monitoring a vulnerability database and detecting new vulnerability information. It includes a means for checking whether a fixed version exists for a detected vulnerability. It includes a means for automatically updating the target open source software if a fixed version exists. It also provides a means for running a system test after the update and evaluating the test results. If a fixed version does not exist, it provides a means for evaluating the extent of the impact on the system and includes a means for automatically generating and applying temporary fix code based on the evaluation results. Finally, it provides a means for notifying the user if the security of the system cannot be maintained, thereby ensuring that the security of the system can always be maintained.

[0006] A "vulnerability database" is a database for storing and monitoring information on known security vulnerabilities.

[0007] "Vulnerability information" is information about security weaknesses or flaws that exist in software.

[0008] A "compatible version" is a new version of the software released to fix a detected vulnerability.

[0009] "Open source software (OSS)" is software whose source code is publicly available and can be freely used, modified, and distributed.

[0010] An "update" is the act of changing software to a new version, with the purpose of adding features or fixing vulnerabilities.

[0011] A "system test" is a test to check the operation of the entire system and detect any defects that may occur after a software update.

[0012] "Scope of impact" refers to the extent to which a vulnerability affects the entire system or part of it.

[0013] "Temporary fix code" refers to source code created as a temporary solution until an official fix is ​​provided.

[0014] "Notifying users" refers to the act of informing system administrators and users of important information, and is intended to encourage emergency response.

[0015] "Mitigation" means a temporary measure taken to minimize the impact of a vulnerability. [Brief explanation of the drawings]

[0016] [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. [Figure 11] FIG. 3 is a sequence diagram showing a processing flow of the data processing system according to the first embodiment. [Figure 12]FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 1. [Figure 13] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system according to the second embodiment when an emotion engine is combined. [Figure 14] FIG. 10 is a sequence diagram showing the flow of processing in the data processing system in Application Example 2 when an emotion engine is combined. DETAILED DESCRIPTION OF THE INVENTION

[0017] 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.

[0018] First, the terms used in the following description will be explained.

[0019] 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, a 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), and an APU (Accelerated Processing Unit).

[0020] 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.

[0021] 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.

[0022] 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), Bluetooth (registered trademark), etc.

[0023] 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."

[0024] [First embodiment]

[0025] FIG. 1 shows an example of the configuration of a data processing system 10 according to the first embodiment.

[0026] 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.

[0027] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0028] 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.

[0029] The reception device 38 includes a touch panel 38A, a microphone 38B, and the like, and receives user input. The touch panel 38A detects contact with an indicator (for example, a pen or a finger) to receive user input by the touch of the indicator. 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 acquires the data indicating the user input.

[0030] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form of expression that the user 20 can perceive (for example, audio and / or text). The display 40A displays visible information such as text and images in accordance with instructions from the processor 46. The speaker 40B outputs audio in accordance with instructions from the processor 46. The 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.

[0031] 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.

[0032] FIG. 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0033] 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.

[0034] 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.

[0035] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 60. The reception output program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0036] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0037] An embodiment of the present invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS) to maintain system security at all times. This system is primarily managed by a server, and notifies users and system administrators.

[0038] System configuration

[0039] 1. Vulnerability detection

[0040] The server periodically monitors vulnerability databases to detect new vulnerability information. For example, it checks databases such as the National Vulnerability Database (NVD) and OSS-Security once a day.

[0041] 2. Check the version that addresses the vulnerability

[0042] For vulnerabilities detected by the server, check the official OSS repository or distributor to determine whether a corresponding version has been released.

[0043] 3. Supported version update

[0044] If a compatible version is available, the server automatically updates the target OSS to the new version, running a test suite for the entire system to verify that the system operates normally after the update.

[0045] 4. Impact assessment

[0046] If a compatible version is not available, the server analyzes the extent of the impact on the system, for example, assessing whether the vulnerability is limited to a specific module or function.

[0047] 5. Application of the provisional revised code

[0048] If necessary, the server will automatically generate temporary fixes and apply them to the affected OSS after assessing the scope of the impact. For example, if the vulnerability exists within a function, it will take temporary measures such as disabling that function.

[0049] 6. Implementing mitigation measures

[0050] If remediation is difficult, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0051] 7. Notice to Users

[0052] If the server cannot resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator, prompting them to take manual action.

[0053] Specific examples

[0054] Example 1: Library vulnerability

[0055] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that version 1.2.4, which addresses the vulnerability, has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally.

[0056] Example 2: Determining unsupported versions

[0057] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B. However, a version that addresses the vulnerability has not yet been released, so the scope of the impact is confirmed. It is confirmed that the vulnerability is related only to a specific function, and that function is temporarily disabled. At the same time, a temporary patch code is generated and applied, maintaining the security of the system until the next update.

[0058] To effectively implement this invention, it is important to regularly monitor the vulnerability database and perform automated tests on the entire system. This system significantly reduces the workload of users and system administrators in dealing with vulnerabilities and allows the system security to be maintained at an optimal state at all times.

[0059] The processing flow will be explained below.

[0060] Step 1:

[0061] The server periodically monitors vulnerability databases (NVD, OSS-Security, etc.) to check whether new vulnerability information has been published.

[0062] Step 2:

[0063] When the server detects new vulnerability information, it obtains detailed information (CVE number, scope of impact, recommended countermeasures, etc.) and checks the version and settings of the OSS used in the system.

[0064] Step 3:

[0065] Check the official repository or distributor to see if there is a version that addresses the vulnerability detected on the server.

[0066] Step 4:

[0067] If a compatible version is available on the server, the target OSS will be automatically updated to the compatible version.

[0068] Step 5:

[0069] After the server is updated, it runs a full system test suite to detect and evaluate any issues that the update may have introduced into the system.

[0070] Step 6:

[0071] The server evaluates the test results and if all are successful, the new version is officially applied to the system. If the tests fail, the update is rolled back and an alert is sent to the user.

[0072] Step 7:

[0073] If the server does not have a compatible version, an analysis is performed to determine the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[0074] Step 8:

[0075] The server identifies the scope of the impact and, if necessary, automatically generates temporary fixes and applies them to the affected OSS. For example, if a vulnerability exists in a function or method, the code in that part will be temporarily disabled.

[0076] Step 9:

[0077] If the server is difficult to fix, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0078] Step 10:

[0079] If the server is severely affected and temporary fixes or mitigation measures are not sufficient, users and system administrators will be notified and prompted to take emergency manual action.

[0080] Step 11:

[0081] The server periodically checks for the release of subsequent versions that address vulnerabilities, and if one is released, automatically performs the update again.

[0082] Step 12:

[0083] The server periodically reassess the scope of impact and the effectiveness of provisional fixes, and implements new countermeasures as necessary.

[0084] Example 1

[0085] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0086] The use of open source software (OSS) is increasing in modern software systems. However, vulnerabilities in OSS can pose a serious threat to the security of the entire system. To address this issue, it is necessary to quickly detect and respond to vulnerabilities, but many system administrators perform this process manually, which makes it difficult and time-consuming to respond quickly. Furthermore, if a compatible version does not exist, it can be difficult to assess the scope of the impact and generate and apply temporary fixes.

[0087] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0088] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, means for automatically analyzing the scope of impact if a fixed version does not exist, and means for generating and applying temporary fix code based on the results of the impact scope evaluation. This enables quick and automatic detection and response to vulnerabilities, reduces the burden on system administrators, and improves system security.

[0089] A "vulnerability database" is a database that collects, stores, and provides information about known software vulnerabilities.

[0090] "Vulnerability information" is information about potential security risks and problems that exist in software.

[0091] A "compatible version" is a new software version that fixes a specific vulnerability.

[0092] "Software" generally refers to programs that run on a computer system and associated data.

[0093] An "update" is the act of installing a new version of the software and improving the existing version.

[0094] "System testing" is a general term for tests conducted to check the operation and functionality of the entire system.

[0095] "Scope of impact" refers to the extent to which a particular vulnerability affects the entire system or some of its functions.

[0096] "Analysis" is the process of examining data or information in detail to arrive at an understanding or conclusion.

[0097] "Temporary fix code" is program code for temporarily fixing a vulnerability.

[0098] "Mitigation" is an alternative or measure to minimize impact when direct correction is difficult.

[0099] "Notification" is the act or means of communicating specific information to another person.

[0100] The system of this invention maintains the security of systems by automatically detecting, correcting, and mitigating vulnerabilities in open source software (OSS). This system is primarily managed by a server, and notifies users and system administrators in real time.

[0101] The system uses the following hardware and software:

[0102] Hardware: Server with high-performance computing resources

[0103] Software: Vulnerability databases (e.g., National Vulnerability Database, OSS-Security), CI / CD tools (e.g., Jenkins), test frameworks (e.g., JUnit), source code static analysis tools (e.g., SonarQube), vulnerability management tools (e.g., curl, firewall configuration tools)

[0104] The server works as follows:

[0105] 1. Vulnerability database monitoring

[0106] The server uses the curl command to retrieve the latest vulnerability information from the vulnerability database once a day, and stores this information in a local database.

[0107] 2. Vulnerability Information Detection

[0108] The server analyzes the vulnerability information and identifies vulnerabilities related to specific OSS. In the process, it extracts CVE (Common Vulnerabilities and Exposures) numbers and associates them with each OSS version.

[0109] 3. Check the compatible version

[0110] The server checks whether a new version that addresses the detected vulnerability has been released in the official repository or from the distribution source, using a GitHub API request.

[0111] 4. Update and test compatible versions

[0112] If a compatible version is available, the server will automatically update the OSS using the apt-get upgrade or yum update command, then run a full system test using a test framework such as JUnit to ensure proper operation.

[0113] 5. Impact assessment

[0114] If a compatible version does not exist, the server will use a source code static analysis tool (e.g., SonarQube) to analyze the scope of the vulnerability.

[0115] 6. Generate and apply temporary correction code

[0116] Based on the results of the impact assessment, the server generates provisional fixes and applies them to the source code. For example, if a vulnerability exists in a function, it generates code to disable that function.

[0117] 7. Implementing mitigation measures

[0118] If remediation is difficult, the server will apply mitigation measures to minimize the impact on the system, such as changing firewall rules to disable certain ports.

[0119] 8. Notice to Users

[0120] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator. The notification is sent via a mail server.

[0121] Specific examples

[0122] Example 1: Library vulnerability

[0123] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that the corresponding version 1.2.4 has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally.

[0124] Example 2: Determining unsupported versions

[0125] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library, Library B. However, since a fixed version has not yet been released, the server checks the scope of the impact. It verifies that the vulnerability is related only to a specific function and temporarily disables that function. At the same time, it generates and applies a temporary fix, maintaining the security of the system until the next update.

[0126] Prompt Sentence Examples

[0127] Please explain the program for the system that automatically detects and fixes OSS vulnerabilities. In particular, I would like to know how to monitor the vulnerability database and subsequently check the supported versions.

[0128] Please explain in detail how your system retrieves new information from vulnerability databases, identifies vulnerabilities, and then fixes or provisionally addresses them, including what software and tools you use.

[0129] By introducing this system, users and system administrators can significantly reduce the time and effort required to respond to vulnerabilities and maintain the security of their systems.

[0130] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0131] Specific processing steps

[0132] Step 1: Monitor the vulnerability database

[0133] The server uses the curl command to monitor the vulnerability database. Once a day, the server retrieves the latest vulnerability information from NVD and OSS-Security and stores it in a local database.

[0134] Input: NVD or OSS-Security API endpoint

[0135] Output: Data containing the latest vulnerability information

[0136] Specific operation: The server executes the curl command to retrieve data from the API, parses the data, and stores it in a local database.

[0137] Step 2: Detecting vulnerability information

[0138] The server analyzes the vulnerability information stored in the local database to identify new vulnerabilities, which are then associated with the appropriate software.

[0139] Input: Vulnerability information in the local database

[0140] Output: Identified vulnerability information and related software

[0141] What it does: The server queries a local database, extracts new CVE numbers, and links them to the associated software versions.

[0142] Step 3: Check the supported version

[0143] The server checks whether a patch version exists for the detected vulnerability, and sends an API request to the official repository or distributor to obtain the latest release information.

[0144] Input: Identified vulnerability information and associated software

[0145] Output: Supported versions and detailed information

[0146] Specific operation: The server makes an API request to the GitHub API or the official website of each OSS and checks the release information of the corresponding version.

[0147] Step 4: Update and test compatible versions

[0148] The server will automatically update the software to the new version if one is available, then run system-wide tests using a testing framework such as JUnit and evaluate the results.

[0149] Input: Release information and software for the supported version

[0150] Output: Updated software and test results

[0151] Specific operation: The server executes the apt-get upgrade or yum update command to install the new version, and then runs a system-wide test using JUnit to verify normal operation after the installation.

[0152] Step 5: Assess the impact

[0153] If a supported version is not available, the server analyzes the scope of the vulnerability by using a source code static analysis tool (e.g., SonarQube) to identify which parts of the code are affected by the vulnerability.

[0154] Input: Identified vulnerability information and software source code

[0155] Output: Detailed information on the affected area

[0156] Specific operation: The server runs a source code static analysis tool to analyze which part of the code the vulnerability affects.

[0157] Step 6: Generate and apply temporary fix code

[0158] Based on the assessment results of the extent of the impact, the server automatically generates provisional fix code and applies it to the software.

[0159] Input: Detailed information about the affected area

[0160] Output: The applied temporary correction code

[0161] Specific operation: The server generates temporary code to fix the program and applies it to the source code. For example, if a function is vulnerable, it generates code to disable that function.

[0162] Step 7: Implementing mitigation measures

[0163] If a fix is ​​difficult, the server will automatically apply mitigation measures, such as closing specific ports.

[0164] Input: Vulnerability information and detailed information on the scope of impact

[0165] Output: Mitigations applied

[0166] Specific action: The server applies mitigation measures such as modifying firewall rules and disabling certain ports.

[0167] Step 8: Notify users

[0168] If the server cannot resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator.

[0169] Input: Vulnerability information that requires action

[0170] Output: Notification to users and system administrators

[0171] What it does: The server uses a mail server to automatically send email notifications containing vulnerability information that needs to be addressed.

[0172] The above are the specific processing steps of the program of this system.

[0173] (Application example 1)

[0174] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0175] As the use of open source software (OSS) expands, vulnerabilities are discovered more frequently, creating a need for rapid and efficient response to these vulnerabilities. However, traditional methods require manual detection, correction, and system testing, which takes time and effort. Furthermore, when a compatible version does not exist or when mitigation measures need to be applied, it is difficult to take appropriate action. Furthermore, delayed notification to users and system administrators can jeopardize the security of the system.

[0176] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0177] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, and means for notifying the user. This enables automatic detection and correction of vulnerabilities, automatic testing, and user notification. Furthermore, if a fixed version does not exist, means for assessing the extent of the impact on the system, generating and applying temporary patch code, and means for taking mitigation measures for the discovered vulnerability enable a rapid response while maintaining system security. Furthermore, periodic updates based on monitored vulnerability information can be performed to maintain system security.

[0178] A "vulnerability database" is a database that aggregates information on security vulnerabilities in open source software and other software.

[0179] "Means for detecting new vulnerability information" refers to a function that has the ability to monitor the vulnerability database and automatically detect new vulnerability information.

[0180] "Means for checking the existence of a compatible version" is a function that checks whether a software version exists that can fix a detected vulnerability.

[0181] "Means for automatically updating the target software" refers to a function that automatically updates to a version that addresses the detected vulnerability if one is available.

[0182] "Means for executing system tests and evaluating the test results" refers to a function for checking the operation of the entire system after an update and evaluating the results.

[0183] "Means of notifying users" refers to a function that notifies users and system administrators of important information such as vulnerability information, update status, and application of temporary patch code.

[0184] "Means for assessing the scope of impact on the system" is a function for assessing the impact that a vulnerability will have on the entire system when no corresponding version exists.

[0185] The "means for generating and applying temporary correction code" is a function that generates correction code as a temporary measure when a compatible version does not exist and applies it to the system.

[0186] "Mitigation measures" are functions that automatically take measures to minimize the impact of vulnerabilities when they exist.

[0187] "Means of maintaining system security through regular updates" refers to the function of regularly monitoring vulnerability databases and software status, and performing system updates as necessary.

[0188] The present invention provides a system that automatically detects and fixes vulnerabilities in open source software (OSS). This system is primarily managed by a server, monitors a vulnerability database to detect new vulnerability information, automatically updates the software if a corresponding version is available, and performs a system test after the update to confirm normal operation. A specific embodiment of this system is described below.

[0189] System configuration

[0190] The system includes the following equipment and software:

[0191] 1. Vulnerability database monitoring and detection methods:

[0192] The server regularly monitors vulnerability databases such as NVD (National Vulnerability Database) and OSS-Security to detect new vulnerability information.

[0193] The software used is the Python Requests library.

[0194] 2. How to check for the existence of a compatible version:

[0195] For vulnerabilities detected on the server, check whether a corresponding version has been published in the official repository or from the distributor.

[0196] 3. Automatic Update Method:

[0197] If a compatible version is available, the server will automatically update the software to the new version.

[0198] After the update, the server runs a full system test suite to ensure proper operation.

[0199] 4. Impact assessment method:

[0200] If a compatible version does not exist, the server analyzes the extent of the impact on the system.

[0201] 5. How to generate and apply temporary fix code:

[0202] If necessary, the server generates temporary fixes and applies them to the target software.

[0203] 6. Mitigation measures:

[0204] If it is difficult to fix the code, the server will automatically apply mitigation measures such as disabling certain functions or strengthening access control.

[0205] 7. User Notification Methods:

[0206] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator.

[0207] Hardware and software used

[0208] Hardware: Servers (including cloud servers), smartphones

[0209] Software: Python, Requests library, NVD API

[0210] Data processing and calculation

[0211] This system obtains vulnerability information in real time and uses that information to update software or implement temporary fixes. Specifically, it uses the Python Requests library to obtain data from a vulnerability database, analyzes it to confirm the existence of a compatible version, and evaluates the extent of the impact.

[0212] Specific examples

[0213] For example, if the popular OSS library "ExampleLib" has a vulnerability, the server will monitor the library and automatically update the version when a vulnerability is reported. It will then notify the user that "ExampleLib has been updated to version 1.2.4. Your system is now secure."

[0214] Example prompts to input to a generative AI model:

[0215] Please explain the code and its processing for the AutoSecGuard application. The following conditions must be met: it must automatically detect and fix vulnerabilities in OSS, run on smartphones, and notify users as necessary. Please also provide specific code and its explanation.

[0216] The above is a specific embodiment for carrying out the present invention. This system significantly reduces the workload of users and system administrators in dealing with vulnerabilities, and enables the system security to be maintained at an optimum state at all times.

[0217] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0218] Step 1:

[0219] Obtaining vulnerability information

[0220] The server periodically monitors vulnerability databases (such as NVD or OSS-Security) to obtain new vulnerability information. It uses the Python Requests library to retrieve the latest information from the database API. The input is the API endpoint and access key, and the output is vulnerability information in JSON format. The server stores this information in an internal database.

[0221] Step 2:

[0222] Check compatible versions

[0223] The server analyzes the vulnerability information it has obtained and checks whether it is compatible with the target software. It also checks whether a compatible version exists in the official repository. The input is the vulnerability information obtained in the previous step, and the output is a boolean value indicating whether a compatible version exists, and if so, the version number. Tools such as a JSON parser are used for analysis.

[0224] Step 3:

[0225] Performing automatic updates

[0226] The server will perform an automatic update to a supported version if one exists, using shell scripts or specific package managers (e.g., pip, apt) to update the software. The input is the version number of the supported version, and the output is the success status of the update.

[0227] Step 4:

[0228] System test execution and evaluation

[0229] After the update, the server runs all system tests and evaluates the test results. For this, a continuous integration tool (e.g., Jenkins, GitLab CI) is used. The input is the updated software and test scripts, and the output is the test result (pass / fail).

[0230] Step 5:

[0231] Impact assessment

[0232] If a compatible version is not available, the server evaluates the extent of the impact on the system. It uses a static analysis tool (e.g., SonarQube) to analyze which modules and functions the vulnerability affects. The input is vulnerability information and system configuration information, and the output is an impact analysis report.

[0233] Step 6:

[0234] Generate and apply temporary fix code

[0235] If necessary, the server generates temporary fixes to temporarily fix vulnerabilities and applies them to the system using an automatic code generation tool or a scripting language (e.g., Python). The input is the scope of impact and directives on how to fix the vulnerability, and the output is the applied temporary fix.

[0236] Step 7:

[0237] Implementing mitigation measures

[0238] If modifying the code is difficult, the server automatically applies mitigation measures such as disabling specific functions or strengthening access control. It uses configuration change tools and firewall settings (e.g., iptables). The input is the directive for the mitigation to be applied, and the output is the execution status of the mitigation measures.

[0239] Step 8:

[0240] User Notifications

[0241] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator. To do this, it uses a mail server or notification service (e.g., Twilio). The input is the notification content, and the output is the transmission result.

[0242] Through these steps, the system enables rapid and automatic response to vulnerabilities.

[0243] Furthermore, an emotion engine that estimates the user's emotion may be combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0244] An embodiment of the present invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS), and optimizes the response by recognizing user emotions. This system is mainly managed by a server, and notifies and provides feedback to users.

[0245] System configuration

[0246] 1. Vulnerability detection

[0247] The server periodically monitors vulnerability databases (e.g., NVD or OSS-Security) to detect new vulnerability information.

[0248] 2. Check the version that addresses the vulnerability

[0249] Check with the official repository or distributor to see if a fixed version has been released for the vulnerability detected on the server.

[0250] 3. Supported version update

[0251] If a compatible version is available, the server automatically updates the target OSS to the compatible version, then runs a full system test suite to verify that the update has not adversely affected the system.

[0252] 4. Impact assessment

[0253] If a compatible version is not available, the server analyzes the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[0254] 5. Application of the provisional revised code

[0255] The server assesses the scope of the impact and, if necessary, automatically generates temporary fix code and applies it to the affected OSS.

[0256] 6. Implementing mitigation measures

[0257] If remediation is difficult, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0258] 7. Notice to Users

[0259] If the server is unable to resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator and prompt them to take manual action.

[0260] 8. Emotion Engine

[0261] The server uses an emotion engine to recognize and analyze the user's emotions based on the vulnerability response results. For example, it determines the user's emotions (e.g., relief, anxiety, irritation, etc.) based on the user's reaction to the notification content and operation logs.

[0262] 9. Customized notifications and feedback

[0263] The server customizes the notification content and provides appropriate feedback based on the user's emotions analyzed by the emotion engine. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[0264] 10. Building an Emotion Database

[0265] The server accumulates user emotional data and builds it into an emotional database, which makes it possible to utilize past emotional data when responding to future vulnerabilities and provide more effective notifications and support.

[0266] Specific examples

[0267] Example 1: Library vulnerabilities and sentiment analysis

[0268] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that version 1.2.4, which addresses the vulnerability, has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally. Furthermore, the server uses an emotion engine to analyze the user's feelings about the vulnerability response results, and sends a customized notification to alleviate the user's anxiety.

[0269] Example 2: Judging unsupported versions and emotional feedback

[0270] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B. However, since a version that addresses the vulnerability has not yet been released, it checks the scope of the impact. It confirms that the vulnerability is related only to a specific function and temporarily disables that function. At the same time, it generates and applies a temporary patch. Furthermore, the server recognizes the user's emotions and sends a notification containing detailed instructions and support information to provide reassurance.

[0271] The present invention aims not only to automatically ensure the safety of systems when responding to OSS vulnerabilities, but also to improve the user experience by recognizing user emotions and providing appropriate feedback.

[0272] The processing flow will be explained below.

[0273] Step 1:

[0274] The server periodically monitors vulnerability databases (NVD, OSS-Security, etc.) to check whether new vulnerability information has been published.

[0275] Step 2:

[0276] When the server detects new vulnerability information, it obtains detailed information (CVE number, scope of impact, recommended countermeasures, etc.) and checks the version and settings of the OSS used in the system.

[0277] Step 3:

[0278] Check the official repository or distributor to see if there is a version that addresses the vulnerability detected on the server.

[0279] Step 4:

[0280] If a compatible version is available on the server, the target OSS will be automatically updated to the compatible version.

[0281] Step 5:

[0282] After the server is updated, it runs a full system test suite to detect and evaluate any issues that the update may have introduced into the system.

[0283] Step 6:

[0284] The server evaluates the test results and if all are successful, the new version is officially applied to the system. If the tests fail, the update is rolled back and an alert is sent to the user.

[0285] Step 7:

[0286] If the server does not have a compatible version, an analysis is performed to determine the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[0287] Step 8:

[0288] The server identifies the scope of the impact and, if necessary, automatically generates temporary fixes and applies them to the affected OSS. For example, if a vulnerability exists in a function or method, the code in that part will be temporarily disabled.

[0289] Step 9:

[0290] If the server is difficult to fix, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0291] Step 10:

[0292] If the server is severely affected and temporary fixes or mitigation measures are not sufficient, users and system administrators will be notified and prompted to take emergency manual action.

[0293] Step 11:

[0294] The server uses an emotion engine to recognize and analyze the user's emotions based on the vulnerability response results. For example, the server determines the user's emotions based on the user's reaction to the notification content and the operation log.

[0295] Step 12:

[0296] The server customizes the notification content based on the user's emotions analyzed by the emotion engine. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[0297] Step 13:

[0298] The server accumulates user emotional data and builds an emotional database. When responding to future vulnerabilities, past emotional data can be used to provide more effective notifications and support.

[0299] Example 2

[0300] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0301] While traditional open source software (OSS) vulnerability response systems can detect vulnerabilities and automate updates, they lack feedback and notifications that take user emotions into consideration. As a result, users may feel anxious or skeptical about vulnerability response, resulting in a poor user experience. Furthermore, due to a lack of temporary fix code generation when a compatible version does not exist and a lack of notification of appropriate mitigation measures to users, the safety and reliability of the system's operation were not sufficiently ensured.

[0302] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0303] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, emotion engine means for analyzing user emotions, and means for customizing notification content based on the emotion engine to match the user's emotions. This not only automatically ensures the safety of the system when responding to OSS vulnerabilities, but also makes it possible to recognize user emotions and provide appropriate feedback.

[0304] A "vulnerability database" is a database that collects known security vulnerabilities and related information.

[0305] "New vulnerability information" is information about vulnerabilities that have been newly added to the vulnerability database.

[0306] A "compatible version" is a version of software that has been fixed for a specific vulnerability and can be used safely.

[0307] "Software" means technical means including a program running on a computer and its associated data.

[0308] An "update" is the act of updating software to the latest version and fixing vulnerabilities and bugs.

[0309] "System testing" is a set of testing processes and methods used to verify that software works as expected.

[0310] An "emotion engine" is a technical means for analyzing a user's emotions and detecting a specific emotional state.

[0311] "User emotions" refers to the user's psychological reactions and feedback to system notifications and operations.

[0312] "Customizing notification content" refers to the act of changing the content of the notification message depending on the user's emotional state, thereby providing more appropriate information.

[0313] "Temporary fix code" refers to a code change that is temporarily applied before an official fix version is released, in order to temporarily fix a vulnerability.

[0314] "Mitigation" is a set of security measures implemented to minimize the impact of a vulnerability.

[0315] The "emotion database" is a database that stores data on users' emotions and uses it for later analysis and notification improvement.

[0316] "Feedback" refers to information including reactions and opinions from users regarding notifications and operations of the system.

[0317] This invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS), and also recognizes user emotions to optimize the response. This system is mainly managed by a server, and notifies and gives feedback to users.

[0318] System configuration

[0319] 1. Vulnerability detection

[0320] The server periodically scans vulnerability databases (e.g., NVD and OSS-Security) using monitoring tools (e.g., OpenVAS) to detect new vulnerability information. Detected vulnerability information is automatically recorded in a log.

[0321] 2. Check the version that addresses the vulnerability

[0322] For the vulnerability detected by the server, the server uses an API request to check whether a fixed version has been published in the official repository or distribution source (e.g., GitHub, GitLab). If a fixed version is confirmed to exist, the server proceeds to the next processing step based on that information.

[0323] 3. Supported version update

[0324] The server uses automation tools such as Ansible or Chef to update the target OSS to the corresponding version, then runs a full system test suite (e.g., Jenkins, Selenium) to verify that the update has not adversely affected the system.

[0325] 4. Impact assessment

[0326] If a compatible version is not available, the server will use static analysis tools such as SonarQube or dynamic analysis tools such as OWASP ZAP to assess the extent of the impact on the system and identify which functions and modules are affected by the vulnerability.

[0327] 5. Application of the provisional revised code

[0328] The server uses PatchBank and Ghidra to assess the extent of the impact and generates a temporary fix. This temporary fix is ​​then applied to the target OSS, and system testing is performed again to confirm its effectiveness.

[0329] 6. Implementing mitigation measures

[0330] If remediation is difficult, apply mitigation measures such as strengthening the server's firewall settings (e.g., IPTables) or access control (e.g., SELinux).

[0331] 7. Notice to Users

[0332] The server uses Nagios or Grafana to notify users and system administrators of the vulnerability, along with details of potential fixes or mitigation measures, and manual action is required if necessary.

[0333] 8. User sentiment analysis using an emotion engine

[0334] The server uses a natural language processing engine such as the Google Cloud Natural Language API to analyze emotions from the user's operation logs and reactions to notifications, and detects specific emotional states (e.g., anxiety, relief, etc.).

[0335] 9. Customized notifications and feedback

[0336] The server customizes the notification content based on the analysis results of the emotion engine and provides feedback tailored to the user's emotions. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[0337] 10. Building an Emotion Database

[0338] The server collects user emotional data and stores it in an emotional database, which enables the system to utilize past emotional data when responding to future vulnerabilities and provide more effective notifications and support.

[0339] Specific examples

[0340] Example 1: Library vulnerabilities and sentiment analysis

[0341] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS Library A using OpenVAS and confirms via the GitHub API that version 1.2.4 has been released to address the vulnerability. The server updates Library A to the latest version using Ansible and runs a full system test using Jenkins. The server then analyzes user sentiment regarding the vulnerability response results using the Google Cloud Natural Language API and sends a customized notification to reduce anxiety.

[0342] Example prompt sentence:

[0343] "A new vulnerability (CVE-XXXX-YYYY) has been detected in OSS library A. It has been updated to version 1.2.4 and the test results are normal. Please rest assured."

[0344] Example 2: Judging unsupported versions and emotional feedback

[0345] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B using OpenVAS. However, because a fixed version has not yet been released, the server uses SonarQube to confirm the scope of the impact, confirms that it is only related to a specific function, and temporarily disables that function. After generating and applying a temporary fix using PatchBank, the server uses the Google Cloud Natural Language API to analyze user sentiment and sends a notification containing detailed instructions and support information to provide reassurance.

[0346] Example prompt sentence:

[0347] Regarding the vulnerability in Library B (CVE-XXXX-ZZZZ), a fixed version has not yet been released, but we have limited the scope of impact and temporarily disabled certain functions. We will send you a detailed guide, so please check it.

[0348] This system aims to improve the user experience while automatically ensuring the safety of the system by integrating OSS vulnerability response and user emotion management.

[0349] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0350] Step 1:

[0351] Vulnerability detection

[0352] The server periodically scans vulnerability databases (e.g., NVD or OSS-Security) using a monitoring tool (e.g., OpenVAS).

[0353] The server obtains new vulnerability information from the vulnerability database (input: vulnerability database, output: vulnerability information).

[0354] The server automatically logs new vulnerability information.

[0355] Specific behavior:

[0356] The server starts OpenVAS and scans the vulnerability database. If it finds a new vulnerability, it logs it as "CVE-XXXX-YYYY" and proceeds to the next step.

[0357] Step 2:

[0358] Check the version that addresses the vulnerability

[0359] For vulnerabilities detected by the server, an API request is used to check whether a fixed version is available in an official repository (e.g., GitHub, GitLab) or from the distribution source.

[0360] The server checks whether a compatible version exists (input: vulnerability information, output: compatible version information).

[0361] Specific behavior:

[0362] The server sends a request to the GitHub API to get the latest version of Library A. It verifies that the corresponding version 1.2.4 is available.

[0363] Step 3:

[0364] Supported version update

[0365] The server uses automation tools such as Ansible or Chef to update the target OSS to a compatible version.

[0366] The server runs a full system test suite (e.g., Jenkins, Selenium) to ensure the update has not adversely affected the system (input: target software, output: update results).

[0367] Specific behavior:

[0368] The server uses Ansible to update Library A to version 1.2.4, then runs all system tests in Jenkins and checks the test results.

[0369] Step 4:

[0370] Impact assessment

[0371] If a compatible version does not exist, the server will assess the extent of the impact on the system using static analysis tools such as SonarQube or dynamic analysis tools such as OWASP ZAP.

[0372] The server identifies which functions and modules are affected by the vulnerability (input: vulnerability information, output: impact assessment results).

[0373] Specific behavior:

[0374] The server launches SonarQube, which analyzes the code across the entire system, and based on the results of the analysis, identifies the affected modules and logs them.

[0375] Step 5:

[0376] Application of provisional amendment code

[0377] The server uses PatchBank and Ghidra to assess the extent of the impact and generate provisional fix code.

[0378] The server applies the provisional fix code to the target OSS and performs the necessary tests (input: impact assessment results, output: provisional fix code).

[0379] Specific behavior:

[0380] The server uses PatchBank to generate provisional fixes for the identified vulnerabilities, applies the fixes, and re-runs the system tests.

[0381] Step 6:

[0382] Implementing mitigation measures

[0383] If remediation is difficult, apply mitigation measures such as strengthening the server's firewall settings (e.g., IPTables) or access control (e.g., SELinux) (Input: vulnerability information, Output: mitigation measures).

[0384] Specific behavior:

[0385] The server changes the IPTables settings to restrict communication within the scope affected by the vulnerability. Test the changed settings to confirm their effectiveness.

[0386] Step 7:

[0387] User Notification

[0388] The server uses Nagios or Grafana to notify users and system administrators of the vulnerability information, provisional fix code, or details of mitigation measures, requiring manual action if necessary (input: vulnerability response information, output: notification).

[0389] Specific behavior:

[0390] The server will then send an email notification to the system administrator via Nagios, containing the vulnerability detected and recommended manual steps to take.

[0391] Step 8:

[0392] User sentiment analysis using an emotion engine

[0393] The server uses a natural language processing engine such as Google Cloud Natural Language API to analyze emotions from the user's operation log and reactions to notifications (input: user operation log, output: emotion analysis results).

[0394] Specific behavior:

[0395] The server collects user operation logs and performs sentiment analysis via the Google Cloud Natural Language API to determine whether the user is feeling "anxiety."

[0396] Step 9:

[0397] Customized notifications and feedback

[0398] The server customizes the notification content based on the analysis results of the emotion engine and provides feedback tailored to the user's emotions (input: emotion analysis results, output: customized notification).

[0399] Specific behavior:

[0400] The server analyzes the user's emotional data, and based on the user's anxiety, creates detailed guidance to provide reassurance and sends a notification.

[0401] Step 10:

[0402] Building an emotion database

[0403] The server collects user emotional data and stores it in an emotional database. Past emotional data can be used to provide more effective notifications and support when responding to future vulnerabilities (input: emotional data, output: emotional database).

[0404] Specific behavior:

[0405] The server stores the emotion data in a database and updates the emotion database for future analysis and notifications.

[0406] (Application example 2)

[0407] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart device 14 will be referred to as a "terminal."

[0408] Vulnerabilities in open source software (OSS) are a serious problem that threatens the security of the entire system. Conventional methods require manual detection of vulnerabilities and the application of fixed versions, which is extremely time-consuming and laborious. In addition, users often feel uneasy about how to respond to such vulnerabilities, and if they are not properly addressed, it can cause further problems. It is necessary to solve these problems and promote automation and improve the user experience.

[0409] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target open source software if a fixed version exists, means for running a system test after the update and evaluating the test results, and means for recognizing user emotions and optimizing feedback. This enables automatic response to OSS vulnerabilities, reduces user anxiety, and enables optimal feedback to be provided quickly.

[0410] The "Vulnerability Database" is a database for recording and managing information on known vulnerabilities related to open source software (OSS).

[0411] "Vulnerabilities" refer to security flaws or weaknesses that exist in open source software.

[0412] "Corrected Version" means a corrected version of the software provided to address a particular vulnerability.

[0413] "Open source software" refers to software that is free for anyone to use, modify, and distribute.

[0414] "Means for recognizing emotions" refers to technology that analyzes a user's operations and reactions and estimates the user's emotional state.

[0415] "Feedback optimization" refers to technology that provides the most appropriate response or notification content based on the user's emotional state.

[0416] "Means for assessing the scope of impact" refers to technology for analyzing the extent to which a detected vulnerability will affect the entire system.

[0417] "Temporary fix code" is provisional code generated to temporarily fix a vulnerability.

[0418] "Mitigations" are temporary measures to minimize the impact on a system until a vulnerability is fully fixed.

[0419] This invention is a system for automatically managing vulnerabilities in open source software (OSS) and optimizing responses by recognizing user emotions. This system is primarily run on a server and interacts with user devices.

[0420] System configuration

[0421] 1. Vulnerability detection and monitoring

[0422] The server periodically monitors vulnerability databases (e.g., NVD and OSS-Security) to detect new vulnerability information, which includes obtaining and analyzing vulnerability data.

[0423] 2. Check the version that addresses the vulnerability

[0424] When a vulnerability is detected, the server checks the official repository or distributor to see if a compatible version is available.

[0425] 3. Automatic Updates

[0426] If a compatible version is available, the server will automatically update the target open source software. After the update, a system test will be run and the test results will be evaluated.

[0427] 4. Impact assessment

[0428] If a compatible version does not exist, the server assesses the extent of the impact on the system and generates and applies temporary fixes as necessary.

[0429] 5. Emotion Recognition and Feedback

[0430] The server uses emotion recognition technology (EmotionRecognizer) to recognize the user's emotions. It analyzes emotions based on the user's operation log and feedback, and optimizes the feedback.

[0431] 6. Customized Notifications

[0432] The server provides customized notifications and support information based on the user's emotions, which can reduce the user's anxiety and encourage appropriate responses.

[0433] Hardware and Software Examples

[0434] EmotionRecognizer: An emotion recognition module for analyzing user emotions.

[0435] VulnerabilityScanner: A vulnerability scanning tool for scanning OSS vulnerabilities.

[0436] NotificationSystem: The notification system for sending notifications to the user.

[0437] Python: The main programming language for this system.

[0438] Smartphone: The device (Android / iOS) on which the user uses the application.

[0439] Example

[0440] Example 1: The server detects a new vulnerability in an OSS library, and if a fixed version is available, it automatically updates the library and runs a full system test. After confirming that it works properly, it analyzes user sentiment and sends detailed feedback.

[0441] Example 2: If a compatible version is not available, the server assesses the extent of the impact, generates and applies a temporary fix, and if the user is concerned, sends a notification with detailed support information.

[0442] Prompt Sentence Examples

[0443] "Create a prototype of an application that detects OSS vulnerabilities and provides appropriate feedback by analyzing user emotions. Specifically, use Python and the EmotionRecognizer, VulnerabilityScanner, and NotificationSystem modules."

[0444] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0445] Step 1:

[0446] The server monitors vulnerability databases (e.g., NVD or OSS-Security) to detect new vulnerability information. As input, it uses the latest vulnerability information obtained from the vulnerability database. This is used to detect whether a vulnerability exists in the system. To obtain data, it periodically accesses the database via a REST API. As output, it obtains newly detected vulnerability information.

[0447] Step 2:

[0448] The server checks official repositories or distribution sources to see if a fixed version exists for the detected vulnerability. The vulnerability information obtained in step 1 is used as input. In this step, the server obtains information about the fixed version through web scraping or API calls. The output is the presence or absence of a fixed version.

[0449] Step 3:

[0450] The server automatically updates the target open source software if a compatible version exists. As input, it uses the results of step 2 and the URL of the compatible version. In this step, it executes the software update using a package management tool (e.g., pip or npm). As output, it reports that the update was successful.

[0451] Step 4:

[0452] After the update, the server runs the system tests and evaluates the test results. It uses the latest software state and test scripts as input. In this step, it runs the system tests using an automated testing tool (e.g., JUnit or pytest). As output, it obtains a log of the test results and a verdict.

[0453] Step 5:

[0454] If a compatible version is not available, the server evaluates the extent of the impact on the system. It uses the vulnerability information from step 1 as input. In this step, it uses a program analysis tool to identify the modules and functions affected by the vulnerability. It obtains information on the extent of the impact as output.

[0455] Step 6:

[0456] The server automatically generates and applies a temporary fix based on the impact range. The impact range information from step 5 is used as input. In this step, a program fix tool is used to generate a temporary fix and apply it to the system. The output is the state in which the temporary fix has been applied.

[0457] Step 7:

[0458] The server analyzes emotions based on the user's operation log and feedback. The user's operation log and feedback information are used as input. In this step, emotion recognition technology (EmotionRecognizer) is used to determine the user's emotions. The output is the user's emotional information.

[0459] Step 8:

[0460] The server provides customized notifications and support information based on the user's emotions. As input, it uses the user's emotion information obtained in step 7. In this step, it sends appropriate feedback and support information to the user through the notification system. As output, a notification is sent to the user's device.

[0461] 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.

[0462] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0463] In the above embodiment, an example in which the specific process is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific process may be performed by the smart device 14.

[0464] [Second embodiment]

[0465] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0466] 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.

[0467] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0468] 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.

[0469] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0470] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0471] 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.

[0472] 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.

[0473] 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 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.

[0474] 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.

[0475] In the smart glasses 214, the reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0476] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal."

[0477] An embodiment of the present invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS) to maintain system security at all times. This system is primarily managed by a server, and notifies users and system administrators.

[0478] System configuration

[0479] 1. Vulnerability detection

[0480] The server periodically monitors vulnerability databases to detect new vulnerability information. For example, it checks databases such as the National Vulnerability Database (NVD) and OSS-Security once a day.

[0481] 2. Check the version that addresses the vulnerability

[0482] For vulnerabilities detected by the server, check the official OSS repository or distributor to determine whether a corresponding version has been released.

[0483] 3. Supported version update

[0484] If a compatible version is available, the server automatically updates the target OSS to the new version, running a test suite for the entire system to verify that the system operates normally after the update.

[0485] 4. Impact assessment

[0486] If a compatible version is not available, the server analyzes the extent of the impact on the system, for example, assessing whether the vulnerability is limited to a specific module or function.

[0487] 5. Application of the provisional revised code

[0488] If necessary, the server will automatically generate temporary fixes and apply them to the affected OSS after assessing the scope of the impact. For example, if the vulnerability exists within a function, it will take temporary measures such as disabling that function.

[0489] 6. Implementing mitigation measures

[0490] If remediation is difficult, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0491] 7. Notice to Users

[0492] If the server cannot resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator, prompting them to take manual action.

[0493] Specific examples

[0494] Example 1: Library vulnerability

[0495] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that version 1.2.4, which addresses the vulnerability, has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally.

[0496] Example 2: Determining unsupported versions

[0497] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B. However, a version that addresses the vulnerability has not yet been released, so the scope of the impact is confirmed. It is confirmed that the vulnerability is related only to a specific function, and that function is temporarily disabled. At the same time, a temporary patch code is generated and applied, maintaining the security of the system until the next update.

[0498] To effectively implement this invention, it is important to regularly monitor the vulnerability database and perform automated tests on the entire system. This system significantly reduces the workload of users and system administrators in dealing with vulnerabilities and allows the system security to be maintained at an optimal state at all times.

[0499] The processing flow will be explained below.

[0500] Step 1:

[0501] The server periodically monitors vulnerability databases (NVD, OSS-Security, etc.) to check whether new vulnerability information has been published.

[0502] Step 2:

[0503] When the server detects new vulnerability information, it obtains detailed information (CVE number, scope of impact, recommended countermeasures, etc.) and checks the version and settings of the OSS used in the system.

[0504] Step 3:

[0505] Check the official repository or distributor to see if there is a version that addresses the vulnerability detected on the server.

[0506] Step 4:

[0507] If a compatible version is available on the server, the target OSS will be automatically updated to the compatible version.

[0508] Step 5:

[0509] After the server is updated, it runs a full system test suite to detect and evaluate any issues that the update may have introduced into the system.

[0510] Step 6:

[0511] The server evaluates the test results and if all are successful, the new version is officially applied to the system. If the tests fail, the update is rolled back and an alert is sent to the user.

[0512] Step 7:

[0513] If the server does not have a compatible version, an analysis is performed to determine the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[0514] Step 8:

[0515] The server identifies the scope of the impact and, if necessary, automatically generates temporary fixes and applies them to the affected OSS. For example, if a vulnerability exists in a function or method, the code in that part will be temporarily disabled.

[0516] Step 9:

[0517] If the server is difficult to fix, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0518] Step 10:

[0519] If the server is severely affected and temporary fixes or mitigation measures are not sufficient, users and system administrators will be notified and prompted to take emergency manual action.

[0520] Step 11:

[0521] The server periodically checks for the release of subsequent versions that address vulnerabilities, and if one is released, automatically performs the update again.

[0522] Step 12:

[0523] The server periodically reassess the scope of impact and the effectiveness of provisional fixes, and implements new countermeasures as necessary.

[0524] Example 1

[0525] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0526] The use of open source software (OSS) is increasing in modern software systems. However, vulnerabilities in OSS can pose a serious threat to the security of the entire system. To address this issue, it is necessary to quickly detect and respond to vulnerabilities, but many system administrators perform this process manually, which makes it difficult and time-consuming to respond quickly. Furthermore, if a compatible version does not exist, it can be difficult to assess the scope of the impact and generate and apply temporary fixes.

[0527] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0528] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, means for automatically analyzing the scope of impact if a fixed version does not exist, and means for generating and applying temporary fix code based on the results of the impact scope evaluation. This enables quick and automatic detection and response to vulnerabilities, reduces the burden on system administrators, and improves system security.

[0529] A "vulnerability database" is a database that collects, stores, and provides information about known software vulnerabilities.

[0530] "Vulnerability information" is information about potential security risks and problems that exist in software.

[0531] A "compatible version" is a new software version that fixes a specific vulnerability.

[0532] "Software" generally refers to programs that run on a computer system and associated data.

[0533] An "update" is the act of installing a new version of the software and improving the existing version.

[0534] "System testing" is a general term for tests conducted to check the operation and functionality of the entire system.

[0535] "Scope of impact" refers to the extent to which a particular vulnerability affects the entire system or some of its functions.

[0536] "Analysis" is the process of examining data or information in detail to arrive at an understanding or conclusion.

[0537] "Temporary fix code" is program code for temporarily fixing a vulnerability.

[0538] "Mitigation" is an alternative or measure to minimize impact when direct correction is difficult.

[0539] "Notification" is the act or means of communicating specific information to another person.

[0540] The system of this invention maintains the security of systems by automatically detecting, correcting, and mitigating vulnerabilities in open source software (OSS). This system is primarily managed by a server, and notifies users and system administrators in real time.

[0541] The system uses the following hardware and software:

[0542] Hardware: Server with high-performance computing resources

[0543] Software: Vulnerability databases (e.g., National Vulnerability Database, OSS-Security), CI / CD tools (e.g., Jenkins), test frameworks (e.g., JUnit), source code static analysis tools (e.g., SonarQube), vulnerability management tools (e.g., curl, firewall configuration tools)

[0544] The server works as follows:

[0545] 1. Vulnerability database monitoring

[0546] The server uses the curl command to retrieve the latest vulnerability information from the vulnerability database once a day, and stores this information in a local database.

[0547] 2. Vulnerability Information Detection

[0548] The server analyzes the vulnerability information and identifies vulnerabilities related to specific OSS. In the process, it extracts CVE (Common Vulnerabilities and Exposures) numbers and associates them with each OSS version.

[0549] 3. Check the compatible version

[0550] The server checks whether a new version that addresses the detected vulnerability has been released in the official repository or from the distribution source, using a GitHub API request.

[0551] 4. Update and test compatible versions

[0552] If a compatible version is available, the server will automatically update the OSS using the apt-get upgrade or yum update command, then run a full system test using a test framework such as JUnit to ensure proper operation.

[0553] 5. Impact assessment

[0554] If a compatible version does not exist, the server will use a source code static analysis tool (e.g., SonarQube) to analyze the scope of the vulnerability.

[0555] 6. Generate and apply temporary correction code

[0556] Based on the results of the impact assessment, the server generates provisional fixes and applies them to the source code. For example, if a vulnerability exists in a function, it generates code to disable that function.

[0557] 7. Implementing mitigation measures

[0558] If remediation is difficult, the server will apply mitigation measures to minimize the impact on the system, such as changing firewall rules to disable certain ports.

[0559] 8. Notice to Users

[0560] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator. The notification is sent via a mail server.

[0561] Specific examples

[0562] Example 1: Library vulnerability

[0563] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that the corresponding version 1.2.4 has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally.

[0564] Example 2: Determining unsupported versions

[0565] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library, Library B. However, since a fixed version has not yet been released, the server checks the scope of the impact. It verifies that the vulnerability is related only to a specific function and temporarily disables that function. At the same time, it generates and applies a temporary fix, maintaining the security of the system until the next update.

[0566] Prompt Sentence Examples

[0567] Please explain the program for the system that automatically detects and fixes OSS vulnerabilities. In particular, I would like to know how to monitor the vulnerability database and subsequently check the supported versions.

[0568] Please explain in detail how your system retrieves new information from vulnerability databases, identifies vulnerabilities, and then fixes or provisionally addresses them, including what software and tools you use.

[0569] By introducing this system, users and system administrators can significantly reduce the time and effort required to respond to vulnerabilities and maintain the security of their systems.

[0570] The flow of the identification process in the first embodiment will be described with reference to FIG.

[0571] Specific processing steps

[0572] Step 1: Monitor the vulnerability database

[0573] The server uses the curl command to monitor the vulnerability database. Once a day, the server retrieves the latest vulnerability information from NVD and OSS-Security and stores it in a local database.

[0574] Input: NVD or OSS-Security API endpoint

[0575] Output: Data containing the latest vulnerability information

[0576] Specific operation: The server executes the curl command to retrieve data from the API, parses the data, and stores it in a local database.

[0577] Step 2: Detecting vulnerability information

[0578] The server analyzes the vulnerability information stored in the local database to identify new vulnerabilities, which are then associated with the appropriate software.

[0579] Input: Vulnerability information in the local database

[0580] Output: Identified vulnerability information and related software

[0581] What it does: The server queries a local database, extracts new CVE numbers, and links them to the associated software versions.

[0582] Step 3: Check the supported version

[0583] The server checks whether a patch version exists for the detected vulnerability, and sends an API request to the official repository or distributor to obtain the latest release information.

[0584] Input: Identified vulnerability information and associated software

[0585] Output: Supported versions and detailed information

[0586] Specific operation: The server makes an API request to the GitHub API or the official website of each OSS and checks the release information of the corresponding version.

[0587] Step 4: Update and test compatible versions

[0588] The server will automatically update the software to the new version if one is available, then run system-wide tests using a testing framework such as JUnit and evaluate the results.

[0589] Input: Release information and software for the supported version

[0590] Output: Updated software and test results

[0591] Specific operation: The server executes the apt-get upgrade or yum update command to install the new version, and then runs a system-wide test using JUnit to verify normal operation after the installation.

[0592] Step 5: Assess the impact

[0593] If a supported version is not available, the server analyzes the scope of the vulnerability by using a source code static analysis tool (e.g., SonarQube) to identify which parts of the code are affected by the vulnerability.

[0594] Input: Identified vulnerability information and software source code

[0595] Output: Detailed information on the affected area

[0596] Specific operation: The server runs a source code static analysis tool to analyze which part of the code the vulnerability affects.

[0597] Step 6: Generate and apply temporary fix code

[0598] Based on the assessment results of the extent of the impact, the server automatically generates provisional fix code and applies it to the software.

[0599] Input: Detailed information about the affected area

[0600] Output: The applied temporary correction code

[0601] Specific operation: The server generates temporary code to fix the program and applies it to the source code. For example, if a function is vulnerable, it generates code to disable that function.

[0602] Step 7: Implementing mitigation measures

[0603] If a fix is ​​difficult, the server will automatically apply mitigation measures, such as closing specific ports.

[0604] Input: Vulnerability information and detailed information on the scope of impact

[0605] Output: Mitigations applied

[0606] Specific action: The server applies mitigation measures such as modifying firewall rules and disabling certain ports.

[0607] Step 8: Notify users

[0608] If the server cannot resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator.

[0609] Input: Vulnerability information that requires action

[0610] Output: Notification to users and system administrators

[0611] What it does: The server uses a mail server to automatically send email notifications containing vulnerability information that needs to be addressed.

[0612] The above are the specific processing steps of the program of this system.

[0613] (Application example 1)

[0614] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0615] As the use of open source software (OSS) expands, vulnerabilities are discovered more frequently, creating a need for rapid and efficient response to these vulnerabilities. However, traditional methods require manual detection, correction, and system testing, which takes time and effort. Furthermore, when a compatible version does not exist or when mitigation measures need to be applied, it is difficult to take appropriate action. Furthermore, delayed notification to users and system administrators can jeopardize the security of the system.

[0616] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[0617] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, and means for notifying the user. This enables automatic detection and correction of vulnerabilities, automatic testing, and user notification. Furthermore, if a fixed version does not exist, means for assessing the extent of the impact on the system, generating and applying temporary patch code, and means for taking mitigation measures for the discovered vulnerability enable a rapid response while maintaining system security. Furthermore, periodic updates based on monitored vulnerability information can be performed to maintain system security.

[0618] A "vulnerability database" is a database that aggregates information on security vulnerabilities in open source software and other software.

[0619] "Means for detecting new vulnerability information" refers to a function that has the ability to monitor the vulnerability database and automatically detect new vulnerability information.

[0620] "Means for checking the existence of a compatible version" is a function that checks whether a software version exists that can fix a detected vulnerability.

[0621] "Means for automatically updating the target software" refers to a function that automatically updates to a version that addresses the detected vulnerability if one is available.

[0622] "Means for executing system tests and evaluating the test results" refers to a function for checking the operation of the entire system after an update and evaluating the results.

[0623] "Means of notifying users" refers to a function that notifies users and system administrators of important information such as vulnerability information, update status, and application of temporary patch code.

[0624] "Means for assessing the scope of impact on the system" is a function for assessing the impact that a vulnerability will have on the entire system when no corresponding version exists.

[0625] The "means for generating and applying temporary correction code" is a function that generates correction code as a temporary measure when a compatible version does not exist and applies it to the system.

[0626] "Mitigation measures" are functions that automatically take measures to minimize the impact of vulnerabilities when they exist.

[0627] "Means of maintaining system security through regular updates" refers to the function of regularly monitoring vulnerability databases and software status, and performing system updates as necessary.

[0628] The present invention provides a system that automatically detects and fixes vulnerabilities in open source software (OSS). This system is primarily managed by a server, monitors a vulnerability database to detect new vulnerability information, automatically updates the software if a corresponding version is available, and performs a system test after the update to confirm normal operation. A specific embodiment of this system is described below.

[0629] System configuration

[0630] The system includes the following equipment and software:

[0631] 1. Vulnerability database monitoring and detection methods:

[0632] The server regularly monitors vulnerability databases such as NVD (National Vulnerability Database) and OSS-Security to detect new vulnerability information.

[0633] The software used is the Python Requests library.

[0634] 2. How to check for the existence of a compatible version:

[0635] For vulnerabilities detected on the server, check whether a corresponding version has been published in the official repository or from the distributor.

[0636] 3. Automatic Update Method:

[0637] If a compatible version is available, the server will automatically update the software to the new version.

[0638] After the update, the server runs a full system test suite to ensure proper operation.

[0639] 4. Impact assessment method:

[0640] If a compatible version does not exist, the server analyzes the extent of the impact on the system.

[0641] 5. How to generate and apply temporary fix code:

[0642] If necessary, the server generates temporary fixes and applies them to the target software.

[0643] 6. Mitigation measures:

[0644] If it is difficult to fix the code, the server will automatically apply mitigation measures such as disabling certain functions or strengthening access control.

[0645] 7. User Notification Methods:

[0646] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator.

[0647] Hardware and software used

[0648] Hardware: Servers (including cloud servers), smartphones

[0649] Software: Python, Requests library, NVD API

[0650] Data processing and calculation

[0651] This system obtains vulnerability information in real time and uses that information to update software or implement temporary fixes. Specifically, it uses the Python Requests library to obtain data from a vulnerability database, analyzes it to confirm the existence of a compatible version, and evaluates the extent of the impact.

[0652] Specific examples

[0653] For example, if the popular OSS library "ExampleLib" has a vulnerability, the server will monitor the library and automatically update the version when a vulnerability is reported. It will then notify the user that "ExampleLib has been updated to version 1.2.4. Your system is now secure."

[0654] Example prompts to input to a generative AI model:

[0655] Please explain the code and its processing for the AutoSecGuard application. The following conditions must be met: it must automatically detect and fix vulnerabilities in OSS, run on smartphones, and notify users as necessary. Please also provide specific code and its explanation.

[0656] The above is a specific embodiment for carrying out the present invention. This system significantly reduces the workload of users and system administrators in dealing with vulnerabilities, and enables the system security to be maintained at an optimum state at all times.

[0657] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[0658] Step 1:

[0659] Obtaining vulnerability information

[0660] The server periodically monitors vulnerability databases (such as NVD or OSS-Security) to obtain new vulnerability information. It uses the Python Requests library to retrieve the latest information from the database API. The input is the API endpoint and access key, and the output is vulnerability information in JSON format. The server stores this information in an internal database.

[0661] Step 2:

[0662] Check compatible versions

[0663] The server analyzes the vulnerability information it has obtained and checks whether it is compatible with the target software. It also checks whether a compatible version exists in the official repository. The input is the vulnerability information obtained in the previous step, and the output is a boolean value indicating whether a compatible version exists, and if so, the version number. Tools such as a JSON parser are used for analysis.

[0664] Step 3:

[0665] Performing automatic updates

[0666] The server will perform an automatic update to a supported version if one exists, using shell scripts or specific package managers (e.g., pip, apt) to update the software. The input is the version number of the supported version, and the output is the success status of the update.

[0667] Step 4:

[0668] System test execution and evaluation

[0669] After the update, the server runs all system tests and evaluates the test results. For this, a continuous integration tool (e.g., Jenkins, GitLab CI) is used. The input is the updated software and test scripts, and the output is the test result (pass / fail).

[0670] Step 5:

[0671] Impact assessment

[0672] If a compatible version is not available, the server evaluates the extent of the impact on the system. It uses a static analysis tool (e.g., SonarQube) to analyze which modules and functions the vulnerability affects. The input is vulnerability information and system configuration information, and the output is an impact analysis report.

[0673] Step 6:

[0674] Generate and apply temporary fix code

[0675] If necessary, the server generates temporary fixes to temporarily fix vulnerabilities and applies them to the system using an automatic code generation tool or a scripting language (e.g., Python). The input is the scope of impact and directives on how to fix the vulnerability, and the output is the applied temporary fix.

[0676] Step 7:

[0677] Implementing mitigation measures

[0678] If modifying the code is difficult, the server automatically applies mitigation measures such as disabling specific functions or strengthening access control. It uses configuration change tools and firewall settings (e.g., iptables). The input is the directive for the mitigation to be applied, and the output is the execution status of the mitigation measures.

[0679] Step 8:

[0680] User Notifications

[0681] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator. To do this, it uses a mail server or notification service (e.g., Twilio). The input is the notification content, and the output is the transmission result.

[0682] Through these steps, the system enables rapid and automatic response to vulnerabilities.

[0683] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[0684] An embodiment of the present invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS), and optimizes the response by recognizing user emotions. This system is mainly managed by a server, and notifies and provides feedback to users.

[0685] System configuration

[0686] 1. Vulnerability detection

[0687] The server periodically monitors vulnerability databases (e.g., NVD or OSS-Security) to detect new vulnerability information.

[0688] 2. Check the version that addresses the vulnerability

[0689] Check with the official repository or distributor to see if a fixed version has been released for the vulnerability detected on the server.

[0690] 3. Supported version update

[0691] If a compatible version is available, the server automatically updates the target OSS to the compatible version, then runs a full system test suite to verify that the update has not adversely affected the system.

[0692] 4. Impact assessment

[0693] If a compatible version is not available, the server analyzes the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[0694] 5. Application of the provisional revised code

[0695] The server assesses the scope of the impact and, if necessary, automatically generates temporary fix code and applies it to the affected OSS.

[0696] 6. Implementing mitigation measures

[0697] If remediation is difficult, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0698] 7. Notice to Users

[0699] If the server is unable to resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator and prompt them to take manual action.

[0700] 8. Emotion Engine

[0701] The server uses an emotion engine to recognize and analyze the user's emotions based on the vulnerability response results. For example, it determines the user's emotions (e.g., relief, anxiety, irritation, etc.) based on the user's reaction to the notification content and operation logs.

[0702] 9. Customized notifications and feedback

[0703] The server customizes the notification content and provides appropriate feedback based on the user's emotions analyzed by the emotion engine. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[0704] 10. Building an Emotion Database

[0705] The server accumulates user emotional data and builds it into an emotional database, which makes it possible to utilize past emotional data when responding to future vulnerabilities and provide more effective notifications and support.

[0706] Specific examples

[0707] Example 1: Library vulnerabilities and sentiment analysis

[0708] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that version 1.2.4, which addresses the vulnerability, has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally. Furthermore, the server uses an emotion engine to analyze the user's feelings about the vulnerability response results, and sends a customized notification to alleviate the user's anxiety.

[0709] Example 2: Judging unsupported versions and emotional feedback

[0710] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B. However, since a version that addresses the vulnerability has not yet been released, it checks the scope of the impact. It confirms that the vulnerability is related only to a specific function and temporarily disables that function. At the same time, it generates and applies a temporary patch. Furthermore, the server recognizes the user's emotions and sends a notification containing detailed instructions and support information to provide reassurance.

[0711] The present invention aims not only to automatically ensure the safety of systems when responding to OSS vulnerabilities, but also to improve the user experience by recognizing user emotions and providing appropriate feedback.

[0712] The processing flow will be explained below.

[0713] Step 1:

[0714] The server periodically monitors vulnerability databases (NVD, OSS-Security, etc.) to check whether new vulnerability information has been published.

[0715] Step 2:

[0716] When the server detects new vulnerability information, it obtains detailed information (CVE number, scope of impact, recommended countermeasures, etc.) and checks the version and settings of the OSS used in the system.

[0717] Step 3:

[0718] Check the official repository or distributor to see if there is a version that addresses the vulnerability detected on the server.

[0719] Step 4:

[0720] If a compatible version is available on the server, the target OSS will be automatically updated to the compatible version.

[0721] Step 5:

[0722] After the server is updated, it runs a full system test suite to detect and evaluate any issues that the update may have introduced into the system.

[0723] Step 6:

[0724] The server evaluates the test results and if all are successful, the new version is officially applied to the system. If the tests fail, the update is rolled back and an alert is sent to the user.

[0725] Step 7:

[0726] If the server does not have a compatible version, an analysis is performed to determine the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[0727] Step 8:

[0728] The server identifies the scope of the impact and, if necessary, automatically generates temporary fixes and applies them to the affected OSS. For example, if a vulnerability exists in a function or method, the code in that part will be temporarily disabled.

[0729] Step 9:

[0730] If the server is difficult to fix, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0731] Step 10:

[0732] If the server is severely affected and temporary fixes or mitigation measures are not sufficient, users and system administrators will be notified and prompted to take emergency manual action.

[0733] Step 11:

[0734] The server uses an emotion engine to recognize and analyze the user's emotions based on the vulnerability response results. For example, the server determines the user's emotions based on the user's reaction to the notification content and the operation log.

[0735] Step 12:

[0736] The server customizes the notification content based on the user's emotions analyzed by the emotion engine. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[0737] Step 13:

[0738] The server accumulates user emotional data and builds an emotional database. When responding to future vulnerabilities, past emotional data can be used to provide more effective notifications and support.

[0739] Example 2

[0740] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0741] While traditional open source software (OSS) vulnerability response systems can detect vulnerabilities and automate updates, they lack feedback and notifications that take user emotions into consideration. As a result, users may feel anxious or skeptical about vulnerability response, resulting in a poor user experience. Furthermore, due to a lack of temporary fix code generation when a compatible version does not exist and a lack of notification of appropriate mitigation measures to users, the safety and reliability of the system's operation were not sufficiently ensured.

[0742] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[0743] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, emotion engine means for analyzing user emotions, and means for customizing notification content based on the emotion engine to match the user's emotions. This not only automatically ensures the safety of the system when responding to OSS vulnerabilities, but also makes it possible to recognize user emotions and provide appropriate feedback.

[0744] A "vulnerability database" is a database that collects known security vulnerabilities and related information.

[0745] "New vulnerability information" is information about vulnerabilities that have been newly added to the vulnerability database.

[0746] A "compatible version" is a version of software that has been fixed for a specific vulnerability and can be used safely.

[0747] "Software" means technical means including a program running on a computer and its associated data.

[0748] An "update" is the act of updating software to the latest version and fixing vulnerabilities and bugs.

[0749] "System testing" is a set of testing processes and methods used to verify that software works as expected.

[0750] An "emotion engine" is a technical means for analyzing a user's emotions and detecting a specific emotional state.

[0751] "User emotions" refers to the user's psychological reactions and feedback to system notifications and operations.

[0752] "Customizing notification content" refers to the act of changing the content of the notification message depending on the user's emotional state, thereby providing more appropriate information.

[0753] "Temporary fix code" refers to a code change that is temporarily applied before an official fix version is released, in order to temporarily fix a vulnerability.

[0754] "Mitigation" is a set of security measures implemented to minimize the impact of a vulnerability.

[0755] The "emotion database" is a database that stores data on users' emotions and uses it for later analysis and notification improvement.

[0756] "Feedback" refers to information including reactions and opinions from users regarding notifications and operations of the system.

[0757] This invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS), and also recognizes user emotions to optimize the response. This system is mainly managed by a server, and notifies and gives feedback to users.

[0758] System configuration

[0759] 1. Vulnerability detection

[0760] The server periodically scans vulnerability databases (e.g., NVD and OSS-Security) using monitoring tools (e.g., OpenVAS) to detect new vulnerability information. Detected vulnerability information is automatically recorded in a log.

[0761] 2. Check the version that addresses the vulnerability

[0762] For the vulnerability detected by the server, the server uses an API request to check whether a fixed version has been published in the official repository or distribution source (e.g., GitHub, GitLab). If a fixed version is confirmed to exist, the server proceeds to the next processing step based on that information.

[0763] 3. Supported version update

[0764] The server uses automation tools such as Ansible or Chef to update the target OSS to the corresponding version, then runs a full system test suite (e.g., Jenkins, Selenium) to verify that the update has not adversely affected the system.

[0765] 4. Impact assessment

[0766] If a compatible version is not available, the server will use static analysis tools such as SonarQube or dynamic analysis tools such as OWASP ZAP to assess the extent of the impact on the system and identify which functions and modules are affected by the vulnerability.

[0767] 5. Application of the provisional revised code

[0768] The server uses PatchBank and Ghidra to assess the extent of the impact and generates a temporary fix. This temporary fix is ​​then applied to the target OSS, and system testing is performed again to confirm its effectiveness.

[0769] 6. Implementing mitigation measures

[0770] If remediation is difficult, apply mitigation measures such as strengthening the server's firewall settings (e.g., IPTables) or access control (e.g., SELinux).

[0771] 7. Notice to Users

[0772] The server uses Nagios or Grafana to notify users and system administrators of the vulnerability, along with details of potential fixes or mitigation measures, and manual action is required if necessary.

[0773] 8. User sentiment analysis using an emotion engine

[0774] The server uses a natural language processing engine such as the Google Cloud Natural Language API to analyze emotions from the user's operation logs and reactions to notifications, and detects specific emotional states (e.g., anxiety, relief, etc.).

[0775] 9. Customized notifications and feedback

[0776] The server customizes the notification content based on the analysis results of the emotion engine and provides feedback tailored to the user's emotions. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[0777] 10. Building an Emotion Database

[0778] The server collects user emotional data and stores it in an emotional database, which enables the system to utilize past emotional data when responding to future vulnerabilities and provide more effective notifications and support.

[0779] Specific examples

[0780] Example 1: Library vulnerabilities and sentiment analysis

[0781] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS Library A using OpenVAS and confirms via the GitHub API that version 1.2.4 has been released to address the vulnerability. The server updates Library A to the latest version using Ansible and runs a full system test using Jenkins. The server then analyzes user sentiment regarding the vulnerability response results using the Google Cloud Natural Language API and sends a customized notification to reduce anxiety.

[0782] Example prompt sentence:

[0783] "A new vulnerability (CVE-XXXX-YYYY) has been detected in OSS library A. It has been updated to version 1.2.4 and the test results are normal. Please rest assured."

[0784] Example 2: Judging unsupported versions and emotional feedback

[0785] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B using OpenVAS. However, because a fixed version has not yet been released, the server uses SonarQube to confirm the scope of the impact, confirms that it is only related to a specific function, and temporarily disables that function. After generating and applying a temporary fix using PatchBank, the server uses the Google Cloud Natural Language API to analyze user sentiment and sends a notification containing detailed instructions and support information to provide reassurance.

[0786] Example prompt sentence:

[0787] Regarding the vulnerability in Library B (CVE-XXXX-ZZZZ), a fixed version has not yet been released, but we have limited the scope of impact and temporarily disabled certain functions. We will send you a detailed guide, so please check it.

[0788] This system aims to improve the user experience while automatically ensuring the safety of the system by integrating OSS vulnerability response and user emotion management.

[0789] The flow of the identification process in the second embodiment will be described with reference to FIG.

[0790] Step 1:

[0791] Vulnerability detection

[0792] The server periodically scans vulnerability databases (e.g., NVD or OSS-Security) using a monitoring tool (e.g., OpenVAS).

[0793] The server obtains new vulnerability information from the vulnerability database (input: vulnerability database, output: vulnerability information).

[0794] The server automatically logs new vulnerability information.

[0795] Specific behavior:

[0796] The server starts OpenVAS and scans the vulnerability database. If it finds a new vulnerability, it logs it as "CVE-XXXX-YYYY" and proceeds to the next step.

[0797] Step 2:

[0798] Check the version that addresses the vulnerability

[0799] For vulnerabilities detected by the server, an API request is used to check whether a fixed version is available in an official repository (e.g., GitHub, GitLab) or from the distribution source.

[0800] The server checks whether a compatible version exists (input: vulnerability information, output: compatible version information).

[0801] Specific behavior:

[0802] The server sends a request to the GitHub API to get the latest version of Library A. It verifies that the corresponding version 1.2.4 is available.

[0803] Step 3:

[0804] Supported version update

[0805] The server uses automation tools such as Ansible or Chef to update the target OSS to a compatible version.

[0806] The server runs a full system test suite (e.g., Jenkins, Selenium) to ensure the update has not adversely affected the system (input: target software, output: update results).

[0807] Specific behavior:

[0808] The server uses Ansible to update Library A to version 1.2.4, then runs all system tests in Jenkins and checks the test results.

[0809] Step 4:

[0810] Impact assessment

[0811] If a compatible version does not exist, the server will assess the extent of the impact on the system using static analysis tools such as SonarQube or dynamic analysis tools such as OWASP ZAP.

[0812] The server identifies which functions and modules are affected by the vulnerability (input: vulnerability information, output: impact assessment results).

[0813] Specific behavior:

[0814] The server launches SonarQube, which analyzes the code across the entire system, and based on the results of the analysis, identifies the affected modules and logs them.

[0815] Step 5:

[0816] Application of provisional amendment code

[0817] The server uses PatchBank and Ghidra to assess the extent of the impact and generate provisional fix code.

[0818] The server applies the provisional fix code to the target OSS and performs the necessary tests (input: impact assessment results, output: provisional fix code).

[0819] Specific behavior:

[0820] The server uses PatchBank to generate provisional fixes for the identified vulnerabilities, applies the fixes, and re-runs the system tests.

[0821] Step 6:

[0822] Implementing mitigation measures

[0823] If remediation is difficult, apply mitigation measures such as strengthening the server's firewall settings (e.g., IPTables) or access control (e.g., SELinux) (Input: vulnerability information, Output: mitigation measures).

[0824] Specific behavior:

[0825] The server changes the IPTables settings to restrict communication within the scope affected by the vulnerability. Test the changed settings to confirm their effectiveness.

[0826] Step 7:

[0827] User Notification

[0828] The server uses Nagios or Grafana to notify users and system administrators of the vulnerability information, provisional fix code, or details of mitigation measures, requiring manual action if necessary (input: vulnerability response information, output: notification).

[0829] Specific behavior:

[0830] The server will then send an email notification to the system administrator via Nagios, containing the vulnerability detected and recommended manual steps to take.

[0831] Step 8:

[0832] User sentiment analysis using an emotion engine

[0833] The server uses a natural language processing engine such as Google Cloud Natural Language API to analyze emotions from the user's operation log and reactions to notifications (input: user operation log, output: emotion analysis results).

[0834] Specific behavior:

[0835] The server collects user operation logs and performs sentiment analysis via the Google Cloud Natural Language API to determine whether the user is feeling "anxiety."

[0836] Step 9:

[0837] Customized notifications and feedback

[0838] The server customizes the notification content based on the analysis results of the emotion engine and provides feedback tailored to the user's emotions (input: emotion analysis results, output: customized notification).

[0839] Specific behavior:

[0840] The server analyzes the user's emotional data, and based on the user's anxiety, creates detailed guidance to provide reassurance and sends a notification.

[0841] Step 10:

[0842] Building an emotion database

[0843] The server collects user emotional data and stores it in an emotional database. Past emotional data can be used to provide more effective notifications and support when responding to future vulnerabilities (input: emotional data, output: emotional database).

[0844] Specific behavior:

[0845] The server stores the emotion data in a database and updates the emotion database for future analysis and notifications.

[0846] (Application example 2)

[0847] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the smart glasses 214 will be referred to as a "terminal."

[0848] Vulnerabilities in open source software (OSS) are a serious problem that threatens the security of the entire system. Conventional methods require manual detection of vulnerabilities and the application of fixed versions, which is extremely time-consuming and laborious. In addition, users often feel uneasy about how to respond to such vulnerabilities, and if they are not properly addressed, it can cause further problems. It is necessary to solve these problems and promote automation and improve the user experience.

[0849] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target open source software if a fixed version exists, means for running a system test after the update and evaluating the test results, and means for recognizing user emotions and optimizing feedback. This enables automatic response to OSS vulnerabilities, reduces user anxiety, and enables optimal feedback to be provided quickly.

[0850] The "Vulnerability Database" is a database for recording and managing information on known vulnerabilities related to open source software (OSS).

[0851] "Vulnerabilities" refer to security flaws or weaknesses that exist in open source software.

[0852] "Corrected Version" means a corrected version of the software provided to address a particular vulnerability.

[0853] "Open source software" refers to software that is free for anyone to use, modify, and distribute.

[0854] "Means for recognizing emotions" refers to technology that analyzes a user's operations and reactions and estimates the user's emotional state.

[0855] "Feedback optimization" refers to technology that provides the most appropriate response or notification content based on the user's emotional state.

[0856] "Means for assessing the scope of impact" refers to technology for analyzing the extent to which a detected vulnerability will affect the entire system.

[0857] "Temporary fix code" is provisional code generated to temporarily fix a vulnerability.

[0858] "Mitigations" are temporary measures to minimize the impact on a system until a vulnerability is fully fixed.

[0859] This invention is a system for automatically managing vulnerabilities in open source software (OSS) and optimizing responses by recognizing user emotions. This system is primarily run on a server and interacts with user devices.

[0860] System configuration

[0861] 1. Vulnerability detection and monitoring

[0862] The server periodically monitors vulnerability databases (e.g., NVD and OSS-Security) to detect new vulnerability information, which includes obtaining and analyzing vulnerability data.

[0863] 2. Check the version that addresses the vulnerability

[0864] When a vulnerability is detected, the server checks the official repository or distributor to see if a compatible version is available.

[0865] 3. Automatic Updates

[0866] If a compatible version is available, the server will automatically update the target open source software. After the update, a system test will be run and the test results will be evaluated.

[0867] 4. Impact assessment

[0868] If a compatible version does not exist, the server assesses the extent of the impact on the system and generates and applies temporary fixes as necessary.

[0869] 5. Emotion Recognition and Feedback

[0870] The server uses emotion recognition technology (EmotionRecognizer) to recognize the user's emotions. It analyzes emotions based on the user's operation log and feedback, and optimizes the feedback.

[0871] 6. Customized Notifications

[0872] The server provides customized notifications and support information based on the user's emotions, which can reduce the user's anxiety and encourage appropriate responses.

[0873] Hardware and Software Examples

[0874] EmotionRecognizer: An emotion recognition module for analyzing user emotions.

[0875] VulnerabilityScanner: A vulnerability scanning tool for scanning OSS vulnerabilities.

[0876] NotificationSystem: The notification system for sending notifications to the user.

[0877] Python: The main programming language for this system.

[0878] Smartphone: The device (Android / iOS) on which the user uses the application.

[0879] Example

[0880] Example 1: The server detects a new vulnerability in an OSS library, and if a fixed version is available, it automatically updates the library and runs a full system test. After confirming that it works properly, it analyzes user sentiment and sends detailed feedback.

[0881] Example 2: If a compatible version is not available, the server assesses the extent of the impact, generates and applies a temporary fix, and if the user is concerned, sends a notification with detailed support information.

[0882] Prompt Sentence Examples

[0883] "Create a prototype of an application that detects OSS vulnerabilities and provides appropriate feedback by analyzing user emotions. Specifically, use Python and the EmotionRecognizer, VulnerabilityScanner, and NotificationSystem modules."

[0884] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[0885] Step 1:

[0886] The server monitors vulnerability databases (e.g., NVD or OSS-Security) to detect new vulnerability information. As input, it uses the latest vulnerability information obtained from the vulnerability database. This is used to detect whether a vulnerability exists in the system. To obtain data, it periodically accesses the database via a REST API. As output, it obtains newly detected vulnerability information.

[0887] Step 2:

[0888] The server checks official repositories or distribution sources to see if a fixed version exists for the detected vulnerability. The vulnerability information obtained in step 1 is used as input. In this step, the server obtains information about the fixed version through web scraping or API calls. The output is the presence or absence of a fixed version.

[0889] Step 3:

[0890] The server automatically updates the target open source software if a compatible version exists. As input, it uses the results of step 2 and the URL of the compatible version. In this step, it executes the software update using a package management tool (e.g., pip or npm). As output, it reports that the update was successful.

[0891] Step 4:

[0892] After the update, the server runs the system tests and evaluates the test results. It uses the latest software state and test scripts as input. In this step, it runs the system tests using an automated testing tool (e.g., JUnit or pytest). As output, it obtains a log of the test results and a verdict.

[0893] Step 5:

[0894] If a compatible version is not available, the server evaluates the extent of the impact on the system. It uses the vulnerability information from step 1 as input. In this step, it uses a program analysis tool to identify the modules and functions affected by the vulnerability. It obtains information on the extent of the impact as output.

[0895] Step 6:

[0896] The server automatically generates and applies a temporary fix based on the impact range. The impact range information from step 5 is used as input. In this step, a program fix tool is used to generate a temporary fix and apply it to the system. The output is the state in which the temporary fix has been applied.

[0897] Step 7:

[0898] The server analyzes emotions based on the user's operation log and feedback. The user's operation log and feedback information are used as input. In this step, emotion recognition technology (EmotionRecognizer) is used to determine the user's emotions. The output is the user's emotional information.

[0899] Step 8:

[0900] The server provides customized notifications and support information based on the user's emotions. As input, it uses the user's emotion information obtained in step 7. In this step, it sends appropriate feedback and support information to the user through the notification system. As output, a notification is sent to the user's device.

[0901] 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.

[0902] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[0903] In the above embodiment, an example in which the specific processing is performed by the data processing device 12 has been given, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the smart glasses 214.

[0904] [Third embodiment]

[0905] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0906] 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.

[0907] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[0908] 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.

[0909] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[0910] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0911] 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.

[0912] 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.

[0913] 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 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.

[0914] 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.

[0915] In the headset type terminal 314, a reception output process is performed by the processor 46. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[0916] Next, a description will be given of the identification process performed by the identification processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as the "server" and the headset type terminal 314 will be referred to as the "terminal."

[0917] An embodiment of the present invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS) to maintain system security at all times. This system is primarily managed by a server, and notifies users and system administrators.

[0918] System configuration

[0919] 1. Vulnerability detection

[0920] The server periodically monitors vulnerability databases to detect new vulnerability information. For example, it checks databases such as the National Vulnerability Database (NVD) and OSS-Security once a day.

[0921] 2. Check the version that addresses the vulnerability

[0922] For vulnerabilities detected by the server, check the official OSS repository or distributor to determine whether a corresponding version has been released.

[0923] 3. Supported version update

[0924] If a compatible version is available, the server automatically updates the target OSS to the new version, running a test suite for the entire system to verify that the system operates normally after the update.

[0925] 4. Impact assessment

[0926] If a compatible version is not available, the server analyzes the extent of the impact on the system, for example, assessing whether the vulnerability is limited to a specific module or function.

[0927] 5. Application of the provisional revised code

[0928] If necessary, the server will automatically generate temporary fixes and apply them to the affected OSS after assessing the scope of the impact. For example, if the vulnerability exists within a function, it will take temporary measures such as disabling that function.

[0929] 6. Implementing mitigation measures

[0930] If remediation is difficult, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0931] 7. Notice to Users

[0932] If the server cannot resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator, prompting them to take manual action.

[0933] Specific examples

[0934] Example 1: Library vulnerability

[0935] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that version 1.2.4, which addresses the vulnerability, has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally.

[0936] Example 2: Determining unsupported versions

[0937] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B. However, a version that addresses the vulnerability has not yet been released, so the scope of the impact is confirmed. It is confirmed that the vulnerability is related only to a specific function, and that function is temporarily disabled. At the same time, a temporary patch code is generated and applied, maintaining the security of the system until the next update.

[0938] To effectively implement this invention, it is important to regularly monitor the vulnerability database and perform automated tests on the entire system. This system significantly reduces the workload of users and system administrators in dealing with vulnerabilities and allows the system security to be maintained at an optimal state at all times.

[0939] The processing flow will be explained below.

[0940] Step 1:

[0941] The server periodically monitors vulnerability databases (NVD, OSS-Security, etc.) to check whether new vulnerability information has been published.

[0942] Step 2:

[0943] When the server detects new vulnerability information, it obtains detailed information (CVE number, scope of impact, recommended countermeasures, etc.) and checks the version and settings of the OSS used in the system.

[0944] Step 3:

[0945] Check the official repository or distributor to see if there is a version that addresses the vulnerability detected on the server.

[0946] Step 4:

[0947] If a compatible version is available on the server, the target OSS will be automatically updated to the compatible version.

[0948] Step 5:

[0949] After the server is updated, it runs a full system test suite to detect and evaluate any issues that the update may have introduced into the system.

[0950] Step 6:

[0951] The server evaluates the test results and if all are successful, the new version is officially applied to the system. If the tests fail, the update is rolled back and an alert is sent to the user.

[0952] Step 7:

[0953] If the server does not have a compatible version, an analysis is performed to determine the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[0954] Step 8:

[0955] The server identifies the scope of the impact and, if necessary, automatically generates temporary fixes and applies them to the affected OSS. For example, if a vulnerability exists in a function or method, the code in that part will be temporarily disabled.

[0956] Step 9:

[0957] If the server is difficult to fix, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[0958] Step 10:

[0959] If the server is severely affected and temporary fixes or mitigation measures are not sufficient, users and system administrators will be notified and prompted to take emergency manual action.

[0960] Step 11:

[0961] The server periodically checks for the release of subsequent versions that address vulnerabilities, and if one is released, automatically performs the update again.

[0962] Step 12:

[0963] The server periodically reassess the scope of impact and the effectiveness of provisional fixes, and implements new countermeasures as necessary.

[0964] Example 1

[0965] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[0966] The use of open source software (OSS) is increasing in modern software systems. However, vulnerabilities in OSS can pose a serious threat to the security of the entire system. To address this issue, it is necessary to quickly detect and respond to vulnerabilities, but many system administrators perform this process manually, which makes it difficult and time-consuming to respond quickly. Furthermore, if a compatible version does not exist, it can be difficult to assess the scope of the impact and generate and apply temporary fixes.

[0967] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[0968] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, means for automatically analyzing the scope of impact if a fixed version does not exist, and means for generating and applying temporary fix code based on the results of the impact scope evaluation. This enables quick and automatic detection and response to vulnerabilities, reduces the burden on system administrators, and improves system security.

[0969] A "vulnerability database" is a database that collects, stores, and provides information about known software vulnerabilities.

[0970] "Vulnerability information" is information about potential security risks and problems that exist in software.

[0971] A "compatible version" is a new software version that fixes a specific vulnerability.

[0972] "Software" generally refers to programs that run on a computer system and associated data.

[0973] An "update" is the act of installing a new version of the software and improving the existing version.

[0974] "System testing" is a general term for tests conducted to check the operation and functionality of the entire system.

[0975] "Scope of impact" refers to the extent to which a particular vulnerability affects the entire system or some of its functions.

[0976] "Analysis" is the process of examining data or information in detail to arrive at an understanding or conclusion.

[0977] "Temporary fix code" is program code for temporarily fixing a vulnerability.

[0978] "Mitigation" is an alternative or measure to minimize impact when direct correction is difficult.

[0979] "Notification" is the act or means of communicating specific information to another person.

[0980] The system of this invention maintains the security of systems by automatically detecting, correcting, and mitigating vulnerabilities in open source software (OSS). This system is primarily managed by a server, and notifies users and system administrators in real time.

[0981] The system uses the following hardware and software:

[0982] Hardware: Server with high-performance computing resources

[0983] Software: Vulnerability databases (e.g., National Vulnerability Database, OSS-Security), CI / CD tools (e.g., Jenkins), test frameworks (e.g., JUnit), source code static analysis tools (e.g., SonarQube), vulnerability management tools (e.g., curl, firewall configuration tools)

[0984] The server works as follows:

[0985] 1. Vulnerability database monitoring

[0986] The server uses the curl command to retrieve the latest vulnerability information from the vulnerability database once a day, and stores this information in a local database.

[0987] 2. Vulnerability Information Detection

[0988] The server analyzes the vulnerability information and identifies vulnerabilities related to specific OSS. In the process, it extracts CVE (Common Vulnerabilities and Exposures) numbers and associates them with each OSS version.

[0989] 3. Check the compatible version

[0990] The server checks whether a new version that addresses the detected vulnerability has been released in the official repository or from the distribution source, using a GitHub API request.

[0991] 4. Update and test compatible versions

[0992] If a compatible version is available, the server will automatically update the OSS using the apt-get upgrade or yum update command, then run a full system test using a test framework such as JUnit to ensure proper operation.

[0993] 5. Impact assessment

[0994] If a compatible version does not exist, the server will use a source code static analysis tool (e.g., SonarQube) to analyze the scope of the vulnerability.

[0995] 6. Generate and apply temporary correction code

[0996] Based on the results of the impact assessment, the server generates provisional fixes and applies them to the source code. For example, if a vulnerability exists in a function, it generates code to disable that function.

[0997] 7. Implementing mitigation measures

[0998] If remediation is difficult, the server will apply mitigation measures to minimize the impact on the system, such as changing firewall rules to disable certain ports.

[0999] 8. Notice to Users

[1000] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator. The notification is sent via a mail server.

[1001] Specific examples

[1002] Example 1: Library vulnerability

[1003] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that the corresponding version 1.2.4 has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally.

[1004] Example 2: Determining unsupported versions

[1005] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library, Library B. However, since a fixed version has not yet been released, the server checks the scope of the impact. It verifies that the vulnerability is related only to a specific function and temporarily disables that function. At the same time, it generates and applies a temporary fix, maintaining the security of the system until the next update.

[1006] Prompt Sentence Examples

[1007] Please explain the program for the system that automatically detects and fixes OSS vulnerabilities. In particular, I would like to know how to monitor the vulnerability database and subsequently check the supported versions.

[1008] Please explain in detail how your system retrieves new information from vulnerability databases, identifies vulnerabilities, and then fixes or provisionally addresses them, including what software and tools you use.

[1009] By introducing this system, users and system administrators can significantly reduce the time and effort required to respond to vulnerabilities and maintain the security of their systems.

[1010] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1011] Specific processing steps

[1012] Step 1: Monitor the vulnerability database

[1013] The server uses the curl command to monitor the vulnerability database. Once a day, the server retrieves the latest vulnerability information from NVD and OSS-Security and stores it in a local database.

[1014] Input: NVD or OSS-Security API endpoint

[1015] Output: Data containing the latest vulnerability information

[1016] Specific operation: The server executes the curl command to retrieve data from the API, parses the data, and stores it in a local database.

[1017] Step 2: Detecting vulnerability information

[1018] The server analyzes the vulnerability information stored in the local database to identify new vulnerabilities, which are then associated with the appropriate software.

[1019] Input: Vulnerability information in the local database

[1020] Output: Identified vulnerability information and related software

[1021] What it does: The server queries a local database, extracts new CVE numbers, and links them to the associated software versions.

[1022] Step 3: Check the supported version

[1023] The server checks whether a patch version exists for the detected vulnerability, and sends an API request to the official repository or distributor to obtain the latest release information.

[1024] Input: Identified vulnerability information and associated software

[1025] Output: Supported versions and detailed information

[1026] Specific operation: The server makes an API request to the GitHub API or the official website of each OSS and checks the release information of the corresponding version.

[1027] Step 4: Update and test compatible versions

[1028] The server will automatically update the software to the new version if one is available, then run system-wide tests using a testing framework such as JUnit and evaluate the results.

[1029] Input: Release information and software for the supported version

[1030] Output: Updated software and test results

[1031] Specific operation: The server executes the apt-get upgrade or yum update command to install the new version, and then runs a system-wide test using JUnit to verify normal operation after the installation.

[1032] Step 5: Assess the impact

[1033] If a supported version is not available, the server analyzes the scope of the vulnerability by using a source code static analysis tool (e.g., SonarQube) to identify which parts of the code are affected by the vulnerability.

[1034] Input: Identified vulnerability information and software source code

[1035] Output: Detailed information on the affected area

[1036] Specific operation: The server runs a source code static analysis tool to analyze which part of the code the vulnerability affects.

[1037] Step 6: Generate and apply temporary fix code

[1038] Based on the assessment results of the extent of the impact, the server automatically generates provisional fix code and applies it to the software.

[1039] Input: Detailed information about the affected area

[1040] Output: The applied temporary correction code

[1041] Specific operation: The server generates temporary code to fix the program and applies it to the source code. For example, if a function is vulnerable, it generates code to disable that function.

[1042] Step 7: Implementing mitigation measures

[1043] If a fix is ​​difficult, the server will automatically apply mitigation measures, such as closing specific ports.

[1044] Input: Vulnerability information and detailed information on the scope of impact

[1045] Output: Mitigations applied

[1046] Specific action: The server applies mitigation measures such as modifying firewall rules and disabling certain ports.

[1047] Step 8: Notify users

[1048] If the server cannot resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator.

[1049] Input: Vulnerability information that requires action

[1050] Output: Notification to users and system administrators

[1051] What it does: The server uses a mail server to automatically send email notifications containing vulnerability information that needs to be addressed.

[1052] The above are the specific processing steps of the program of this system.

[1053] (Application example 1)

[1054] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1055] As the use of open source software (OSS) expands, vulnerabilities are discovered more frequently, creating a need for rapid and efficient response to these vulnerabilities. However, traditional methods require manual detection, correction, and system testing, which takes time and effort. Furthermore, when a compatible version does not exist or when mitigation measures need to be applied, it is difficult to take appropriate action. Furthermore, delayed notification to users and system administrators can jeopardize the security of the system.

[1056] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1057] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, and means for notifying the user. This enables automatic detection and correction of vulnerabilities, automatic testing, and user notification. Furthermore, if a fixed version does not exist, means for assessing the extent of the impact on the system, generating and applying temporary patch code, and means for taking mitigation measures for the discovered vulnerability enable a rapid response while maintaining system security. Furthermore, periodic updates based on monitored vulnerability information can be performed to maintain system security.

[1058] A "vulnerability database" is a database that aggregates information on security vulnerabilities in open source software and other software.

[1059] "Means for detecting new vulnerability information" refers to a function that has the ability to monitor the vulnerability database and automatically detect new vulnerability information.

[1060] "Means for checking the existence of a compatible version" is a function that checks whether a software version exists that can fix a detected vulnerability.

[1061] "Means for automatically updating the target software" refers to a function that automatically updates to a version that addresses the detected vulnerability if one is available.

[1062] "Means for executing system tests and evaluating the test results" refers to a function for checking the operation of the entire system after an update and evaluating the results.

[1063] "Means of notifying users" refers to a function that notifies users and system administrators of important information such as vulnerability information, update status, and application of temporary patch code.

[1064] "Means for assessing the scope of impact on the system" is a function for assessing the impact that a vulnerability will have on the entire system when no corresponding version exists.

[1065] The "means for generating and applying temporary correction code" is a function that generates correction code as a temporary measure when a compatible version does not exist and applies it to the system.

[1066] "Mitigation measures" are functions that automatically take measures to minimize the impact of vulnerabilities when they exist.

[1067] "Means of maintaining system security through regular updates" refers to the function of regularly monitoring vulnerability databases and software status, and performing system updates as necessary.

[1068] The present invention provides a system that automatically detects and fixes vulnerabilities in open source software (OSS). This system is primarily managed by a server, monitors a vulnerability database to detect new vulnerability information, automatically updates the software if a corresponding version is available, and performs a system test after the update to confirm normal operation. A specific embodiment of this system is described below.

[1069] System configuration

[1070] The system includes the following equipment and software:

[1071] 1. Vulnerability database monitoring and detection methods:

[1072] The server regularly monitors vulnerability databases such as NVD (National Vulnerability Database) and OSS-Security to detect new vulnerability information.

[1073] The software used is the Python Requests library.

[1074] 2. How to check for the existence of a compatible version:

[1075] For vulnerabilities detected on the server, check whether a corresponding version has been published in the official repository or from the distributor.

[1076] 3. Automatic Update Method:

[1077] If a compatible version is available, the server will automatically update the software to the new version.

[1078] After the update, the server runs a full system test suite to ensure proper operation.

[1079] 4. Impact assessment method:

[1080] If a compatible version does not exist, the server analyzes the extent of the impact on the system.

[1081] 5. How to generate and apply temporary fix code:

[1082] If necessary, the server generates temporary fixes and applies them to the target software.

[1083] 6. Mitigation measures:

[1084] If it is difficult to fix the code, the server will automatically apply mitigation measures such as disabling certain functions or strengthening access control.

[1085] 7. User Notification Methods:

[1086] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator.

[1087] Hardware and software used

[1088] Hardware: Servers (including cloud servers), smartphones

[1089] Software: Python, Requests library, NVD API

[1090] Data processing and calculation

[1091] This system obtains vulnerability information in real time and uses that information to update software or implement temporary fixes. Specifically, it uses the Python Requests library to obtain data from a vulnerability database, analyzes it to confirm the existence of a compatible version, and evaluates the extent of the impact.

[1092] Specific examples

[1093] For example, if the popular OSS library "ExampleLib" has a vulnerability, the server will monitor the library and automatically update the version when a vulnerability is reported. It will then notify the user that "ExampleLib has been updated to version 1.2.4. Your system is now secure."

[1094] Example prompts to input to a generative AI model:

[1095] Please explain the code and its processing for the AutoSecGuard application. The following conditions must be met: it must automatically detect and fix vulnerabilities in OSS, run on smartphones, and notify users as necessary. Please also provide specific code and its explanation.

[1096] The above is a specific embodiment for carrying out the present invention. This system significantly reduces the workload of users and system administrators in dealing with vulnerabilities, and enables the system security to be maintained at an optimum state at all times.

[1097] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1098] Step 1:

[1099] Obtaining vulnerability information

[1100] The server periodically monitors vulnerability databases (such as NVD or OSS-Security) to obtain new vulnerability information. It uses the Python Requests library to retrieve the latest information from the database API. The input is the API endpoint and access key, and the output is vulnerability information in JSON format. The server stores this information in an internal database.

[1101] Step 2:

[1102] Check compatible versions

[1103] The server analyzes the vulnerability information it has obtained and checks whether it is compatible with the target software. It also checks whether a compatible version exists in the official repository. The input is the vulnerability information obtained in the previous step, and the output is a boolean value indicating whether a compatible version exists, and if so, the version number. Tools such as a JSON parser are used for analysis.

[1104] Step 3:

[1105] Performing automatic updates

[1106] The server will perform an automatic update to a supported version if one exists, using shell scripts or specific package managers (e.g., pip, apt) to update the software. The input is the version number of the supported version, and the output is the success status of the update.

[1107] Step 4:

[1108] System test execution and evaluation

[1109] After the update, the server runs all system tests and evaluates the test results. For this, a continuous integration tool (e.g., Jenkins, GitLab CI) is used. The input is the updated software and test scripts, and the output is the test result (pass / fail).

[1110] Step 5:

[1111] Impact assessment

[1112] If a compatible version is not available, the server evaluates the extent of the impact on the system. It uses a static analysis tool (e.g., SonarQube) to analyze which modules and functions the vulnerability affects. The input is vulnerability information and system configuration information, and the output is an impact analysis report.

[1113] Step 6:

[1114] Generate and apply temporary fix code

[1115] If necessary, the server generates temporary fixes to temporarily fix vulnerabilities and applies them to the system using an automatic code generation tool or a scripting language (e.g., Python). The input is the scope of impact and directives on how to fix the vulnerability, and the output is the applied temporary fix.

[1116] Step 7:

[1117] Implementing mitigation measures

[1118] If modifying the code is difficult, the server automatically applies mitigation measures such as disabling specific functions or strengthening access control. It uses configuration change tools and firewall settings (e.g., iptables). The input is the directive for the mitigation to be applied, and the output is the execution status of the mitigation measures.

[1119] Step 8:

[1120] User Notifications

[1121] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator. To do this, it uses a mail server or notification service (e.g., Twilio). The input is the notification content, and the output is the transmission result.

[1122] Through these steps, the system enables rapid and automatic response to vulnerabilities.

[1123] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1124] An embodiment of the present invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS), and optimizes the response by recognizing user emotions. This system is mainly managed by a server, and notifies and provides feedback to users.

[1125] System configuration

[1126] 1. Vulnerability detection

[1127] The server periodically monitors vulnerability databases (e.g., NVD or OSS-Security) to detect new vulnerability information.

[1128] 2. Check the version that addresses the vulnerability

[1129] Check with the official repository or distributor to see if a fixed version has been released for the vulnerability detected on the server.

[1130] 3. Supported version update

[1131] If a compatible version is available, the server automatically updates the target OSS to the compatible version, then runs a full system test suite to verify that the update has not adversely affected the system.

[1132] 4. Impact assessment

[1133] If a compatible version is not available, the server analyzes the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[1134] 5. Application of the provisional revised code

[1135] The server assesses the scope of the impact and, if necessary, automatically generates temporary fix code and applies it to the affected OSS.

[1136] 6. Implementing mitigation measures

[1137] If remediation is difficult, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[1138] 7. Notice to Users

[1139] If the server is unable to resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator and prompt them to take manual action.

[1140] 8. Emotion Engine

[1141] The server uses an emotion engine to recognize and analyze the user's emotions based on the vulnerability response results. For example, it determines the user's emotions (e.g., relief, anxiety, irritation, etc.) based on the user's reaction to the notification content and operation logs.

[1142] 9. Customized notifications and feedback

[1143] The server customizes the notification content and provides appropriate feedback based on the user's emotions analyzed by the emotion engine. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[1144] 10. Building an Emotion Database

[1145] The server accumulates user emotional data and builds it into an emotional database, which makes it possible to utilize past emotional data when responding to future vulnerabilities and provide more effective notifications and support.

[1146] Specific examples

[1147] Example 1: Library vulnerabilities and sentiment analysis

[1148] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that version 1.2.4, which addresses the vulnerability, has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally. Furthermore, the server uses an emotion engine to analyze the user's feelings about the vulnerability response results, and sends a customized notification to alleviate the user's anxiety.

[1149] Example 2: Judging unsupported versions and emotional feedback

[1150] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B. However, since a version that addresses the vulnerability has not yet been released, it checks the scope of the impact. It confirms that the vulnerability is related only to a specific function and temporarily disables that function. At the same time, it generates and applies a temporary patch. Furthermore, the server recognizes the user's emotions and sends a notification containing detailed instructions and support information to provide reassurance.

[1151] The present invention aims not only to automatically ensure the safety of systems when responding to OSS vulnerabilities, but also to improve the user experience by recognizing user emotions and providing appropriate feedback.

[1152] The processing flow will be explained below.

[1153] Step 1:

[1154] The server periodically monitors vulnerability databases (NVD, OSS-Security, etc.) to check whether new vulnerability information has been published.

[1155] Step 2:

[1156] When the server detects new vulnerability information, it obtains detailed information (CVE number, scope of impact, recommended countermeasures, etc.) and checks the version and settings of the OSS used in the system.

[1157] Step 3:

[1158] Check the official repository or distributor to see if there is a version that addresses the vulnerability detected on the server.

[1159] Step 4:

[1160] If a compatible version is available on the server, the target OSS will be automatically updated to the compatible version.

[1161] Step 5:

[1162] After the server is updated, it runs a full system test suite to detect and evaluate any issues that the update may have introduced into the system.

[1163] Step 6:

[1164] The server evaluates the test results and if all are successful, the new version is officially applied to the system. If the tests fail, the update is rolled back and an alert is sent to the user.

[1165] Step 7:

[1166] If the server does not have a compatible version, an analysis is performed to determine the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[1167] Step 8:

[1168] The server identifies the scope of the impact and, if necessary, automatically generates temporary fixes and applies them to the affected OSS. For example, if a vulnerability exists in a function or method, the code in that part will be temporarily disabled.

[1169] Step 9:

[1170] If the server is difficult to fix, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[1171] Step 10:

[1172] If the server is severely affected and temporary fixes or mitigation measures are not sufficient, users and system administrators will be notified and prompted to take emergency manual action.

[1173] Step 11:

[1174] The server uses an emotion engine to recognize and analyze the user's emotions based on the vulnerability response results. For example, the server determines the user's emotions based on the user's reaction to the notification content and the operation log.

[1175] Step 12:

[1176] The server customizes the notification content based on the user's emotions analyzed by the emotion engine. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[1177] Step 13:

[1178] The server accumulates user emotional data and builds an emotional database. When responding to future vulnerabilities, past emotional data can be used to provide more effective notifications and support.

[1179] Example 2

[1180] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1181] While traditional open source software (OSS) vulnerability response systems can detect vulnerabilities and automate updates, they lack feedback and notifications that take user emotions into consideration. As a result, users may feel anxious or skeptical about vulnerability response, resulting in a poor user experience. Furthermore, due to a lack of temporary fix code generation when a compatible version does not exist and a lack of notification of appropriate mitigation measures to users, the safety and reliability of the system's operation were not sufficiently ensured.

[1182] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1183] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, emotion engine means for analyzing user emotions, and means for customizing notification content based on the emotion engine to match the user's emotions. This not only automatically ensures the safety of the system when responding to OSS vulnerabilities, but also makes it possible to recognize user emotions and provide appropriate feedback.

[1184] A "vulnerability database" is a database that collects known security vulnerabilities and related information.

[1185] "New vulnerability information" is information about vulnerabilities that have been newly added to the vulnerability database.

[1186] A "compatible version" is a version of software that has been fixed for a specific vulnerability and can be used safely.

[1187] "Software" means technical means including a program running on a computer and its associated data.

[1188] An "update" is the act of updating software to the latest version and fixing vulnerabilities and bugs.

[1189] "System testing" is a set of testing processes and methods used to verify that software works as expected.

[1190] An "emotion engine" is a technical means for analyzing a user's emotions and detecting a specific emotional state.

[1191] "User emotions" refers to the user's psychological reactions and feedback to system notifications and operations.

[1192] "Customizing notification content" refers to the act of changing the content of the notification message depending on the user's emotional state, thereby providing more appropriate information.

[1193] "Temporary fix code" refers to a code change that is temporarily applied before an official fix version is released, in order to temporarily fix a vulnerability.

[1194] "Mitigation" is a set of security measures implemented to minimize the impact of a vulnerability.

[1195] The "emotion database" is a database that stores data on users' emotions and uses it for later analysis and notification improvement.

[1196] "Feedback" refers to information including reactions and opinions from users regarding notifications and operations of the system.

[1197] This invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS), and also recognizes user emotions to optimize the response. This system is mainly managed by a server, and notifies and gives feedback to users.

[1198] System configuration

[1199] 1. Vulnerability detection

[1200] The server periodically scans vulnerability databases (e.g., NVD and OSS-Security) using monitoring tools (e.g., OpenVAS) to detect new vulnerability information. Detected vulnerability information is automatically recorded in a log.

[1201] 2. Check the version that addresses the vulnerability

[1202] For the vulnerability detected by the server, the server uses an API request to check whether a fixed version has been published in the official repository or distribution source (e.g., GitHub, GitLab). If a fixed version is confirmed to exist, the server proceeds to the next processing step based on that information.

[1203] 3. Supported version update

[1204] The server uses automation tools such as Ansible or Chef to update the target OSS to the corresponding version, then runs a full system test suite (e.g., Jenkins, Selenium) to verify that the update has not adversely affected the system.

[1205] 4. Impact assessment

[1206] If a compatible version is not available, the server will use static analysis tools such as SonarQube or dynamic analysis tools such as OWASP ZAP to assess the extent of the impact on the system and identify which functions and modules are affected by the vulnerability.

[1207] 5. Application of the provisional revised code

[1208] The server uses PatchBank and Ghidra to assess the extent of the impact and generates a temporary fix. This temporary fix is ​​then applied to the target OSS, and system testing is performed again to confirm its effectiveness.

[1209] 6. Implementing mitigation measures

[1210] If remediation is difficult, apply mitigation measures such as strengthening the server's firewall settings (e.g., IPTables) or access control (e.g., SELinux).

[1211] 7. Notice to Users

[1212] The server uses Nagios or Grafana to notify users and system administrators of the vulnerability, along with details of potential fixes or mitigation measures, and manual action is required if necessary.

[1213] 8. User sentiment analysis using an emotion engine

[1214] The server uses a natural language processing engine such as the Google Cloud Natural Language API to analyze emotions from the user's operation logs and reactions to notifications, and detects specific emotional states (e.g., anxiety, relief, etc.).

[1215] 9. Customized notifications and feedback

[1216] The server customizes the notification content based on the analysis results of the emotion engine and provides feedback tailored to the user's emotions. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[1217] 10. Building an Emotion Database

[1218] The server collects user emotional data and stores it in an emotional database, which enables the system to utilize past emotional data when responding to future vulnerabilities and provide more effective notifications and support.

[1219] Specific examples

[1220] Example 1: Library vulnerabilities and sentiment analysis

[1221] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS Library A using OpenVAS and confirms via the GitHub API that version 1.2.4 has been released to address the vulnerability. The server updates Library A to the latest version using Ansible and runs a full system test using Jenkins. The server then analyzes user sentiment regarding the vulnerability response results using the Google Cloud Natural Language API and sends a customized notification to reduce anxiety.

[1222] Example prompt sentence:

[1223] "A new vulnerability (CVE-XXXX-YYYY) has been detected in OSS library A. It has been updated to version 1.2.4 and the test results are normal. Please rest assured."

[1224] Example 2: Judging unsupported versions and emotional feedback

[1225] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B using OpenVAS. However, because a fixed version has not yet been released, the server uses SonarQube to confirm the scope of the impact, confirms that it is only related to a specific function, and temporarily disables that function. After generating and applying a temporary fix using PatchBank, the server uses the Google Cloud Natural Language API to analyze user sentiment and sends a notification containing detailed instructions and support information to provide reassurance.

[1226] Example prompt sentence:

[1227] Regarding the vulnerability in Library B (CVE-XXXX-ZZZZ), a fixed version has not yet been released, but we have limited the scope of impact and temporarily disabled certain functions. We will send you a detailed guide, so please check it.

[1228] This system aims to improve the user experience while automatically ensuring the safety of the system by integrating OSS vulnerability response and user emotion management.

[1229] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1230] Step 1:

[1231] Vulnerability detection

[1232] The server periodically scans vulnerability databases (e.g., NVD or OSS-Security) using a monitoring tool (e.g., OpenVAS).

[1233] The server obtains new vulnerability information from the vulnerability database (input: vulnerability database, output: vulnerability information).

[1234] The server automatically logs new vulnerability information.

[1235] Specific behavior:

[1236] The server starts OpenVAS and scans the vulnerability database. If it finds a new vulnerability, it logs it as "CVE-XXXX-YYYY" and proceeds to the next step.

[1237] Step 2:

[1238] Check the version that addresses the vulnerability

[1239] For vulnerabilities detected by the server, an API request is used to check whether a fixed version is available in an official repository (e.g., GitHub, GitLab) or from the distribution source.

[1240] The server checks whether a compatible version exists (input: vulnerability information, output: compatible version information).

[1241] Specific behavior:

[1242] The server sends a request to the GitHub API to get the latest version of Library A. It verifies that the corresponding version 1.2.4 is available.

[1243] Step 3:

[1244] Supported version update

[1245] The server uses automation tools such as Ansible or Chef to update the target OSS to a compatible version.

[1246] The server runs a full system test suite (e.g., Jenkins, Selenium) to ensure the update has not adversely affected the system (input: target software, output: update results).

[1247] Specific behavior:

[1248] The server uses Ansible to update Library A to version 1.2.4, then runs all system tests in Jenkins and checks the test results.

[1249] Step 4:

[1250] Impact assessment

[1251] If a compatible version does not exist, the server will assess the extent of the impact on the system using static analysis tools such as SonarQube or dynamic analysis tools such as OWASP ZAP.

[1252] The server identifies which functions and modules are affected by the vulnerability (input: vulnerability information, output: impact assessment results).

[1253] Specific behavior:

[1254] The server launches SonarQube, which analyzes the code across the entire system, and based on the results of the analysis, identifies the affected modules and logs them.

[1255] Step 5:

[1256] Application of provisional amendment code

[1257] The server uses PatchBank and Ghidra to assess the extent of the impact and generate provisional fix code.

[1258] The server applies the provisional fix code to the target OSS and performs the necessary tests (input: impact assessment results, output: provisional fix code).

[1259] Specific behavior:

[1260] The server uses PatchBank to generate provisional fixes for the identified vulnerabilities, applies the fixes, and re-runs the system tests.

[1261] Step 6:

[1262] Implementing mitigation measures

[1263] If remediation is difficult, apply mitigation measures such as strengthening the server's firewall settings (e.g., IPTables) or access control (e.g., SELinux) (Input: vulnerability information, Output: mitigation measures).

[1264] Specific behavior:

[1265] The server changes the IPTables settings to restrict communication within the scope affected by the vulnerability. Test the changed settings to confirm their effectiveness.

[1266] Step 7:

[1267] User Notification

[1268] The server uses Nagios or Grafana to notify users and system administrators of the vulnerability information, provisional fix code, or details of mitigation measures, requiring manual action if necessary (input: vulnerability response information, output: notification).

[1269] Specific behavior:

[1270] The server will then send an email notification to the system administrator via Nagios, containing the vulnerability detected and recommended manual steps to take.

[1271] Step 8:

[1272] User sentiment analysis using an emotion engine

[1273] The server uses a natural language processing engine such as Google Cloud Natural Language API to analyze emotions from the user's operation log and reactions to notifications (input: user operation log, output: emotion analysis results).

[1274] Specific behavior:

[1275] The server collects user operation logs and performs sentiment analysis via the Google Cloud Natural Language API to determine whether the user is feeling "anxiety."

[1276] Step 9:

[1277] Customized notifications and feedback

[1278] The server customizes the notification content based on the analysis results of the emotion engine and provides feedback tailored to the user's emotions (input: emotion analysis results, output: customized notification).

[1279] Specific behavior:

[1280] The server analyzes the user's emotional data, and based on the user's anxiety, creates detailed guidance to provide reassurance and sends a notification.

[1281] Step 10:

[1282] Building an emotion database

[1283] The server collects user emotional data and stores it in an emotional database. Past emotional data can be used to provide more effective notifications and support when responding to future vulnerabilities (input: emotional data, output: emotional database).

[1284] Specific behavior:

[1285] The server stores the emotion data in a database and updates the emotion database for future analysis and notifications.

[1286] (Application example 2)

[1287] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the headset type terminal 314 will be referred to as a "terminal."

[1288] Vulnerabilities in open source software (OSS) are a serious problem that threatens the security of the entire system. Conventional methods require manual detection of vulnerabilities and the application of fixed versions, which is extremely time-consuming and laborious. In addition, users often feel uneasy about how to respond to such vulnerabilities, and if they are not properly addressed, it can cause further problems. It is necessary to solve these problems and promote automation and improve the user experience.

[1289] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target open source software if a fixed version exists, means for running a system test after the update and evaluating the test results, and means for recognizing user emotions and optimizing feedback. This enables automatic response to OSS vulnerabilities, reduces user anxiety, and enables optimal feedback to be provided quickly.

[1290] The "Vulnerability Database" is a database for recording and managing information on known vulnerabilities related to open source software (OSS).

[1291] "Vulnerabilities" refer to security flaws or weaknesses that exist in open source software.

[1292] "Corrected Version" means a corrected version of the software provided to address a particular vulnerability.

[1293] "Open source software" refers to software that is free for anyone to use, modify, and distribute.

[1294] "Means for recognizing emotions" refers to technology that analyzes a user's operations and reactions and estimates the user's emotional state.

[1295] "Feedback optimization" refers to technology that provides the most appropriate response or notification content based on the user's emotional state.

[1296] "Means for assessing the scope of impact" refers to technology for analyzing the extent to which a detected vulnerability will affect the entire system.

[1297] "Temporary fix code" is provisional code generated to temporarily fix a vulnerability.

[1298] "Mitigations" are temporary measures to minimize the impact on a system until a vulnerability is fully fixed.

[1299] This invention is a system for automatically managing vulnerabilities in open source software (OSS) and optimizing responses by recognizing user emotions. This system is primarily run on a server and interacts with user devices.

[1300] System configuration

[1301] 1. Vulnerability detection and monitoring

[1302] The server periodically monitors vulnerability databases (e.g., NVD and OSS-Security) to detect new vulnerability information, which includes obtaining and analyzing vulnerability data.

[1303] 2. Check the version that addresses the vulnerability

[1304] When a vulnerability is detected, the server checks the official repository or distributor to see if a compatible version is available.

[1305] 3. Automatic Updates

[1306] If a compatible version is available, the server will automatically update the target open source software. After the update, a system test will be run and the test results will be evaluated.

[1307] 4. Impact assessment

[1308] If a compatible version does not exist, the server assesses the extent of the impact on the system and generates and applies temporary fixes as necessary.

[1309] 5. Emotion Recognition and Feedback

[1310] The server uses emotion recognition technology (EmotionRecognizer) to recognize the user's emotions. It analyzes emotions based on the user's operation log and feedback, and optimizes the feedback.

[1311] 6. Customized Notifications

[1312] The server provides customized notifications and support information based on the user's emotions, which can reduce the user's anxiety and encourage appropriate responses.

[1313] Hardware and Software Examples

[1314] EmotionRecognizer: An emotion recognition module for analyzing user emotions.

[1315] VulnerabilityScanner: A vulnerability scanning tool for scanning OSS vulnerabilities.

[1316] NotificationSystem: The notification system for sending notifications to the user.

[1317] Python: The main programming language for this system.

[1318] Smartphone: The device (Android / iOS) on which the user uses the application.

[1319] Example

[1320] Example 1: The server detects a new vulnerability in an OSS library, and if a fixed version is available, it automatically updates the library and runs a full system test. After confirming that it works properly, it analyzes user sentiment and sends detailed feedback.

[1321] Example 2: If a compatible version is not available, the server assesses the extent of the impact, generates and applies a temporary fix, and if the user is concerned, sends a notification with detailed support information.

[1322] Prompt Sentence Examples

[1323] "Create a prototype of an application that detects OSS vulnerabilities and provides appropriate feedback by analyzing user emotions. Specifically, use Python and the EmotionRecognizer, VulnerabilityScanner, and NotificationSystem modules."

[1324] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1325] Step 1:

[1326] The server monitors vulnerability databases (e.g., NVD or OSS-Security) to detect new vulnerability information. As input, it uses the latest vulnerability information obtained from the vulnerability database. This is used to detect whether a vulnerability exists in the system. To obtain data, it periodically accesses the database via a REST API. As output, it obtains newly detected vulnerability information.

[1327] Step 2:

[1328] The server checks official repositories or distribution sources to see if a fixed version exists for the detected vulnerability. The vulnerability information obtained in step 1 is used as input. In this step, the server obtains information about the fixed version through web scraping or API calls. The output is the presence or absence of a fixed version.

[1329] Step 3:

[1330] The server automatically updates the target open source software if a compatible version exists. As input, it uses the results of step 2 and the URL of the compatible version. In this step, it executes the software update using a package management tool (e.g., pip or npm). As output, it reports that the update was successful.

[1331] Step 4:

[1332] After the update, the server runs the system tests and evaluates the test results. It uses the latest software state and test scripts as input. In this step, it runs the system tests using an automated testing tool (e.g., JUnit or pytest). As output, it obtains a log of the test results and a verdict.

[1333] Step 5:

[1334] If a compatible version is not available, the server evaluates the extent of the impact on the system. It uses the vulnerability information from step 1 as input. In this step, it uses a program analysis tool to identify the modules and functions affected by the vulnerability. It obtains information on the extent of the impact as output.

[1335] Step 6:

[1336] The server automatically generates and applies a temporary fix based on the impact range. The impact range information from step 5 is used as input. In this step, a program fix tool is used to generate a temporary fix and apply it to the system. The output is the state in which the temporary fix has been applied.

[1337] Step 7:

[1338] The server analyzes emotions based on the user's operation log and feedback. The user's operation log and feedback information are used as input. In this step, emotion recognition technology (EmotionRecognizer) is used to determine the user's emotions. The output is the user's emotional information.

[1339] Step 8:

[1340] The server provides customized notifications and support information based on the user's emotions. As input, it uses the user's emotion information obtained in step 7. In this step, it sends appropriate feedback and support information to the user through the notification system. As output, a notification is sent to the user's device.

[1341] 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.

[1342] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1343] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the headset type terminal 314.

[1344] [Fourth embodiment]

[1345] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[1346] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[1347] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. 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).

[1348] 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.

[1349] The microphone 238 receives instructions and the like from the user 20 by receiving voice uttered by the user 20. The microphone 238 captures the voice uttered by the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio in accordance with instructions from the processor 46.

[1350] 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 surroundings of user 20 (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[1351] 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.

[1352] The control object 443 includes a display device, LEDs in the eyes, and motors for driving 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.

[1353] 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.

[1354] 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 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.

[1355] 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.

[1356] In the robot 414, the processor 46 performs the reception output process. A reception output program 60 is stored in the storage 50. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 60 executed on the RAM 48.

[1357] Next, a description will be given of the specific processing performed by the specific processing unit 290 of the data processing device 12. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1358] An embodiment of the present invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS) to maintain system security at all times. This system is primarily managed by a server, and notifies users and system administrators.

[1359] System configuration

[1360] 1. Vulnerability detection

[1361] The server periodically monitors vulnerability databases to detect new vulnerability information. For example, it checks databases such as the National Vulnerability Database (NVD) and OSS-Security once a day.

[1362] 2. Check the version that addresses the vulnerability

[1363] For vulnerabilities detected by the server, check the official OSS repository or distributor to determine whether a corresponding version has been released.

[1364] 3. Supported version update

[1365] If a compatible version is available, the server automatically updates the target OSS to the new version, running a test suite for the entire system to verify that the system operates normally after the update.

[1366] 4. Impact assessment

[1367] If a compatible version is not available, the server analyzes the extent of the impact on the system, for example, assessing whether the vulnerability is limited to a specific module or function.

[1368] 5. Application of the provisional revised code

[1369] If necessary, the server will automatically generate temporary fixes and apply them to the affected OSS after assessing the scope of the impact. For example, if the vulnerability exists within a function, it will take temporary measures such as disabling that function.

[1370] 6. Implementing mitigation measures

[1371] If remediation is difficult, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[1372] 7. Notice to Users

[1373] If the server cannot resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator, prompting them to take manual action.

[1374] Specific examples

[1375] Example 1: Library vulnerability

[1376] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that version 1.2.4, which addresses the vulnerability, has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally.

[1377] Example 2: Determining unsupported versions

[1378] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B. However, a version that addresses the vulnerability has not yet been released, so the scope of the impact is confirmed. It is confirmed that the vulnerability is related only to a specific function, and that function is temporarily disabled. At the same time, a temporary patch code is generated and applied, maintaining the security of the system until the next update.

[1379] To effectively implement this invention, it is important to regularly monitor the vulnerability database and perform automated tests on the entire system. This system significantly reduces the workload of users and system administrators in dealing with vulnerabilities and allows the system security to be maintained at an optimal state at all times.

[1380] The processing flow will be explained below.

[1381] Step 1:

[1382] The server periodically monitors vulnerability databases (NVD, OSS-Security, etc.) to check whether new vulnerability information has been published.

[1383] Step 2:

[1384] When the server detects new vulnerability information, it obtains detailed information (CVE number, scope of impact, recommended countermeasures, etc.) and checks the version and settings of the OSS used in the system.

[1385] Step 3:

[1386] Check the official repository or distributor to see if there is a version that addresses the vulnerability detected on the server.

[1387] Step 4:

[1388] If a compatible version is available on the server, the target OSS will be automatically updated to the compatible version.

[1389] Step 5:

[1390] After the server is updated, it runs a full system test suite to detect and evaluate any issues that the update may have introduced into the system.

[1391] Step 6:

[1392] The server evaluates the test results and if all are successful, the new version is officially applied to the system. If the tests fail, the update is rolled back and an alert is sent to the user.

[1393] Step 7:

[1394] If the server does not have a compatible version, an analysis is performed to determine the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[1395] Step 8:

[1396] The server identifies the scope of the impact and, if necessary, automatically generates temporary fixes and applies them to the affected OSS. For example, if a vulnerability exists in a function or method, the code in that part will be temporarily disabled.

[1397] Step 9:

[1398] If the server is difficult to fix, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[1399] Step 10:

[1400] If the server is severely affected and temporary fixes or mitigation measures are not sufficient, users and system administrators will be notified and prompted to take emergency manual action.

[1401] Step 11:

[1402] The server periodically checks for the release of subsequent versions that address vulnerabilities, and if one is released, automatically performs the update again.

[1403] Step 12:

[1404] The server periodically reassess the scope of impact and the effectiveness of provisional fixes, and implements new countermeasures as necessary.

[1405] Example 1

[1406] Next, a description will be given of Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1407] The use of open source software (OSS) is increasing in modern software systems. However, vulnerabilities in OSS can pose a serious threat to the security of the entire system. To address this issue, it is necessary to quickly detect and respond to vulnerabilities, but many system administrators perform this process manually, which makes it difficult and time-consuming to respond quickly. Furthermore, if a compatible version does not exist, it can be difficult to assess the scope of the impact and generate and apply temporary fixes.

[1408] The specific processing by the specific processing unit 290 of the data processing device 12 in the first embodiment is realized by the following means.

[1409] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, means for automatically analyzing the scope of impact if a fixed version does not exist, and means for generating and applying temporary fix code based on the results of the impact scope evaluation. This enables quick and automatic detection and response to vulnerabilities, reduces the burden on system administrators, and improves system security.

[1410] A "vulnerability database" is a database that collects, stores, and provides information about known software vulnerabilities.

[1411] "Vulnerability information" is information about potential security risks and problems that exist in software.

[1412] A "compatible version" is a new software version that fixes a specific vulnerability.

[1413] "Software" generally refers to programs that run on a computer system and associated data.

[1414] An "update" is the act of installing a new version of the software and improving the existing version.

[1415] "System testing" is a general term for tests conducted to check the operation and functionality of the entire system.

[1416] "Scope of impact" refers to the extent to which a particular vulnerability affects the entire system or some of its functions.

[1417] "Analysis" is the process of examining data or information in detail to arrive at an understanding or conclusion.

[1418] "Temporary fix code" is program code for temporarily fixing a vulnerability.

[1419] "Mitigation" is an alternative or measure to minimize impact when direct correction is difficult.

[1420] "Notification" is the act or means of communicating specific information to another person.

[1421] The system of this invention maintains the security of systems by automatically detecting, correcting, and mitigating vulnerabilities in open source software (OSS). This system is primarily managed by a server, and notifies users and system administrators in real time.

[1422] The system uses the following hardware and software:

[1423] Hardware: Server with high-performance computing resources

[1424] Software: Vulnerability databases (e.g., National Vulnerability Database, OSS-Security), CI / CD tools (e.g., Jenkins), test frameworks (e.g., JUnit), source code static analysis tools (e.g., SonarQube), vulnerability management tools (e.g., curl, firewall configuration tools)

[1425] The server works as follows:

[1426] 1. Vulnerability database monitoring

[1427] The server uses the curl command to retrieve the latest vulnerability information from the vulnerability database once a day, and stores this information in a local database.

[1428] 2. Vulnerability Information Detection

[1429] The server analyzes the vulnerability information and identifies vulnerabilities related to specific OSS. In the process, it extracts CVE (Common Vulnerabilities and Exposures) numbers and associates them with each OSS version.

[1430] 3. Check the compatible version

[1431] The server checks whether a new version that addresses the detected vulnerability has been released in the official repository or from the distribution source, using a GitHub API request.

[1432] 4. Update and test compatible versions

[1433] If a compatible version is available, the server will automatically update the OSS using the apt-get upgrade or yum update command, then run a full system test using a test framework such as JUnit to ensure proper operation.

[1434] 5. Impact assessment

[1435] If a compatible version does not exist, the server will use a source code static analysis tool (e.g., SonarQube) to analyze the scope of the vulnerability.

[1436] 6. Generate and apply temporary correction code

[1437] Based on the results of the impact assessment, the server generates provisional fixes and applies them to the source code. For example, if a vulnerability exists in a function, it generates code to disable that function.

[1438] 7. Implementing mitigation measures

[1439] If remediation is difficult, the server will apply mitigation measures to minimize the impact on the system, such as changing firewall rules to disable certain ports.

[1440] 8. Notice to Users

[1441] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator. The notification is sent via a mail server.

[1442] Specific examples

[1443] Example 1: Library vulnerability

[1444] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that the corresponding version 1.2.4 has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally.

[1445] Example 2: Determining unsupported versions

[1446] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library, Library B. However, since a fixed version has not yet been released, the server checks the scope of the impact. It verifies that the vulnerability is related only to a specific function and temporarily disables that function. At the same time, it generates and applies a temporary fix, maintaining the security of the system until the next update.

[1447] Prompt Sentence Examples

[1448] Please explain the program for the system that automatically detects and fixes OSS vulnerabilities. In particular, I would like to know how to monitor the vulnerability database and subsequently check the supported versions.

[1449] Please explain in detail how your system retrieves new information from vulnerability databases, identifies vulnerabilities, and then fixes or provisionally addresses them, including what software and tools you use.

[1450] By introducing this system, users and system administrators can significantly reduce the time and effort required to respond to vulnerabilities and maintain the security of their systems.

[1451] The flow of the identification process in the first embodiment will be described with reference to FIG.

[1452] Specific processing steps

[1453] Step 1: Monitor the vulnerability database

[1454] The server uses the curl command to monitor the vulnerability database. Once a day, the server retrieves the latest vulnerability information from NVD and OSS-Security and stores it in a local database.

[1455] Input: NVD or OSS-Security API endpoint

[1456] Output: Data containing the latest vulnerability information

[1457] Specific operation: The server executes the curl command to retrieve data from the API, parses the data, and stores it in a local database.

[1458] Step 2: Detecting vulnerability information

[1459] The server analyzes the vulnerability information stored in the local database to identify new vulnerabilities, which are then associated with the appropriate software.

[1460] Input: Vulnerability information in the local database

[1461] Output: Identified vulnerability information and related software

[1462] What it does: The server queries a local database, extracts new CVE numbers, and links them to the associated software versions.

[1463] Step 3: Check the supported version

[1464] The server checks whether a patch version exists for the detected vulnerability, and sends an API request to the official repository or distributor to obtain the latest release information.

[1465] Input: Identified vulnerability information and associated software

[1466] Output: Supported versions and detailed information

[1467] Specific operation: The server makes an API request to the GitHub API or the official website of each OSS and checks the release information of the corresponding version.

[1468] Step 4: Update and test compatible versions

[1469] The server will automatically update the software to the new version if one is available, then run system-wide tests using a testing framework such as JUnit and evaluate the results.

[1470] Input: Release information and software for the supported version

[1471] Output: Updated software and test results

[1472] Specific operation: The server executes the apt-get upgrade or yum update command to install the new version, and then runs a system-wide test using JUnit to verify normal operation after the installation.

[1473] Step 5: Assess the impact

[1474] If a supported version is not available, the server analyzes the scope of the vulnerability by using a source code static analysis tool (e.g., SonarQube) to identify which parts of the code are affected by the vulnerability.

[1475] Input: Identified vulnerability information and software source code

[1476] Output: Detailed information on the affected area

[1477] Specific operation: The server runs a source code static analysis tool to analyze which part of the code the vulnerability affects.

[1478] Step 6: Generate and apply temporary fix code

[1479] Based on the assessment results of the extent of the impact, the server automatically generates provisional fix code and applies it to the software.

[1480] Input: Detailed information about the affected area

[1481] Output: The applied temporary correction code

[1482] Specific operation: The server generates temporary code to fix the program and applies it to the source code. For example, if a function is vulnerable, it generates code to disable that function.

[1483] Step 7: Implementing mitigation measures

[1484] If a fix is ​​difficult, the server will automatically apply mitigation measures, such as closing specific ports.

[1485] Input: Vulnerability information and detailed information on the scope of impact

[1486] Output: Mitigations applied

[1487] Specific action: The server applies mitigation measures such as modifying firewall rules and disabling certain ports.

[1488] Step 8: Notify users

[1489] If the server cannot resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator.

[1490] Input: Vulnerability information that requires action

[1491] Output: Notification to users and system administrators

[1492] What it does: The server uses a mail server to automatically send email notifications containing vulnerability information that needs to be addressed.

[1493] The above are the specific processing steps of the program of this system.

[1494] (Application example 1)

[1495] Next, a description will be given of Application Example 1. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1496] As the use of open source software (OSS) expands, vulnerabilities are discovered more frequently, creating a need for rapid and efficient response to these vulnerabilities. However, traditional methods require manual detection, correction, and system testing, which takes time and effort. Furthermore, when a compatible version does not exist or when mitigation measures need to be applied, it is difficult to take appropriate action. Furthermore, delayed notification to users and system administrators can jeopardize the security of the system.

[1497] The specific processing by the specific processing unit 290 of the data processing device 12 in the application example 1 is realized by the following means.

[1498] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, and means for notifying the user. This enables automatic detection and correction of vulnerabilities, automatic testing, and user notification. Furthermore, if a fixed version does not exist, means for assessing the extent of the impact on the system, generating and applying temporary patch code, and means for taking mitigation measures for the discovered vulnerability enable a rapid response while maintaining system security. Furthermore, periodic updates based on monitored vulnerability information can be performed to maintain system security.

[1499] A "vulnerability database" is a database that aggregates information on security vulnerabilities in open source software and other software.

[1500] "Means for detecting new vulnerability information" refers to a function that has the ability to monitor the vulnerability database and automatically detect new vulnerability information.

[1501] "Means for checking the existence of a compatible version" is a function that checks whether a software version exists that can fix a detected vulnerability.

[1502] "Means for automatically updating the target software" refers to a function that automatically updates to a version that addresses the detected vulnerability if one is available.

[1503] "Means for executing system tests and evaluating the test results" refers to a function for checking the operation of the entire system after an update and evaluating the results.

[1504] "Means of notifying users" refers to a function that notifies users and system administrators of important information such as vulnerability information, update status, and application of temporary patch code.

[1505] "Means for assessing the scope of impact on the system" is a function for assessing the impact that a vulnerability will have on the entire system when no corresponding version exists.

[1506] The "means for generating and applying temporary correction code" is a function that generates correction code as a temporary measure when a compatible version does not exist and applies it to the system.

[1507] "Mitigation measures" are functions that automatically take measures to minimize the impact of vulnerabilities when they exist.

[1508] "Means of maintaining system security through regular updates" refers to the function of regularly monitoring vulnerability databases and software status, and performing system updates as necessary.

[1509] The present invention provides a system that automatically detects and fixes vulnerabilities in open source software (OSS). This system is primarily managed by a server, monitors a vulnerability database to detect new vulnerability information, automatically updates the software if a corresponding version is available, and performs a system test after the update to confirm normal operation. A specific embodiment of this system is described below.

[1510] System configuration

[1511] The system includes the following equipment and software:

[1512] 1. Vulnerability database monitoring and detection methods:

[1513] The server regularly monitors vulnerability databases such as NVD (National Vulnerability Database) and OSS-Security to detect new vulnerability information.

[1514] The software used is the Python Requests library.

[1515] 2. How to check for the existence of a compatible version:

[1516] For vulnerabilities detected on the server, check whether a corresponding version has been published in the official repository or from the distributor.

[1517] 3. Automatic Update Method:

[1518] If a compatible version is available, the server will automatically update the software to the new version.

[1519] After the update, the server runs a full system test suite to ensure proper operation.

[1520] 4. Impact assessment method:

[1521] If a compatible version does not exist, the server analyzes the extent of the impact on the system.

[1522] 5. How to generate and apply temporary fix code:

[1523] If necessary, the server generates temporary fixes and applies them to the target software.

[1524] 6. Mitigation measures:

[1525] If it is difficult to fix the code, the server will automatically apply mitigation measures such as disabling certain functions or strengthening access control.

[1526] 7. User Notification Methods:

[1527] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator.

[1528] Hardware and software used

[1529] Hardware: Servers (including cloud servers), smartphones

[1530] Software: Python, Requests library, NVD API

[1531] Data processing and calculation

[1532] This system obtains vulnerability information in real time and uses that information to update software or implement temporary fixes. Specifically, it uses the Python Requests library to obtain data from a vulnerability database, analyzes it to confirm the existence of a compatible version, and evaluates the extent of the impact.

[1533] Specific examples

[1534] For example, if the popular OSS library "ExampleLib" has a vulnerability, the server will monitor the library and automatically update the version when a vulnerability is reported. It will then notify the user that "ExampleLib has been updated to version 1.2.4. Your system is now secure."

[1535] Example prompts to input to a generative AI model:

[1536] Please explain the code and its processing for the AutoSecGuard application. The following conditions must be met: it must automatically detect and fix vulnerabilities in OSS, run on smartphones, and notify users as necessary. Please also provide specific code and its explanation.

[1537] The above is a specific embodiment for carrying out the present invention. This system significantly reduces the workload of users and system administrators in dealing with vulnerabilities, and enables the system security to be maintained at an optimum state at all times.

[1538] The flow of the specific processing in the application example 1 will be described with reference to FIG.

[1539] Step 1:

[1540] Obtaining vulnerability information

[1541] The server periodically monitors vulnerability databases (such as NVD or OSS-Security) to obtain new vulnerability information. It uses the Python Requests library to retrieve the latest information from the database API. The input is the API endpoint and access key, and the output is vulnerability information in JSON format. The server stores this information in an internal database.

[1542] Step 2:

[1543] Check compatible versions

[1544] The server analyzes the vulnerability information it has obtained and checks whether it is compatible with the target software. It also checks whether a compatible version exists in the official repository. The input is the vulnerability information obtained in the previous step, and the output is a boolean value indicating whether a compatible version exists, and if so, the version number. Tools such as a JSON parser are used for analysis.

[1545] Step 3:

[1546] Performing automatic updates

[1547] The server will perform an automatic update to a supported version if one exists, using shell scripts or specific package managers (e.g., pip, apt) to update the software. The input is the version number of the supported version, and the output is the success status of the update.

[1548] Step 4:

[1549] System test execution and evaluation

[1550] After the update, the server runs all system tests and evaluates the test results. For this, a continuous integration tool (e.g., Jenkins, GitLab CI) is used. The input is the updated software and test scripts, and the output is the test result (pass / fail).

[1551] Step 5:

[1552] Impact assessment

[1553] If a compatible version is not available, the server evaluates the extent of the impact on the system. It uses a static analysis tool (e.g., SonarQube) to analyze which modules and functions the vulnerability affects. The input is vulnerability information and system configuration information, and the output is an impact analysis report.

[1554] Step 6:

[1555] Generate and apply temporary fix code

[1556] If necessary, the server generates temporary fixes to temporarily fix vulnerabilities and applies them to the system using an automatic code generation tool or a scripting language (e.g., Python). The input is the scope of impact and directives on how to fix the vulnerability, and the output is the applied temporary fix.

[1557] Step 7:

[1558] Implementing mitigation measures

[1559] If modifying the code is difficult, the server automatically applies mitigation measures such as disabling specific functions or strengthening access control. It uses configuration change tools and firewall settings (e.g., iptables). The input is the directive for the mitigation to be applied, and the output is the execution status of the mitigation measures.

[1560] Step 8:

[1561] User Notifications

[1562] If the problem cannot be resolved with temporary fixes or mitigation measures, the server will notify the user or system administrator. To do this, it uses a mail server or notification service (e.g., Twilio). The input is the notification content, and the output is the transmission result.

[1563] Through these steps, the system enables rapid and automatic response to vulnerabilities.

[1564] Furthermore, an emotion engine that estimates the user's emotion may be further combined. That is, the identification processing unit 290 may estimate the user's emotion using the emotion identification model 59, and perform identification processing using the user's emotion.

[1565] An embodiment of the present invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS), and optimizes the response by recognizing user emotions. This system is mainly managed by a server, and notifies and provides feedback to users.

[1566] System configuration

[1567] 1. Vulnerability detection

[1568] The server periodically monitors vulnerability databases (e.g., NVD or OSS-Security) to detect new vulnerability information.

[1569] 2. Check the version that addresses the vulnerability

[1570] Check with the official repository or distributor to see if a fixed version has been released for the vulnerability detected on the server.

[1571] 3. Supported version update

[1572] If a compatible version is available, the server automatically updates the target OSS to the compatible version, then runs a full system test suite to verify that the update has not adversely affected the system.

[1573] 4. Impact assessment

[1574] If a compatible version is not available, the server analyzes the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[1575] 5. Application of the provisional revised code

[1576] The server assesses the scope of the impact and, if necessary, automatically generates temporary fix code and applies it to the affected OSS.

[1577] 6. Implementing mitigation measures

[1578] If remediation is difficult, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[1579] 7. Notice to Users

[1580] If the server is unable to resolve the issue with temporary fixes or mitigation measures, it will notify the user or system administrator and prompt them to take manual action.

[1581] 8. Emotion Engine

[1582] The server uses an emotion engine to recognize and analyze the user's emotions based on the vulnerability response results. For example, it determines the user's emotions (e.g., relief, anxiety, irritation, etc.) based on the user's reaction to the notification content and operation logs.

[1583] 9. Customized notifications and feedback

[1584] The server customizes the notification content and provides appropriate feedback based on the user's emotions analyzed by the emotion engine. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[1585] 10. Building an Emotion Database

[1586] The server accumulates user emotional data and builds it into an emotional database, which makes it possible to utilize past emotional data when responding to future vulnerabilities and provide more effective notifications and support.

[1587] Specific examples

[1588] Example 1: Library vulnerabilities and sentiment analysis

[1589] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS library A. It confirms that version 1.2.4, which addresses the vulnerability, has been released, and automatically updates library A to version 1.2.4. It then runs a full system test to confirm that it operates normally. Furthermore, the server uses an emotion engine to analyze the user's feelings about the vulnerability response results, and sends a customized notification to alleviate the user's anxiety.

[1590] Example 2: Judging unsupported versions and emotional feedback

[1591] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B. However, since a version that addresses the vulnerability has not yet been released, it checks the scope of the impact. It confirms that the vulnerability is related only to a specific function and temporarily disables that function. At the same time, it generates and applies a temporary patch. Furthermore, the server recognizes the user's emotions and sends a notification containing detailed instructions and support information to provide reassurance.

[1592] The present invention aims not only to automatically ensure the safety of systems when responding to OSS vulnerabilities, but also to improve the user experience by recognizing user emotions and providing appropriate feedback.

[1593] The processing flow will be explained below.

[1594] Step 1:

[1595] The server periodically monitors vulnerability databases (NVD, OSS-Security, etc.) to check whether new vulnerability information has been published.

[1596] Step 2:

[1597] When the server detects new vulnerability information, it obtains detailed information (CVE number, scope of impact, recommended countermeasures, etc.) and checks the version and settings of the OSS used in the system.

[1598] Step 3:

[1599] Check the official repository or distributor to see if there is a version that addresses the vulnerability detected on the server.

[1600] Step 4:

[1601] If a compatible version is available on the server, the target OSS will be automatically updated to the compatible version.

[1602] Step 5:

[1603] After the server is updated, it runs a full system test suite to detect and evaluate any issues that the update may have introduced into the system.

[1604] Step 6:

[1605] The server evaluates the test results and if all are successful, the new version is officially applied to the system. If the tests fail, the update is rolled back and an alert is sent to the user.

[1606] Step 7:

[1607] If the server does not have a compatible version, an analysis is performed to determine the extent of the impact on the system, assessing which functions and modules are affected by the vulnerability.

[1608] Step 8:

[1609] The server identifies the scope of the impact and, if necessary, automatically generates temporary fixes and applies them to the affected OSS. For example, if a vulnerability exists in a function or method, the code in that part will be temporarily disabled.

[1610] Step 9:

[1611] If the server is difficult to fix, mitigation measures are automatically applied, such as disabling certain functions or strengthening access controls.

[1612] Step 10:

[1613] If the server is severely affected and temporary fixes or mitigation measures are not sufficient, users and system administrators will be notified and prompted to take emergency manual action.

[1614] Step 11:

[1615] The server uses an emotion engine to recognize and analyze the user's emotions based on the vulnerability response results. For example, the server determines the user's emotions based on the user's reaction to the notification content and the operation log.

[1616] Step 12:

[1617] The server customizes the notification content based on the user's emotions analyzed by the emotion engine. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[1618] Step 13:

[1619] The server accumulates user emotional data and builds an emotional database. When responding to future vulnerabilities, past emotional data can be used to provide more effective notifications and support.

[1620] Example 2

[1621] Next, a description will be given of Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1622] While traditional open source software (OSS) vulnerability response systems can detect vulnerabilities and automate updates, they lack feedback and notifications that take user emotions into consideration. As a result, users may feel anxious or skeptical about vulnerability response, resulting in a poor user experience. Furthermore, due to a lack of temporary fix code generation when a compatible version does not exist and a lack of notification of appropriate mitigation measures to users, the safety and reliability of the system's operation were not sufficiently ensured.

[1623] The specific processing by the specific processing unit 290 of the data processing device 12 in the second embodiment is realized by the following means.

[1624] In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target software if a fixed version exists, means for running a system test after the update and evaluating the test results, emotion engine means for analyzing user emotions, and means for customizing notification content based on the emotion engine to match the user's emotions. This not only automatically ensures the safety of the system when responding to OSS vulnerabilities, but also makes it possible to recognize user emotions and provide appropriate feedback.

[1625] A "vulnerability database" is a database that collects known security vulnerabilities and related information.

[1626] "New vulnerability information" is information about vulnerabilities that have been newly added to the vulnerability database.

[1627] A "compatible version" is a version of software that has been fixed for a specific vulnerability and can be used safely.

[1628] "Software" means technical means including a program running on a computer and its associated data.

[1629] An "update" is the act of updating software to the latest version and fixing vulnerabilities and bugs.

[1630] "System testing" is a set of testing processes and methods used to verify that software works as expected.

[1631] An "emotion engine" is a technical means for analyzing a user's emotions and detecting a specific emotional state.

[1632] "User emotions" refers to the user's psychological reactions and feedback to system notifications and operations.

[1633] "Customizing notification content" refers to the act of changing the content of the notification message depending on the user's emotional state, thereby providing more appropriate information.

[1634] "Temporary fix code" refers to a code change that is temporarily applied before an official fix version is released, in order to temporarily fix a vulnerability.

[1635] "Mitigation" is a set of security measures implemented to minimize the impact of a vulnerability.

[1636] The "emotion database" is a database that stores data on users' emotions and uses it for later analysis and notification improvement.

[1637] "Feedback" refers to information including reactions and opinions from users regarding notifications and operations of the system.

[1638] This invention is a system that automatically detects and fixes vulnerabilities in open source software (OSS), and also recognizes user emotions to optimize the response. This system is mainly managed by a server, and notifies and gives feedback to users.

[1639] System configuration

[1640] 1. Vulnerability detection

[1641] The server periodically scans vulnerability databases (e.g., NVD and OSS-Security) using monitoring tools (e.g., OpenVAS) to detect new vulnerability information. Detected vulnerability information is automatically recorded in a log.

[1642] 2. Check the version that addresses the vulnerability

[1643] For the vulnerability detected by the server, the server uses an API request to check whether a fixed version has been published in the official repository or distribution source (e.g., GitHub, GitLab). If a fixed version is confirmed to exist, the server proceeds to the next processing step based on that information.

[1644] 3. Supported version update

[1645] The server uses automation tools such as Ansible or Chef to update the target OSS to the corresponding version, then runs a full system test suite (e.g., Jenkins, Selenium) to verify that the update has not adversely affected the system.

[1646] 4. Impact assessment

[1647] If a compatible version is not available, the server will use static analysis tools such as SonarQube or dynamic analysis tools such as OWASP ZAP to assess the extent of the impact on the system and identify which functions and modules are affected by the vulnerability.

[1648] 5. Application of the provisional revised code

[1649] The server uses PatchBank and Ghidra to assess the extent of the impact and generates a temporary fix. This temporary fix is ​​then applied to the target OSS, and system testing is performed again to confirm its effectiveness.

[1650] 6. Implementing mitigation measures

[1651] If remediation is difficult, apply mitigation measures such as strengthening the server's firewall settings (e.g., IPTables) or access control (e.g., SELinux).

[1652] 7. Notice to Users

[1653] The server uses Nagios or Grafana to notify users and system administrators of the vulnerability, along with details of potential fixes or mitigation measures, and manual action is required if necessary.

[1654] 8. User sentiment analysis using an emotion engine

[1655] The server uses a natural language processing engine such as the Google Cloud Natural Language API to analyze emotions from the user's operation logs and reactions to notifications, and detects specific emotional states (e.g., anxiety, relief, etc.).

[1656] 9. Customized notifications and feedback

[1657] The server customizes the notification content based on the analysis results of the emotion engine and provides feedback tailored to the user's emotions. For example, if the user is feeling anxious, it adds detailed explanations and support information.

[1658] 10. Building an Emotion Database

[1659] The server collects user emotional data and stores it in an emotional database, which enables the system to utilize past emotional data when responding to future vulnerabilities and provide more effective notifications and support.

[1660] Specific examples

[1661] Example 1: Library vulnerabilities and sentiment analysis

[1662] The server detects a new vulnerability (CVE-XXXX-YYYY) in OSS Library A using OpenVAS and confirms via the GitHub API that version 1.2.4 has been released to address the vulnerability. The server updates Library A to the latest version using Ansible and runs a full system test using Jenkins. The server then analyzes user sentiment regarding the vulnerability response results using the Google Cloud Natural Language API and sends a customized notification to reduce anxiety.

[1663] Example prompt sentence:

[1664] "A new vulnerability (CVE-XXXX-YYYY) has been detected in OSS library A. It has been updated to version 1.2.4 and the test results are normal. Please rest assured."

[1665] Example 2: Judging unsupported versions and emotional feedback

[1666] The server detects a vulnerability (CVE-XXXX-ZZZZ) in another library B using OpenVAS. However, because a fixed version has not yet been released, the server uses SonarQube to confirm the scope of the impact, confirms that it is only related to a specific function, and temporarily disables that function. After generating and applying a temporary fix using PatchBank, the server uses the Google Cloud Natural Language API to analyze user sentiment and sends a notification containing detailed instructions and support information to provide reassurance.

[1667] Example prompt sentence:

[1668] Regarding the vulnerability in Library B (CVE-XXXX-ZZZZ), a fixed version has not yet been released, but we have limited the scope of impact and temporarily disabled certain functions. We will send you a detailed guide, so please check it.

[1669] This system aims to improve the user experience while automatically ensuring the safety of the system by integrating OSS vulnerability response and user emotion management.

[1670] The flow of the identification process in the second embodiment will be described with reference to FIG.

[1671] Step 1:

[1672] Vulnerability detection

[1673] The server periodically scans vulnerability databases (e.g., NVD or OSS-Security) using a monitoring tool (e.g., OpenVAS).

[1674] The server obtains new vulnerability information from the vulnerability database (input: vulnerability database, output: vulnerability information).

[1675] The server automatically logs new vulnerability information.

[1676] Specific behavior:

[1677] The server starts OpenVAS and scans the vulnerability database. If it finds a new vulnerability, it logs it as "CVE-XXXX-YYYY" and proceeds to the next step.

[1678] Step 2:

[1679] Check the version that addresses the vulnerability

[1680] For vulnerabilities detected by the server, an API request is used to check whether a fixed version is available in an official repository (e.g., GitHub, GitLab) or from the distribution source.

[1681] The server checks whether a compatible version exists (input: vulnerability information, output: compatible version information).

[1682] Specific behavior:

[1683] The server sends a request to the GitHub API to get the latest version of Library A. It verifies that the corresponding version 1.2.4 is available.

[1684] Step 3:

[1685] Supported version update

[1686] The server uses automation tools such as Ansible or Chef to update the target OSS to a compatible version.

[1687] The server runs a full system test suite (e.g., Jenkins, Selenium) to ensure the update has not adversely affected the system (input: target software, output: update results).

[1688] Specific behavior:

[1689] The server uses Ansible to update Library A to version 1.2.4, then runs all system tests in Jenkins and checks the test results.

[1690] Step 4:

[1691] Impact assessment

[1692] If a compatible version does not exist, the server will assess the extent of the impact on the system using static analysis tools such as SonarQube or dynamic analysis tools such as OWASP ZAP.

[1693] The server identifies which functions and modules are affected by the vulnerability (input: vulnerability information, output: impact assessment results).

[1694] Specific behavior:

[1695] The server launches SonarQube, which analyzes the code across the entire system, and based on the results of the analysis, identifies the affected modules and logs them.

[1696] Step 5:

[1697] Application of provisional amendment code

[1698] The server uses PatchBank and Ghidra to assess the extent of the impact and generate provisional fix code.

[1699] The server applies the provisional fix code to the target OSS and performs the necessary tests (input: impact assessment results, output: provisional fix code).

[1700] Specific behavior:

[1701] The server uses PatchBank to generate provisional fixes for the identified vulnerabilities, applies the fixes, and re-runs the system tests.

[1702] Step 6:

[1703] Implementing mitigation measures

[1704] If remediation is difficult, apply mitigation measures such as strengthening the server's firewall settings (e.g., IPTables) or access control (e.g., SELinux) (Input: vulnerability information, Output: mitigation measures).

[1705] Specific behavior:

[1706] The server changes the IPTables settings to restrict communication within the scope affected by the vulnerability. Test the changed settings to confirm their effectiveness.

[1707] Step 7:

[1708] User Notification

[1709] The server uses Nagios or Grafana to notify users and system administrators of the vulnerability information, provisional fix code, or details of mitigation measures, requiring manual action if necessary (input: vulnerability response information, output: notification).

[1710] Specific behavior:

[1711] The server will then send an email notification to the system administrator via Nagios, containing the vulnerability detected and recommended manual steps to take.

[1712] Step 8:

[1713] User sentiment analysis using an emotion engine

[1714] The server uses a natural language processing engine such as Google Cloud Natural Language API to analyze emotions from the user's operation log and reactions to notifications (input: user operation log, output: emotion analysis results).

[1715] Specific behavior:

[1716] The server collects user operation logs and performs sentiment analysis via the Google Cloud Natural Language API to determine whether the user is feeling "anxiety."

[1717] Step 9:

[1718] Customized notifications and feedback

[1719] The server customizes the notification content based on the analysis results of the emotion engine and provides feedback tailored to the user's emotions (input: emotion analysis results, output: customized notification).

[1720] Specific behavior:

[1721] The server analyzes the user's emotional data, and based on the user's anxiety, creates detailed guidance to provide reassurance and sends a notification.

[1722] Step 10:

[1723] Building an emotion database

[1724] The server collects user emotional data and stores it in an emotional database. Past emotional data can be used to provide more effective notifications and support when responding to future vulnerabilities (input: emotional data, output: emotional database).

[1725] Specific behavior:

[1726] The server stores the emotion data in a database and updates the emotion database for future analysis and notifications.

[1727] (Application example 2)

[1728] Next, a description will be given of Application Example 2. In the following description, the data processing device 12 will be referred to as a "server" and the robot 414 will be referred to as a "terminal."

[1729] Vulnerabilities in open source software (OSS) are a serious problem that threatens the security of the entire system. Conventional methods require manual detection of vulnerabilities and the application of fixed versions, which is extremely time-consuming and laborious. In addition, users often feel uneasy about how to respond to such vulnerabilities, and if they are not properly addressed, it can cause further problems. It is necessary to solve these problems and promote automation and improve the user experience.

[1730] The identification process by the identification processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means. In this invention, the server includes means for monitoring a vulnerability database and detecting new vulnerability information, means for checking whether a fixed version exists for the detected vulnerability, means for automatically updating the target open source software if a fixed version exists, means for running a system test after the update and evaluating the test results, and means for recognizing user emotions and optimizing feedback. This enables automatic response to OSS vulnerabilities, reduces user anxiety, and enables optimal feedback to be provided quickly.

[1731] The "Vulnerability Database" is a database for recording and managing information on known vulnerabilities related to open source software (OSS).

[1732] "Vulnerabilities" refer to security flaws or weaknesses that exist in open source software.

[1733] "Corrected Version" means a corrected version of the software provided to address a particular vulnerability.

[1734] "Open source software" refers to software that is free for anyone to use, modify, and distribute.

[1735] "Means for recognizing emotions" refers to technology that analyzes a user's operations and reactions and estimates the user's emotional state.

[1736] "Feedback optimization" refers to technology that provides the most appropriate response or notification content based on the user's emotional state.

[1737] "Means for assessing the scope of impact" refers to technology for analyzing the extent to which a detected vulnerability will affect the entire system.

[1738] "Temporary fix code" is provisional code generated to temporarily fix a vulnerability.

[1739] "Mitigations" are temporary measures to minimize the impact on a system until a vulnerability is fully fixed.

[1740] This invention is a system for automatically managing vulnerabilities in open source software (OSS) and optimizing responses by recognizing user emotions. This system is primarily run on a server and interacts with user devices.

[1741] System configuration

[1742] 1. Vulnerability detection and monitoring

[1743] The server periodically monitors vulnerability databases (e.g., NVD and OSS-Security) to detect new vulnerability information, which includes obtaining and analyzing vulnerability data.

[1744] 2. Check the version that addresses the vulnerability

[1745] When a vulnerability is detected, the server checks the official repository or distributor to see if a compatible version is available.

[1746] 3. Automatic Updates

[1747] If a compatible version is available, the server will automatically update the target open source software. After the update, a system test will be run and the test results will be evaluated.

[1748] 4. Impact assessment

[1749] If a compatible version does not exist, the server assesses the extent of the impact on the system and generates and applies temporary fixes as necessary.

[1750] 5. Emotion Recognition and Feedback

[1751] The server uses emotion recognition technology (EmotionRecognizer) to recognize the user's emotions. It analyzes emotions based on the user's operation log and feedback, and optimizes the feedback.

[1752] 6. Customized Notifications

[1753] The server provides customized notifications and support information based on the user's emotions, which can reduce the user's anxiety and encourage appropriate responses.

[1754] Hardware and Software Examples

[1755] EmotionRecognizer: An emotion recognition module for analyzing user emotions.

[1756] VulnerabilityScanner: A vulnerability scanning tool for scanning OSS vulnerabilities.

[1757] NotificationSystem: The notification system for sending notifications to the user.

[1758] Python: The main programming language for this system.

[1759] Smartphone: The device (Android / iOS) on which the user uses the application.

[1760] Example

[1761] Example 1: The server detects a new vulnerability in an OSS library, and if a fixed version is available, it automatically updates the library and runs a full system test. After confirming that it works properly, it analyzes user sentiment and sends detailed feedback.

[1762] Example 2: If a compatible version is not available, the server assesses the extent of the impact, generates and applies a temporary fix, and if the user is concerned, sends a notification with detailed support information.

[1763] Prompt Sentence Examples

[1764] "Create a prototype of an application that detects OSS vulnerabilities and provides appropriate feedback by analyzing user emotions. Specifically, use Python and the EmotionRecognizer, VulnerabilityScanner, and NotificationSystem modules."

[1765] The flow of the specific processing in the application example 2 will be described with reference to FIG.

[1766] Step 1:

[1767] The server monitors vulnerability databases (e.g., NVD or OSS-Security) to detect new vulnerability information. As input, it uses the latest vulnerability information obtained from the vulnerability database. This is used to detect whether a vulnerability exists in the system. To obtain data, it periodically accesses the database via a REST API. As output, it obtains newly detected vulnerability information.

[1768] Step 2:

[1769] The server checks official repositories or distribution sources to see if a fixed version exists for the detected vulnerability. The vulnerability information obtained in step 1 is used as input. In this step, the server obtains information about the fixed version through web scraping or API calls. The output is the presence or absence of a fixed version.

[1770] Step 3:

[1771] The server automatically updates the target open source software if a compatible version exists. As input, it uses the results of step 2 and the URL of the compatible version. In this step, it executes the software update using a package management tool (e.g., pip or npm). As output, it reports that the update was successful.

[1772] Step 4:

[1773] After the update, the server runs the system tests and evaluates the test results. It uses the latest software state and test scripts as input. In this step, it runs the system tests using an automated testing tool (e.g., JUnit or pytest). As output, it obtains a log of the test results and a verdict.

[1774] Step 5:

[1775] If a compatible version is not available, the server evaluates the extent of the impact on the system. It uses the vulnerability information from step 1 as input. In this step, it uses a program analysis tool to identify the modules and functions affected by the vulnerability. It obtains information on the extent of the impact as output.

[1776] Step 6:

[1777] The server automatically generates and applies a temporary fix based on the impact range. The impact range information from step 5 is used as input. In this step, a program fix tool is used to generate a temporary fix and apply it to the system. The output is the state in which the temporary fix has been applied.

[1778] Step 7:

[1779] The server analyzes emotions based on the user's operation log and feedback. The user's operation log and feedback information are used as input. In this step, emotion recognition technology (EmotionRecognizer) is used to determine the user's emotions. The output is the user's emotional information.

[1780] Step 8:

[1781] The server provides customized notifications and support information based on the user's emotions. As input, it uses the user's emotion information obtained in step 7. In this step, it sends appropriate feedback and support information to the user through the notification system. As output, a notification is sent to the user's device.

[1782] 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.

[1783] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search <url: https: gemini.google.com ?hl="ja">) and other generation AIs. 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 voice, text data indicating text, and image data indicating an image is also input. 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.

[1784] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of the present disclosure is not limited to this, and the specific processing may be performed by the robot 414.

[1785] 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.

[1786] FIG. 9 is a diagram illustrating 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 actions arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion includes both affect 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.

[1787] 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.

[1788] 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).

[1789] 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 indicated, and when they approach the ideal, a state of pleasure is indicated. Emotions can also be created for robots, automobiles, 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 indicated, and when they approach the ideal, a state of pleasure is indicated. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on Voice Emotion Recognition and Emotional Brain Physiological Signal Analysis Systems, Tokushima University, Doctoral Dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[1790] 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."

[1791] 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.

[1792] The system according to the present disclosure has been described above mainly with respect to the functions of the data processing device 12, but the system according to the present disclosure is not necessarily implemented on a server. The system according to the present disclosure may be implemented as a general information processing system. The present disclosure may be implemented, for example, as a software program running on a personal computer or an application running on a smartphone, etc. The method according to the present disclosure may be provided to users in the form of SaaS (Software as a Service).

[1793] In the above embodiment, an example was given in which the specific processing is performed by one computer 22, but the technology of the present disclosure is not limited to this, and the specific processing may be distributed and performed by a plurality of computers including the computer 22. For example, the data generation model 58 may be provided in an external device of the data processing device 12, and data may be generated in the external device in accordance with input data.

[1794] 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.

[1795] 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.

[1796] 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.

[1797] The hardware resource for executing a specific process can be any of the following processors: An example of a processor 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. Another example of a processor is 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.

[1798] The hardware resource that executes the specific processing 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 processing may be a single processor.

[1799] 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.

[1800] 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.

[1801] 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.

[1802] 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.

[1803] The following is further disclosed regarding the above embodiment.

[1804] (Claim 1)

[1805] A means of monitoring the vulnerability database and detecting new vulnerability information;

[1806] A means of verifying the existence of a version that addresses the detected vulnerability;

[1807] A method to automatically update the target open source software when a compatible version is available, and

[1808] A means of running system tests after an update and evaluating the test results;

[1809] A system including:

[1810] (Claim 2)

[1811] A means for assessing the extent of impact on a system when a compatible version does not exist;

[1812] a means for automatically generating and applying a provisional correction code based on the evaluation results;

[1813] a means of notifying the user if the system becomes insecure;

[1814] The system of claim 1 further comprising:

[1815] (Claim 3)

[1816] 10. The system of claim 1, further comprising means for automatically applying mitigation measures to minimize system impact.

[1817] "Example 1"

[1818] (Claim 1)

[1819] A means of monitoring the vulnerability database and detecting new vulnerability information;

[1820] A means of verifying the existence of a version that addresses the detected vulnerability;

[1821] If a compatible version is available, a method to automatically update the target software,

[1822] A means of running system tests after an update and evaluating the test results;

[1823] If a compatible version does not exist, there is a way to automatically analyze the scope of the impact,

[1824] A means for generating and applying provisional correction code as a result of the impact assessment;

[1825] A system including:

[1826] (Claim 2)

[1827] 10. The system of claim 1, further comprising means for automatically applying mitigation measures to maintain system security.

[1828] (Claim 3)

[1829] 2. The system of claim 1, further comprising means for notifying a user or administrator when the corrective or mitigating measures are insufficient.

[1830] "Application Example 1"

[1831] (Claim 1)

[1832] A means of monitoring the vulnerability database and detecting new vulnerability information;

[1833] A means of verifying the existence of a version that addresses the detected vulnerability;

[1834] If a compatible version is available, a method to automatically update the target software,

[1835] A means of running system tests after an update and evaluating the test results;

[1836] a means for notifying a user;

[1837] A system including:

[1838] (Claim 2)

[1839] A means for assessing the extent of impact on a system when a compatible version does not exist;

[1840] a means for automatically generating and applying a provisional correction code based on the evaluation results;

[1841] a means of notifying the user if the system becomes insecure;

[1842] Measures to mitigate discovered vulnerabilities; and

[1843] The system of claim 1 further comprising:

[1844] (Claim 3)

[1845] 2. The system according to claim 1, further comprising means for periodically updating the system based on the monitored vulnerability information to maintain the security of the system.

[1846] "Example 2: Combining Emotion Engines"

[1847] (Claim 1)

[1848] A means of monitoring the vulnerability database and detecting new vulnerability information;

[1849] A means of verifying the existence of a version that addresses the detected vulnerability;

[1850] If a compatible version is available, a method to automatically update the target software,

[1851] A means of running system tests after an update and evaluating the test results;

[1852] emotion engine means for analyzing the emotion of a user;

[1853] A means for customizing notification content according to a user's emotions based on an emotion engine;

[1854] A system including:

[1855] (Claim 2)

[1856] A means for assessing the extent of impact on a system when a compatible version does not exist;

[1857] a means for automatically generating and applying a provisional correction code based on the evaluation results;

[1858] a means of notifying the user if the system becomes insecure;

[1859] A means for accumulating user emotion data and constructing an emotion database;

[1860] 10. The system of claim 1, comprising:

[1861] (Claim 3)

[1862] Automatically apply mitigation measures to minimize system impact, and

[1863] A means of providing automatically generated notifications and feedback to users;

[1864] 10. The system of claim 1, comprising:

[1865] "Application example 2 when combining emotion engines"

[1866] (Claim 1)

[1867] A means of monitoring the vulnerability database and detecting new vulnerability information;

[1868] A means of verifying the existence of a version that addresses the detected vulnerability;

[1869] A method to automatically update the target open source software when a compatible version is available, and

[1870] A means of running system tests after an update and evaluating the test results;

[1871] A means for recognizing user emotions and optimizing feedback;

[1872] A system including:

[1873] (Claim 2)

[1874] A means for assessing the extent of impact on a system when a compatible version does not exist;

[1875] a means for automatically generating and applying a provisional correction code based on the evaluation results;

[1876] a means of notifying the user if the system becomes insecure;

[1877] means for providing customized notifications and feedback based on user sentiment;

[1878] The system of claim 1 further comprising:

[1879] (Claim 3)

[1880] 10. The system of claim 1, further comprising means for automatically applying mitigation measures to minimize system impact. [Explanation of symbols]

[1881] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot< / url:> < / url:> < / url:> < / url:>

Claims

1. A means of monitoring the vulnerability database and detecting new vulnerability information; A means of verifying the existence of a version that addresses the detected vulnerability; A method to automatically update the target open source software when a compatible version is available, and A means of running system tests after an update and evaluating the test results; A system including:

2. A means for assessing the extent of impact on a system when a compatible version does not exist; a means for automatically generating and applying a provisional correction code based on the evaluation results; a means of notifying the user if the system becomes insecure; The system of claim 1 further comprising:

3. The system of claim 1 further comprising means for automatically applying mitigation measures to minimize system impact.

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