system

Abstract

We provide the system. [Solution] A method for generating vulnerability test items by automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence, A means for autonomously performing vulnerability testing on an information processing device 24 hours a day, 365 days a year using the generated test items, A means for analyzing test results and generating a report that shows vulnerabilities and proposed solutions, A filtering means that monitors the displayed content and detects potentially fraudulent patterns in a terminal device operated by a user, A warning system that notifies users of information indicating suspicious activity, A system that includes this.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In modern information processing devices and information providing devices, with the complication of their functions, the risk of potential vulnerabilities occurring is increasing. These vulnerabilities may be exploited by malicious attackers and are serious problems threatening the security of the system. Also, in the conventional methods, a great deal of time and human resources are required for vulnerability diagnosis, and there is a problem that efficient and highly accurate diagnosis is difficult. This invention is proposed to solve these problems.

Means for Solving the Problems

[0005] This invention is characterized by utilizing generative artificial intelligence to automatically analyze the display screen of an information processing device and generate vulnerability test items. Using the generated test items, the system autonomously performs vulnerability testing on the target information processing device without interruption, and generates a report that identifies vulnerabilities and proposes remediation measures based on the test results, thereby achieving efficient security inspection. This system enables highly accurate vulnerability assessment with fewer human resources compared to conventional methods.

[0006] "Generative artificial intelligence" is a system of technology that learns from large amounts of data and performs pattern recognition and information generation, possessing the ability to creatively and automatically generate output in response to new inputs for specific tasks.

[0007] An "information processing device" is a device equipped with the functions of inputting, processing, and outputting data, and is a general term that includes various computer systems such as computers and smartphones.

[0008] A "display screen" is an interface on a display device intended to provide visual information to the user, and includes user interface elements for interaction.

[0009] "Vulnerability test items" refer to specific test content and methods used to confirm whether an information processing device has security risks such as unauthorized access or information leakage.

[0010] "Vulnerability testing" is an inspection procedure conducted to detect security flaws in information processing equipment, and it evaluates the degree to which the equipment is resistant to attacks and misuse.

[0011] A "report" is a document that analyzes the results of vulnerability testing and provides a detailed explanation of the problems found and the countermeasures taken to address them. [Brief explanation of the drawing]

[0012] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of the data processing device and smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Figure 11] This is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] This is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] This is a sequence diagram showing the processing flow of the data processing system in Example 2, when an emotion engine is combined. [Figure 14] This is a sequence diagram showing the processing flow of the data processing system in Application Example 2, which combines an emotion engine. [Modes for carrying out the invention]

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the terms used in the following description will be explained.

[0015] In the following embodiments, the numbered processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the numbered RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the numbered storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, and the like.

[0018] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna, etc. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0019] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." That is, "A and / or B" means that it may be A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] 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 perceptible to the user 20 (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0027] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] As shown in Figure 2, in the data processing device 12, a specific processing is performed by the processor 28. A specific processing program 56 is stored in the storage 32. The specific processing program 56 is an example of a "program" related to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0030] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] The system according to the present invention aims to automatically identify vulnerabilities from the display screen of an information processing device and conduct efficient security inspections by utilizing generative artificial intelligence. This system is mainly realized through the interaction of a server, a terminal, and a user. Its specific form is shown below.

[0034] First, the server provides a platform equipped with an AI agent. This platform operates 24 / 7 on the server, receiving data from information processing devices, generating test items, conducting tests, and analyzing the results. Based on an updated vulnerability database, it autonomously repeats these steps, ensuring that it always has defenses against the latest attack methods.

[0035] The terminal is an information processing device operated by the user, and as the user uses it as usual, it sends the displayed screen and input information to the server. The terminal monitors all interface elements on the screen, such as text input fields and buttons, in real time and provides this information to the server.

[0036] As an example, consider a scenario where a user interacts with the login screen of a web application. The terminal sends structural information of this screen to the server. Based on this information, an AI agent on the server automatically identifies appropriate vulnerabilities and generates test items for attack methods such as SQL injection and cross-site scripting.

[0037] During this process, the server performs tests and analyzes the results. The analysis results are generated as a report that includes details of the detected vulnerabilities and suggested remediation measures. This report is provided to the user to help them take appropriate security measures in response to the issues found.

[0038] This system, implemented in this way, helps users maintain a consistently high level of security, even without specialized security knowledge.

[0039] The following describes the processing flow.

[0040] Step 1:

[0041] The device analyzes the displayed screen and collects UI information. The device scans all interface elements on the screen that the user is interacting with and analyzes this information (for example, form field attributes and button labels).

[0042] Step 2:

[0043] The device sends collected UI information to the server. The device packages the analyzed screen information and sends it to the server as data for security testing.

[0044] Step 3:

[0045] The server activates an AI agent and analyzes the UI information. Based on the received UI information, the server automatically generates the test items necessary for vulnerability assessment. The AI ​​agent analyzes this information and determines which vulnerability tests should be performed.

[0046] Step 4:

[0047] The server executes the test items. The server sequentially executes each test item generated by the AI ​​agent to check for vulnerabilities. Specifically, it performs attack simulations such as SQL injection and XSS.

[0048] Step 5:

[0049] The server analyzes the test results and generates a report. The server analyzes the test execution results in detail and generates a report summarizing the detected vulnerabilities, their severity, and recommended countermeasures.

[0050] Step 6:

[0051] The user receives and reviews the report. The user reviews the report sent from the server and takes necessary security measures. The report clearly indicates high-priority issues and prompts the user to take action.

[0052] (Example 1)

[0053] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0054] Traditional security testing systems have the problem of being unable to efficiently and continuously address the latest vulnerabilities because detecting and mitigating vulnerabilities requires a great deal of time and specialized knowledge. Furthermore, manual input analysis and test item generation consume human resources and can affect the accuracy of the tests. As a result, many companies and individuals are unable to implement adequate security measures and are exposed to potential risks.

[0055] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0056] In this invention, the server includes means for analyzing input information based on screen information acquired from an information processing device using generative artificial intelligence to generate test items, means for receiving and monitoring displayed screen information in real time, and means for analyzing test results to generate a report including details of vulnerabilities and proposed remediation measures. This enables efficient vulnerability countermeasures that are always in response to the latest security threats, even for users who do not possess special security expertise.

[0057] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to automatically learn from data and generate new data through pattern recognition and prediction.

[0058] An "information processing device" refers to an electronic computing device such as a computer or smartphone, which has the function of inputting, processing, and outputting data.

[0059] "Screen information" refers to the visual data displayed on the display device of an information processing device, and includes text, images, and interface elements.

[0060] "Input information" refers to data provided to an information processing device by a user or system, and includes keyboard input, mouse operations, and touch input.

[0061] A "test item" refers to a specific set of conditions or procedures that should be verified during a security inspection, and is generated to identify vulnerabilities.

[0062] "Vulnerability testing" refers to inspections performed on information processing equipment to identify security flaws and weaknesses, thereby enabling the evaluation of the system's security.

[0063] A "report" refers to a document generated based on test results, which contains details of the vulnerabilities detected and proposed fixes.

[0064] A "user interface" refers to the point of contact when a user interacts with an information processing device or software, and it includes graphical elements and controls.

[0065] This invention is centered around three elements: a server, a terminal, and a user. The invention is implemented through a security inspection system utilizing generative artificial intelligence technology.

[0066] The server provides a platform equipped with a generative AI model, receiving and monitoring screen information transmitted from information processing devices. Specifically, the server operates 24 / 7, acquiring data from information processing devices in real time and generating security-related test items based on that data. These include threats such as SQL injection and cross-site scripting. Once test items are generated, the server automatically performs security checks, analyzes the test results, and generates a report. This report includes details of the detected vulnerabilities and proposed remediation measures.

[0067] The terminal provides the means for the user to operate the information processing device. During normal use, the terminal transmits the displayed screen and user input information to the server. Data automatically collected by the terminal assists in security checks performed by the server.

[0068] Users can utilize the system without requiring special security expertise. For example, when a user interacts with the login screen of a web application, the terminal sends screen information to the server. Based on this information, a generation AI model on the server automatically generates appropriate test items and conducts the inspection.

[0069] An example of a prompt message would be, "Identify vulnerabilities in the web application's login screen and generate appropriate security test items." This allows users to benefit from advanced system security measures in their daily work.

[0070] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0071] Step 1:

[0072] The terminal monitors the display screen and input information as the user operates the information processing device as usual. Specifically, the terminal captures interface elements such as text input fields and buttons in real time and extracts that information as data. It acquires screen information resulting from user operations as input and prepares to send it to the server as output.

[0073] Step 2:

[0074] The server receives screen information data sent from the terminal. The server then analyzes the received data and generates security test items using a generation AI model. This process uses screen information as input data and generates test items to identify vulnerabilities as output. Specifically, the AI ​​model considers the possibility of SQL injection and cross-site scripting attacks and automatically creates corresponding test items.

[0075] Step 3:

[0076] The server automatically performs security checks based on the generated test items. During the test, it attempts to induce vulnerabilities using hypothetical attack patterns. The generated test items are used as input, and the test results are obtained as output. The specific operation of this test includes a process of simulating several hypothetical user input scenarios to check for unauthorized intrusion.

[0077] Step 4:

[0078] The server analyzes the results of security checks and generates a report. Here, the result data obtained from the tests is used as input, processed by an analytical AI model, and outputs a report that includes details of vulnerabilities and suggested remediation methods. Specifically, the report describes the location of the vulnerability, the severity of its impact, and detailed information on how to fix it.

[0079] Step 5:

[0080] Users can receive reports provided by the server and take security measures. The input received by the user is a report generated by the server, and the output is the implementation of corrective actions based on that report. Specifically, users improve the application's code and configuration based on the report to eliminate potential vulnerabilities.

[0081] (Application Example 1)

[0082] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0083] In recent years, cyberattacks on information processing equipment have been increasing, with phishing scams and attacks exploiting vulnerabilities being particularly problematic. While security measures to counter these attacks are becoming more sophisticated, it remains difficult for users without specialized knowledge to implement appropriate defenses. Furthermore, real-time vulnerability detection and rapid response are required, necessitating effective and user-friendly solutions.

[0084] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0085] In this invention, the server includes means for automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence and generating vulnerability test items; means for autonomously performing vulnerability tests on the information processing device 24 hours a day, 365 days a year using the generated test items; means for analyzing the test results and generating a report showing vulnerabilities and proposed corrections; filtering means for monitoring the display content and detecting potentially fraudulent patterns on a terminal device operated by the user; and warning means for notifying the user of information indicating suspicious activity. As a result, even if the user does not have specialized knowledge, they can be protected from threats such as phishing scams in real time, and at the same time, the latest defensive measures can be taken regarding vulnerabilities in the information processing device.

[0086] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to learn from past data and generate new information and patterns.

[0087] An "information processing device" is a device used for inputting, processing, storing, and outputting data, and primarily refers to a computer.

[0088] A "display screen" refers to the interface that an information processing device uses to visually convey data to the user, and includes monitors and displays.

[0089] "Input content" refers to the data and information that the user provides to the information processing device, and includes keyboard input and touch input.

[0090] "Vulnerability test items" are specific inspection criteria and procedures set up to detect potential security holes in a system or application.

[0091] A "terminal device" is an information processing device that is directly operated by the user, and includes smartphones, tablets, and other similar devices.

[0092] "Potential fraud patterns" refer to suspicious behaviors or characteristics used to identify phishing sites, malware, and other malicious activities.

[0093] "Warning measures" refer to methods and tools for informing users of dangers or anomalies, and include functions for issuing alert messages and notifications.

[0094] A "report" is a document that summarizes test results and analysis results, and includes information on security threats and countermeasures.

[0095] In implementing this invention, the system, as a security monitoring platform utilizing generative artificial intelligence, comprises three main elements: a server, a terminal, and a user.

[0096] The server, equipped with generative artificial intelligence, receives data transmitted from information processing devices 24 hours a day, 365 days a year. Built using programming languages ​​such as Python, it utilizes natural language processing libraries and machine learning frameworks (e.g., SpaCy and TENSORFLOW®). The server analyzes data and web page structures entered in real time from user terminals, automatically generating test items against attacks such as SQL injection and cross-site scripting. It also analyzes the test results, creates a report including details on vulnerabilities and suggested fixes, and provides it to the user.

[0097] On the device, when applications and websites that the user uses daily are displayed, their structure is automatically analyzed and sent to the server. The device monitors the content of the user interface and detects potentially fraudulent patterns. This protects the user from phishing sites and malicious activity.

[0098] As a concrete example, when a user accesses an online banking site, the terminal analyzes all input fields on that page in real time and provides the data to the server. Based on the entered information, the server automatically assesses for vulnerabilities and immediately displays a warning if it detects unsafe behavior. This warning allows the user to avoid further risks.

[0099] Examples of prompts for a generative AI model include the following:

[0100] "You are a phishing site detection agent. Analyze the structure and content of web pages that users visit and look for patterns that indicate suspicious activity. When you find signs of phishing, generate a warning message."

[0101] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0102] Step 1:

[0103] The terminal monitors user operations and retrieves the data actually entered from the display screen of the information processing device. This input consists of text and form information directly entered by the user on a web page or application. The terminal then sends this data to the server. The output of this step is the data actually entered by the user.

[0104] Step 2:

[0105] The server receives input data sent from the terminal and begins analyzing this data using a generative AI model. This analysis uses a natural language processing library (e.g., SpaCy) to scrutinize the input and generate initial test items to discover potential vulnerabilities. The input is the user's input data, and the output is the generated test items.

[0106] Step 3:

[0107] Based on test items generated by the server, the system autonomously performs vulnerability testing on the information processing device. Here, a machine learning framework (e.g., TensorFlow) is used to detect patterns that could be considered vulnerabilities. The input is the generated test items, and the output is the test results regarding the vulnerabilities.

[0108] Step 4:

[0109] The server analyzes the test results and generates a report containing details of the detected vulnerabilities and suggested remediations. This analysis includes a statistical assessment based on the test results, listing the most critical vulnerabilities. The input is the results of the vulnerability tests, and the output is the completed report.

[0110] Step 5:

[0111] The server sends the completed report to the terminal and notifies the user. Based on this information, the terminal displays a summary of the report and warnings on the screen, prompting the user to take appropriate action. The input is the report from the server, and the output is the warning notification and report display received by the user.

[0112] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0113] This invention provides a system that combines an AI agent equipped with generative artificial intelligence and an emotion engine, offering a new method for enhancing the security of information processing devices. This system analyzes input content from the display screen of the information processing device and automatically and autonomously performs vulnerability testing. Furthermore, it recognizes the user's emotions using the emotion engine and appropriately adjusts the execution of tests and the presentation of reports accordingly.

[0114] The server provides a platform that integrates an AI agent and an emotion engine. The AI ​​agent analyzes user interface information sent from the terminal and generates test items based on vulnerability assessments. This process enables the security of information processing devices to be maintained 24 / 7.

[0115] The emotion engine monitors the user's facial expressions, voice, input speed, and operation patterns via the device to estimate the user's emotional state. For example, if the user is experiencing stress, the emotion engine can instruct the server to temporarily ease the test or adjust the report to be more concise and easier to understand.

[0116] As a concrete example, while a user logs into the system and performs their normal tasks, the terminal extracts emotional data from its operation patterns and speed, as well as background audio. The server analyzes this data, and if it detects a moment when the user is particularly anxious or stressed, it immediately refrains from submitting a vulnerability report or softens its wording.

[0117] Ultimately, the server generates and provides the user with a report based on the test results and the sentiment engine's analysis. This report includes the severity of the detected vulnerabilities and the priority of countermeasures based on the user's sentiment state, making it a crucial source of information for the user to take effective action.

[0118] Therefore, in addition to conventional vulnerability assessments using AI agents, this system utilizes an emotion engine to optimize the user experience, achieving both enhanced security and user-friendly support.

[0119] The following describes the processing flow.

[0120] Step 1:

[0121] The device monitors the user's actions. The device collects information such as the user's action speed, input patterns, facial expressions, and voice to supply to the emotion engine. This information is later analyzed by the emotion engine.

[0122] Step 2:

[0123] The device sends structural information of the display screen to the server. The device analyzes the UI information of the application the user is operating and sends that data to the AI ​​agent on the server.

[0124] Step 3:

[0125] The server uses an AI agent to generate vulnerability test items based on UI information. The AI ​​agent autonomously analyzes the received information and determines how to proceed with the testing.

[0126] Step 4:

[0127] The server uses an emotion engine to evaluate the user's emotional state. The emotion engine analyzes the user's emotional data sent from the terminal in real time and estimates the user's mental state.

[0128] Step 5:

[0129] Based on the evaluation of the emotion engine, the server adjusts the execution of the test. For example, if the user is experiencing stress, the server adjusts the frequency and timing of the test to reduce the user's burden.

[0130] Step 6:

[0131] The server performs vulnerability testing and collects the results. It executes test items generated by an AI agent and records in detail the problems detected during the testing and their impact.

[0132] Step 7:

[0133] The server analyzes the test results and generates a report that takes emotional states into account. The report includes detected vulnerabilities, estimated user emotional states, and priority actions.

[0134] Step 8:

[0135] The user receives the report and reviews its contents. Based on the information provided, the user takes necessary security measures to ensure the system's security.

[0136] (Example 2)

[0137] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0138] Traditional vulnerability testing systems fail to consider the emotional state of users, resulting in an inability to adequately address users experiencing stress or anxiety. Furthermore, if the timing of testing or the content of reports are not user-friendly, many users will find them difficult to understand, leading to delays in implementing countermeasures. To address these issues, a system is needed that assesses users' emotional states in real time and adapts tests and reports accordingly.

[0139] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0140] In this invention, the server includes means for automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence and generating vulnerability test items; means for autonomously performing vulnerability tests on the information processing device 24 hours a day, 365 days a year using the generated test items; means for analyzing the test results and generating a report showing vulnerabilities and proposed corrections; means for estimating the user's emotional state using emotional data including facial expressions, voice, input speed, and operation patterns collected from the terminal; and means for adjusting the content of the vulnerability test and the expression of the results according to the estimated emotional state. This makes it possible to provide effective and user-friendly security measures that take into account the user's emotional state.

[0141] "Generative artificial intelligence" refers to an artificial intelligence system that has the ability to autonomously generate knowledge based on data and perform new information processing.

[0142] An "information processing device" is a device, including computers and their associated devices, used for collecting, analyzing, storing, and communicating data.

[0143] "Means for analyzing input content" refers to a method or process for analyzing and understanding data obtained from a user interface.

[0144] "Vulnerability testing items" refer to the specific tests and inspections performed to identify security weaknesses in information processing equipment.

[0145] "Emotional data" refers to information such as facial expressions, voice, input speed, and operation patterns that are collected to evaluate the user's current emotional state.

[0146] "Means for estimating emotional state" refers to a method or process for analyzing emotional data to identify a user's current psychological state.

[0147] "Means of analyzing test results" refers to methods or processes for evaluating the results of vulnerability testing and understanding the security issues and areas for improvement discovered.

[0148] "Means of generating a report" refers to a method or process for documenting information to be provided to the user based on the results and analysis of the test.

[0149] This invention is a system that combines an AI agent and an emotion engine to enhance the security of information processing devices. The entire system consists of a server, terminals, and interfaces for each user.

[0150] The server provides a platform that includes both generative artificial intelligence (AI agent) and an emotion engine. The AI ​​agent analyzes user interface information transmitted from the user's terminal and generates test items based on vulnerability assessments. The AI ​​agent uses deep learning algorithms to automatically perform data analysis and test item generation.

[0151] The device collects data in real time during interactions with the user. In particular, it monitors the user's facial expressions, voice, input speed, and operation patterns, and sends this data to the server as emotional data. The device has a built-in camera and microphone, and acquires user data through smooth interface operation.

[0152] For users, emotional data is implicitly collected while they perform their daily tasks and operations. The actions users actually take are routine operations and do not require any special preparation or attention. For example, while a user logs into the system and operates a work application, the terminal monitors these actions and collects data.

[0153] The emotion engine runs on the server and analyzes emotion data received from the terminal. This allows it to detect whether the user is experiencing stress and provides feedback to the server based on the detection results. The server then uses this feedback to adjust vulnerability testing and report presentation.

[0154] For example, if a user performs an action that causes them anxiety, the system will immediately mitigate the test and simplify the results report. An example of a prompt used as part of this process is: "Quickly identify the emotional state of the user as they log into the system and perform routine tasks, and adaptively adjust the security report accordingly."

[0155] This invention enables effective vulnerability management by providing user-friendly support in addition to automated security diagnostics.

[0156] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0157] Step 1:

[0158] The device monitors user actions. Specifically, it captures the user's facial expressions with its camera and acquires audio with its microphone. It also records the speed and patterns of keyboard and mouse operations. This input data is immediately transmitted to the server. The output is collected emotion data packets.

[0159] Step 2:

[0160] The server analyzes the emotional data received from the terminal. The emotion engine analyzes facial expressions, voice, and operation patterns to estimate the user's emotional state. Deep learning technology is used to quantify stress and anxiety levels. The output is an evaluation result indicating the user's emotional state.

[0161] Step 3:

[0162] The server uses an AI agent to analyze information from the user interface. Specifically, it uses a generative AI model to automatically generate vulnerability test items from the user's interface operation history. At this time, the AI, which has learned from past data and patterns, determines the appropriate test content. The output is a list of generated test items.

[0163] Step 4:

[0164] The server performs vulnerability testing based on the estimated emotional state and generated test items. The AI ​​agent automatically performs each test based on the list of test items, but adjusts the timing and content of the tests if the user is experiencing stress. The output is the result data of the tests performed.

[0165] Step 5:

[0166] The server analyzes the test results and generates a report based on those results. It adjusts the report's wording, taking sentiment evaluation into account, to make it user-friendly. Specifically, it organizes the test results in stages and proposes corrective actions based on their importance. The output is the final vulnerability assessment report.

[0167] (Application Example 2)

[0168] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0169] Conventional security systems for information processing devices often fail to consider the user's emotional state in vulnerability testing and notification of results, which can compromise the user experience. In particular, when users are experiencing stress or anxiety, reports and alerts may be presented in overly technical or harsh language, making it difficult for them to properly address vulnerabilities.

[0170] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0171] In this invention, the server includes means for automatically analyzing input content from a display device using generative artificial intelligence and generating vulnerability test items, means for autonomously performing vulnerability tests 24 hours a day, 365 days a year, and means for evaluating the user's emotional state using emotion recognition means and adjusting the content of security notifications and reports according to the emotion. This enables flexible security responses in accordance with the user's emotional state, simultaneously achieving optimization of the user experience and enhancement of security.

[0172] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data within an information processing device to generate new information or test items.

[0173] "Vulnerability testing" is a test conducted to detect security weaknesses in information processing equipment and to identify those problems.

[0174] "Emotion recognition means" refers to technology that analyzes a user's emotional state based on their facial expressions, voice, and input patterns.

[0175] A "security notification" is a notification about the security status and threats related to information processing equipment, informing users of vulnerabilities and related recommended countermeasures.

[0176] "User experience" is a general term for the experiences and sensations that users have when using information processing devices and their functions.

[0177] The system of the present invention enhances security and user experience by acquiring information from the user's terminal and processing it on a server. Specifically, the terminal uses a camera and microphone to collect the user's facial expressions and voice in real time and estimate their emotions. This estimated emotion data is then transmitted to the server.

[0178] The server uses a generative AI model to analyze the received data and adjusts vulnerability testing and report generation based on the user's emotional state. Specifically, if the server determines that the user is in a high-stress state, it will adjust the content of security alerts and reports to be more gentle. This optimizes the user experience and ensures that users do not feel stressed while using the interface.

[0179] The technology used involves performing sentiment analysis using Microsoft® Azure® sentiment recognition APIs and generating reports and notifications in appropriate formats using generative AI models (for example, OpenAI® APIs). This enables flexible responses that align with the user's emotions.

[0180] As a concrete example, if the terminal detects user stress during financial transactions, the server should immediately refrain from displaying a detailed vulnerability report and instead provide a more easily understandable report later when the user is relaxed. An example of a prompt message would be, "If the user's emotional state indicates stress, please adjust the wording of the security report to be concise and gentle."

[0181] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0182] Step 1:

[0183] The user begins operating the information processing device via the terminal. The terminal's camera and microphone collect the user's facial expressions and voice in real time. The input data consists of facial expression data and voice data, which serve as foundational information for emotion recognition.

[0184] Step 2:

[0185] The device sends the collected facial and voice data to the Microsoft Azure emotion recognition API. This API analyzes the data and estimates and outputs the user's emotional state. Specifically, it analyzes smiles and changes in voice tone to identify emotions such as excitement, calmness, and stress. The output is an emotion label and its intensity.

[0186] Step 3:

[0187] The server receives output from the emotion recognition API and uses a generative AI model to generate prompt messages tailored to the current emotional state. The input consists of emotion labels and their intensity, which are used to determine the appropriate report format. The output is a security notification or report in a gentle tone that matches the user's emotions.

[0188] Step 4:

[0189] While the user continues to operate the system, the server autonomously performs vulnerability testing. Based on the content of the prompt messages generated in the previous step, it generates a test results report and adjusts the content as needed. It performs actual checks based on the test items and outputs the results as a report based on the prompt messages.

[0190] Step 5:

[0191] The server sends a refined report to the user's device. The user can then review the report on their device and take appropriate action based on the security information provided. The goal here is to optimize the user experience, ensuring that the security information is presented in an easy-to-understand format without causing stress.

[0192] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0193] Data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0194] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0195] [Second Embodiment]

[0196] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0197] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0198] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0199] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0200] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0201] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0202] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0203] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0204] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0205] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0206] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0207] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. 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".

[0208] The system according to the present invention aims to automatically identify vulnerabilities from the display screen of an information processing device and conduct efficient security inspections by utilizing generative artificial intelligence. This system is mainly realized through the interaction of a server, a terminal, and a user. Its specific form is shown below.

[0209] First, the server provides a platform equipped with an AI agent. This platform operates 24 / 7 on the server, receiving data from information processing devices, generating test items, conducting tests, and analyzing the results. Based on an updated vulnerability database, it autonomously repeats these steps, ensuring that it always has defenses against the latest attack methods.

[0210] The terminal is an information processing device operated by the user, and as the user uses it as usual, it sends the displayed screen and input information to the server. The terminal monitors all interface elements on the screen, such as text input fields and buttons, in real time and provides this information to the server.

[0211] As an example, consider a scenario where a user interacts with the login screen of a web application. The terminal sends structural information of this screen to the server. Based on this information, an AI agent on the server automatically identifies appropriate vulnerabilities and generates test items for attack methods such as SQL injection and cross-site scripting.

[0212] During this process, the server performs tests and analyzes the results. The analysis results are generated as a report that includes details of the detected vulnerabilities and suggested remediation measures. This report is provided to the user to help them take appropriate security measures in response to the issues found.

[0213] This system, implemented in this way, helps users maintain a consistently high level of security, even without specialized security knowledge.

[0214] The following describes the processing flow.

[0215] Step 1:

[0216] The device analyzes the displayed screen and collects UI information. The device scans all interface elements on the screen that the user is interacting with and analyzes this information (for example, form field attributes and button labels).

[0217] Step 2:

[0218] The device sends collected UI information to the server. The device packages the analyzed screen information and sends it to the server as data for security testing.

[0219] Step 3:

[0220] The server activates an AI agent and analyzes the UI information. Based on the received UI information, the server automatically generates the test items necessary for vulnerability assessment. The AI ​​agent analyzes this information and determines which vulnerability tests should be performed.

[0221] Step 4:

[0222] The server executes the test items. The server sequentially executes each test item generated by the AI ​​agent to check for vulnerabilities. Specifically, it performs attack simulations such as SQL injection and XSS.

[0223] Step 5:

[0224] The server analyzes the test results and generates a report. The server analyzes the test execution results in detail and generates a report summarizing the detected vulnerabilities, their severity, and recommended countermeasures.

[0225] Step 6:

[0226] The user receives and reviews the report. The user reviews the report sent from the server and takes necessary security measures. The report clearly indicates high-priority issues and prompts the user to take action.

[0227] (Example 1)

[0228] Next, we will describe Example 1. 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."

[0229] Traditional security testing systems have the problem of being unable to efficiently and continuously address the latest vulnerabilities because detecting and mitigating vulnerabilities requires a great deal of time and specialized knowledge. Furthermore, manual input analysis and test item generation consume human resources and can affect the accuracy of the tests. As a result, many companies and individuals are unable to implement adequate security measures and are exposed to potential risks.

[0230] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0231] In this invention, the server includes means for analyzing input information based on screen information acquired from an information processing device using generative artificial intelligence to generate test items, means for receiving and monitoring displayed screen information in real time, and means for analyzing test results to generate a report including details of vulnerabilities and proposed remediation measures. This enables efficient vulnerability countermeasures that are always in response to the latest security threats, even for users who do not possess special security expertise.

[0232] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to automatically learn from data and generate new data through pattern recognition and prediction.

[0233] An "information processing device" refers to an electronic computing device such as a computer or smartphone, which has the function of inputting, processing, and outputting data.

[0234] "Screen information" refers to the visual data displayed on the display device of an information processing device, and includes text, images, and interface elements.

[0235] "Input information" refers to data provided to an information processing device by a user or system, and includes keyboard input, mouse operations, and touch input.

[0236] A "test item" refers to a specific set of conditions or procedures that should be verified during a security inspection, and is generated to identify vulnerabilities.

[0237] "Vulnerability testing" refers to inspections performed on information processing equipment to identify security flaws and weaknesses, thereby enabling the evaluation of the system's security.

[0238] A "report" refers to a document generated based on test results, which contains details of the vulnerabilities detected and proposed fixes.

[0239] A "user interface" refers to the point of contact when a user interacts with an information processing device or software, and it includes graphical elements and controls.

[0240] This invention is centered around three elements: a server, a terminal, and a user. The invention is implemented through a security inspection system utilizing generative artificial intelligence technology.

[0241] The server provides a platform equipped with a generative AI model, receiving and monitoring screen information transmitted from information processing devices. Specifically, the server operates 24 / 7, acquiring data from information processing devices in real time and generating security-related test items based on that data. These include threats such as SQL injection and cross-site scripting. Once test items are generated, the server automatically performs security checks, analyzes the test results, and generates a report. This report includes details of the detected vulnerabilities and proposed remediation measures.

[0242] The terminal provides the means for the user to operate the information processing device. During normal use, the terminal transmits the displayed screen and user input information to the server. Data automatically collected by the terminal assists in security checks performed by the server.

[0243] Users can utilize the system without requiring special security expertise. For example, when a user interacts with the login screen of a web application, the terminal sends screen information to the server. Based on this information, a generation AI model on the server automatically generates appropriate test items and conducts the inspection.

[0244] An example of a prompt message would be, "Identify vulnerabilities in the web application's login screen and generate appropriate security test items." This allows users to benefit from advanced system security measures in their daily work.

[0245] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0246] Step 1:

[0247] The terminal monitors the display screen and input information as the user operates the information processing device as usual. Specifically, the terminal captures interface elements such as text input fields and buttons in real time and extracts that information as data. It acquires screen information resulting from user operations as input and prepares to send it to the server as output.

[0248] Step 2:

[0249] The server receives screen information data sent from the terminal. The server then analyzes the received data and generates security test items using a generation AI model. This process uses screen information as input data and generates test items to identify vulnerabilities as output. Specifically, the AI ​​model considers the possibility of SQL injection and cross-site scripting attacks and automatically creates corresponding test items.

[0250] Step 3:

[0251] The server automatically performs security checks based on the generated test items. During the test, it attempts to induce vulnerabilities using hypothetical attack patterns. The generated test items are used as input, and the test results are obtained as output. The specific operation of this test includes a process of simulating several hypothetical user input scenarios to check for unauthorized intrusion.

[0252] Step 4:

[0253] The server analyzes the results of security checks and generates a report. Here, the result data obtained from the tests is used as input, processed by an analytical AI model, and outputs a report that includes details of vulnerabilities and suggested remediation methods. Specifically, the report describes the location of the vulnerability, the severity of its impact, and detailed information on how to fix it.

[0254] Step 5:

[0255] Users can receive reports provided by the server and take security measures. The input received by the user is a report generated by the server, and the output is the implementation of corrective actions based on that report. Specifically, users improve the application's code and configuration based on the report to eliminate potential vulnerabilities.

[0256] (Application Example 1)

[0257] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] In recent years, cyberattacks on information processing equipment have been increasing, with phishing scams and attacks exploiting vulnerabilities being particularly problematic. While security measures to counter these attacks are becoming more sophisticated, it remains difficult for users without specialized knowledge to implement appropriate defenses. Furthermore, real-time vulnerability detection and rapid response are required, necessitating effective and user-friendly solutions.

[0259] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0260] In this invention, the server includes means for automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence and generating vulnerability test items; means for autonomously performing vulnerability tests on the information processing device 24 hours a day, 365 days a year using the generated test items; means for analyzing the test results and generating a report showing vulnerabilities and proposed corrections; filtering means for monitoring the display content and detecting potentially fraudulent patterns on a terminal device operated by the user; and warning means for notifying the user of information indicating suspicious activity. As a result, even if the user does not have specialized knowledge, they can be protected from threats such as phishing scams in real time, and at the same time, the latest defensive measures can be taken regarding vulnerabilities in the information processing device.

[0261] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to learn from past data and generate new information and patterns.

[0262] An "information processing device" is a device used for inputting, processing, storing, and outputting data, and primarily refers to a computer.

[0263] A "display screen" refers to the interface that an information processing device uses to visually convey data to the user, and includes monitors and displays.

[0264] "Input content" refers to the data and information that the user provides to the information processing device, and includes keyboard input and touch input.

[0265] "Vulnerability test items" are specific inspection criteria and procedures set up to detect potential security holes in a system or application.

[0266] A "terminal device" is an information processing device that is directly operated by the user, and includes smartphones, tablets, and other similar devices.

[0267] "Potential fraud patterns" refer to suspicious behaviors or characteristics used to identify phishing sites, malware, and other malicious activities.

[0268] "Warning measures" refer to methods and tools for informing users of dangers or anomalies, and include functions for issuing alert messages and notifications.

[0269] A "report" is a document that summarizes test results and analysis results, and includes information on security threats and countermeasures.

[0270] In implementing this invention, the system, as a security monitoring platform utilizing generative artificial intelligence, comprises three main elements: a server, a terminal, and a user.

[0271] The server, equipped with generative artificial intelligence, receives data transmitted from information processing devices 24 hours a day, 365 days a year. Built using programming languages ​​such as Python, it utilizes natural language processing libraries and machine learning frameworks (e.g., SpaCy and TensorFlow). The server analyzes data and web page structures entered in real time from user terminals, automatically generating test items against attacks such as SQL injection and cross-site scripting. It also analyzes the test results, creates a report including details on vulnerabilities and suggested fixes, and provides it to the user.

[0272] On the device, when applications and websites that the user uses daily are displayed, their structure is automatically analyzed and sent to the server. The device monitors the content of the user interface and detects potentially fraudulent patterns. This protects the user from phishing sites and malicious activity.

[0273] As a concrete example, when a user accesses an online banking site, the terminal analyzes all input fields on that page in real time and provides the data to the server. Based on the entered information, the server automatically assesses for vulnerabilities and immediately displays a warning if it detects unsafe behavior. This warning allows the user to avoid further risks.

[0274] Examples of prompts for a generative AI model include the following:

[0275] "You are a phishing site detection agent. Analyze the structure and content of web pages that users visit and look for patterns that indicate suspicious activity. When you find signs of phishing, generate a warning message."

[0276] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0277] Step 1:

[0278] The terminal monitors the user's operations and acquires the data actually input from the display screen of the information processing device. The input here is the text and form information directly input by the user on the web page or application. The terminal transmits the data to the server. The output of this step is the data actually input by the user.

[0279] Step 2:

[0280] The server receives the input data transmitted from the terminal and starts analyzing this data using the generated AI model. In this analysis operation, a natural language processing library (for example, SpaCy) is used to scrutinize the input content, and initial test items for discovering potential vulnerabilities are generated. The input is the user's input data, and the output is the generated test items.

[0281] Step 3:

[0282] Based on the test items generated by the server, a vulnerability test is autonomously executed on the information processing device. Here, a machine learning framework (for example, TensorFlow) is utilized to detect patterns considered as vulnerabilities. The input is the generated test items, and the output is the test results regarding vulnerabilities.

[0283] Step 4:

[0284] The server analyzes the test results and generates a report including details of the detected vulnerabilities and modification proposals. This analysis includes a statistical evaluation based on the test results and lists up the most important vulnerabilities. The input is the results of the vulnerability test, and the output is the completed report.

[0285] Step 5:

[0286] The server sends the completed report to the terminal and notifies the user. Based on this information, the terminal displays the summary and warnings of the report on the screen and prompts the user to take appropriate measures. The input is the report from the server, and the outputs are the warning notifications received by the user and the display of the report.

[0287] Furthermore, an emotion engine for estimating the user's emotion may be combined. That is, the specific processing unit 290 may estimate the user's emotion using the emotion recognition model 59 and perform specific processing using the user's emotion.

[0288] The present invention is a system that combines an AI agent equipped with generative artificial intelligence and an emotion engine, and provides a new method for strengthening the security of an information processing device. This system not only analyzes the input content from the display screen of the information processing device and automatically and autonomously conducts vulnerability tests, but also recognizes the user's emotion by the emotion engine and appropriately adjusts the implementation of the tests and the presentation of reports accordingly.

[0289] The server provides a platform in which an AI agent and an emotion engine are integrated. The AI agent analyzes the information of the user interface transmitted from the terminal and generates test items based on the evaluation of vulnerabilities. This process enables the security of the information processing device to be maintained 24 hours a day, 365 days a year.

[0290] The emotion engine monitors the user's expression, voice, input speed, operation pattern, etc. via the terminal and estimates the user's emotional state. For example, when the user is feeling stressed, the emotion engine instructs the server to temporarily relax the implementation of the test or has the function of adjusting the content of the report to be concise and easy to understand.

[0291] As a concrete example, while a user logs into the system and performs their normal tasks, the terminal extracts emotional data from its operation patterns and speed, as well as background audio. The server analyzes this data, and if it detects a moment when the user is particularly anxious or stressed, it immediately refrains from submitting a vulnerability report or softens its wording.

[0292] Ultimately, the server generates and provides the user with a report based on the test results and the sentiment engine's analysis. This report includes the severity of the detected vulnerabilities and the priority of countermeasures based on the user's sentiment state, making it a crucial source of information for the user to take effective action.

[0293] Therefore, in addition to conventional vulnerability assessments using AI agents, this system utilizes an emotion engine to optimize the user experience, achieving both enhanced security and user-friendly support.

[0294] The following describes the processing flow.

[0295] Step 1:

[0296] The device monitors the user's actions. The device collects information such as the user's action speed, input patterns, facial expressions, and voice to supply to the emotion engine. This information is later analyzed by the emotion engine.

[0297] Step 2:

[0298] The device sends structural information of the display screen to the server. The device analyzes the UI information of the application the user is operating and sends that data to the AI ​​agent on the server.

[0299] Step 3:

[0300] The server uses an AI agent to generate vulnerability test items based on UI information. The AI agent autonomously analyzes the received information and determines how to proceed with the test.

[0301] Step 4:

[0302] The server uses an emotion engine to evaluate the user's emotional state. The emotion engine analyzes the user's emotional data transmitted from the terminal in real time and estimates the user's mental state.

[0303] Step 5:

[0304] According to the evaluation of the emotion engine, the server adjusts the implementation of the test. For example, if the user is feeling stressed, the server adjusts the frequency and timing of the test to reduce the user's burden.

[0305] Step 6:

[0306] The server executes the vulnerability test and collects the results. It executes the test items generated by the AI agent and details the problems detected in the test and their impacts.

[0307] Step 7:

[0308] The server analyzes the results of the test and generates a report considering the emotional state. The report includes the detected vulnerabilities, the estimated emotional state of the user, and the countermeasures to be prioritized.

[0309] Step 8:

[0310] The user receives the report and checks the content. Based on the provided information, the user takes the necessary security measures to ensure the security of the system.

[0311] (Example 2)

[0312] Next, we will describe Example 2. 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".

[0313] Traditional vulnerability testing systems fail to consider the emotional state of users, resulting in an inability to adequately address users experiencing stress or anxiety. Furthermore, if the timing of testing or the content of reports are not user-friendly, many users will find them difficult to understand, leading to delays in implementing countermeasures. To address these issues, a system is needed that assesses users' emotional states in real time and adapts tests and reports accordingly.

[0314] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0315] In this invention, the server includes means for automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence and generating vulnerability test items; means for autonomously performing vulnerability tests on the information processing device 24 hours a day, 365 days a year using the generated test items; means for analyzing the test results and generating a report showing vulnerabilities and proposed corrections; means for estimating the user's emotional state using emotional data including facial expressions, voice, input speed, and operation patterns collected from the terminal; and means for adjusting the content of the vulnerability test and the expression of the results according to the estimated emotional state. This makes it possible to provide effective and user-friendly security measures that take into account the user's emotional state.

[0316] "Generative artificial intelligence" refers to an artificial intelligence system that has the ability to autonomously generate knowledge based on data and perform new information processing.

[0317] An "information processing device" is a device, including computers and their associated devices, used for collecting, analyzing, storing, and communicating data.

[0318] "Means for analyzing input content" refers to a method or process for analyzing and understanding data obtained from a user interface.

[0319] "Vulnerability testing items" refer to the specific tests and inspections performed to identify security weaknesses in information processing equipment.

[0320] "Emotional data" refers to information such as facial expressions, voice, input speed, and operation patterns that are collected to evaluate the user's current emotional state.

[0321] "Means for estimating emotional state" refers to a method or process for analyzing emotional data to identify a user's current psychological state.

[0322] "Means of analyzing test results" refers to methods or processes for evaluating the results of vulnerability testing and understanding the security issues and areas for improvement discovered.

[0323] "Means of generating a report" refers to a method or process for documenting information to be provided to the user based on the results and analysis of the test.

[0324] This invention is a system that combines an AI agent and an emotion engine to enhance the security of information processing devices. The entire system consists of a server, terminals, and interfaces for each user.

[0325] The server provides a platform that includes both generative artificial intelligence (AI agent) and an emotion engine. The AI ​​agent analyzes user interface information transmitted from the user's terminal and generates test items based on vulnerability assessments. The AI ​​agent uses deep learning algorithms to automatically perform data analysis and test item generation.

[0326] The device collects data in real time during interactions with the user. In particular, it monitors the user's facial expressions, voice, input speed, and operation patterns, and sends this data to the server as emotional data. The device has a built-in camera and microphone, and acquires user data through smooth interface operation.

[0327] For users, emotional data is implicitly collected while they perform their daily tasks and operations. The actions users actually take are routine operations and do not require any special preparation or attention. For example, while a user logs into the system and operates a work application, the terminal monitors these actions and collects data.

[0328] The emotion engine runs on the server and analyzes emotion data received from the terminal. This allows it to detect whether the user is experiencing stress and provides feedback to the server based on the detection results. The server then uses this feedback to adjust vulnerability testing and report presentation.

[0329] For example, if a user performs an action that causes them anxiety, the system will immediately mitigate the test and simplify the results report. An example of a prompt used as part of this process is: "Quickly identify the emotional state of the user as they log into the system and perform routine tasks, and adaptively adjust the security report accordingly."

[0330] This invention enables effective vulnerability management by providing user-friendly support in addition to automated security diagnostics.

[0331] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0332] Step 1:

[0333] The device monitors user actions. Specifically, it captures the user's facial expressions with its camera and acquires audio with its microphone. It also records the speed and patterns of keyboard and mouse operations. This input data is immediately transmitted to the server. The output is collected emotion data packets.

[0334] Step 2:

[0335] The server analyzes the emotional data received from the terminal. The emotion engine analyzes facial expressions, voice, and operation patterns to estimate the user's emotional state. Deep learning technology is used to quantify stress and anxiety levels. The output is an evaluation result indicating the user's emotional state.

[0336] Step 3:

[0337] The server uses an AI agent to analyze information from the user interface. Specifically, it uses a generative AI model to automatically generate vulnerability test items from the user's interface operation history. At this time, the AI, which has learned from past data and patterns, determines the appropriate test content. The output is a list of generated test items.

[0338] Step 4:

[0339] The server performs vulnerability testing based on the estimated emotional state and generated test items. The AI ​​agent automatically performs each test based on the list of test items, but adjusts the timing and content of the tests if the user is experiencing stress. The output is the result data of the tests performed.

[0340] Step 5:

[0341] The server analyzes the test results and generates a report based on those results. It adjusts the report's wording, taking sentiment evaluation into account, to make it user-friendly. Specifically, it organizes the test results in stages and proposes corrective actions based on their importance. The output is the final vulnerability assessment report.

[0342] (Application Example 2)

[0343] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the smart glasses 214 as the "terminal".

[0344] Conventional security systems for information processing devices often fail to consider the user's emotional state in vulnerability testing and notification of results, which can compromise the user experience. In particular, when users are experiencing stress or anxiety, reports and alerts may be presented in overly technical or harsh language, making it difficult for them to properly address vulnerabilities.

[0345] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0346] In this invention, the server includes means for automatically analyzing input content from a display device using generative artificial intelligence and generating vulnerability test items, means for autonomously performing vulnerability tests 24 hours a day, 365 days a year, and means for evaluating the user's emotional state using emotion recognition means and adjusting the content of security notifications and reports according to the emotion. This enables flexible security responses in accordance with the user's emotional state, simultaneously achieving optimization of the user experience and enhancement of security.

[0347] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data within an information processing device to generate new information or test items.

[0348] "Vulnerability testing" is a test conducted to detect security weaknesses in information processing equipment and to identify those problems.

[0349] "Emotion recognition means" refers to technology that analyzes a user's emotional state based on their facial expressions, voice, and input patterns.

[0350] A "security notification" is a notification about the security status and threats related to information processing equipment, informing users of vulnerabilities and related recommended countermeasures.

[0351] "User experience" is a general term for the experiences and sensations that users have when using information processing devices and their functions.

[0352] The system of the present invention enhances security and user experience by acquiring information from the user's terminal and processing it on a server. Specifically, the terminal uses a camera and microphone to collect the user's facial expressions and voice in real time and estimate their emotions. This estimated emotion data is then transmitted to the server.

[0353] The server uses a generative AI model to analyze the received data and adjusts vulnerability testing and report generation based on the user's emotional state. Specifically, if the server determines that the user is in a high-stress state, it will adjust the content of security alerts and reports to be more gentle. This optimizes the user experience and ensures that users do not feel stressed while using the interface.

[0354] The technology used involves performing sentiment analysis using Microsoft Azure's sentiment recognition API and generating reports and notifications in appropriate formats using a generative AI model (for example, OpenAI's API). This enables flexible responses that align with the user's emotions.

[0355] As a concrete example, if the terminal detects user stress during financial transactions, the server should immediately refrain from displaying a detailed vulnerability report and instead provide a more easily understandable report later when the user is relaxed. An example of a prompt message would be, "If the user's emotional state indicates stress, please adjust the wording of the security report to be concise and gentle."

[0356] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0357] Step 1:

[0358] The user begins operating the information processing device via the terminal. The terminal's camera and microphone collect the user's facial expressions and voice in real time. The input data consists of facial expression data and voice data, which serve as foundational information for emotion recognition.

[0359] Step 2:

[0360] The device sends the collected facial and voice data to the Microsoft Azure emotion recognition API. This API analyzes the data and estimates and outputs the user's emotional state. Specifically, it analyzes smiles and changes in voice tone to identify emotions such as excitement, calmness, and stress. The output is an emotion label and its intensity.

[0361] Step 3:

[0362] The server receives output from the emotion recognition API and uses a generative AI model to generate prompt messages tailored to the current emotional state. The input consists of emotion labels and their intensity, which are used to determine the appropriate report format. The output is a security notification or report in a gentle tone that matches the user's emotions.

[0363] Step 4:

[0364] While the user continues to operate the system, the server autonomously performs vulnerability testing. Based on the content of the prompt messages generated in the previous step, it generates a test results report and adjusts the content as needed. It performs actual checks based on the test items and outputs the results as a report based on the prompt messages.

[0365] Step 5:

[0366] The server sends a refined report to the user's device. The user can then review the report on their device and take appropriate action based on the security information provided. The goal here is to optimize the user experience, ensuring that the security information is presented in an easy-to-understand format without causing stress.

[0367] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0368] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0369] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0370] [Third Embodiment]

[0371] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0372] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0373] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0374] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0375] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0376] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0377] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0378] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0379] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0380] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0381] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0382] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0383] The system according to the present invention aims to automatically identify vulnerabilities from the display screen of an information processing device and conduct efficient security inspections by utilizing generative artificial intelligence. This system is mainly realized through the interaction of a server, a terminal, and a user. Its specific form is shown below.

[0384] First, the server provides a platform equipped with an AI agent. This platform operates 24 / 7 on the server, receiving data from information processing devices, generating test items, conducting tests, and analyzing the results. Based on an updated vulnerability database, it autonomously repeats these steps, ensuring that it always has defenses against the latest attack methods.

[0385] The terminal is an information processing device operated by the user, and as the user uses it as usual, it sends the displayed screen and input information to the server. The terminal monitors all interface elements on the screen, such as text input fields and buttons, in real time and provides this information to the server.

[0386] As an example, consider a scenario where a user interacts with the login screen of a web application. The terminal sends structural information of this screen to the server. Based on this information, an AI agent on the server automatically identifies appropriate vulnerabilities and generates test items for attack methods such as SQL injection and cross-site scripting.

[0387] During this process, the server performs tests and analyzes the results. The analysis results are generated as a report that includes details of the detected vulnerabilities and suggested remediation measures. This report is provided to the user to help them take appropriate security measures in response to the issues found.

[0388] This system, implemented in this way, helps users maintain a consistently high level of security, even without specialized security knowledge.

[0389] The following describes the processing flow.

[0390] Step 1:

[0391] The device analyzes the displayed screen and collects UI information. The device scans all interface elements on the screen that the user is interacting with and analyzes this information (for example, form field attributes and button labels).

[0392] Step 2:

[0393] The device sends collected UI information to the server. The device packages the analyzed screen information and sends it to the server as data for security testing.

[0394] Step 3:

[0395] The server activates an AI agent and analyzes the UI information. Based on the received UI information, the server automatically generates the test items necessary for vulnerability assessment. The AI ​​agent analyzes this information and determines which vulnerability tests should be performed.

[0396] Step 4:

[0397] The server executes the test items. The server sequentially executes each test item generated by the AI ​​agent to check for vulnerabilities. Specifically, it performs attack simulations such as SQL injection and XSS.

[0398] Step 5:

[0399] The server analyzes the test results and generates a report. The server analyzes the test execution results in detail and generates a report summarizing the detected vulnerabilities, their severity, and recommended countermeasures.

[0400] Step 6:

[0401] The user receives and reviews the report. The user reviews the report sent from the server and takes necessary security measures. The report clearly indicates high-priority issues and prompts the user to take action.

[0402] (Example 1)

[0403] Next, we will describe Example 1. 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."

[0404] Traditional security testing systems have the problem of being unable to efficiently and continuously address the latest vulnerabilities because detecting and mitigating vulnerabilities requires a great deal of time and specialized knowledge. Furthermore, manual input analysis and test item generation consume human resources and can affect the accuracy of the tests. As a result, many companies and individuals are unable to implement adequate security measures and are exposed to potential risks.

[0405] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0406] In this invention, the server includes means for analyzing input information based on screen information acquired from an information processing device using generative artificial intelligence to generate test items, means for receiving and monitoring displayed screen information in real time, and means for analyzing test results to generate a report including details of vulnerabilities and proposed remediation measures. This enables efficient vulnerability countermeasures that are always in response to the latest security threats, even for users who do not possess special security expertise.

[0407] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to automatically learn from data and generate new data through pattern recognition and prediction.

[0408] An "information processing device" refers to an electronic computing device such as a computer or smartphone, which has the function of inputting, processing, and outputting data.

[0409] "Screen information" refers to the visual data displayed on the display device of an information processing device, and includes text, images, and interface elements.

[0410] "Input information" refers to data provided to an information processing device by a user or system, and includes keyboard input, mouse operations, and touch input.

[0411] A "test item" refers to a specific set of conditions or procedures that should be verified during a security inspection, and is generated to identify vulnerabilities.

[0412] "Vulnerability testing" refers to inspections performed on information processing equipment to identify security flaws and weaknesses, thereby enabling the evaluation of the system's security.

[0413] A "report" refers to a document generated based on test results, which contains details of the vulnerabilities detected and proposed fixes.

[0414] A "user interface" refers to the point of contact when a user interacts with an information processing device or software, and it includes graphical elements and controls.

[0415] This invention is centered around three elements: a server, a terminal, and a user. The invention is implemented through a security inspection system utilizing generative artificial intelligence technology.

[0416] The server provides a platform equipped with a generative AI model, receiving and monitoring screen information transmitted from information processing devices. Specifically, the server operates 24 / 7, acquiring data from information processing devices in real time and generating security-related test items based on that data. These include threats such as SQL injection and cross-site scripting. Once test items are generated, the server automatically performs security checks, analyzes the test results, and generates a report. This report includes details of the detected vulnerabilities and proposed remediation measures.

[0417] The terminal provides the means for the user to operate the information processing device. During normal use, the terminal transmits the displayed screen and user input information to the server. Data automatically collected by the terminal assists in security checks performed by the server.

[0418] Users can utilize the system without requiring special security expertise. For example, when a user interacts with the login screen of a web application, the terminal sends screen information to the server. Based on this information, a generation AI model on the server automatically generates appropriate test items and conducts the inspection.

[0419] An example of a prompt message would be, "Identify vulnerabilities in the web application's login screen and generate appropriate security test items." This allows users to benefit from advanced system security measures in their daily work.

[0420] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0421] Step 1:

[0422] The terminal monitors the display screen and input information as the user operates the information processing device as usual. Specifically, the terminal captures interface elements such as text input fields and buttons in real time and extracts that information as data. It acquires screen information resulting from user operations as input and prepares to send it to the server as output.

[0423] Step 2:

[0424] The server receives screen information data sent from the terminal. The server then analyzes the received data and generates security test items using a generation AI model. This process uses screen information as input data and generates test items to identify vulnerabilities as output. Specifically, the AI ​​model considers the possibility of SQL injection and cross-site scripting attacks and automatically creates corresponding test items.

[0425] Step 3:

[0426] The server automatically performs security checks based on the generated test items. During the test, it attempts to induce vulnerabilities using hypothetical attack patterns. The generated test items are used as input, and the test results are obtained as output. The specific operation of this test includes a process of simulating several hypothetical user input scenarios to check for unauthorized intrusion.

[0427] Step 4:

[0428] The server analyzes the results of security checks and generates a report. Here, the result data obtained from the tests is used as input, processed by an analytical AI model, and outputs a report that includes details of vulnerabilities and suggested remediation methods. Specifically, the report describes the location of the vulnerability, the severity of its impact, and detailed information on how to fix it.

[0429] Step 5:

[0430] Users can receive reports provided by the server and take security measures. The input received by the user is a report generated by the server, and the output is the implementation of corrective actions based on that report. Specifically, users improve the application's code and configuration based on the report to eliminate potential vulnerabilities.

[0431] (Application Example 1)

[0432] Next, we will explain Application Example 1. In the following explanation, 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."

[0433] In recent years, cyberattacks on information processing equipment have been increasing, with phishing scams and attacks exploiting vulnerabilities being particularly problematic. While security measures to counter these attacks are becoming more sophisticated, it remains difficult for users without specialized knowledge to implement appropriate defenses. Furthermore, real-time vulnerability detection and rapid response are required, necessitating effective and user-friendly solutions.

[0434] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0435] In this invention, the server includes means for automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence and generating vulnerability test items; means for autonomously performing vulnerability tests on the information processing device 24 hours a day, 365 days a year using the generated test items; means for analyzing the test results and generating a report showing vulnerabilities and proposed corrections; filtering means for monitoring the display content and detecting potentially fraudulent patterns on a terminal device operated by the user; and warning means for notifying the user of information indicating suspicious activity. As a result, even if the user does not have specialized knowledge, they can be protected from threats such as phishing scams in real time, and at the same time, the latest defensive measures can be taken regarding vulnerabilities in the information processing device.

[0436] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to learn from past data and generate new information and patterns.

[0437] An "information processing device" is a device used for inputting, processing, storing, and outputting data, and primarily refers to a computer.

[0438] A "display screen" refers to the interface that an information processing device uses to visually convey data to the user, and includes monitors and displays.

[0439] "Input content" refers to the data and information that the user provides to the information processing device, and includes keyboard input and touch input.

[0440] "Vulnerability test items" are specific inspection criteria and procedures set up to detect potential security holes in a system or application.

[0441] A "terminal device" is an information processing device that is directly operated by the user, and includes smartphones, tablets, and other similar devices.

[0442] "Potential fraud patterns" refer to suspicious behaviors or characteristics used to identify phishing sites, malware, and other malicious activities.

[0443] "Warning measures" refer to methods and tools for informing users of dangers or anomalies, and include functions for issuing alert messages and notifications.

[0444] A "report" is a document that summarizes test results and analysis results, and includes information on security threats and countermeasures.

[0445] In implementing this invention, the system, as a security monitoring platform utilizing generative artificial intelligence, comprises three main elements: a server, a terminal, and a user.

[0446] The server, equipped with generative artificial intelligence, receives data transmitted from information processing devices 24 hours a day, 365 days a year. Built using programming languages ​​such as Python, it utilizes natural language processing libraries and machine learning frameworks (e.g., SpaCy and TensorFlow). The server analyzes data and web page structures entered in real time from user terminals, automatically generating test items against attacks such as SQL injection and cross-site scripting. It also analyzes the test results, creates a report including details on vulnerabilities and suggested fixes, and provides it to the user.

[0447] On the device, when applications and websites that the user uses daily are displayed, their structure is automatically analyzed and sent to the server. The device monitors the content of the user interface and detects potentially fraudulent patterns. This protects the user from phishing sites and malicious activity.

[0448] As a concrete example, when a user accesses an online banking site, the terminal analyzes all input fields on that page in real time and provides the data to the server. Based on the entered information, the server automatically assesses for vulnerabilities and immediately displays a warning if it detects unsafe behavior. This warning allows the user to avoid further risks.

[0449] Examples of prompts for a generative AI model include the following:

[0450] "You are a phishing site detection agent. Analyze the structure and content of web pages that users visit and look for patterns that indicate suspicious activity. When you find signs of phishing, generate a warning message."

[0451] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0452] Step 1:

[0453] The terminal monitors user operations and retrieves the data actually entered from the display screen of the information processing device. This input consists of text and form information directly entered by the user on a web page or application. The terminal then sends this data to the server. The output of this step is the data actually entered by the user.

[0454] Step 2:

[0455] The server receives input data sent from the terminal and begins analyzing this data using a generative AI model. This analysis uses a natural language processing library (e.g., SpaCy) to scrutinize the input and generate initial test items to discover potential vulnerabilities. The input is the user's input data, and the output is the generated test items.

[0456] Step 3:

[0457] Based on test items generated by the server, the system autonomously performs vulnerability testing on the information processing device. Here, a machine learning framework (e.g., TensorFlow) is used to detect patterns that could be considered vulnerabilities. The input is the generated test items, and the output is the test results regarding the vulnerabilities.

[0458] Step 4:

[0459] The server analyzes the test results and generates a report containing details of the detected vulnerabilities and suggested remediations. This analysis includes a statistical assessment based on the test results, listing the most critical vulnerabilities. The input is the results of the vulnerability tests, and the output is the completed report.

[0460] Step 5:

[0461] The server sends the completed report to the terminal and notifies the user. Based on this information, the terminal displays a summary of the report and warnings on the screen, prompting the user to take appropriate action. The input is the report from the server, and the output is the warning notification and report display received by the user.

[0462] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0463] This invention provides a system that combines an AI agent equipped with generative artificial intelligence and an emotion engine, offering a new method for enhancing the security of information processing devices. This system analyzes input content from the display screen of the information processing device and automatically and autonomously performs vulnerability testing. Furthermore, it recognizes the user's emotions using the emotion engine and appropriately adjusts the execution of tests and the presentation of reports accordingly.

[0464] The server provides a platform that integrates an AI agent and an emotion engine. The AI ​​agent analyzes user interface information sent from the terminal and generates test items based on vulnerability assessments. This process enables the security of information processing devices to be maintained 24 / 7.

[0465] The emotion engine monitors the user's facial expressions, voice, input speed, and operation patterns via the device to estimate the user's emotional state. For example, if the user is experiencing stress, the emotion engine can instruct the server to temporarily ease the test or adjust the report to be more concise and easier to understand.

[0466] As a concrete example, while a user logs into the system and performs their normal tasks, the terminal extracts emotional data from its operation patterns and speed, as well as background audio. The server analyzes this data, and if it detects a moment when the user is particularly anxious or stressed, it immediately refrains from submitting a vulnerability report or softens its wording.

[0467] Ultimately, the server generates and provides the user with a report based on the test results and the sentiment engine's analysis. This report includes the severity of the detected vulnerabilities and the priority of countermeasures based on the user's sentiment state, making it a crucial source of information for the user to take effective action.

[0468] Therefore, in addition to conventional vulnerability assessments using AI agents, this system utilizes an emotion engine to optimize the user experience, achieving both enhanced security and user-friendly support.

[0469] The following describes the processing flow.

[0470] Step 1:

[0471] The device monitors the user's actions. The device collects information such as the user's action speed, input patterns, facial expressions, and voice to supply to the emotion engine. This information is later analyzed by the emotion engine.

[0472] Step 2:

[0473] The device sends structural information of the display screen to the server. The device analyzes the UI information of the application the user is operating and sends that data to the AI ​​agent on the server.

[0474] Step 3:

[0475] The server uses an AI agent to generate vulnerability test items based on UI information. The AI ​​agent autonomously analyzes the received information and determines how to proceed with the testing.

[0476] Step 4:

[0477] The server uses an emotion engine to evaluate the user's emotional state. The emotion engine analyzes the user's emotional data sent from the terminal in real time and estimates the user's mental state.

[0478] Step 5:

[0479] Based on the evaluation of the emotion engine, the server adjusts the execution of the test. For example, if the user is experiencing stress, the server adjusts the frequency and timing of the test to reduce the user's burden.

[0480] Step 6:

[0481] The server performs vulnerability testing and collects the results. It executes test items generated by an AI agent and records in detail the problems detected during the testing and their impact.

[0482] Step 7:

[0483] The server analyzes the test results and generates a report that takes emotional states into account. The report includes detected vulnerabilities, estimated user emotional states, and priority actions.

[0484] Step 8:

[0485] The user receives the report and reviews its contents. Based on the information provided, the user takes necessary security measures to ensure the system's security.

[0486] (Example 2)

[0487] Next, we will describe Example 2. 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."

[0488] Traditional vulnerability testing systems fail to consider the emotional state of users, resulting in an inability to adequately address users experiencing stress or anxiety. Furthermore, if the timing of testing or the content of reports are not user-friendly, many users will find them difficult to understand, leading to delays in implementing countermeasures. To address these issues, a system is needed that assesses users' emotional states in real time and adapts tests and reports accordingly.

[0489] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0490] In this invention, the server includes means for automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence and generating vulnerability test items; means for autonomously performing vulnerability tests on the information processing device 24 hours a day, 365 days a year using the generated test items; means for analyzing the test results and generating a report showing vulnerabilities and proposed corrections; means for estimating the user's emotional state using emotional data including facial expressions, voice, input speed, and operation patterns collected from the terminal; and means for adjusting the content of the vulnerability test and the expression of the results according to the estimated emotional state. This makes it possible to provide effective and user-friendly security measures that take into account the user's emotional state.

[0491] "Generative artificial intelligence" refers to an artificial intelligence system that has the ability to autonomously generate knowledge based on data and perform new information processing.

[0492] An "information processing device" is a device, including computers and their associated devices, used for collecting, analyzing, storing, and communicating data.

[0493] "Means for analyzing input content" refers to a method or process for analyzing and understanding data obtained from a user interface.

[0494] "Vulnerability testing items" refer to the specific tests and inspections performed to identify security weaknesses in information processing equipment.

[0495] "Emotional data" refers to information such as facial expressions, voice, input speed, and operation patterns that are collected to evaluate the user's current emotional state.

[0496] "Means for estimating emotional state" refers to a method or process for analyzing emotional data to identify a user's current psychological state.

[0497] "Means of analyzing test results" refers to methods or processes for evaluating the results of vulnerability testing and understanding the security issues and areas for improvement discovered.

[0498] "Means of generating a report" refers to a method or process for documenting information to be provided to the user based on the results and analysis of the test.

[0499] This invention is a system that combines an AI agent and an emotion engine to enhance the security of information processing devices. The entire system consists of a server, terminals, and interfaces for each user.

[0500] The server provides a platform that includes both generative artificial intelligence (AI agent) and an emotion engine. The AI ​​agent analyzes user interface information transmitted from the user's terminal and generates test items based on vulnerability assessments. The AI ​​agent uses deep learning algorithms to automatically perform data analysis and test item generation.

[0501] The device collects data in real time during interactions with the user. In particular, it monitors the user's facial expressions, voice, input speed, and operation patterns, and sends this data to the server as emotional data. The device has a built-in camera and microphone, and acquires user data through smooth interface operation.

[0502] For users, emotional data is implicitly collected while they perform their daily tasks and operations. The actions users actually take are routine operations and do not require any special preparation or attention. For example, while a user logs into the system and operates a work application, the terminal monitors these actions and collects data.

[0503] The emotion engine runs on the server and analyzes emotion data received from the terminal. This allows it to detect whether the user is experiencing stress and provides feedback to the server based on the detection results. The server then uses this feedback to adjust vulnerability testing and report presentation.

[0504] For example, if a user performs an action that causes them anxiety, the system will immediately mitigate the test and simplify the results report. An example of a prompt used as part of this process is: "Quickly identify the emotional state of the user as they log into the system and perform routine tasks, and adaptively adjust the security report accordingly."

[0505] This invention enables effective vulnerability management by providing user-friendly support in addition to automated security diagnostics.

[0506] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0507] Step 1:

[0508] The device monitors user actions. Specifically, it captures the user's facial expressions with its camera and acquires audio with its microphone. It also records the speed and patterns of keyboard and mouse operations. This input data is immediately transmitted to the server. The output is collected emotion data packets.

[0509] Step 2:

[0510] The server analyzes the emotional data received from the terminal. The emotion engine analyzes facial expressions, voice, and operation patterns to estimate the user's emotional state. Deep learning technology is used to quantify stress and anxiety levels. The output is an evaluation result indicating the user's emotional state.

[0511] Step 3:

[0512] The server uses an AI agent to analyze information from the user interface. Specifically, it uses a generative AI model to automatically generate vulnerability test items from the user's interface operation history. At this time, the AI, which has learned from past data and patterns, determines the appropriate test content. The output is a list of generated test items.

[0513] Step 4:

[0514] The server performs vulnerability testing based on the estimated emotional state and generated test items. The AI ​​agent automatically performs each test based on the list of test items, but adjusts the timing and content of the tests if the user is experiencing stress. The output is the result data of the tests performed.

[0515] Step 5:

[0516] The server analyzes the test results and generates a report based on those results. It adjusts the report's wording, taking sentiment evaluation into account, to make it user-friendly. Specifically, it organizes the test results in stages and proposes corrective actions based on their importance. The output is the final vulnerability assessment report.

[0517] (Application Example 2)

[0518] Next, we will explain Application Example 2. In the following explanation, 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."

[0519] Conventional security systems for information processing devices often fail to consider the user's emotional state in vulnerability testing and notification of results, which can compromise the user experience. In particular, when users are experiencing stress or anxiety, reports and alerts may be presented in overly technical or harsh language, making it difficult for them to properly address vulnerabilities.

[0520] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0521] In this invention, the server includes means for automatically analyzing input content from a display device using generative artificial intelligence and generating vulnerability test items, means for autonomously performing vulnerability tests 24 hours a day, 365 days a year, and means for evaluating the user's emotional state using emotion recognition means and adjusting the content of security notifications and reports according to the emotion. This enables flexible security responses in accordance with the user's emotional state, simultaneously achieving optimization of the user experience and enhancement of security.

[0522] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data within an information processing device to generate new information or test items.

[0523] "Vulnerability testing" is a test conducted to detect security weaknesses in information processing equipment and to identify those problems.

[0524] "Emotion recognition means" refers to technology that analyzes a user's emotional state based on their facial expressions, voice, and input patterns.

[0525] A "security notification" is a notification about the security status and threats related to information processing equipment, informing users of vulnerabilities and related recommended countermeasures.

[0526] "User experience" is a general term for the experiences and sensations that users have when using information processing devices and their functions.

[0527] The system of the present invention enhances security and user experience by acquiring information from the user's terminal and processing it on a server. Specifically, the terminal uses a camera and microphone to collect the user's facial expressions and voice in real time and estimate their emotions. This estimated emotion data is then transmitted to the server.

[0528] The server uses a generative AI model to analyze the received data and adjusts vulnerability testing and report generation based on the user's emotional state. Specifically, if the server determines that the user is in a high-stress state, it will adjust the content of security alerts and reports to be more gentle. This optimizes the user experience and ensures that users do not feel stressed while using the interface.

[0529] The technology used involves performing sentiment analysis using Microsoft Azure's sentiment recognition API and generating reports and notifications in appropriate formats using a generative AI model (for example, OpenAI's API). This enables flexible responses that align with the user's emotions.

[0530] As a concrete example, if the terminal detects user stress during financial transactions, the server should immediately refrain from displaying a detailed vulnerability report and instead provide a more easily understandable report later when the user is relaxed. An example of a prompt message would be, "If the user's emotional state indicates stress, please adjust the wording of the security report to be concise and gentle."

[0531] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0532] Step 1:

[0533] The user begins operating the information processing device via the terminal. The terminal's camera and microphone collect the user's facial expressions and voice in real time. The input data consists of facial expression data and voice data, which serve as foundational information for emotion recognition.

[0534] Step 2:

[0535] The device sends the collected facial and voice data to the Microsoft Azure emotion recognition API. This API analyzes the data and estimates and outputs the user's emotional state. Specifically, it analyzes smiles and changes in voice tone to identify emotions such as excitement, calmness, and stress. The output is an emotion label and its intensity.

[0536] Step 3:

[0537] The server receives output from the emotion recognition API and uses a generative AI model to generate prompt messages tailored to the current emotional state. The input consists of emotion labels and their intensity, which are used to determine the appropriate report format. The output is a security notification or report in a gentle tone that matches the user's emotions.

[0538] Step 4:

[0539] While the user continues to operate the system, the server autonomously performs vulnerability testing. Based on the content of the prompt messages generated in the previous step, it generates a test results report and adjusts the content as needed. It performs actual checks based on the test items and outputs the results as a report based on the prompt messages.

[0540] Step 5:

[0541] The server sends a refined report to the user's device. The user can then review the report on their device and take appropriate action based on the security information provided. The goal here is to optimize the user experience, ensuring that the security information is presented in an easy-to-understand format without causing stress.

[0542] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0543] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0544] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0545] [Fourth Embodiment]

[0546] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0547] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0548] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN (Wide Area Network) and / or a LAN (Local Area Network).

[0549] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0550] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0551] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0552] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0553] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0554] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0555] The specific processing program 56 is an example of a "program" relating to the technology of this disclosure. The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0556] The storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290.

[0557] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. 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 processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0558] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0559] The system according to the present invention aims to automatically identify vulnerabilities from the display screen of an information processing device and conduct efficient security inspections by utilizing generative artificial intelligence. This system is mainly realized through the interaction of a server, a terminal, and a user. Its specific form is shown below.

[0560] First, the server provides a platform equipped with an AI agent. This platform operates 24 / 7 on the server, receiving data from information processing devices, generating test items, conducting tests, and analyzing the results. Based on an updated vulnerability database, it autonomously repeats these steps, ensuring that it always has defenses against the latest attack methods.

[0561] The terminal is an information processing device operated by the user, and as the user uses it as usual, it sends the displayed screen and input information to the server. The terminal monitors all interface elements on the screen, such as text input fields and buttons, in real time and provides this information to the server.

[0562] As an example, consider a scenario where a user interacts with the login screen of a web application. The terminal sends structural information of this screen to the server. Based on this information, an AI agent on the server automatically identifies appropriate vulnerabilities and generates test items for attack methods such as SQL injection and cross-site scripting.

[0563] During this process, the server performs tests and analyzes the results. The analysis results are generated as a report that includes details of the detected vulnerabilities and suggested remediation measures. This report is provided to the user to help them take appropriate security measures in response to the issues found.

[0564] This system, implemented in this way, helps users maintain a consistently high level of security, even without specialized security knowledge.

[0565] The following describes the processing flow.

[0566] Step 1:

[0567] The device analyzes the displayed screen and collects UI information. The device scans all interface elements on the screen that the user is interacting with and analyzes this information (for example, form field attributes and button labels).

[0568] Step 2:

[0569] The device sends collected UI information to the server. The device packages the analyzed screen information and sends it to the server as data for security testing.

[0570] Step 3:

[0571] The server activates an AI agent and analyzes the UI information. Based on the received UI information, the server automatically generates the test items necessary for vulnerability assessment. The AI ​​agent analyzes this information and determines which vulnerability tests should be performed.

[0572] Step 4:

[0573] The server executes the test items. The server sequentially executes each test item generated by the AI ​​agent to check for vulnerabilities. Specifically, it performs attack simulations such as SQL injection and XSS.

[0574] Step 5:

[0575] The server analyzes the test results and generates a report. The server analyzes the test execution results in detail and generates a report summarizing the detected vulnerabilities, their severity, and recommended countermeasures.

[0576] Step 6:

[0577] The user receives and reviews the report. The user reviews the report sent from the server and takes necessary security measures. The report clearly indicates high-priority issues and prompts the user to take action.

[0578] (Example 1)

[0579] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0580] Traditional security testing systems have the problem of being unable to efficiently and continuously address the latest vulnerabilities because detecting and mitigating vulnerabilities requires a great deal of time and specialized knowledge. Furthermore, manual input analysis and test item generation consume human resources and can affect the accuracy of the tests. As a result, many companies and individuals are unable to implement adequate security measures and are exposed to potential risks.

[0581] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0582] In this invention, the server includes means for analyzing input information based on screen information acquired from an information processing device using generative artificial intelligence to generate test items, means for receiving and monitoring displayed screen information in real time, and means for analyzing test results to generate a report including details of vulnerabilities and proposed remediation measures. This enables efficient vulnerability countermeasures that are always in response to the latest security threats, even for users who do not possess special security expertise.

[0583] "Generative artificial intelligence" is an artificial intelligence technology that has the ability to automatically learn from data and generate new data through pattern recognition and prediction.

[0584] An "information processing device" refers to an electronic computing device such as a computer or smartphone, which has the function of inputting, processing, and outputting data.

[0585] "Screen information" refers to the visual data displayed on the display device of an information processing device, and includes text, images, and interface elements.

[0586] "Input information" refers to data provided to an information processing device by a user or system, and includes keyboard input, mouse operations, and touch input.

[0587] A "test item" refers to a specific set of conditions or procedures that should be verified during a security inspection, and is generated to identify vulnerabilities.

[0588] "Vulnerability testing" refers to inspections performed on information processing equipment to identify security flaws and weaknesses, thereby enabling the evaluation of the system's security.

[0589] A "report" refers to a document generated based on test results, which contains details of the vulnerabilities detected and proposed fixes.

[0590] A "user interface" refers to the point of contact when a user interacts with an information processing device or software, and it includes graphical elements and controls.

[0591] This invention is centered around three elements: a server, a terminal, and a user. The invention is implemented through a security inspection system utilizing generative artificial intelligence technology.

[0592] The server provides a platform equipped with a generative AI model, receiving and monitoring screen information transmitted from information processing devices. Specifically, the server operates 24 / 7, acquiring data from information processing devices in real time and generating security-related test items based on that data. These include threats such as SQL injection and cross-site scripting. Once test items are generated, the server automatically performs security checks, analyzes the test results, and generates a report. This report includes details of the detected vulnerabilities and proposed remediation measures.

[0593] The terminal provides the means for the user to operate the information processing device. During normal use, the terminal transmits the displayed screen and user input information to the server. Data automatically collected by the terminal assists in security checks performed by the server.

[0594] Users can utilize the system without requiring special security expertise. For example, when a user interacts with the login screen of a web application, the terminal sends screen information to the server. Based on this information, a generation AI model on the server automatically generates appropriate test items and conducts the inspection.

[0595] An example of a prompt message would be, "Identify vulnerabilities in the web application's login screen and generate appropriate security test items." This allows users to benefit from advanced system security measures in their daily work.

[0596] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0597] Step 1:

[0598] The terminal monitors the display screen and input information as the user operates the information processing device as usual. Specifically, the terminal captures interface elements such as text input fields and buttons in real time and extracts that information as data. It acquires screen information resulting from user operations as input and prepares to send it to the server as output.

[0599] Step 2:

[0600] The server receives screen information data sent from the terminal. The server then analyzes the received data and generates security test items using a generation AI model. This process uses screen information as input data and generates test items to identify vulnerabilities as output. Specifically, the AI ​​model considers the possibility of SQL injection and cross-site scripting attacks and automatically creates corresponding test items.

[0601] Step 3:

[0602] The server automatically performs security checks based on the generated test items. During the test, it attempts to induce vulnerabilities using hypothetical attack patterns. The generated test items are used as input, and the test results are obtained as output. The specific operation of this test includes a process of simulating several hypothetical user input scenarios to check for unauthorized intrusion.

[0603] Step 4:

[0604] The server analyzes the results of security checks and generates a report. Here, the result data obtained from the tests is used as input, processed by an analytical AI model, and outputs a report that includes details of vulnerabilities and suggested remediation methods. Specifically, the report describes the location of the vulnerability, the severity of its impact, and detailed information on how to fix it.

[0605] Step 5:

[0606] Users can receive reports provided by the server and take security measures. The input received by the user is a report generated by the server, and the output is the implementation of corrective actions based on that report. Specifically, users improve the application's code and configuration based on the report to eliminate potential vulnerabilities.

[0607] (Application Example 1)

[0608] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0609] In recent years, cyberattacks on information processing equipment have been increasing, with phishing scams and attacks exploiting vulnerabilities being particularly problematic. While security measures to counter these attacks are becoming more sophisticated, it remains difficult for users without specialized knowledge to implement appropriate defenses. Furthermore, real-time vulnerability detection and rapid response are required, necessitating effective and user-friendly solutions.

[0610] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0611] In this invention, the server includes means for automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence and generating vulnerability test items; means for autonomously performing vulnerability tests on the information processing device 24 hours a day, 365 days a year using the generated test items; means for analyzing the test results and generating a report showing vulnerabilities and proposed corrections; filtering means for monitoring the display content and detecting potentially fraudulent patterns on a terminal device operated by the user; and warning means for notifying the user of information indicating suspicious activity. As a result, even if the user does not have specialized knowledge, they can be protected from threats such as phishing scams in real time, and at the same time, the latest defensive measures can be taken regarding vulnerabilities in the information processing device.

[0612] "Generative artificial intelligence" is a type of artificial intelligence that has the ability to learn from past data and generate new information and patterns.

[0613] An "information processing device" is a device used for inputting, processing, storing, and outputting data, and primarily refers to a computer.

[0614] A "display screen" refers to the interface that an information processing device uses to visually convey data to the user, and includes monitors and displays.

[0615] "Input content" refers to the data and information that the user provides to the information processing device, and includes keyboard input and touch input.

[0616] "Vulnerability test items" are specific inspection criteria and procedures set up to detect potential security holes in a system or application.

[0617] A "terminal device" is an information processing device that is directly operated by the user, and includes smartphones, tablets, and other similar devices.

[0618] "Potential fraud patterns" refer to suspicious behaviors or characteristics used to identify phishing sites, malware, and other malicious activities.

[0619] "Warning measures" refer to methods and tools for informing users of dangers or anomalies, and include functions for issuing alert messages and notifications.

[0620] A "report" is a document that summarizes test results and analysis results, and includes information on security threats and countermeasures.

[0621] In implementing this invention, the system, as a security monitoring platform utilizing generative artificial intelligence, comprises three main elements: a server, a terminal, and a user.

[0622] The server, equipped with generative artificial intelligence, receives data transmitted from information processing devices 24 hours a day, 365 days a year. Built using programming languages ​​such as Python, it utilizes natural language processing libraries and machine learning frameworks (e.g., SpaCy and TensorFlow). The server analyzes data and web page structures entered in real time from user terminals, automatically generating test items against attacks such as SQL injection and cross-site scripting. It also analyzes the test results, creates a report including details on vulnerabilities and suggested fixes, and provides it to the user.

[0623] On the device, when applications and websites that the user uses daily are displayed, their structure is automatically analyzed and sent to the server. The device monitors the content of the user interface and detects potentially fraudulent patterns. This protects the user from phishing sites and malicious activity.

[0624] As a concrete example, when a user accesses an online banking site, the terminal analyzes all input fields on that page in real time and provides the data to the server. Based on the entered information, the server automatically assesses for vulnerabilities and immediately displays a warning if it detects unsafe behavior. This warning allows the user to avoid further risks.

[0625] Examples of prompts for a generative AI model include the following:

[0626] "You are a phishing site detection agent. Analyze the structure and content of web pages that users visit and look for patterns that indicate suspicious activity. When you find signs of phishing, generate a warning message."

[0627] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0628] Step 1:

[0629] The terminal monitors user operations and retrieves the data actually entered from the display screen of the information processing device. This input consists of text and form information directly entered by the user on a web page or application. The terminal then sends this data to the server. The output of this step is the data actually entered by the user.

[0630] Step 2:

[0631] The server receives input data sent from the terminal and begins analyzing this data using a generative AI model. This analysis uses a natural language processing library (e.g., SpaCy) to scrutinize the input and generate initial test items to discover potential vulnerabilities. The input is the user's input data, and the output is the generated test items.

[0632] Step 3:

[0633] Based on test items generated by the server, the system autonomously performs vulnerability testing on the information processing device. Here, a machine learning framework (e.g., TensorFlow) is used to detect patterns that could be considered vulnerabilities. The input is the generated test items, and the output is the test results regarding the vulnerabilities.

[0634] Step 4:

[0635] The server analyzes the test results and generates a report containing details of the detected vulnerabilities and suggested remediations. This analysis includes a statistical assessment based on the test results, listing the most critical vulnerabilities. The input is the results of the vulnerability tests, and the output is the completed report.

[0636] Step 5:

[0637] The server sends the completed report to the terminal and notifies the user. Based on this information, the terminal displays a summary of the report and warnings on the screen, prompting the user to take appropriate action. The input is the report from the server, and the output is the warning notification and report display received by the user.

[0638] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0639] This invention provides a system that combines an AI agent equipped with generative artificial intelligence and an emotion engine, offering a new method for enhancing the security of information processing devices. This system analyzes input content from the display screen of the information processing device and automatically and autonomously performs vulnerability testing. Furthermore, it recognizes the user's emotions using the emotion engine and appropriately adjusts the execution of tests and the presentation of reports accordingly.

[0640] The server provides a platform that integrates an AI agent and an emotion engine. The AI ​​agent analyzes user interface information sent from the terminal and generates test items based on vulnerability assessments. This process enables the security of information processing devices to be maintained 24 / 7.

[0641] The emotion engine monitors the user's facial expressions, voice, input speed, and operation patterns via the device to estimate the user's emotional state. For example, if the user is experiencing stress, the emotion engine can instruct the server to temporarily ease the test or adjust the report to be more concise and easier to understand.

[0642] As a concrete example, while a user logs into the system and performs their normal tasks, the terminal extracts emotional data from its operation patterns and speed, as well as background audio. The server analyzes this data, and if it detects a moment when the user is particularly anxious or stressed, it immediately refrains from submitting a vulnerability report or softens its wording.

[0643] Ultimately, the server generates and provides the user with a report based on the test results and the sentiment engine's analysis. This report includes the severity of the detected vulnerabilities and the priority of countermeasures based on the user's sentiment state, making it a crucial source of information for the user to take effective action.

[0644] Therefore, in addition to conventional vulnerability assessments using AI agents, this system utilizes an emotion engine to optimize the user experience, achieving both enhanced security and user-friendly support.

[0645] The following describes the processing flow.

[0646] Step 1:

[0647] The device monitors the user's actions. The device collects information such as the user's action speed, input patterns, facial expressions, and voice to supply to the emotion engine. This information is later analyzed by the emotion engine.

[0648] Step 2:

[0649] The device sends structural information of the display screen to the server. The device analyzes the UI information of the application the user is operating and sends that data to the AI ​​agent on the server.

[0650] Step 3:

[0651] The server uses an AI agent to generate vulnerability test items based on UI information. The AI ​​agent autonomously analyzes the received information and determines how to proceed with the testing.

[0652] Step 4:

[0653] The server uses an emotion engine to evaluate the user's emotional state. The emotion engine analyzes the user's emotional data sent from the terminal in real time and estimates the user's mental state.

[0654] Step 5:

[0655] Based on the evaluation of the emotion engine, the server adjusts the execution of the test. For example, if the user is experiencing stress, the server adjusts the frequency and timing of the test to reduce the user's burden.

[0656] Step 6:

[0657] The server performs vulnerability testing and collects the results. It executes test items generated by an AI agent and records in detail the problems detected during the testing and their impact.

[0658] Step 7:

[0659] The server analyzes the test results and generates a report that takes emotional states into account. The report includes detected vulnerabilities, estimated user emotional states, and priority actions.

[0660] Step 8:

[0661] The user receives the report and reviews its contents. Based on the information provided, the user takes necessary security measures to ensure the system's security.

[0662] (Example 2)

[0663] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0664] Traditional vulnerability testing systems fail to consider the emotional state of users, resulting in an inability to adequately address users experiencing stress or anxiety. Furthermore, if the timing of testing or the content of reports are not user-friendly, many users will find them difficult to understand, leading to delays in implementing countermeasures. To address these issues, a system is needed that assesses users' emotional states in real time and adapts tests and reports accordingly.

[0665] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0666] In this invention, the server includes means for automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence and generating vulnerability test items; means for autonomously performing vulnerability tests on the information processing device 24 hours a day, 365 days a year using the generated test items; means for analyzing the test results and generating a report showing vulnerabilities and proposed corrections; means for estimating the user's emotional state using emotional data including facial expressions, voice, input speed, and operation patterns collected from the terminal; and means for adjusting the content of the vulnerability test and the expression of the results according to the estimated emotional state. This makes it possible to provide effective and user-friendly security measures that take into account the user's emotional state.

[0667] "Generative artificial intelligence" refers to an artificial intelligence system that has the ability to autonomously generate knowledge based on data and perform new information processing.

[0668] An "information processing device" is a device, including computers and their associated devices, used for collecting, analyzing, storing, and communicating data.

[0669] "Means for analyzing input content" refers to a method or process for analyzing and understanding data obtained from a user interface.

[0670] "Vulnerability testing items" refer to the specific tests and inspections performed to identify security weaknesses in information processing equipment.

[0671] "Emotional data" refers to information such as facial expressions, voice, input speed, and operation patterns that are collected to evaluate the user's current emotional state.

[0672] "Means for estimating emotional state" refers to a method or process for analyzing emotional data to identify a user's current psychological state.

[0673] "Means of analyzing test results" refers to methods or processes for evaluating the results of vulnerability testing and understanding the security issues and areas for improvement discovered.

[0674] "Means of generating a report" refers to a method or process for documenting information to be provided to the user based on the results and analysis of the test.

[0675] This invention is a system that combines an AI agent and an emotion engine to enhance the security of information processing devices. The entire system consists of a server, terminals, and interfaces for each user.

[0676] The server provides a platform that includes both generative artificial intelligence (AI agent) and an emotion engine. The AI ​​agent analyzes user interface information transmitted from the user's terminal and generates test items based on vulnerability assessments. The AI ​​agent uses deep learning algorithms to automatically perform data analysis and test item generation.

[0677] The device collects data in real time during interactions with the user. In particular, it monitors the user's facial expressions, voice, input speed, and operation patterns, and sends this data to the server as emotional data. The device has a built-in camera and microphone, and acquires user data through smooth interface operation.

[0678] For users, emotional data is implicitly collected while they perform their daily tasks and operations. The actions users actually take are routine operations and do not require any special preparation or attention. For example, while a user logs into the system and operates a work application, the terminal monitors these actions and collects data.

[0679] The emotion engine runs on the server and analyzes emotion data received from the terminal. This allows it to detect whether the user is experiencing stress and provides feedback to the server based on the detection results. The server then uses this feedback to adjust vulnerability testing and report presentation.

[0680] For example, if a user performs an action that causes them anxiety, the system will immediately mitigate the test and simplify the results report. An example of a prompt used as part of this process is: "Quickly identify the emotional state of the user as they log into the system and perform routine tasks, and adaptively adjust the security report accordingly."

[0681] This invention enables effective vulnerability management by providing user-friendly support in addition to automated security diagnostics.

[0682] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0683] Step 1:

[0684] The device monitors user actions. Specifically, it captures the user's facial expressions with its camera and acquires audio with its microphone. It also records the speed and patterns of keyboard and mouse operations. This input data is immediately transmitted to the server. The output is collected emotion data packets.

[0685] Step 2:

[0686] The server analyzes the emotional data received from the terminal. The emotion engine analyzes facial expressions, voice, and operation patterns to estimate the user's emotional state. Deep learning technology is used to quantify stress and anxiety levels. The output is an evaluation result indicating the user's emotional state.

[0687] Step 3:

[0688] The server uses an AI agent to analyze information from the user interface. Specifically, it uses a generative AI model to automatically generate vulnerability test items from the user's interface operation history. At this time, the AI, which has learned from past data and patterns, determines the appropriate test content. The output is a list of generated test items.

[0689] Step 4:

[0690] The server performs vulnerability testing based on the estimated emotional state and generated test items. The AI ​​agent automatically performs each test based on the list of test items, but adjusts the timing and content of the tests if the user is experiencing stress. The output is the result data of the tests performed.

[0691] Step 5:

[0692] The server analyzes the test results and generates a report based on those results. It adjusts the report's wording, taking sentiment evaluation into account, to make it user-friendly. Specifically, it organizes the test results in stages and proposes corrective actions based on their importance. The output is the final vulnerability assessment report.

[0693] (Application Example 2)

[0694] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0695] Conventional security systems for information processing devices often fail to consider the user's emotional state in vulnerability testing and notification of results, which can compromise the user experience. In particular, when users are experiencing stress or anxiety, reports and alerts may be presented in overly technical or harsh language, making it difficult for them to properly address vulnerabilities.

[0696] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0697] In this invention, the server includes means for automatically analyzing input content from a display device using generative artificial intelligence and generating vulnerability test items, means for autonomously performing vulnerability tests 24 hours a day, 365 days a year, and means for evaluating the user's emotional state using emotion recognition means and adjusting the content of security notifications and reports according to the emotion. This enables flexible security responses in accordance with the user's emotional state, simultaneously achieving optimization of the user experience and enhancement of security.

[0698] "Generative artificial intelligence" is an artificial intelligence technology that analyzes data within an information processing device to generate new information or test items.

[0699] "Vulnerability testing" is a test conducted to detect security weaknesses in information processing equipment and to identify those problems.

[0700] "Emotion recognition means" refers to technology that analyzes a user's emotional state based on their facial expressions, voice, and input patterns.

[0701] A "security notification" is a notification about the security status and threats related to information processing equipment, informing users of vulnerabilities and related recommended countermeasures.

[0702] "User experience" is a general term for the experiences and sensations that users have when using information processing devices and their functions.

[0703] The system of the present invention enhances security and user experience by acquiring information from the user's terminal and processing it on a server. Specifically, the terminal uses a camera and microphone to collect the user's facial expressions and voice in real time and estimate their emotions. This estimated emotion data is then transmitted to the server.

[0704] The server uses a generative AI model to analyze the received data and adjusts vulnerability testing and report generation based on the user's emotional state. Specifically, if the server determines that the user is in a high-stress state, it will adjust the content of security alerts and reports to be more gentle. This optimizes the user experience and ensures that users do not feel stressed while using the interface.

[0705] The technology used involves performing sentiment analysis using Microsoft Azure's sentiment recognition API and generating reports and notifications in appropriate formats using a generative AI model (for example, OpenAI's API). This enables flexible responses that align with the user's emotions.

[0706] As a concrete example, if the terminal detects user stress during financial transactions, the server should immediately refrain from displaying a detailed vulnerability report and instead provide a more easily understandable report later when the user is relaxed. An example of a prompt message would be, "If the user's emotional state indicates stress, please adjust the wording of the security report to be concise and gentle."

[0707] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0708] Step 1:

[0709] The user begins operating the information processing device via the terminal. The terminal's camera and microphone collect the user's facial expressions and voice in real time. The input data consists of facial expression data and voice data, which serve as foundational information for emotion recognition.

[0710] Step 2:

[0711] The device sends the collected facial and voice data to the Microsoft Azure emotion recognition API. This API analyzes the data and estimates and outputs the user's emotional state. Specifically, it analyzes smiles and changes in voice tone to identify emotions such as excitement, calmness, and stress. The output is an emotion label and its intensity.

[0712] Step 3:

[0713] The server receives output from the emotion recognition API and uses a generative AI model to generate prompt messages tailored to the current emotional state. The input consists of emotion labels and their intensity, which are used to determine the appropriate report format. The output is a security notification or report in a gentle tone that matches the user's emotions.

[0714] Step 4:

[0715] While the user continues to operate the system, the server autonomously performs vulnerability testing. Based on the content of the prompt messages generated in the previous step, it generates a test results report and adjusts the content as needed. It performs actual checks based on the test items and outputs the results as a report based on the prompt messages.

[0716] Step 5:

[0717] The server sends a refined report to the user's device. The user can then review the report on their device and take appropriate action based on the security information provided. The goal here is to optimize the user experience, ensuring that the security information is presented in an easy-to-understand format without causing stress.

[0718] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0719] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0720] 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 this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0721] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0722] Figure 9 shows an emotion map 400 in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0723] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0724] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0725] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, motorcycles, etc., emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0726] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0727] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0728] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0729] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0730] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0731] 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.

[0732] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0733] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0734] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0735] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0736] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0737] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and the like that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0738] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0739] The following is further disclosed regarding the embodiments described above.

[0740] (Claim 1)

[0741] A method for generating vulnerability test items by automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence,

[0742] A means for autonomously performing vulnerability testing on an information processing device 24 hours a day, 365 days a year using the generated test items,

[0743] A means for analyzing test results and generating a report that shows vulnerabilities and proposed fixes,

[0744] A system that includes this.

[0745] (Claim 2)

[0746] The system according to claim 1, wherein the means for analyzing the input content automatically grasps the structure of the displayed user interface to refine the test items.

[0747] (Claim 3)

[0748] The system according to claim 1, wherein the means for generating the report includes information that evaluates the severity of the detected vulnerabilities and indicates priority actions.

[0749] "Example 1"

[0750] (Claim 1)

[0751] A means for generating security-related test items by analyzing input information based on screen information acquired from an information processing device using generative artificial intelligence,

[0752] The information processing device is provided with means to autonomously perform security checks 24 hours a day, 365 days a year using the generated test items,

[0753] A means for analyzing test results and generating a report that includes details of the detected vulnerabilities and proposed remediation measures,

[0754] A means for receiving and monitoring screen information transmitted from an information processing device in real time,

[0755] A means of keeping defenses up-to-date by using an updated vulnerability database,

[0756] A system that includes this.

[0757] (Claim 2)

[0758] The system according to claim 1, wherein the means for analyzing the input information optimizes the test items by automatically understanding the structure of the transmitted user interface.

[0759] (Claim 3)

[0760] The system according to claim 1, wherein the means for generating the report includes information for evaluating the importance of the identified vulnerabilities and suggesting priority defensive measures.

[0761] "Application Example 1"

[0762] (Claim 1)

[0763] A method for generating vulnerability test items by automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence,

[0764] A means for autonomously performing vulnerability testing on an information processing device 24 hours a day, 365 days a year using the generated test items,

[0765] A means for analyzing test results and generating a report that shows vulnerabilities and proposed solutions,

[0766] A filtering means that monitors the displayed content and detects potentially fraudulent patterns in a terminal device operated by a user,

[0767] A warning system that notifies users of information indicating suspicious activity,

[0768] A system that includes this.

[0769] (Claim 2)

[0770] The system according to claim 1, wherein the means for analyzing the input content refines the test items by automatically grasping the structure of the displayed user interface.

[0771] (Claim 3)

[0772] The system according to claim 1, wherein the means for generating the report includes information that assesses the severity of the detected vulnerabilities and indicates priority actions.

[0773] "Example 2 of combining an emotion engine"

[0774] (Claim 1)

[0775] A method for generating vulnerability test items by automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence,

[0776] A means for autonomously performing vulnerability testing on an information processing device 24 hours a day, 365 days a year using the generated test items,

[0777] A means for analyzing test results and generating a report that shows vulnerabilities and proposed fixes,

[0778] A means for estimating a user's emotional state using emotional data, including facial expressions, voice, input speed, and operation patterns, collected from a device,

[0779] A means of adjusting the content and expression of results of vulnerability tests according to the estimated emotional state,

[0780] A system that includes this.

[0781] (Claim 2)

[0782] The system according to claim 1, wherein the means for analyzing the input content automatically grasps the structure of the displayed user interface to refine the test items.

[0783] (Claim 3)

[0784] The system according to claim 1, wherein the means for generating the report includes information that evaluates the severity of the detected vulnerabilities and indicates priority actions.

[0785] "Application example 2 when combining with an emotional engine"

[0786] (Claim 1)

[0787] A method for generating vulnerability test items by automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence,

[0788] A means for autonomously performing vulnerability testing on an information processing device 24 hours a day, 365 days a year using the generated test items,

[0789] A means for analyzing test results and generating a report that shows vulnerabilities and proposed fixes,

[0790] A means for evaluating the user's emotional state using emotion recognition means and adjusting the content of security notifications and reports according to the emotion,

[0791] A system that includes this.

[0792] (Claim 2)

[0793] The system according to claim 1, wherein the means for analyzing the input content automatically grasps the structure of the displayed user interface to refine the test items.

[0794] (Claim 3)

[0795] The system according to claim 1, wherein the means for generating the report includes information that evaluates the severity of the detected vulnerabilities and indicates priority actions.

[0796] (Claim 4)

[0797] The system according to claim 1, wherein the emotion recognition means acquires the user's state based on facial expressions, voice, and input patterns, and appropriately changes the expression of the notification. [Explanation of Symbols]

[0798] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A method for generating vulnerability test items by automatically analyzing input content from the display screen of an information processing device using generative artificial intelligence, A means for autonomously performing vulnerability testing on an information processing device 24 hours a day, 365 days a year using the generated test items, A means for analyzing test results and generating a report that shows vulnerabilities and proposed solutions, A filtering means that monitors the displayed content and detects potentially fraudulent patterns in a terminal device operated by a user, A warning system that notifies users of information indicating suspicious activity, A system that includes this.

2. The system according to claim 1, wherein the means for analyzing the input content automatically grasps the structure of the displayed user interface to refine the test items.

3. The system according to claim 1, wherein the means for generating the report includes information that assesses the severity of the detected vulnerabilities and indicates priority actions.

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