system

Abstract

We provide the system. [Solution] The information processing device includes means for measuring the user's skill level and generating a virtual scenario corresponding to that skill level. In the aforementioned virtual scenario, means for generating a virtual attack and causing the user to take action to respond to it, A means for evaluating the user's response to the virtual attack and proposing improvement suggestions, 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 method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In recent years, the work of engineers in the field of information processing has become increasingly sophisticated, and in particular, technical capabilities in security operations and troubleshooting are required. Therefore, it is difficult for individuals to acquire general capabilities through self-study, resulting in a shortage of skilled engineers who can be immediately effective. Against this background, there is a need for a new training system for engineers to practically improve skills that are effectively useful in their work.

Means for Solving the Problems

[0005] This invention enables users to improve their ability to respond to attacks in real business scenarios by having an information processing device measure an engineer's current skill level and generate virtual scenarios corresponding to that level. Specifically, it employs a configuration that generates virtual attacks such as SQL injection and mass requests, and teaches the user how to respond to them. Furthermore, it evaluates the user's response results and provides feedback with improvement suggestions as needed. Past response results are stored in a recording device and used to construct the next scenario, enabling continuous and personalized training for each user.

[0006] An "information processing device" is a computer system that processes diverse data and provides users with the information they need.

[0007] "User" refers to an individual who operates a system or program and aims to improve their skills in order to achieve a specific purpose.

[0008] "Technical level" refers to the degree of an engineer's technical knowledge and their ability to apply it.

[0009] A "virtual scenario" refers to setting up a situation in a virtual environment that simulates a real-world security incident.

[0010] A "virtual attack" refers to simulating a simulated intrusion or interference into an information system or network.

[0011] An "improvement plan" is a suggestion for more effective countermeasures or methods, provided after evaluating the user's current response.

[0012] A "recording device" refers to a hardware or software configuration for storing data and information and making it available for later reference and analysis. [Brief explanation of the drawing]

[0013] [Figure 1]This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [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, which incorporates an emotion engine. [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]

[0014] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0015] First, the terms used in the following description will be explained.

[0016] In the following embodiments, the labeled 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.

[0017] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0018] In the following embodiments, the labeled 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, etc.

[0019] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0020] 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."

[0021] [First Embodiment]

[0022] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0023] 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.

[0024] 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).

[0025] 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.

[0026] 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.

[0027] 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.

[0028] 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.

[0029] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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".

[0034] The system of this invention provides a virtual training environment for improving engineers' technical skills using an information processing device. The following describes a specific form for implementing this system.

[0035] The main components of this system consist of an information processing device (server), a terminal that receives user input, and a virtual scenario that the user experiences. The program is executed primarily by the server, and the user accesses it through the terminal.

[0036] The server first asks users to input their skill level in a self-assessment format when they begin training. Based on this information, the server is responsible for generating virtual scenarios of appropriate difficulty. These include simulations of security attacks such as SQL injection and mass requests. The generated scenarios are built as virtual environments, and access permissions are granted to the user's terminal.

[0037] Users connect to a virtual environment using their devices and respond to security incidents simulating real-world business scenarios based on provided scenarios. By detecting virtual attacks and taking appropriate countermeasures, users can hone their practical skills.

[0038] The server has the capability to monitor and record user behavior in real time. It evaluates whether predefined measures have been implemented and how effective they were, and provides users with specific improvement suggestions as feedback, if necessary.

[0039] All past training results are saved in the recording device and used to build training scenarios for subsequent sessions. In this way, personalized training is possible for each user.

[0040] As a concrete example, suppose a user selects the "intermediate" level. In this case, the server generates an intermediate-difficulty SQL injection attack scenario and deploys it to the terminal. The user attempts to prevent the attack by discovering abnormal queries through log analysis and setting up appropriate query filters. Based on the feedback obtained in this process, the user improves their skills and becomes ready to tackle more advanced scenarios.

[0041] The following describes the processing flow.

[0042] Step 1:

[0043] When a user accesses the system, the server prompts them for input regarding their technical level. This input consists of three options: "Beginner," "Intermediate," and "Advanced." The user self-assessses their skill level and selects one. The server stores this information in a database.

[0044] Step 2:

[0045] The server uses a generation AI to generate virtual scenarios of appropriate difficulty based on the user's technical level. These scenarios include virtual attacks such as potential SQL injection attacks and high-volume requests. The generated scenarios are then deployed to the virtual environment.

[0046] Step 3:

[0047] The terminal receives scenario information provided by the server and prepares it for the user to access the virtual environment. The terminal displays connection information and prepares the user to participate in the training.

[0048] Step 4:

[0049] The user connects to the virtual environment from their terminal using the provided access information. The terminal enables the user to operate within the virtual environment and records their actions in real time.

[0050] Step 5:

[0051] Within the virtual environment, users identify potential security incidents and implement necessary countermeasures based on presented scenarios. For example, they might monitor logs to detect unusual queries and develop appropriate countermeasures.

[0052] Step 6:

[0053] The server monitors user responses in real time and records each action. Furthermore, it evaluates how effective the user's actions were, creates improvement suggestions as needed, and provides feedback to the user.

[0054] Step 7:

[0055] The recording device saves the user's responses and feedback. This record is used in subsequent training sessions to form a user-specific learning curve and form the basis for providing more effective training.

[0056] (Example 1)

[0057] 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."

[0058] In the modern information technology field, there is an urgent need to cultivate engineers who can respond quickly to security incidents. However, environments for training with realistic scenarios are limited, resulting in a lack of opportunities for engineers to acquire practical skills. This lack can lead to a decline in actual incident response capabilities, potentially increasing the vulnerability of information systems. Therefore, there is a need for a system that provides virtual scenarios tailored to the technical level of engineers, enabling practical training and efficiently improving each individual's response capabilities.

[0059] 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.

[0060] In this invention, the server includes means for an information processing device to input the user's skill level in a self-assessment format and generate a virtual scenario corresponding to that skill level; means for generating a virtual attack using a generated AI model and having the user implement practical responses; and means for monitoring and evaluating the user's response to the virtual attack in real time and suggesting improvements using prompt messages. This enables engineers to enhance their security response capabilities in a practical and personalized training environment.

[0061] An "information processing device" is a computer device used for collecting, processing, storing, and communicating data.

[0062] "User" refers to an individual or group that operates an information processing device and receives training through a virtual scenario.

[0063] "Technical level" is a measure that indicates the user's technical proficiency and experience.

[0064] A "virtual scenario" is a simulation environment generated by an information processing device for a user to conduct specific training.

[0065] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence techniques to generate scenarios and feedback from a dataset.

[0066] A "virtual attack" is a simulated attack on an information system that is conducted within a virtual scenario.

[0067] "Practical response" refers to a series of preventative or mitigation measures taken by the user against a virtual attack within a virtual scenario.

[0068] "Real-time monitoring" is a method of observing and collecting data on a user's activities in real time.

[0069] A "prompt message" is a text message used by a generative AI model to provide feedback or instructions to the user.

[0070] An "improvement suggestion" is a proposal to make the actions taken by the user within a virtual scenario more effective.

[0071] A "recording device" is a device or mechanism for saving data and user activity history so that it can be used later.

[0072] In this embodiment of the invention, an information processing device plays a central role. The information processing device functions as a server, and users access this system through a terminal. Operations from the terminal are performed via an interface, and the user inputs their technical level. Based on the input technical level, the server generates a virtual scenario optimized for the user. A generation AI model is utilized, and the content of the scenario includes attack simulations such as database attacks and overload requests.

[0073] Specifically, if the user selects the "intermediate" level, the server generates an intermediate-level database attack simulation and builds it as a virtual environment. This environment runs on Docker using virtual container technology. Access information to the environment is provided to the terminal, and it is configured to allow the user to perform virtual incident response. By logging in from the terminal, the user begins training in the virtual environment, detects anomalies in the simulation, and attempts to take appropriate action.

[0074] The server monitors user behavior in real time and records the responses in a database. Evaluation is performed programmatically, and prompt statements are generated. For example, specific feedback is provided, such as "Detect an abnormal database query and how to set up filtering effectively." Through this process, users can improve their practical skills, and individual feedback is accumulated in preparation for the next training session.

[0075] In this way, the entire system works together to support the skill development of engineers, enabling more effective development of technical capabilities.

[0076] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0077] Step 1:

[0078] The user logs in to the information processing device using a terminal. The user enters their ID and password, which are sent to the server. The server compares the entered authentication information with the database and determines whether the authentication is successful. If authentication is successful, the server displays a technical level selection screen on the terminal.

[0079] Step 2:

[0080] The user selects their skill level through a self-assessment format and enters this information on their terminal. The entered skill level data is sent to the server. The server uses the received skill level information and a generative AI model to generate a virtual scenario of the corresponding difficulty level. For example, a scenario involving an SQL injection attack might be selected.

[0081] Step 3:

[0082] The server creates a virtual environment based on the generated virtual scenario. This environment is built on Docker using virtual container technology, and a virtual attack sequence is configured. Access information to the created virtual environment is sent to the terminal. The user connects to the virtual environment after receiving this information.

[0083] Step 4:

[0084] Users log in to a virtual environment via a terminal and begin scenario-based training. They detect anomalies occurring within the simulation and attempt appropriate responses, such as detecting and filtering abnormal queries. This process helps users hone their practical skills.

[0085] Step 5:

[0086] The server monitors the user's responses to the simulation in real time and records all operation logs. The server analyzes the behavioral data and generates prompts using a generative AI model. Based on these prompts, it suggests improvement measures to the user. For example, it might suggest "log monitoring methods to improve the speed of attack detection."

[0087] Step 6:

[0088] After training is complete, the server saves all data to a recording device. This saved data is used as foundational data when customizing the next training scenario. This ensures that an optimal training environment is continuously provided for each user.

[0089] (Application Example 1)

[0090] 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."

[0091] In the modern era, there is a lack of effective means for engineers to improve their skills in defending against cyberattacks. Furthermore, there is a need for personalized training tailored to individual skill levels, but traditional methods are currently insufficient. In addition, there is a growing demand for training environments that are practical, provide immediate feedback, and allow for rapid skill improvement.

[0092] 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.

[0093] In this invention, the server includes means for measuring the user's skill level and generating a virtual scenario corresponding to that skill level; means for generating a simulated attack and prompting the user to implement countermeasures against it; means for evaluating the user's countermeasures against the simulated attack and suggesting improvements; and means for generating appropriate prompt sentences based on the scenario using a generation AI model. This enables efficient and effective skill improvement by providing an optimal training scenario tailored to each individual and through real-time improvement feedback.

[0094] A "computational device" is an electronic device used to measure the user's skill level, generate appropriate virtual scenarios, and perform corresponding processing.

[0095] A "virtual scenario" provides simulated situations and challenges tailored to the user's skill level, creating an environment for acquiring new skills.

[0096] A "simulated attack" is a series of programs that recreates actual cyberattacks in a virtual environment and allows users to implement countermeasures against them.

[0097] A "terminal device" is an electronic device used by a user to access a system and experience virtual scenarios.

[0098] A "training application" is software that allows users to learn and practice skills through virtual scenarios.

[0099] A "generative AI model" is an algorithmic model that automatically generates appropriate prompts and countermeasures based on a virtual scenario and the user's actions.

[0100] A "prompt message" is text created by a generative AI model and used to provide instructions or information to the user.

[0101] To realize this invention, the system used is configured as follows: The server plays a central role as a computing device and has the function of understanding the user's skill level. Based on this, the server acquires self-assessment data entered by the user and generates an appropriate virtual scenario based on that data. The virtual scenario includes a simulated cyberattack and is presented to the user in real time.

[0102] A terminal is a physical device operated by the user, such as a smartphone or personal computer. A training application is installed on this terminal, and the user uses this application to connect to a scenario and deal with a simulated attack through operation.

[0103] The generative AI model operates within the server and monitors user actions in a virtual scenario. Based on these actions, the model generates appropriate feedback and prompts, providing the user with suggested actions and improvements. A specific example of a prompt is: "Describe how the user can learn techniques to detect and defend against a mid-level cyberattack on the virtual network."

[0104] As a concrete example, imagine a user using their smartphone during their commute and activating this system. If the user selects the "intermediate" level, the server generates a scenario simulating a database injection attack and sends it to the device. The user experiences the scenario through the application and performs attack detection and countermeasures.

[0105] As described above, this system, by combining servers, terminal devices, and generated AI models, makes it possible to provide security training optimized for each individual user.

[0106] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0107] Step 1:

[0108] The server receives self-assessment data of the user's technical level performed through the terminal. This input data is analyzed to identify the user's current technical level. Based on the identified technical level, the server prepares to select an appropriate virtual scenario.

[0109] Step 2:

[0110] The server generates a virtual scenario corresponding to the identified technical level. A generation AI model is used to specifically determine the scenario content, and the scenario includes the type of simulated attack. The generated scenario is sent to the user's terminal, initiating the scenario.

[0111] Step 3:

[0112] The user initiates action based on a virtual scenario received on their device. The user uses a training application to actually implement countermeasures against a simulated attack. These actions are logged and sent to the server.

[0113] Step 4:

[0114] The server analyzes user operation logs in real time and evaluates its response to simulated attacks. Based on the evaluation results, it uses a generative AI model to determine areas for improvement and appropriate countermeasures, and generates prompt messages to suggest them.

[0115] Step 5:

[0116] The server sends the generated prompt message to the user's terminal. The user receives the prompt message as input, considers improvement measures based on its content, and takes actions with the intention of incorporating them into the next scenario.

[0117] Step 6:

[0118] The terminal saves all operation results and evaluation feedback to a recording device. This recorded data is prepared to be used as reference material when constructing the next training scenario.

[0119] The above describes the basic program processing flow in this system.

[0120] 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.

[0121] This invention combines an engineering skills training system with an emotion engine that detects the user's emotional state. This provides a more dynamic and personalized learning experience, aiming to improve the user's skills and reduce their psychological burden.

[0122] The server first retrieves data on the user's technical level selection and generates an appropriate virtual scenario based on that information. These virtual scenarios include security attacks such as SQL injection and mass requests. This scenario is then delivered to the virtual environment for the user to experience through their terminal.

[0123] The emotion engine analyzes the user's facial expressions and voice tone in real time via sensors such as cameras and microphones while the user participates in a virtual scenario. Based on this information, the emotion engine evaluates the user's stress level and concentration level and provides feedback to the server.

[0124] Based on information from the emotion engine, the server dynamically adjusts the scenario according to the user's state. For example, if the server detects that the user is under high stress, it may lower the difficulty of the scenario or display a message encouraging them to take a break. Furthermore, if it determines that the user's interest or concentration is waning, it may present a different type of challenge or introduce educational content.

[0125] Users can experience these interactions through their devices and progress through the training at their own pace. This maximizes learning effectiveness and allows them to acquire the necessary skills effectively. The recording device saves the user's emotional data and learning progress, which will be used to generate future scenarios.

[0126] As a concrete example, consider a scenario where a user encounters an unexpectedly difficult scenario during intermediate-level training, temporarily experiencing high stress levels. In this situation, the emotion engine reacts quickly, prompting the server to adjust the scenario or send encouraging messages to alleviate stress. As a result, the user can continue learning at the appropriate time while their psychological pressure is eased. In this way, the system incorporating the emotion engine provides users with a more meaningful and efficient learning environment.

[0127] The following describes the processing flow.

[0128] Step 1:

[0129] When a user accesses the system, the server prompts them for input regarding their technical level. This input consists of three options: "Beginner," "Intermediate," and "Advanced." The user self-assessses their skill level and selects one. The server stores this information in a database.

[0130] Step 2:

[0131] The server generates a virtual scenario tailored to the user's technical level. This scenario includes security attacks such as SQL injection and excessive requests. The generated scenario is then ready to be delivered to the terminal as a virtual environment.

[0132] Step 3:

[0133] The terminal builds a virtual environment based on the scenario information received from the server and prepares it for the user to access. Connection information is displayed on the terminal, and the user is ready to participate in the scenario.

[0134] Step 4:

[0135] Users connect to a virtual environment via a terminal and begin training based on a presented scenario. In the virtual scenario, they identify a security incident and take appropriate corrective actions.

[0136] Step 5:

[0137] The emotion engine uses sensors to monitor the user's facial expressions and voice tone during training, analyzing their emotional state in real time. It generates indicators of stress and concentration levels and sends this information to the server.

[0138] Step 6:

[0139] The server analyzes data from the emotion engine and dynamically adjusts the content of the virtual scenario according to the user's state. For example, if it detects that the user is in a high-stress state, it may lower the difficulty of the scenario or display a message encouraging the user to take a break.

[0140] Step 7:

[0141] The device displays appropriate feedback and adjustments on the user's screen based on instructions from the server. This allows the user to continue learning at their own pace.

[0142] Step 8:

[0143] The recording device saves user emotion data and training progress, which are then used to generate the next virtual scenario. This creates a user-specific training profile, enabling more effective learning.

[0144] (Example 2)

[0145] 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".

[0146] Conventional engineering skills training systems can provide a learning experience tailored to the user's skill level, but they lack the ability to provide an individualized learning environment that takes into account the user's psychological state. In particular, when faced with high stress or low concentration, the user's learning effectiveness may be significantly reduced. Therefore, there is a need for technology that can detect the user's emotional state in real time and dynamically adjust the training scenario accordingly.

[0147] 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.

[0148] In this invention, the server includes means for measuring the user's technical level and generating a virtual scenario corresponding to that level; means for generating a virtual attack in the virtual scenario and having the user respond to it; and means for detecting the user's emotional state and adjusting the virtual scenario in real time. This enables a personalized learning experience tailored to the user's technical skills, thereby reducing psychological burden and maximizing learning effectiveness.

[0149] An "information processing device" is an electronic device that receives data and performs various processes based on that data.

[0150] "Technical level" is an indicator that shows the level of technical ability and knowledge possessed by the user.

[0151] A "virtual scenario" is a simulation environment provided to the user that simulates and reproduces a specific problem.

[0152] A "virtual attack" is a simulated attack that occurs within a simulation and is provided to allow users to learn how to deal with it.

[0153] "Emotional state" refers to the user's psychological state and reactions, and usually refers to stress levels and concentration levels.

[0154] "Real-time adjustment" refers to making immediate changes to ongoing processes or environments.

[0155] This invention begins with the user accessing an interface using a terminal and selecting their skill level. The server retrieves data on the user's selected skill level and generates an appropriate virtual scenario based on that information. The virtual scenario is deployed to an information processing device and provides the user with training against a virtual attack.

[0156] The server utilizes a generative AI model to incorporate SQL injection and massive requests as possible virtual attacks into the scenario. Once the scenario is complete, it is delivered to the terminal, allowing users to experience the scenario firsthand.

[0157] As users experience scenarios through their devices, their facial expressions and voices are recorded and analyzed using cameras and microphones. This recording is processed in real time by an emotion engine, and the user's emotional state is fed back to the server.

[0158] The server dynamically adjusts the scenario to suit the user's emotional state based on information obtained from the emotion engine. Specifically, if high stress is detected, the difficulty of the scenario is reduced or an encouraging message is displayed. Also, if the analysis indicates that the user's concentration level is low, the challenge content is changed or educational content is provided.

[0159] As a practical example, if a user encounters a difficult scenario during intermediate-level training, the emotion engine detects a high-stress state. The server immediately responds, adjusting the difficulty level to reduce the user's psychological burden. Furthermore, emotional data and operation logs are stored in a recording device and used for generating future scenarios.

[0160] An example of a prompt sentence to input into the generative AI model is, "Please tell me how to respond when a user is in a highly stressed state." Based on this prompt sentence, the generative AI model generates the optimal solution, and the server uses it to adaptively adjust its response to each individual user.

[0161] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0162] Step 1:

[0163] The user selects their skill level through the terminal's interface. This selection data is entered into the server. The server analyzes this data to determine the user's skill level.

[0164] Step 2:

[0165] The server generates virtual scenarios tailored to the user's technical level. This scenario generation uses a generative AI model. Specifically, the generative AI model receives user technical level information as input and outputs scenarios that include appropriate virtual attacks (e.g., SQL injection or massive requests).

[0166] Step 3:

[0167] The server delivers the generated virtual scenario to the terminal. The user experiences this scenario on the terminal. The terminal displays the necessary interfaces as the scenario progresses and receives input from the user.

[0168] Step 4:

[0169] The emotion engine operates, capturing the user's facial expressions and voice in real time using the camera and microphone connected to the device. This data is analyzed by the emotion engine, and the user's emotional state (stress level and concentration level) is output.

[0170] Step 5:

[0171] The server receives emotional state data from the emotion engine. Based on this information, the server dynamically adjusts the scenario. Specifically, if the server detects that the user is in a high-stress state, it may lower the difficulty of the scenario or display encouraging messages on the device.

[0172] Step 6:

[0173] The device follows instructions from the server, adjusting the scenario and interface while providing feedback to the user. This allows the user to continue having a comfortable and effective learning experience.

[0174] Step 7:

[0175] The recording device saves user emotion data and scenario progress. This saved data will be used in future scenario generation.

[0176] (Application Example 2)

[0177] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0178] In modern technical training, uniform training that does not consider the user's emotional state can lead to stress and concentration levels affecting individual progress. This is especially true in workplaces such as factories, where practical training tailored to the user's skill level is required. It is necessary to reduce the user's psychological burden and provide an optimal learning experience. However, conventional systems have struggled to adequately monitor emotional states and dynamically adjust training content, so this aspect needs improvement.

[0179] 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.

[0180] In this invention, the server includes an information processing device that measures the user's skill level and generates a virtual scenario corresponding to that skill level, and an emotion recognition device that analyzes the user's facial expressions and tone of voice and evaluates their emotional state, and a means for dynamically adjusting the virtual scenario based on the user's emotional state. This makes it possible to reduce the user's psychological burden and provide an individually optimized learning experience.

[0181] An "information processing device" refers to an entire system that has the function of measuring the user's technical level and generating a virtual scenario based on that level.

[0182] A "virtual scenario" is a computer-generated simulated situation or environment designed to help users acquire practical skills.

[0183] "Emotion recognition means" refers to technologies and devices that analyze a user's facial expressions and tone of voice to evaluate their emotional state in real time.

[0184] "Dynamic adjustment" refers to the process of changing the content and difficulty level of a virtual scenario in a timely manner according to the user's emotional state.

[0185] A "recording device" is hardware or software used to save the user's response results and emotional data, and to utilize them in constructing future scenarios.

[0186] To implement this invention, a system is used that combines an information processing device, an emotion recognition means, a dynamic adjustment means, and a recording device.

[0187] The server measures the user's technical level and generates virtual scenarios according to that level. These virtual scenarios are configured to enable practical training simulating various industrial environments.

[0188] The emotion recognition system uses sensors such as cameras and microphones to analyze the user's facial expressions and voice tone. This process employs emotion analysis models utilizing machine learning libraries such as TENSORFLOW®. The user's emotional state is evaluated, and feedback is sent to the server in real time.

[0189] Based on the user's emotional data, the server dynamically adjusts the scenario difficulty or displays a message encouraging the user to take a break if the user is experiencing stress. The analysis and processing of emotional data is possible using OpenCV, an open-source CV library.

[0190] The device is used as an interface, allowing users to receive feedback through it and progress through their training at their own pace.

[0191] The recording device records collected emotional data and training progress, which are then used to generate the next scenario. This blessing device interacts with a database to support a learning experience optimized for each individual user.

[0192] As a concrete example, consider a scenario where a trainee learning to operate a new robot in a factory encounters a difficult operation and experiences temporary high stress. In this case, the emotion recognition system reacts quickly, the server adjusts the scenario, and the user receives appropriate support. An example of a prompt message provided to the user using a generative AI model is: "The user is experiencing stress due to a difficult operation. How would you like to adjust the training scenario?"

[0193] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0194] Step 1:

[0195] The server receives the user's skill level as input data and generates a virtual scenario based on it. Based on the input data, it designs a virtual scenario with difficulty and content appropriate to the user's skills and sends that scenario to the terminal. This allows the user to access an appropriate training environment.

[0196] Step 2:

[0197] The device collects emotional data, such as the user's facial expressions and voice, through its camera and microphone. The collected data is sent as input to an emotion recognition system and analyzed by a TensorFlow emotion analysis model. This analysis evaluates the user's emotional state, particularly stress and concentration levels, and this information is sent to the server as feedback.

[0198] Step 3:

[0199] The server dynamically adjusts the virtual scenario based on the user's emotional state data received as feedback. For example, if the stress level is high, the difficulty of the scenario is reduced, and an encouraging prompt is generated. This prompt might be something like, "The user is experiencing stress due to a difficult operation. How would you like the training scenario adjusted?" The generated prompt is sent to the terminal and displayed on the user's screen.

[0200] Step 4:

[0201] The device presents dynamically adjusted scenarios and prompts to the user through its user interface. The user can then use these to progress through the training smoothly and with minimal stress. The device's display capabilities are utilized here, and the design ensures that the user experiences a reduction in potential psychological burden.

[0202] Step 5:

[0203] The recording device records the user's emotional data and scenario progress during training. This data is stored in a database for future scenario generation and customization of user learning. This makes it possible to develop an optimal training plan for each user and maintain a system that supports continuous skill improvement.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] [Second Embodiment]

[0208] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0209] 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.

[0210] 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).

[0211] 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.

[0212] 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.

[0213] 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).

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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".

[0220] The system of this invention provides a virtual training environment for improving engineers' technical skills using an information processing device. The following describes a specific embodiment of this system.

[0221] The main components of this system consist of an information processing device (server), a terminal that receives user input, and a virtual scenario that the user experiences. The program is executed primarily by the server, and the user accesses it through the terminal.

[0222] The server first asks users to input their skill level in a self-assessment format when they begin training. Based on this information, the server is responsible for generating virtual scenarios of appropriate difficulty. These include simulations of security attacks such as SQL injection and mass requests. The generated scenarios are built as virtual environments, and access permissions are granted to the user's terminal.

[0223] Users connect to a virtual environment using their devices and respond to security incidents simulating real-world business scenarios based on provided scenarios. By detecting virtual attacks and taking appropriate countermeasures, users can hone their practical skills.

[0224] The server has the capability to monitor and record user behavior in real time. It evaluates whether predefined measures have been implemented and how effective they were, and provides users with specific improvement suggestions as feedback, if necessary.

[0225] All past training results are saved in the recording device and used to build training scenarios for subsequent sessions. In this way, personalized training is possible for each user.

[0226] As a concrete example, suppose a user selects the "intermediate" level. In this case, the server generates an intermediate-difficulty SQL injection attack scenario and deploys it to the terminal. The user attempts to prevent the attack by discovering abnormal queries through log analysis and setting up appropriate query filters. Based on the feedback obtained in this process, the user improves their skills and becomes ready to tackle more advanced scenarios.

[0227] The following describes the processing flow.

[0228] Step 1:

[0229] When a user accesses the system, the server prompts them for input regarding their technical level. This input consists of three options: "Beginner," "Intermediate," and "Advanced." The user self-assessses their skill level and selects one. The server stores this information in a database.

[0230] Step 2:

[0231] The server uses a generation AI to generate virtual scenarios of appropriate difficulty based on the user's technical level. These scenarios include virtual attacks such as potential SQL injection attacks and high-volume requests. The generated scenarios are then deployed to the virtual environment.

[0232] Step 3:

[0233] The terminal receives scenario information provided by the server and prepares it for the user to access the virtual environment. The terminal displays connection information and prepares the user to participate in the training.

[0234] Step 4:

[0235] The user connects to the virtual environment from their terminal using the provided access information. The terminal enables the user to operate within the virtual environment and records their actions in real time.

[0236] Step 5:

[0237] Within the virtual environment, users identify potential security incidents and implement necessary countermeasures based on presented scenarios. For example, they might monitor logs to detect unusual queries and develop appropriate countermeasures.

[0238] Step 6:

[0239] The server monitors user responses in real time and records each action. Furthermore, it evaluates how effective the user's actions were, creates improvement suggestions as needed, and provides feedback to the user.

[0240] Step 7:

[0241] The recording device saves the user's responses and feedback. This record is used in subsequent training sessions to form a user-specific learning curve and form the basis for providing more effective training.

[0242] (Example 1)

[0243] 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."

[0244] In the modern information technology field, there is an urgent need to cultivate engineers who can respond quickly to security incidents. However, environments for training with realistic scenarios are limited, resulting in a lack of opportunities for engineers to acquire practical skills. This lack can lead to a decline in actual incident response capabilities, potentially increasing the vulnerability of information systems. Therefore, there is a need for a system that provides virtual scenarios tailored to the technical level of engineers, enabling practical training and efficiently improving each individual's response capabilities.

[0245] 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.

[0246] In this invention, the server includes means for an information processing device to input the user's skill level in a self-assessment format and generate a virtual scenario corresponding to that skill level; means for generating a virtual attack using a generated AI model and having the user implement practical responses; and means for monitoring and evaluating the user's response to the virtual attack in real time and suggesting improvements using prompt messages. This enables engineers to enhance their security response capabilities in a practical and personalized training environment.

[0247] An "information processing device" is a computer device used for collecting, processing, storing, and communicating data.

[0248] "User" refers to an individual or group that operates an information processing device and receives training through a virtual scenario.

[0249] "Technical level" is a measure that indicates the user's technical proficiency and experience.

[0250] A "virtual scenario" is a simulation environment generated by an information processing device for a user to conduct specific training.

[0251] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence techniques to generate scenarios and feedback from a dataset.

[0252] A "virtual attack" is a simulated attack on an information system that is conducted within a virtual scenario.

[0253] "Practical response" refers to a series of preventative or mitigation measures taken by the user against a virtual attack within a virtual scenario.

[0254] "Real-time monitoring" is a method of observing and collecting data on a user's activities in real time.

[0255] A "prompt message" is a text message used by a generative AI model to provide feedback or instructions to the user.

[0256] An "improvement suggestion" is a proposal to make the actions taken by the user within a virtual scenario more effective.

[0257] A "recording device" is a device or mechanism for saving data and user activity history so that it can be used later.

[0258] In this embodiment of the invention, an information processing device plays a central role. The information processing device functions as a server, and users access this system through a terminal. Operations from the terminal are performed via an interface, and the user inputs their technical level. Based on the input technical level, the server generates a virtual scenario optimized for the user. A generation AI model is utilized, and the content of the scenario includes attack simulations such as database attacks and overload requests.

[0259] Specifically, if the user selects the "intermediate" level, the server generates an intermediate-level database attack simulation and builds it as a virtual environment. This environment runs on Docker using virtual container technology. Access information to the environment is provided to the terminal, and it is configured to allow the user to perform virtual incident response. By logging in from the terminal, the user begins training in the virtual environment, detects anomalies in the simulation, and attempts to take appropriate action.

[0260] The server monitors user behavior in real time and records the responses in a database. Evaluation is performed programmatically, and prompt statements are generated. For example, specific feedback is provided, such as "Detect an abnormal database query and how to set up filtering effectively." Through this process, users can improve their practical skills, and individual feedback is accumulated in preparation for the next training session.

[0261] In this way, the entire system works together to support the skill development of engineers, enabling more effective development of technical capabilities.

[0262] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0263] Step 1:

[0264] The user logs in to the information processing device using a terminal. The user enters their ID and password, which are sent to the server. The server compares the entered authentication information with the database and determines whether the authentication is successful. If authentication is successful, the server displays a technical level selection screen on the terminal.

[0265] Step 2:

[0266] The user selects their skill level through a self-assessment format and enters this information on their terminal. The entered skill level data is sent to the server. The server uses the received skill level information and a generative AI model to generate a virtual scenario of the corresponding difficulty level. For example, a scenario involving an SQL injection attack might be selected.

[0267] Step 3:

[0268] The server creates a virtual environment based on the generated virtual scenario. This environment is built on Docker using virtual container technology, and a virtual attack sequence is configured. Access information to the created virtual environment is sent to the terminal. The user connects to the virtual environment after receiving this information.

[0269] Step 4:

[0270] Users log in to a virtual environment via a terminal and begin scenario-based training. They detect anomalies occurring within the simulation and attempt appropriate responses, such as detecting and filtering abnormal queries. This process helps users hone their practical skills.

[0271] Step 5:

[0272] The server monitors the user's responses to the simulation in real time and records all operation logs. The server analyzes the behavioral data and generates prompts using a generative AI model. Based on these prompts, it suggests improvement measures to the user. For example, it might suggest "log monitoring methods to improve the speed of attack detection."

[0273] Step 6:

[0274] After training is complete, the server saves all data to a recording device. This saved data is used as foundational data when customizing the next training scenario. This ensures that an optimal training environment is continuously provided for each user.

[0275] (Application Example 1)

[0276] 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."

[0277] In the modern era, there is a lack of effective means for engineers to improve their skills in defending against cyberattacks. Furthermore, there is a need for personalized training tailored to individual skill levels, but traditional methods are currently insufficient. In addition, there is a growing demand for training environments that are practical, provide immediate feedback, and allow for rapid skill improvement.

[0278] 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.

[0279] In this invention, the server includes means for measuring the user's technical level and generating a virtual scenario according to the technical level, means for generating a simulated attack and having the user implement countermeasures against it, means for evaluating the user's countermeasures against the simulated attack and presenting improvement suggestions, and means for generating appropriate prompt texts based on the scenario using a generation AI model. Thereby, an optimal training scenario corresponding to each individual is provided, and through real-time improvement feedback, efficient and effective skill improvement becomes possible.

[0280] A "computing device" is an electronic device for measuring the user's technical level, generating an appropriate virtual scenario, and performing corresponding processing.

[0281] A "virtual scenario" provides simulated situations and problems according to the user's technical level and constructs an environment for technology acquisition.

[0282] A "simulated attack" is a series of programs for reproducing a cyber attack that may actually occur in a virtual environment and having the user implement countermeasures against it.

[0283] A "terminal device" is an electronic device used by the user to access the system and experience a virtual scenario.

[0284] A "training application" is software for the user to learn and train technology through a virtual scenario.

[0285] A "generation AI model" is an algorithm model for automatically generating appropriate prompt texts and countermeasures based on a virtual scenario and the user's actions.

[0286] A "prompt text" is text created by a generation AI model and used to give some instructions or convey information to the user.

[0287] To realize this invention, the system used is configured as follows: The server plays a central role as a computing device and has the function of understanding the user's skill level. Based on this, the server acquires self-assessment data entered by the user and generates an appropriate virtual scenario based on that data. The virtual scenario includes a simulated cyberattack and is presented to the user in real time.

[0288] A terminal is a physical device used by the user, such as a smartphone or personal computer. A training application is installed on this terminal, and the user uses this application to connect to a scenario and deal with a simulated attack through their actions.

[0289] The generative AI model operates within the server and monitors user actions in a virtual scenario. Based on these actions, the model generates appropriate feedback and prompts, providing the user with suggested actions and improvements. A specific example of a prompt is: "Describe how the user can learn techniques to detect and defend against a mid-level cyberattack on the virtual network."

[0290] As a concrete example, imagine a user using their smartphone during their commute and activating this system. If the user selects the "intermediate" level, the server generates a scenario simulating a database injection attack and sends it to the device. The user experiences the scenario through the application and performs attack detection and countermeasures.

[0291] As described above, this system, by combining servers, terminal devices, and generated AI models, makes it possible to provide security training optimized for each individual user.

[0292] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0293] Step 1:

[0294] The server receives self-assessment data of the user's technical level performed through the terminal. This input data is analyzed to identify the user's current technical level. Based on the identified technical level, the server prepares to select an appropriate virtual scenario.

[0295] Step 2:

[0296] The server generates a virtual scenario corresponding to the identified technical level. A generation AI model is used to specifically determine the scenario content, and the scenario includes the type of simulated attack. The generated scenario is sent to the user's terminal, initiating the scenario.

[0297] Step 3:

[0298] The user initiates action based on a virtual scenario received on their device. The user uses a training application to actually implement countermeasures against a simulated attack. These actions are logged and sent to the server.

[0299] Step 4:

[0300] The server analyzes user operation logs in real time and evaluates its response to simulated attacks. Based on the evaluation results, it uses a generative AI model to determine areas for improvement and appropriate countermeasures, and then generates prompt messages to suggest them.

[0301] Step 5:

[0302] The server sends the generated prompt message to the user's terminal. The user receives the prompt message as input, considers improvement measures based on its content, and takes actions with the intention of incorporating them into the next scenario.

[0303] Step 6:

[0304] The terminal saves all operation results and evaluation feedback to the recording device. This recorded data is prepared to be used as reference material when constructing the next training scenario.

[0305] The above is the basic program processing flow in this system.

[0306] Furthermore, an emotion engine for estimating the user's emotions may be combined. That is, the specific processing unit 290 may estimate the user's emotions using the emotion specific model 59 and perform specific processing using the user's emotions.

[0307] The present invention combines an emotion engine for detecting the user's emotional state with an engineering skill training system. Thereby, a more dynamic and individualized learning experience can be provided, and the user's skill improvement and psychological burden reduction can be achieved.

[0308] The server first obtains the selection data of the user's own technical level and generates an appropriate virtual scenario based on that information. The virtual scenario includes SQL injection and mass requests as security attacks. This scenario is distributed to the virtual environment for the user to experience through the terminal.

[0309] The emotion engine analyzes the user's facial expressions and voice tones in real time through sensors such as cameras and microphones while the user is participating in the virtual scenario. Based on this information, the emotion engine evaluates the user's stress level and concentration and provides feedback to the server.

[0310] Based on information from the emotion engine, the server dynamically adjusts the scenario according to the user's state. For example, if the server detects that the user is under high stress, it may lower the difficulty of the scenario or display a message encouraging them to take a break. Furthermore, if it determines that the user's interest or concentration is waning, it may present a different type of challenge or introduce educational content.

[0311] Users can experience these interactions through their devices and progress through the training at their own pace. This maximizes learning effectiveness and allows them to acquire the necessary skills effectively. The recording device saves the user's emotional data and learning progress, which will be used to generate future scenarios.

[0312] As a concrete example, consider a scenario where a user encounters an unexpectedly difficult scenario during intermediate-level training, temporarily experiencing high stress levels. In this situation, the emotion engine reacts quickly, prompting the server to adjust the scenario or send encouraging messages to alleviate stress. As a result, the user can continue learning at the appropriate time while their psychological pressure is eased. In this way, the system incorporating the emotion engine provides users with a more meaningful and efficient learning environment.

[0313] The following describes the processing flow.

[0314] Step 1:

[0315] When a user accesses the system, the server prompts them for input regarding their technical level. This input consists of three options: "Beginner," "Intermediate," and "Advanced." The user self-assessses their skill level and selects one. The server stores this information in a database.

[0316] Step 2:

[0317] The server generates a virtual scenario tailored to the user's technical level. This scenario includes security attacks such as SQL injection and excessive requests. The generated scenario is then ready to be delivered to the terminal as a virtual environment.

[0318] Step 3:

[0319] The terminal builds a virtual environment based on the scenario information received from the server and prepares it for the user to access. Connection information is displayed on the terminal, and the user is ready to participate in the scenario.

[0320] Step 4:

[0321] Users connect to a virtual environment via a terminal and begin training based on a presented scenario. In the virtual scenario, they identify a security incident and take appropriate corrective actions.

[0322] Step 5:

[0323] The emotion engine uses sensors to monitor the user's facial expressions and voice tone during training, analyzing their emotional state in real time. It generates indicators of stress and concentration levels and sends this information to the server.

[0324] Step 6:

[0325] The server analyzes data from the emotion engine and dynamically adjusts the content of the virtual scenario according to the user's state. For example, if it detects that the user is in a high-stress state, it may lower the difficulty of the scenario or display a message encouraging the user to take a break.

[0326] Step 7:

[0327] The device displays appropriate feedback and adjustments on the user's screen based on instructions from the server. This allows the user to continue learning at their own pace.

[0328] Step 8:

[0329] The recording device saves user emotion data and training progress, which are then used to generate the next virtual scenario. This creates a user-specific training profile, enabling more effective learning.

[0330] (Example 2)

[0331] 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".

[0332] Conventional engineering skills training systems can provide a learning experience tailored to the user's skill level, but they lack the ability to provide an individualized learning environment that takes into account the user's psychological state. In particular, when faced with high stress or low concentration, the user's learning effectiveness may be significantly reduced. Therefore, there is a need for technology that can detect the user's emotional state in real time and dynamically adjust the training scenario accordingly.

[0333] 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.

[0334] In this invention, the server includes means for measuring the user's technical level and generating a virtual scenario corresponding to that level; means for generating a virtual attack in the virtual scenario and having the user respond to it; and means for detecting the user's emotional state and adjusting the virtual scenario in real time. This enables a personalized learning experience tailored to the user's technical skills, thereby reducing psychological burden and maximizing learning effectiveness.

[0335] An "information processing device" is an electronic device that receives data and performs various processes based on that data.

[0336] "Technical level" is an indicator that shows the level of technical ability and knowledge possessed by the user.

[0337] A "virtual scenario" is a simulation environment provided to the user that simulates and reproduces a specific problem.

[0338] A "virtual attack" is a simulated attack that occurs within a simulation and is provided to allow users to learn how to deal with it.

[0339] "Emotional state" refers to the user's psychological state and reactions, and usually refers to stress levels and concentration levels.

[0340] "Real-time adjustment" refers to making immediate changes to ongoing processes or environments.

[0341] This invention begins with the user accessing an interface using a terminal and selecting their skill level. The server retrieves data on the user's selected skill level and generates an appropriate virtual scenario based on that information. The virtual scenario is deployed to an information processing device and provides the user with training against a virtual attack.

[0342] The server utilizes a generative AI model to incorporate SQL injection and massive requests as possible virtual attacks into the scenario. Once the scenario is complete, it is delivered to the terminal, allowing users to experience the scenario firsthand.

[0343] As users experience scenarios through their devices, their facial expressions and voices are recorded and analyzed using cameras and microphones. This recording is processed in real time by an emotion engine, and the user's emotional state is fed back to the server.

[0344] The server dynamically adjusts the scenario to suit the user's emotional state based on information obtained from the emotion engine. Specifically, if high stress is detected, the difficulty of the scenario is reduced or an encouraging message is displayed. Also, if the analysis indicates that the user's concentration level is low, the challenge content is changed or educational content is provided.

[0345] As a practical example, if a user encounters a difficult scenario during intermediate-level training, the emotion engine detects a high-stress state. The server immediately responds, adjusting the difficulty level to reduce the user's psychological burden. Furthermore, emotional data and operation logs are stored in a recording device and used for generating future scenarios.

[0346] An example of a prompt to input into the generative AI model is, "Please tell me how to respond when a user is in a highly stressed state." Based on this prompt, the generative AI model generates the optimal solution, which the server then uses to adaptively adjust its response to each individual user.

[0347] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0348] Step 1:

[0349] The user selects their skill level through the terminal's interface. This selection data is entered into the server. The server analyzes this data to determine the user's skill level.

[0350] Step 2:

[0351] The server generates virtual scenarios tailored to the user's technical level. This scenario generation uses a generative AI model. Specifically, the generative AI model receives user technical level information as input and outputs scenarios that include appropriate virtual attacks (e.g., SQL injection or massive requests).

[0352] Step 3:

[0353] The server delivers the generated virtual scenario to the terminal. The user experiences this scenario on the terminal. The terminal displays the necessary interfaces as the scenario progresses and receives input from the user.

[0354] Step 4:

[0355] The emotion engine operates, capturing the user's facial expressions and voice in real time using the camera and microphone connected to the device. This data is analyzed by the emotion engine, and the user's emotional state (stress level and concentration level) is output.

[0356] Step 5:

[0357] The server receives emotional state data from the emotion engine. Based on this information, the server dynamically adjusts the scenario. Specifically, if the server detects that the user is in a high-stress state, it may lower the difficulty of the scenario or display encouraging messages on the device.

[0358] Step 6:

[0359] The device follows instructions from the server, adjusting the scenario and interface while providing feedback to the user. This allows the user to continue having a comfortable and effective learning experience.

[0360] Step 7:

[0361] The recording device saves user emotion data and scenario progress. This saved data will be used in future scenario generation.

[0362] (Application Example 2)

[0363] 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".

[0364] In modern technical training, uniform training that does not consider the user's emotional state can lead to stress and concentration levels affecting individual progress. This is especially true in workplaces such as factories, where practical training tailored to the user's skill level is required. It is necessary to reduce the user's psychological burden and provide an optimal learning experience. However, conventional systems have struggled to adequately monitor emotional states and dynamically adjust training content, so this aspect needs improvement.

[0365] 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.

[0366] In this invention, the server includes an information processing device that measures the user's skill level and generates a virtual scenario corresponding to that skill level, and an emotion recognition device that analyzes the user's facial expressions and tone of voice and evaluates their emotional state, and a means for dynamically adjusting the virtual scenario based on the user's emotional state. This makes it possible to reduce the user's psychological burden and provide an individually optimized learning experience.

[0367] An "information processing device" refers to an entire system that has the function of measuring the user's technical level and generating a virtual scenario based on that level.

[0368] A "virtual scenario" is a computer-generated simulated situation or environment designed to help users acquire practical skills.

[0369] "Emotion recognition means" refers to technologies and devices that analyze a user's facial expressions and tone of voice to evaluate their emotional state in real time.

[0370] "Dynamic adjustment" refers to the process of changing the content and difficulty level of a virtual scenario in a timely manner according to the user's emotional state.

[0371] A "recording device" is hardware or software used to save the user's response results and emotional data, and to utilize them in constructing future scenarios.

[0372] To implement this invention, a system is used that combines an information processing device, an emotion recognition means, a dynamic adjustment means, and a recording device.

[0373] The server measures the user's technical level and generates virtual scenarios according to that level. These virtual scenarios are configured to enable practical training simulating various industrial environments.

[0374] The emotion recognition system uses sensors such as cameras and microphones to analyze the user's facial expressions and voice tone. This process employs emotion analysis models utilizing machine learning libraries such as TensorFlow. The user's emotional state is evaluated, and feedback is sent to the server in real time.

[0375] Based on the user's emotional data, the server dynamically adjusts the scenario difficulty or displays a message encouraging the user to take a break if the user is experiencing stress. The analysis and processing of emotional data is possible using OpenCV, an open-source CV library.

[0376] The device is used as an interface, allowing users to receive feedback through it and progress through their training at their own pace.

[0377] The recording device records collected emotional data and training progress, which are then used to generate the next scenario. This blessing device interacts with a database to support a learning experience optimized for each individual user.

[0378] As a concrete example, consider a scenario where a trainee learning to operate a new robot in a factory encounters a difficult operation and experiences temporary high stress. In this case, the emotion recognition system reacts quickly, the server adjusts the scenario, and the user receives appropriate support. An example of a prompt message provided to the user using a generative AI model is: "The user is experiencing stress due to a difficult operation. How would you like to adjust the training scenario?"

[0379] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0380] Step 1:

[0381] The server receives the user's skill level as input data and generates a virtual scenario based on it. Based on the input data, it designs a virtual scenario with difficulty and content appropriate to the user's skills and sends that scenario to the terminal. This allows the user to access an appropriate training environment.

[0382] Step 2:

[0383] The device collects emotional data, such as the user's facial expressions and voice, through its camera and microphone. The collected data is sent as input to an emotion recognition system and analyzed by a TensorFlow emotion analysis model. This analysis evaluates the user's emotional state, particularly stress and concentration levels, and this information is sent to the server as feedback.

[0384] Step 3:

[0385] The server dynamically adjusts the virtual scenario based on the user's emotional state data received as feedback. For example, if the stress level is high, the difficulty of the scenario is reduced, and an encouraging prompt is generated. This prompt might be something like, "The user is experiencing stress due to a difficult operation. How would you like the training scenario adjusted?" The generated prompt is sent to the terminal and displayed on the user's screen.

[0386] Step 4:

[0387] The device presents dynamically adjusted scenarios and prompts to the user through its user interface. The user can then use these to progress through the training smoothly and with minimal stress. The device's display capabilities are utilized here, and the design ensures that the user experiences a reduction in potential psychological burden.

[0388] Step 5:

[0389] The recording device records the user's emotional data and scenario progress during training. This data is stored in a database for future scenario generation and customization of user learning. This makes it possible to develop an optimal training plan for each user and maintain a system that supports continuous skill improvement.

[0390] 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.

[0391] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0392] 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.

[0393] [Third Embodiment]

[0394] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0395] 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.

[0396] 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).

[0397] 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.

[0398] 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.

[0399] 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).

[0400] 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.

[0401] 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.

[0402] 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.

[0403] 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.

[0404] 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.

[0405] 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".

[0406] The system of this invention provides a virtual training environment for improving engineers' technical skills using an information processing device. The following describes a specific embodiment of this system.

[0407] The main components of this system consist of an information processing device (server), a terminal that receives user input, and a virtual scenario that the user experiences. The program is executed primarily by the server, and the user accesses it through the terminal.

[0408] The server first asks users to input their skill level in a self-assessment format when they begin training. Based on this information, the server is responsible for generating virtual scenarios of appropriate difficulty. These include simulations of security attacks such as SQL injection and mass requests. The generated scenarios are built as virtual environments, and access permissions are granted to the user's terminal.

[0409] Users connect to a virtual environment using their devices and respond to security incidents simulating real-world business scenarios based on provided scenarios. By detecting virtual attacks and taking appropriate countermeasures, users can hone their practical skills.

[0410] The server has the capability to monitor and record user behavior in real time. It evaluates whether predefined measures have been implemented and how effective they were, and provides users with specific improvement suggestions as feedback, if necessary.

[0411] All past training results are saved in the recording device and used to build training scenarios for subsequent sessions. In this way, personalized training is possible for each user.

[0412] As a concrete example, suppose a user selects the "intermediate" level. In this case, the server generates an intermediate-difficulty SQL injection attack scenario and deploys it to the terminal. The user attempts to prevent the attack by discovering abnormal queries through log analysis and setting up appropriate query filters. Based on the feedback obtained in this process, the user improves their skills and becomes ready to tackle more advanced scenarios.

[0413] The following describes the processing flow.

[0414] Step 1:

[0415] When a user accesses the system, the server prompts them for input regarding their technical level. This input consists of three options: "Beginner," "Intermediate," and "Advanced." The user self-assessses their skill level and selects one. The server stores this information in a database.

[0416] Step 2:

[0417] The server uses a generation AI to generate virtual scenarios of appropriate difficulty based on the user's technical level. These scenarios include virtual attacks such as potential SQL injection attacks and high-volume requests. The generated scenarios are then deployed to the virtual environment.

[0418] Step 3:

[0419] The terminal receives scenario information provided by the server and prepares it for the user to access the virtual environment. The terminal displays connection information and prepares the user to participate in the training.

[0420] Step 4:

[0421] The user connects to the virtual environment from their terminal using the provided access information. The terminal enables the user to operate within the virtual environment and records their actions in real time.

[0422] Step 5:

[0423] Within the virtual environment, users identify potential security incidents and implement necessary countermeasures based on presented scenarios. For example, they might monitor logs to detect unusual queries and develop appropriate countermeasures.

[0424] Step 6:

[0425] The server monitors user responses in real time and records each action. Furthermore, it evaluates how effective the user's actions were, creates improvement suggestions as needed, and provides feedback to the user.

[0426] Step 7:

[0427] The recording device saves the user's responses and feedback. This record is used in subsequent training sessions to form a user-specific learning curve and form the basis for providing more effective training.

[0428] (Example 1)

[0429] 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."

[0430] In the modern information technology field, there is an urgent need to cultivate engineers who can respond quickly to security incidents. However, environments for training with realistic scenarios are limited, resulting in a lack of opportunities for engineers to acquire practical skills. This lack can lead to a decline in actual incident response capabilities, potentially increasing the vulnerability of information systems. Therefore, there is a need for a system that provides virtual scenarios tailored to the technical level of engineers, enabling practical training and efficiently improving each individual's response capabilities.

[0431] 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.

[0432] In this invention, the server includes means for an information processing device to input the user's skill level in a self-assessment format and generate a virtual scenario corresponding to that skill level; means for generating a virtual attack using a generated AI model and having the user implement practical responses; and means for monitoring and evaluating the user's response to the virtual attack in real time and suggesting improvements using prompt messages. This enables engineers to enhance their security response capabilities in a practical and personalized training environment.

[0433] An "information processing device" is a computer device used for collecting, processing, storing, and communicating data.

[0434] "User" refers to an individual or group that operates an information processing device and receives training through a virtual scenario.

[0435] "Technical level" is a measure that indicates the user's technical proficiency and experience.

[0436] A "virtual scenario" is a simulation environment generated by an information processing device for a user to conduct specific training.

[0437] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence techniques to generate scenarios and feedback from a dataset.

[0438] A "virtual attack" is a simulated attack on an information system that is conducted within a virtual scenario.

[0439] "Practical response" refers to a series of preventative or mitigation measures taken by the user against a virtual attack within a virtual scenario.

[0440] "Real-time monitoring" is a method of observing and collecting data on a user's activities in real time.

[0441] A "prompt message" is a text message used by a generative AI model to provide feedback or instructions to the user.

[0442] An "improvement suggestion" is a proposal to make the actions taken by the user within a virtual scenario more effective.

[0443] A "recording device" is a device or mechanism for saving data and user activity history so that it can be used later.

[0444] In this embodiment of the invention, an information processing device plays a central role. The information processing device functions as a server, and users access this system through a terminal. Operations from the terminal are performed via an interface, and the user inputs their technical level. Based on the input technical level, the server generates a virtual scenario optimized for the user. A generation AI model is utilized, and the content of the scenario includes attack simulations such as database attacks and overload requests.

[0445] Specifically, if the user selects the "intermediate" level, the server generates an intermediate-level database attack simulation and builds it as a virtual environment. This environment runs on Docker using virtual container technology. Access information to the environment is provided to the terminal, and it is configured to allow the user to perform virtual incident response. By logging in from the terminal, the user begins training in the virtual environment, detects anomalies in the simulation, and attempts to take appropriate action.

[0446] The server monitors user behavior in real time and records the responses in a database. Evaluation is performed programmatically, and prompt statements are generated. For example, specific feedback is provided, such as "Detect an abnormal database query and how to set up filtering effectively." Through this process, users can improve their practical skills, and individual feedback is accumulated in preparation for the next training session.

[0447] In this way, the entire system works together to support the skill development of engineers, enabling more effective development of technical capabilities.

[0448] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0449] Step 1:

[0450] The user logs in to the information processing device using a terminal. The user enters their ID and password, which are sent to the server. The server compares the entered authentication information with the database and determines whether the authentication is successful. If authentication is successful, the server displays a technical level selection screen on the terminal.

[0451] Step 2:

[0452] The user selects their skill level through a self-assessment format and enters this information on their terminal. The entered skill level data is sent to the server. The server uses the received skill level information and a generative AI model to generate a virtual scenario of the corresponding difficulty level. For example, a scenario involving an SQL injection attack might be selected.

[0453] Step 3:

[0454] The server creates a virtual environment based on the generated virtual scenario. This environment is built on Docker using virtual container technology, and a virtual attack sequence is configured. Access information to the created virtual environment is sent to the terminal. The user connects to the virtual environment after receiving this information.

[0455] Step 4:

[0456] Users log in to a virtual environment via a terminal and begin scenario-based training. They detect anomalies occurring within the simulation and attempt appropriate responses, such as detecting and filtering abnormal queries. This process helps users hone their practical skills.

[0457] Step 5:

[0458] The server monitors the user's responses to the simulation in real time and records all operation logs. The server analyzes the behavioral data and generates prompts using a generative AI model. Based on these prompts, it suggests improvement measures to the user. For example, it might suggest "log monitoring methods to improve the speed of attack detection."

[0459] Step 6:

[0460] After training is complete, the server saves all data to a recording device. This saved data is used as foundational data when customizing the next training scenario. This ensures that an optimal training environment is continuously provided for each user.

[0461] (Application Example 1)

[0462] 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."

[0463] In the modern era, there is a lack of effective means for engineers to improve their skills in defending against cyberattacks. Furthermore, there is a need for personalized training tailored to individual skill levels, but traditional methods are currently insufficient. In addition, there is a growing demand for training environments that are practical, provide immediate feedback, and allow for rapid skill improvement.

[0464] 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.

[0465] In this invention, the server includes means for measuring the user's skill level and generating a virtual scenario corresponding to that skill level; means for generating a simulated attack and prompting the user to implement countermeasures against it; means for evaluating the user's countermeasures against the simulated attack and suggesting improvements; and means for generating appropriate prompt sentences based on the scenario using a generation AI model. This enables efficient and effective skill improvement by providing an optimal training scenario tailored to each individual and through real-time improvement feedback.

[0466] A "computational device" is an electronic device used to measure the user's skill level, generate appropriate virtual scenarios, and perform corresponding processing.

[0467] A "virtual scenario" provides simulated situations and challenges tailored to the user's skill level, creating an environment for acquiring new skills.

[0468] A "simulated attack" is a series of programs that recreates actual cyberattacks in a virtual environment and allows users to implement countermeasures against them.

[0469] A "terminal device" is an electronic device used by a user to access a system and experience virtual scenarios.

[0470] A "training application" is software that allows users to learn and practice skills through virtual scenarios.

[0471] A "generative AI model" is an algorithmic model that automatically generates appropriate prompts and countermeasures based on a virtual scenario and the user's actions.

[0472] A "prompt message" is text created by a generative AI model and used to provide instructions or information to the user.

[0473] To realize this invention, the system used is configured as follows: The server plays a central role as a computing device and has the function of understanding the user's skill level. Based on this, the server acquires self-assessment data entered by the user and generates an appropriate virtual scenario based on that data. The virtual scenario includes a simulated cyberattack and is presented to the user in real time.

[0474] A terminal is a physical device used by the user, such as a smartphone or personal computer. A training application is installed on this terminal, and the user uses this application to connect to a scenario and deal with a simulated attack through their actions.

[0475] The generative AI model operates within the server and monitors user actions in a virtual scenario. Based on these actions, the model generates appropriate feedback and prompts, providing the user with suggested actions and improvements. A specific example of a prompt is: "Describe how the user can learn techniques to detect and defend against a mid-level cyberattack on the virtual network."

[0476] As a concrete example, imagine a user using their smartphone during their commute and activating this system. If the user selects the "intermediate" level, the server generates a scenario simulating a database injection attack and sends it to the device. The user experiences the scenario through the application and performs attack detection and countermeasures.

[0477] As described above, this system, by combining servers, terminal devices, and generated AI models, makes it possible to provide security training optimized for each individual user.

[0478] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0479] Step 1:

[0480] The server receives self-assessment data of the user's technical level performed through the terminal. This input data is analyzed to identify the user's current technical level. Based on the identified technical level, the server prepares to select an appropriate virtual scenario.

[0481] Step 2:

[0482] The server generates a virtual scenario corresponding to the identified technical level. A generation AI model is used to specifically determine the scenario content, and the scenario includes the type of simulated attack. The generated scenario is sent to the user's terminal, initiating the scenario.

[0483] Step 3:

[0484] The user initiates action based on a virtual scenario received on their device. The user uses a training application to actually implement countermeasures against a simulated attack. These actions are logged and sent to the server.

[0485] Step 4:

[0486] The server analyzes user operation logs in real time and evaluates its response to simulated attacks. Based on the evaluation results, it uses a generative AI model to determine areas for improvement and appropriate countermeasures, and then generates prompt messages to suggest them.

[0487] Step 5:

[0488] The server sends the generated prompt message to the user's terminal. The user receives the prompt message as input, considers improvement measures based on its content, and takes actions with the intention of incorporating them into the next scenario.

[0489] Step 6:

[0490] The terminal saves all operation results and evaluation feedback to a recording device. This recorded data is prepared to be used as reference material when constructing the next training scenario.

[0491] The above describes the basic program processing flow in this system.

[0492] 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.

[0493] This invention combines an engineering skills training system with an emotion engine that detects the user's emotional state. This provides a more dynamic and personalized learning experience, aiming to improve the user's skills and reduce their psychological burden.

[0494] The server first obtains data on the user's technical level selection and generates an appropriate virtual scenario based on that information. These virtual scenarios include security attacks such as SQL injection and mass requests. This scenario is then delivered to the virtual environment for the user to experience through their terminal.

[0495] The emotion engine analyzes the user's facial expressions and voice tone in real time via sensors such as cameras and microphones while the user participates in a virtual scenario. Based on this information, the emotion engine evaluates the user's stress level and concentration level and provides feedback to the server.

[0496] Based on information from the emotion engine, the server dynamically adjusts the scenario according to the user's state. For example, if the server detects that the user is under high stress, it may lower the difficulty of the scenario or display a message encouraging them to take a break. Furthermore, if it determines that the user's interest or concentration is waning, it may present a different type of challenge or introduce educational content.

[0497] Users can experience these interactions through their devices and progress through the training at their own pace. This maximizes learning effectiveness and allows them to acquire the necessary skills effectively. The recording device saves the user's emotional data and learning progress, which will be used to generate future scenarios.

[0498] As a concrete example, consider a scenario where a user encounters an unexpectedly difficult scenario during intermediate-level training, temporarily experiencing high stress levels. In this situation, the emotion engine reacts quickly, prompting the server to adjust the scenario or send encouraging messages to alleviate stress. As a result, the user can continue learning at the appropriate time while their psychological pressure is eased. In this way, the system incorporating the emotion engine provides users with a more meaningful and efficient learning environment.

[0499] The following describes the processing flow.

[0500] Step 1:

[0501] When a user accesses the system, the server prompts them for input regarding their technical level. This input consists of three options: "Beginner," "Intermediate," and "Advanced." The user self-assessses their skill level and selects one. The server stores this information in a database.

[0502] Step 2:

[0503] The server generates a virtual scenario tailored to the user's technical level. This scenario includes security attacks such as SQL injection and excessive requests. The generated scenario is then ready to be delivered to the terminal as a virtual environment.

[0504] Step 3:

[0505] The terminal builds a virtual environment based on the scenario information received from the server and prepares it for the user to access. Connection information is displayed on the terminal, and the user is ready to participate in the scenario.

[0506] Step 4:

[0507] Users connect to a virtual environment via a terminal and begin training based on a presented scenario. In the virtual scenario, they identify a security incident and take appropriate corrective actions.

[0508] Step 5:

[0509] The emotion engine uses sensors to monitor the user's facial expressions and voice tone during training, analyzing their emotional state in real time. It generates indicators of stress and concentration levels and sends this information to the server.

[0510] Step 6:

[0511] The server analyzes data from the emotion engine and dynamically adjusts the content of the virtual scenario according to the user's state. For example, if it detects that the user is in a high-stress state, it may lower the difficulty of the scenario or display a message encouraging the user to take a break.

[0512] Step 7:

[0513] The device displays appropriate feedback and adjustments on the user's screen based on instructions from the server. This allows the user to continue learning at their own pace.

[0514] Step 8:

[0515] The recording device saves user emotion data and training progress, which are then used to generate the next virtual scenario. This creates a user-specific training profile, enabling more effective learning.

[0516] (Example 2)

[0517] 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."

[0518] Conventional engineering skills training systems can provide a learning experience tailored to the user's skill level, but they lack the ability to provide an individualized learning environment that takes into account the user's psychological state. In particular, when faced with high stress or low concentration, the user's learning effectiveness may be significantly reduced. Therefore, there is a need for technology that can detect the user's emotional state in real time and dynamically adjust the training scenario accordingly.

[0519] 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.

[0520] In this invention, the server includes means for measuring the user's technical level and generating a virtual scenario corresponding to that level; means for generating a virtual attack in the virtual scenario and having the user respond to it; and means for detecting the user's emotional state and adjusting the virtual scenario in real time. This enables a personalized learning experience tailored to the user's technical skills, thereby reducing psychological burden and maximizing learning effectiveness.

[0521] An "information processing device" is an electronic device that receives data and performs various processes based on that data.

[0522] "Technical level" is an indicator that shows the level of technical ability and knowledge possessed by the user.

[0523] A "virtual scenario" is a simulation environment provided to the user that simulates and reproduces a specific problem.

[0524] A "virtual attack" is a simulated attack that occurs within a simulation and is provided to allow users to learn how to deal with it.

[0525] "Emotional state" refers to the user's psychological state and reactions, and usually refers to stress levels and concentration levels.

[0526] "Real-time adjustment" refers to making immediate changes to ongoing processes or environments.

[0527] This invention begins with the user accessing an interface using a terminal and selecting their skill level. The server retrieves data on the user's selected skill level and generates an appropriate virtual scenario based on that information. The virtual scenario is deployed to an information processing device and provides the user with training against a virtual attack.

[0528] The server utilizes a generative AI model to incorporate SQL injection and massive requests as possible virtual attacks into the scenario. Once the scenario is complete, it is delivered to the terminal, allowing users to experience the scenario firsthand.

[0529] As users experience scenarios through their devices, their facial expressions and voices are recorded and analyzed using cameras and microphones. This recording is processed in real time by an emotion engine, and the user's emotional state is fed back to the server.

[0530] The server dynamically adjusts the scenario to suit the user's emotional state based on information obtained from the emotion engine. Specifically, if high stress is detected, the difficulty of the scenario is reduced or an encouraging message is displayed. Also, if the analysis indicates that the user's concentration level is low, the challenge content is changed or educational content is provided.

[0531] As a practical example, if a user encounters a difficult scenario during intermediate-level training, the emotion engine detects a high-stress state. The server immediately responds, adjusting the difficulty level to reduce the user's psychological burden. Furthermore, emotional data and operation logs are stored in a recording device and used for generating future scenarios.

[0532] An example of a prompt to input into the generative AI model is, "Please tell me how to respond when a user is in a highly stressed state." Based on this prompt, the generative AI model generates the optimal solution, which the server then uses to adaptively adjust its response to each individual user.

[0533] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0534] Step 1:

[0535] The user selects their skill level through the terminal's interface. This selection data is entered into the server. The server analyzes this data to determine the user's skill level.

[0536] Step 2:

[0537] The server generates virtual scenarios tailored to the user's technical level. This scenario generation uses a generative AI model. Specifically, the generative AI model receives user technical level information as input and outputs scenarios that include appropriate virtual attacks (e.g., SQL injection or massive requests).

[0538] Step 3:

[0539] The server delivers the generated virtual scenario to the terminal. The user experiences this scenario on the terminal. The terminal displays the necessary interfaces as the scenario progresses and receives input from the user.

[0540] Step 4:

[0541] The emotion engine operates, capturing the user's facial expressions and voice in real time using the camera and microphone connected to the device. This data is analyzed by the emotion engine, and the user's emotional state (stress level and concentration level) is output.

[0542] Step 5:

[0543] The server receives emotional state data from the emotion engine. Based on this information, the server dynamically adjusts the scenario. Specifically, if the server detects that the user is in a high-stress state, it may lower the difficulty of the scenario or display encouraging messages on the device.

[0544] Step 6:

[0545] The device follows instructions from the server, adjusting the scenario and interface while providing feedback to the user. This allows the user to continue having a comfortable and effective learning experience.

[0546] Step 7:

[0547] The recording device saves user emotion data and scenario progress. This saved data will be used in future scenario generation.

[0548] (Application Example 2)

[0549] 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."

[0550] In modern technical training, uniform training that does not consider the user's emotional state can lead to stress and concentration levels affecting individual progress. This is especially true in workplaces such as factories, where practical training tailored to the user's skill level is required. It is necessary to reduce the user's psychological burden and provide an optimal learning experience. However, conventional systems have struggled to adequately monitor emotional states and dynamically adjust training content, so this aspect needs improvement.

[0551] 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.

[0552] In this invention, the server includes an information processing device that measures the user's skill level and generates a virtual scenario corresponding to that skill level, and an emotion recognition device that analyzes the user's facial expressions and tone of voice and evaluates their emotional state, and a means for dynamically adjusting the virtual scenario based on the user's emotional state. This makes it possible to reduce the user's psychological burden and provide an individually optimized learning experience.

[0553] An "information processing device" refers to an entire system that has the function of measuring the user's technical level and generating a virtual scenario based on that level.

[0554] A "virtual scenario" is a computer-generated simulated situation or environment designed to help users acquire practical skills.

[0555] "Emotion recognition means" refers to technologies and devices that analyze a user's facial expressions and tone of voice to evaluate their emotional state in real time.

[0556] "Dynamic adjustment" refers to the process of changing the content and difficulty level of a virtual scenario in a timely manner according to the user's emotional state.

[0557] A "recording device" is hardware or software used to save the user's response results and emotional data, and to utilize them in constructing future scenarios.

[0558] To implement this invention, a system is used that combines an information processing device, an emotion recognition means, a dynamic adjustment means, and a recording device.

[0559] The server measures the user's technical level and generates virtual scenarios according to that level. These virtual scenarios are configured to enable practical training simulating various industrial environments.

[0560] The emotion recognition system uses sensors such as cameras and microphones to analyze the user's facial expressions and voice tone. This process employs emotion analysis models utilizing machine learning libraries such as TensorFlow. The user's emotional state is evaluated, and feedback is sent to the server in real time.

[0561] Based on the user's emotional data, the server dynamically adjusts the scenario difficulty or displays a message encouraging the user to take a break if the user is experiencing stress. The analysis and processing of emotional data is possible using OpenCV, an open-source CV library.

[0562] The device is used as an interface, allowing users to receive feedback through it and progress through their training at their own pace.

[0563] The recording device records collected emotional data and training progress, which are then used to generate the next scenario. This blessing device interacts with a database to support a learning experience optimized for each individual user.

[0564] As a concrete example, consider a scenario where a trainee learning to operate a new robot in a factory encounters a difficult operation and experiences temporary high stress. In this case, the emotion recognition system reacts quickly, the server adjusts the scenario, and the user receives appropriate support. An example of a prompt message provided to the user using a generative AI model is: "The user is experiencing stress due to a difficult operation. How would you like to adjust the training scenario?"

[0565] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0566] Step 1:

[0567] The server receives the user's skill level as input data and generates a virtual scenario based on it. Based on the input data, it designs a virtual scenario with difficulty and content appropriate to the user's skills and sends that scenario to the terminal. This allows the user to access an appropriate training environment.

[0568] Step 2:

[0569] The device collects emotional data, such as the user's facial expressions and voice, through its camera and microphone. The collected data is sent as input to an emotion recognition system and analyzed by a TensorFlow emotion analysis model. This analysis evaluates the user's emotional state, particularly stress and concentration levels, and this information is sent to the server as feedback.

[0570] Step 3:

[0571] The server dynamically adjusts the virtual scenario based on the user's emotional state data received as feedback. For example, if the stress level is high, the difficulty of the scenario is reduced, and an encouraging prompt is generated. This prompt might be something like, "The user is experiencing stress due to a difficult operation. How would you like the training scenario adjusted?" The generated prompt is sent to the terminal and displayed on the user's screen.

[0572] Step 4:

[0573] The device presents dynamically adjusted scenarios and prompts to the user through its user interface. The user can then use these to progress through the training smoothly and with minimal stress. The device's display capabilities are utilized here, and the design ensures that the user experiences a reduction in potential psychological burden.

[0574] Step 5:

[0575] The recording device records the user's emotional data and scenario progress during training. This data is stored in a database for future scenario generation and customization of user learning. This makes it possible to develop an optimal training plan for each user and maintain a system that supports continuous skill improvement.

[0576] 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.

[0577] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0578] 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.

[0579] [Fourth Embodiment]

[0580] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0581] 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.

[0582] 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).

[0583] 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.

[0584] 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.

[0585] 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).

[0586] 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.

[0587] 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.

[0588] 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.

[0589] 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.

[0590] 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.

[0591] 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.

[0592] 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".

[0593] The system of this invention provides a virtual training environment for improving engineers' technical skills using an information processing device. The following describes a specific embodiment of this system.

[0594] The main components of this system consist of an information processing device (server), a terminal that receives user input, and a virtual scenario that the user experiences. The program is executed primarily by the server, and the user accesses it through the terminal.

[0595] The server first asks users to input their skill level in a self-assessment format when they begin training. Based on this information, the server is responsible for generating virtual scenarios of appropriate difficulty. These include simulations of security attacks such as SQL injection and mass requests. The generated scenarios are built as virtual environments, and access permissions are granted to the user's terminal.

[0596] Users connect to a virtual environment using their devices and respond to security incidents simulating real-world business scenarios based on provided scenarios. By detecting virtual attacks and taking appropriate countermeasures, users can hone their practical skills.

[0597] The server has the capability to monitor and record user behavior in real time. It evaluates whether predefined measures have been implemented and how effective they were, and provides users with specific improvement suggestions as feedback, if necessary.

[0598] All past training results are saved in the recording device and used to build training scenarios for subsequent sessions. In this way, personalized training is possible for each user.

[0599] As a concrete example, suppose a user selects the "intermediate" level. In this case, the server generates an intermediate-difficulty SQL injection attack scenario and deploys it to the terminal. The user attempts to prevent the attack by discovering abnormal queries through log analysis and setting up appropriate query filters. Based on the feedback obtained in this process, the user improves their skills and becomes ready to tackle more advanced scenarios.

[0600] The following describes the processing flow.

[0601] Step 1:

[0602] When a user accesses the system, the server prompts them for input regarding their technical level. This input consists of three options: "Beginner," "Intermediate," and "Advanced." The user self-assessses their skill level and selects one. The server stores this information in a database.

[0603] Step 2:

[0604] The server uses a generation AI to generate virtual scenarios of appropriate difficulty based on the user's technical level. These scenarios include virtual attacks such as potential SQL injection attacks and high-volume requests. The generated scenarios are then deployed to the virtual environment.

[0605] Step 3:

[0606] The terminal receives scenario information provided by the server and prepares it for the user to access the virtual environment. The terminal displays connection information and prepares the user to participate in the training.

[0607] Step 4:

[0608] The user connects to the virtual environment from their terminal using the provided access information. The terminal enables the user to operate within the virtual environment and records their actions in real time.

[0609] Step 5:

[0610] Within the virtual environment, users identify potential security incidents and implement necessary countermeasures based on presented scenarios. For example, they might monitor logs to detect unusual queries and develop appropriate countermeasures.

[0611] Step 6:

[0612] The server monitors user responses in real time and records each action. Furthermore, it evaluates how effective the user's actions were, creates improvement suggestions as needed, and provides feedback to the user.

[0613] Step 7:

[0614] The recording device saves the user's responses and feedback. This record is used in subsequent training sessions to form a user-specific learning curve and form the basis for providing more effective training.

[0615] (Example 1)

[0616] 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".

[0617] In the modern information technology field, there is an urgent need to cultivate engineers who can respond quickly to security incidents. However, environments for training with realistic scenarios are limited, resulting in a lack of opportunities for engineers to acquire practical skills. This lack can lead to a decline in actual incident response capabilities, potentially increasing the vulnerability of information systems. Therefore, there is a need for a system that provides virtual scenarios tailored to the technical level of engineers, enabling practical training and efficiently improving each individual's response capabilities.

[0618] 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.

[0619] In this invention, the server includes means for an information processing device to input the user's skill level in a self-assessment format and generate a virtual scenario corresponding to that skill level; means for generating a virtual attack using a generated AI model and having the user implement practical responses; and means for monitoring and evaluating the user's response to the virtual attack in real time and suggesting improvements using prompt messages. This enables engineers to enhance their security response capabilities in a practical and personalized training environment.

[0620] An "information processing device" is a computer device used for collecting, processing, storing, and communicating data.

[0621] "User" refers to an individual or group that operates an information processing device and receives training through a virtual scenario.

[0622] "Technical level" is a measure that indicates the user's technical proficiency and experience.

[0623] A "virtual scenario" is a simulation environment generated by an information processing device for a user to conduct specific training.

[0624] A "generative AI model" is an algorithm that uses machine learning and artificial intelligence techniques to generate scenarios and feedback from a dataset.

[0625] A "virtual attack" is a simulated attack on an information system that is conducted within a virtual scenario.

[0626] "Practical response" refers to a series of preventative or mitigation measures taken by the user against a virtual attack within a virtual scenario.

[0627] "Real-time monitoring" is a method of observing and collecting data on a user's activities in real time.

[0628] A "prompt message" is a text message used by a generative AI model to provide feedback or instructions to the user.

[0629] An "improvement suggestion" is a proposal to make the actions taken by the user within a virtual scenario more effective.

[0630] A "recording device" is a device or mechanism for saving data and user activity history so that it can be used later.

[0631] In this embodiment of the invention, an information processing device plays a central role. The information processing device functions as a server, and users access this system through a terminal. Operations from the terminal are performed via an interface, and the user inputs their technical level. Based on the input technical level, the server generates a virtual scenario optimized for the user. A generation AI model is utilized, and the content of the scenario includes attack simulations such as database attacks and overload requests.

[0632] Specifically, if the user selects the "intermediate" level, the server generates an intermediate-level database attack simulation and builds it as a virtual environment. This environment runs on Docker using virtual container technology. Access information to the environment is provided to the terminal, and it is configured to allow the user to perform virtual incident response. By logging in from the terminal, the user begins training in the virtual environment, detects anomalies in the simulation, and attempts to take appropriate action.

[0633] The server monitors user behavior in real time and records the responses in a database. Evaluation is performed programmatically, and prompt statements are generated. For example, specific feedback is provided, such as "Detect an abnormal database query and how to set up filtering effectively." Through this process, users can improve their practical skills, and individual feedback is accumulated in preparation for the next training session.

[0634] In this way, the entire system works together to support the skill development of engineers, enabling more effective development of technical capabilities.

[0635] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0636] Step 1:

[0637] The user logs in to the information processing device using a terminal. The user enters their ID and password, which are sent to the server. The server compares the entered authentication information with the database and determines whether the authentication is successful. If authentication is successful, the server displays a technical level selection screen on the terminal.

[0638] Step 2:

[0639] The user selects their skill level through a self-assessment format and enters this information on their terminal. The entered skill level data is sent to the server. The server uses the received skill level information and a generative AI model to generate a virtual scenario of the corresponding difficulty level. For example, a scenario involving an SQL injection attack might be selected.

[0640] Step 3:

[0641] The server creates a virtual environment based on the generated virtual scenario. This environment is built on Docker using virtual container technology, and a virtual attack sequence is configured. Access information to the created virtual environment is sent to the terminal. The user connects to the virtual environment after receiving this information.

[0642] Step 4:

[0643] Users log in to a virtual environment via a terminal and begin scenario-based training. They detect anomalies occurring within the simulation and attempt appropriate responses, such as detecting and filtering abnormal queries. This process helps users hone their practical skills.

[0644] Step 5:

[0645] The server monitors the user's responses to the simulation in real time and records all operation logs. The server analyzes the behavioral data and generates prompts using a generative AI model. Based on these prompts, it suggests improvement measures to the user. For example, it might suggest "log monitoring methods to improve the speed of attack detection."

[0646] Step 6:

[0647] After training is complete, the server saves all data to a recording device. This saved data is used as foundational data when customizing the next training scenario. This ensures that an optimal training environment is continuously provided for each user.

[0648] (Application Example 1)

[0649] 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".

[0650] In the modern era, there is a lack of effective means for engineers to improve their skills in defending against cyberattacks. Furthermore, there is a need for personalized training tailored to individual skill levels, but traditional methods are currently insufficient. In addition, there is a growing demand for training environments that are practical, provide immediate feedback, and allow for rapid skill improvement.

[0651] 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.

[0652] In this invention, the server includes means for measuring the user's skill level and generating a virtual scenario corresponding to that skill level; means for generating a simulated attack and prompting the user to implement countermeasures against it; means for evaluating the user's countermeasures against the simulated attack and suggesting improvements; and means for generating appropriate prompt sentences based on the scenario using a generation AI model. This enables efficient and effective skill improvement by providing an optimal training scenario tailored to each individual and through real-time improvement feedback.

[0653] A "computational device" is an electronic device used to measure the user's skill level, generate appropriate virtual scenarios, and perform corresponding processing.

[0654] A "virtual scenario" provides simulated situations and challenges tailored to the user's skill level, creating an environment for acquiring new skills.

[0655] A "simulated attack" is a series of programs that recreates actual cyberattacks in a virtual environment and allows users to implement countermeasures against them.

[0656] A "terminal device" is an electronic device used by a user to access a system and experience virtual scenarios.

[0657] A "training application" is software that allows users to learn and practice skills through virtual scenarios.

[0658] A "generative AI model" is an algorithmic model that automatically generates appropriate prompts and countermeasures based on a virtual scenario and the user's actions.

[0659] A "prompt message" is text created by a generative AI model and used to provide instructions or information to the user.

[0660] To realize this invention, the system used is configured as follows: The server plays a central role as a computing device and has the function of understanding the user's skill level. Based on this, the server acquires self-assessment data entered by the user and generates an appropriate virtual scenario based on that data. The virtual scenario includes a simulated cyberattack and is presented to the user in real time.

[0661] A terminal is a physical device used by the user, such as a smartphone or personal computer. A training application is installed on this terminal, and the user uses this application to connect to a scenario and deal with a simulated attack through their actions.

[0662] The generative AI model operates within the server and monitors user actions in a virtual scenario. Based on these actions, the model generates appropriate feedback and prompts, providing the user with suggested actions and improvements. A specific example of a prompt is: "Describe how the user can learn techniques to detect and defend against a mid-level cyberattack on the virtual network."

[0663] As a concrete example, imagine a user using their smartphone during their commute and activating this system. If the user selects the "intermediate" level, the server generates a scenario simulating a database injection attack and sends it to the device. The user experiences the scenario through the application and performs attack detection and countermeasures.

[0664] As described above, this system, by combining servers, terminal devices, and generated AI models, makes it possible to provide security training optimized for each individual user.

[0665] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0666] Step 1:

[0667] The server receives self-assessment data of the user's technical level performed through the terminal. This input data is analyzed to identify the user's current technical level. Based on the identified technical level, the server prepares to select an appropriate virtual scenario.

[0668] Step 2:

[0669] The server generates a virtual scenario corresponding to the identified technical level. A generation AI model is used to specifically determine the scenario content, and the scenario includes the type of simulated attack. The generated scenario is sent to the user's terminal, initiating the scenario.

[0670] Step 3:

[0671] The user initiates action based on a virtual scenario received on their device. The user uses a training application to actually implement countermeasures against a simulated attack. These actions are logged and sent to the server.

[0672] Step 4:

[0673] The server analyzes user operation logs in real time and evaluates its response to simulated attacks. Based on the evaluation results, it uses a generative AI model to determine areas for improvement and appropriate countermeasures, and then generates prompt messages to suggest them.

[0674] Step 5:

[0675] The server sends the generated prompt message to the user's terminal. The user receives the prompt message as input, considers improvement measures based on its content, and takes actions with the intention of incorporating them into the next scenario.

[0676] Step 6:

[0677] The terminal saves all operation results and evaluation feedback to a recording device. This recorded data is prepared to be used as reference material when constructing the next training scenario.

[0678] The above describes the basic program processing flow in this system.

[0679] 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.

[0680] This invention combines an engineering skills training system with an emotion engine that detects the user's emotional state. This provides a more dynamic and personalized learning experience, aiming to improve the user's skills and reduce their psychological burden.

[0681] The server first obtains data on the user's technical level selection and generates an appropriate virtual scenario based on that information. These virtual scenarios include security attacks such as SQL injection and mass requests. This scenario is then delivered to the virtual environment for the user to experience through their terminal.

[0682] The emotion engine analyzes the user's facial expressions and voice tone in real time via sensors such as cameras and microphones while the user participates in a virtual scenario. Based on this information, the emotion engine evaluates the user's stress level and concentration level and provides feedback to the server.

[0683] Based on information from the emotion engine, the server dynamically adjusts the scenario according to the user's state. For example, if the server detects that the user is under high stress, it may lower the difficulty of the scenario or display a message encouraging them to take a break. Furthermore, if it determines that the user's interest or concentration is waning, it may present a different type of challenge or introduce educational content.

[0684] Users can experience these interactions through their devices and progress through the training at their own pace. This maximizes learning effectiveness and allows them to acquire the necessary skills effectively. The recording device saves the user's emotional data and learning progress, which will be used to generate future scenarios.

[0685] As a concrete example, consider a scenario where a user encounters an unexpectedly difficult scenario during intermediate-level training, temporarily experiencing high stress levels. In this situation, the emotion engine reacts quickly, prompting the server to adjust the scenario or send encouraging messages to alleviate stress. As a result, the user can continue learning at the appropriate time while their psychological pressure is eased. In this way, the system incorporating the emotion engine provides users with a more meaningful and efficient learning environment.

[0686] The following describes the processing flow.

[0687] Step 1:

[0688] When a user accesses the system, the server prompts them for input regarding their technical level. This input consists of three options: "Beginner," "Intermediate," and "Advanced." The user self-assessses their skill level and selects one. The server stores this information in a database.

[0689] Step 2:

[0690] The server generates a virtual scenario tailored to the user's technical level. This scenario includes security attacks such as SQL injection and excessive requests. The generated scenario is then ready to be delivered to the terminal as a virtual environment.

[0691] Step 3:

[0692] The terminal builds a virtual environment based on the scenario information received from the server and prepares it for the user to access. Connection information is displayed on the terminal, and the user is ready to participate in the scenario.

[0693] Step 4:

[0694] Users connect to a virtual environment via a terminal and begin training based on a presented scenario. In the virtual scenario, they identify a security incident and take appropriate corrective actions.

[0695] Step 5:

[0696] The emotion engine uses sensors to monitor the user's facial expressions and voice tone during training, analyzing their emotional state in real time. It generates indicators of stress and concentration levels and sends this information to the server.

[0697] Step 6:

[0698] The server analyzes data from the emotion engine and dynamically adjusts the content of the virtual scenario according to the user's state. For example, if it detects that the user is in a high-stress state, it may lower the difficulty of the scenario or display a message encouraging the user to take a break.

[0699] Step 7:

[0700] The device displays appropriate feedback and adjustments on the user's screen based on instructions from the server. This allows the user to continue learning at their own pace.

[0701] Step 8:

[0702] The recording device saves user emotion data and training progress, which are then used to generate the next virtual scenario. This creates a user-specific training profile, enabling more effective learning.

[0703] (Example 2)

[0704] 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".

[0705] Conventional engineering skills training systems can provide a learning experience tailored to the user's skill level, but they lack the ability to provide an individualized learning environment that takes into account the user's psychological state. In particular, when faced with high stress or low concentration, the user's learning effectiveness may be significantly reduced. Therefore, there is a need for technology that can detect the user's emotional state in real time and dynamically adjust the training scenario accordingly.

[0706] 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.

[0707] In this invention, the server includes means for measuring the user's technical level and generating a virtual scenario corresponding to that level; means for generating a virtual attack in the virtual scenario and having the user respond to it; and means for detecting the user's emotional state and adjusting the virtual scenario in real time. This enables a personalized learning experience tailored to the user's technical skills, thereby reducing psychological burden and maximizing learning effectiveness.

[0708] An "information processing device" is an electronic device that receives data and performs various processes based on that data.

[0709] "Technical level" is an indicator that shows the level of technical ability and knowledge possessed by the user.

[0710] A "virtual scenario" is a simulation environment provided to the user that simulates and reproduces a specific problem.

[0711] A "virtual attack" is a simulated attack that occurs within a simulation and is provided to allow users to learn how to deal with it.

[0712] "Emotional state" refers to the user's psychological state and reactions, and usually refers to stress levels and concentration levels.

[0713] "Real-time adjustment" refers to making immediate changes to ongoing processes or environments.

[0714] This invention begins with the user accessing an interface using a terminal and selecting their skill level. The server retrieves data on the user's selected skill level and generates an appropriate virtual scenario based on that information. The virtual scenario is deployed to an information processing device and provides the user with training against a virtual attack.

[0715] The server utilizes a generative AI model to incorporate SQL injection and massive requests as possible virtual attacks into the scenario. Once the scenario is complete, it is delivered to the terminal, allowing users to experience the scenario firsthand.

[0716] As users experience scenarios through their devices, their facial expressions and voices are recorded and analyzed using cameras and microphones. This recording is processed in real time by an emotion engine, and the user's emotional state is fed back to the server.

[0717] The server dynamically adjusts the scenario to suit the user's emotional state based on information obtained from the emotion engine. Specifically, if high stress is detected, the difficulty of the scenario is reduced or an encouraging message is displayed. Also, if the analysis indicates that the user's concentration level is low, the challenge content is changed or educational content is provided.

[0718] As a practical example, if a user encounters a difficult scenario during intermediate-level training, the emotion engine detects a high-stress state. The server immediately responds, adjusting the difficulty level to reduce the user's psychological burden. Furthermore, emotional data and operation logs are stored in a recording device and used for generating future scenarios.

[0719] An example of a prompt to input into the generative AI model is, "Please tell me how to respond when a user is in a highly stressed state." Based on this prompt, the generative AI model generates the optimal solution, which the server then uses to adaptively adjust its response to each individual user.

[0720] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0721] Step 1:

[0722] The user selects their skill level through the terminal's interface. This selection data is entered into the server. The server analyzes this data to determine the user's skill level.

[0723] Step 2:

[0724] The server generates virtual scenarios tailored to the user's technical level. This scenario generation uses a generative AI model. Specifically, the generative AI model receives user technical level information as input and outputs scenarios that include appropriate virtual attacks (e.g., SQL injection or massive requests).

[0725] Step 3:

[0726] The server delivers the generated virtual scenario to the terminal. The user experiences this scenario on the terminal. The terminal displays the necessary interfaces as the scenario progresses and receives input from the user.

[0727] Step 4:

[0728] The emotion engine operates, capturing the user's facial expressions and voice in real time using the camera and microphone connected to the device. This data is analyzed by the emotion engine, and the user's emotional state (stress level and concentration level) is output.

[0729] Step 5:

[0730] The server receives emotional state data from the emotion engine. Based on this information, the server dynamically adjusts the scenario. Specifically, if the server detects that the user is in a high-stress state, it may lower the difficulty of the scenario or display encouraging messages on the device.

[0731] Step 6:

[0732] The device follows instructions from the server, adjusting the scenario and interface while providing feedback to the user. This allows the user to continue having a comfortable and effective learning experience.

[0733] Step 7:

[0734] The recording device saves user emotion data and scenario progress. This saved data will be used in future scenario generation.

[0735] (Application Example 2)

[0736] 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".

[0737] In modern technical training, uniform training that does not consider the user's emotional state can lead to stress and concentration levels affecting individual progress. This is especially true in workplaces such as factories, where practical training tailored to the user's skill level is required. It is necessary to reduce the user's psychological burden and provide an optimal learning experience. However, conventional systems have struggled to adequately monitor emotional states and dynamically adjust training content, so this aspect needs improvement.

[0738] 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.

[0739] In this invention, the server includes an information processing device that measures the user's skill level and generates a virtual scenario corresponding to that skill level, and an emotion recognition device that analyzes the user's facial expressions and tone of voice and evaluates their emotional state, and a means for dynamically adjusting the virtual scenario based on the user's emotional state. This makes it possible to reduce the user's psychological burden and provide an individually optimized learning experience.

[0740] An "information processing device" refers to an entire system that has the function of measuring the user's technical level and generating a virtual scenario based on that level.

[0741] A "virtual scenario" is a computer-generated simulated situation or environment designed to help users acquire practical skills.

[0742] "Emotion recognition means" refers to technologies and devices that analyze a user's facial expressions and tone of voice to evaluate their emotional state in real time.

[0743] "Dynamic adjustment" refers to the process of changing the content and difficulty level of a virtual scenario in a timely manner according to the user's emotional state.

[0744] A "recording device" is hardware or software used to save the user's response results and emotional data, and to utilize them in constructing future scenarios.

[0745] To implement this invention, a system is used that combines an information processing device, an emotion recognition means, a dynamic adjustment means, and a recording device.

[0746] The server measures the user's technical level and generates virtual scenarios according to that level. These virtual scenarios are configured to enable practical training simulating various industrial environments.

[0747] The emotion recognition system uses sensors such as cameras and microphones to analyze the user's facial expressions and voice tone. This process employs emotion analysis models utilizing machine learning libraries such as TensorFlow. The user's emotional state is evaluated, and feedback is sent to the server in real time.

[0748] Based on the user's emotional data, the server dynamically adjusts the scenario difficulty or displays a message encouraging the user to take a break if the user is experiencing stress. The analysis and processing of emotional data is possible using OpenCV, an open-source CV library.

[0749] The device is used as an interface, allowing users to receive feedback through it and progress through their training at their own pace.

[0750] The recording device records collected emotional data and training progress, which are then used to generate the next scenario. This blessing device interacts with a database to support a learning experience optimized for each individual user.

[0751] As a concrete example, consider a scenario where a trainee learning to operate a new robot in a factory encounters a difficult operation and experiences temporary high stress. In this case, the emotion recognition system reacts quickly, the server adjusts the scenario, and the user receives appropriate support. An example of a prompt message provided to the user using a generative AI model is: "The user is experiencing stress due to a difficult operation. How would you like to adjust the training scenario?"

[0752] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0753] Step 1:

[0754] The server receives the user's skill level as input data and generates a virtual scenario based on it. Based on the input data, it designs a virtual scenario with difficulty and content appropriate to the user's skills and sends that scenario to the terminal. This allows the user to access an appropriate training environment.

[0755] Step 2:

[0756] The device collects emotional data, such as the user's facial expressions and voice, through its camera and microphone. The collected data is sent as input to an emotion recognition system and analyzed by a TensorFlow emotion analysis model. This analysis evaluates the user's emotional state, particularly stress and concentration levels, and this information is sent to the server as feedback.

[0757] Step 3:

[0758] The server dynamically adjusts the virtual scenario based on the user's emotional state data received as feedback. For example, if the stress level is high, the difficulty of the scenario is reduced, and an encouraging prompt is generated. This prompt might be something like, "The user is experiencing stress due to a difficult operation. How would you like the training scenario adjusted?" The generated prompt is sent to the terminal and displayed on the user's screen.

[0759] Step 4:

[0760] The device presents dynamically adjusted scenarios and prompts to the user through its user interface. The user can then use these to progress through the training smoothly and with minimal stress. The device's display capabilities are utilized here, and the design ensures that the user experiences a reduction in potential psychological burden.

[0761] Step 5:

[0762] The recording device records the user's emotional data and scenario progress during training. This data is stored in a database for future scenario generation and customization of user learning. This makes it possible to develop an optimal training plan for each user and maintain a system that supports continuous skill improvement.

[0763] 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.

[0764] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One 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.

[0765] 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.

[0766] 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.

[0767] 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.

[0768] 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.

[0769] 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.

[0770] 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.

[0771] 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."

[0772] 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.

[0773] 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.

[0774] 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.

[0775] 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.

[0776] 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.

[0777] 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.

[0778] 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.

[0779] 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.

[0780] 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.

[0781] 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.

[0782] 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.

[0783] 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 as being incorporated by reference.

[0784] The following is further disclosed regarding the embodiments described above.

[0785] (Claim 1)

[0786] The information processing device includes means for measuring the user's skill level and generating a virtual scenario corresponding to that skill level.

[0787] In the aforementioned virtual scenario, means for generating a virtual attack and causing the user to take action to respond to it,

[0788] A means for evaluating the user's response to the virtual attack and proposing improvement suggestions,

[0789] A system that includes this.

[0790] (Claim 2)

[0791] The system according to claim 1, wherein the virtual attack includes SQL injection or a large number of requests.

[0792] (Claim 3)

[0793] The system according to claim 1, wherein the recording device saves the user's response results and uses them for constructing the next scenario.

[0794] "Example 1"

[0795] (Claim 1)

[0796] The information processing device includes means for having the user input their technical level in a self-assessment format and generating a virtual scenario corresponding to that technical level,

[0797] In the aforementioned virtual scenario, a means for generating a virtual attack using a generative AI model and forcing the user to take practical action is provided.

[0798] A means for monitoring and evaluating the user's response to the virtual attack in real time, and for suggesting improvements using prompt messages,

[0799] The recording device includes means for saving the user's behavioral data and using it to construct the next scenario,

[0800] A system that includes this.

[0801] (Claim 2)

[0802] The system according to claim 1, wherein the virtual attack includes a database attack or an overload request.

[0803] (Claim 3)

[0804] The system according to claim 1, wherein the information processing device controls access to a virtual environment and permits a user to connect to an appropriate virtual scenario.

[0805] "Application Example 1"

[0806] (Claim 1)

[0807] The computing device includes means for measuring the user's skill level and generating a virtual scenario corresponding to that skill level,

[0808] In the aforementioned hypothetical scenario, means for generating a simulated attack and causing the user to implement countermeasures against it,

[0809] A means for evaluating the user's countermeasures against simulated attacks and proposing improvement plans,

[0810] A means for providing a training application using the user's terminal device and for the user to learn how to respond,

[0811] A means for generating appropriate prompt sentences based on a scenario using a generative AI model,

[0812] A system that includes this.

[0813] (Claim 2)

[0814] The system according to claim 1, wherein the simulated attack includes database injection or mass requests.

[0815] (Claim 3)

[0816] The system according to claim 1, wherein the recording mechanism saves the user's countermeasure results and uses them for constructing the next scenario.

[0817] "Example 2 of combining an emotion engine"

[0818] (Claim 1)

[0819] The information processing device includes means for measuring the user's skill level and generating a virtual scenario corresponding to that skill level.

[0820] In the aforementioned virtual scenario, means for generating a virtual attack and causing the user to take action to respond to it,

[0821] A means for evaluating the user's response to the virtual attack and proposing improvement suggestions,

[0822] A means for detecting the user's emotional state and adjusting the virtual scenario in real time,

[0823] ...

[0824] A system that includes this.

[0825] (Claim 2)

[0826] The system according to claim 1, wherein the virtual attack includes SQL injection or a large number of requests.

[0827] (Claim 3)

[0828] The system according to claim 1, wherein the recording device stores the user's response results and emotional data and uses them for constructing the next scenario.

[0829] "Application example 2 when combining with an emotional engine"

[0830] (Claim 1)

[0831] The information processing device includes means for measuring the user's skill level and generating a virtual scenario corresponding to that skill level.

[0832] In the aforementioned virtual scenario, means for generating a virtual attack and causing the user to take action to respond to it,

[0833] The emotion recognition means analyzes the user's facial expressions and tone of voice, and evaluates their emotional state.

[0834] Means for dynamically adjusting the virtual scenario based on the user's emotional state,

[0835] A means for evaluating the user's response to the virtual attack and proposing improvement suggestions,

[0836] A system that includes this.

[0837] (Claim 2)

[0838] The system according to claim 1, wherein the virtual attack includes SQL injection or a large number of requests, and further modifies the training content according to the emotional state of the user.

[0839] (Claim 3)

[0840] The system according to claim 1, wherein the recording device stores the user's response results and emotional data and uses them for constructing the next scenario. [Explanation of symbols]

[0841] 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. The information processing device includes means for measuring the user's skill level and generating a virtual scenario corresponding to that skill level. In the aforementioned virtual scenario, means for generating a virtual attack and causing the user to take action to respond to it, A means for evaluating the user's response to the virtual attack and proposing improvement suggestions, A system that includes this.

2. The system according to claim 1, wherein the virtual attack includes SQL injection or a large number of requests.

3. The system according to claim 1, wherein the recording device saves the user's response results and uses them for constructing the next scenario.

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