System

Abstract

A system in accordance with an embodiment aims to provide a realistic and unpredictable security incident training scenario.SOLUTION: A system according to an embodiment includes a scenario generation unit, a randomness providing unit, and a feedback evaluation unit. The scenario generation unit generates a real-time scenario using the generation AI. The randomness imparting unit imparts randomness to the scenario generated by the scenario generation unit. The feedback evaluation unit performs training based on the scenario generated by the randomness providing unit, and evaluates the result.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem of making it difficult to provide realistic and unpredictable scenarios for security incident training.

[0005] The system according to the embodiment aims to provide realistic and unpredictable security incident training scenarios. [Means for solving the problem]

[0006] The system according to the embodiment includes a scenario generation unit, a randomness imparting unit, and a feedback evaluation unit. The scenario generation unit generates a real-time scenario using a generation AI. The randomness imparting unit imparts randomness to the scenario generated by the scenario generation unit. The feedback evaluation unit performs training based on the scenario generated by the randomness imparting unit and evaluates the results. [Effects of the Invention]

[0007] A system according to an embodiment can provide realistic and unpredictable security incident training scenarios. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example 1) A security incident training system according to an embodiment of the present invention utilizes generative AI to provide real-time scenarios. This system improves training and response capabilities by simulating unpredictable situations using the random output of generative AI. This allows the security incident training system to simulate situations that are close to actual security incidents, improving the response capabilities of training participants.

[0029] A security incident training system according to an embodiment includes a scenario generation unit, a randomization unit, and a feedback evaluation unit. The scenario generation unit generates real-time scenarios using a generation AI. For example, the generation AI generates scenarios for security incident training. The generation AI receives inputs from prompts including the training objectives and basic scenario settings, and the generation AI generates scenarios based on the prompts. The randomization unit adds randomness to the scenarios generated by the scenario generation unit. For example, by utilizing the random nature of the generation AI's output, the scenario content is designed to be different each time. This requires training participants to constantly adapt to new situations, thereby improving their ability to respond to actual security incidents. The feedback evaluation unit conducts training based on the scenarios generated by the randomization unit and evaluates the results. For example, the generation AI analyzes the training results and evaluates the participants' responses. This evaluation is based on how the participants responded to the scenarios, and areas for improvement and merit are provided as feedback. As a result, the security incident training system according to an embodiment provides unpredictable scenarios in real time, thereby improving the training participants' ability to respond.

[0030] The scenario generation unit can add realistic elements based on past security incident data. For example, the generation AI in the scenario generation unit analyzes past security incident data and generates realistic scenarios based on that data. For example, specific cases of unauthorized access that occurred in the past can be incorporated into the scenario. The generation AI can also generate scenarios related to specific industries or companies based on past security incident data. For example, it can recreate a data leak incident that occurred in the financial industry. The generation AI can also generate complex scenarios that combine multiple elements based on past security incident data. For example, it can create a scenario in which a phishing attack and internal fraud occur simultaneously. This makes it possible to provide realistic scenarios based on past security incident data and create a more realistic training environment.

[0031] The scenario generation unit can reflect the participant's past training history and customize the scenario to strengthen individual weaknesses. In the scenario generation unit, for example, the generation AI analyzes the participant's past training history and generates a scenario to strengthen individual weaknesses. For example, it retrains attack methods that were not properly addressed in the past. The generation AI also generates scenarios to strengthen specific skills based on the participant's past training history. For example, it creates a scenario to compensate for weaknesses in password management. The generation AI also generates scenarios according to the participant's individual progress based on the participant's past training history. For example, it provides a basic scenario for beginners and an advanced scenario for advanced participants. This makes it possible to customize the scenario to strengthen individual weaknesses based on the participant's past training history.

[0032] The scenario generation unit can generate scenarios that combine physical security elements. For example, the generation AI in the scenario generation unit generates scenarios that combine physical security elements. For example, it simultaneously provides an office intrusion simulation and a data leak scenario. The generation AI also generates scenarios that include physical security elements, enabling comprehensive security training. For example, it creates a scenario in which a physical intrusion and a cyber attack occur simultaneously. The generation AI also generates scenarios that reproduce actual office environments based on physical security elements. For example, it simulates intrusion routes based on the office layout. This makes it possible to provide scenarios that combine physical security elements and enable comprehensive security training.

[0033] The scenario generation unit can generate scenarios specialized for different industries. In the scenario generation unit, for example, the generation AI generates scenarios specialized for different industries. For example, it provides a patient data leak scenario for the medical industry, and a fraudulent transaction scenario for the financial industry. The generation AI also generates scenarios that address security risks for each industry. For example, it creates a production line shutdown scenario for the manufacturing industry, and a student data leak scenario for the education industry. The generation AI also generates industry-specific scenarios to address security risks for each industry. For example, it provides an infrastructure attack scenario for the energy industry. This makes it possible to provide scenarios specialized for different industries and train to address security risks for each industry.

[0034] The randomness imparting unit can combine multiple random elements. For example, the randomness imparting unit uses a generation AI to generate a scenario that combines multiple random elements. For example, it provides a scenario that combines multiple attack methods that occur simultaneously. The generation AI also combines randomly selected elements to generate unpredictable scenarios. For example, it creates a scenario in which a phishing attack and a malware infection occur simultaneously. The generation AI also generates complex scenarios based on multiple random elements. For example, it provides a scenario in which internal fraud and external attacks work together. In this way, by combining multiple random elements, it is possible to provide more complex and unpredictable scenarios.

[0035] The randomization unit can incorporate external data that changes in real time. For example, the generation AI in the randomization unit generates a scenario that incorporates network traffic data that changes in real time. For example, it provides a scenario that responds to a sudden increase in traffic. The generation AI also generates a scenario based on weather information that changes in real time. For example, it simulates physical security risks associated with changes in weather. The generation AI also generates dynamic scenarios based on external data that changes in real time. For example, it provides a scenario that is linked to network anomaly detection. This makes it possible to provide dynamic scenarios by incorporating external data that changes in real time.

[0036] The randomization unit can reflect security risks from different cultures and regions. In the randomization unit, for example, the generation AI generates scenarios that reflect the security risks from different cultures and regions. For example, it simulates cyber attacks that are likely to occur in a specific region. In addition, to enable training from a global perspective, the generation AI generates scenarios that combine security risks from different cultures and regions. For example, it reproduces international cyber attacks. In addition, the generation AI generates region-specific scenarios based on security risks from different cultures and regions. For example, it reproduces security risks in a specific country or region. This allows training from a global perspective to be realized by reflecting security risks from different cultures and regions.

[0037] The randomization unit can support a multiplayer mode in which multiple participants work together simultaneously to respond. For example, the randomization unit generates a scenario in which a generation AI generates a scenario in which multiple participants work together simultaneously to respond. For example, it provides a cyber attack scenario that requires a team response. The generation AI also generates a scenario compatible with the multiplayer mode to train teamwork. For example, it creates a scenario in which participants with multiple roles work together to respond. The generation AI also provides training aimed at strengthening teamwork based on a scenario in which multiple participants work together simultaneously. For example, it provides a scenario that requires a quick response by the entire team. This allows multiple participants to work together simultaneously to respond, thereby training teamwork.

[0038] The feedback evaluation unit can analyze participants' action logs in detail and suggest specific areas for improvement. In the feedback evaluation unit, for example, the generation AI analyzes participants' action logs in detail and suggests specific areas for improvement. For example, it points out delayed responses and incorrect judgments. In addition, a system can be built in which the generation AI suggests specific areas for improvement based on participants' action logs. For example, it points out specific operational errors and errors in judgment and suggests improvement measures. In addition, the generation AI analyzes participants' action logs and provides feedback on specific areas for improvement. For example, it evaluates the speed and accuracy of responses and suggests areas for improvement. In this way, the effectiveness of training can be increased by analyzing participants' action logs in detail and suggesting specific areas for improvement.

[0039] The feedback evaluation unit can compare and evaluate with other participants and clearly indicate the relative skill level. For example, when the generation AI evaluates the training results, the feedback evaluation unit compares and evaluates with other participants. For example, it displays the overall ranking or score. Furthermore, a system can be constructed in which the generation AI clearly indicates the relative skill level based on the comparison and evaluation with other participants. For example, it displays the average score or the top percentage. Furthermore, the generation AI compares and evaluates with other participants based on the training results and provides feedback on the relative skill level. For example, it clearly indicates strengths and weaknesses in specific skills. In this way, by comparing and evaluating with other participants and clearly indicating the relative skill level, it is possible to improve the motivation of participants.

[0040] The feedback evaluation unit can compare performance across different training scenarios and evaluate overall skill improvement. For example, the generation AI compares performance across different training scenarios and evaluates overall skill improvement. For example, it compares response speed and accuracy across multiple scenarios. In addition, a system is constructed in which the generation AI compares and evaluates performance across different scenarios based on the training results. For example, the score in each scenario is reflected in the overall evaluation. In addition, the generation AI analyzes performance across different training scenarios and evaluates overall skill improvement. For example, it evaluates the degree of improvement of a specific skill set. This makes it possible to compare performance across different training scenarios and evaluate overall skill improvement.

[0041] The feedback evaluation unit can provide a visualized report and present areas for improvement in a visually easy-to-understand format. In the feedback evaluation unit, for example, the generation AI visualizes the training results and provides a report that is visually easy to understand. For example, areas for improvement are shown using graphs and charts. In addition, a system is constructed in which the generation AI generates visualized reports based on the training results. For example, response time and accuracy are visually displayed. In addition, the generation AI visualizes the training results and presents areas for improvement in a visually easy-to-understand format. For example, weaknesses are highlighted using a heat map. This makes it possible to provide a visualized report and present areas for improvement in a visually easy-to-understand format.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The security incident training system can further include a voice recognition unit. The voice recognition unit can analyze the voice instructions of the training participants in real time and dynamically change the progress of the scenario. For example, if a participant issues a specific instruction, the development of the scenario can be changed. The voice recognition unit can also adjust the difficulty of the scenario based on the participant's voice instructions. For example, if a participant issues an instruction for an emergency response, the voice recognition unit can increase the complexity of the scenario. The voice recognition unit can also provide feedback based on the participant's voice instructions. For example, the voice recognition unit can evaluate whether the participant's instructions were appropriate and suggest areas for improvement. This makes it possible to dynamically change the scenario and provide feedback using voice recognition.

[0044] The security incident training system can further include a biometric authentication unit. The biometric authentication unit can perform individual authentication using the biometric information of training participants. For example, fingerprint or facial recognition can be used to confirm the identity of participants. The biometric authentication unit can also set situations in which authentication is required in specific scenarios during training. For example, fingerprint authentication can be required when accessing important data. The biometric authentication unit can also suggest areas for improvement in the authentication process based on the training results. For example, it can evaluate the time and accuracy required for authentication and propose improvement measures. This makes it possible to train authentication processes that utilize biometric information.

[0045] The security incident training system can also be equipped with a virtual reality (VR) section. The VR section can provide training participants with a more realistic training environment. For example, a virtual office environment can be recreated using VR goggles. The VR section can also dynamically change the virtual environment depending on the training scenario. For example, it can simulate responses in the virtual environment when a cyberattack occurs. The VR section can also evaluate actions in the virtual environment based on the training results and provide feedback. For example, it can evaluate the speed and accuracy of responses in the virtual environment and suggest areas for improvement. This makes it possible to create a realistic training environment and provide feedback using virtual reality.

[0046] The security incident training system can further include a data analysis unit. The data analysis unit can analyze data collected during the training and evaluate the effectiveness of the training. For example, it can analyze the speed and accuracy of participants' responses and evaluate the effectiveness of the training. The data analysis unit can also suggest specific areas for improvement based on the training results. For example, it can point out specific operational errors or errors in judgment and propose improvement measures. The data analysis unit can also optimize training scenarios based on data collected during the training. For example, it can provide scenarios that are appropriate for the skill level of the participants. This enables effective training evaluation and feedback using data analysis.

[0047] The security incident training system can also generate scenarios that reflect the security risks of different cultures and regions. For example, it can simulate cyberattacks that are likely to occur in a particular region. In addition, to enable training from a global perspective, the generative AI can generate scenarios that combine security risks from different cultures and regions. For example, it can reproduce international cyberattacks. The generative AI can also generate region-specific scenarios based on security risks from different cultures and regions. For example, it can reproduce security risks in a specific country or region. This allows training to be conducted from a global perspective, reflecting the security risks of different cultures and regions.

[0048] The security incident training system can also support a multiplayer mode in which multiple participants work together simultaneously to respond. For example, it can provide a cyberattack scenario that requires a team response. The generation AI can also generate scenarios compatible with the multiplayer mode to train teamwork. For example, it can create a scenario in which participants with multiple roles work together to respond. The generation AI can also provide training aimed at strengthening teamwork based on a scenario in which multiple participants work simultaneously. For example, it can provide a scenario that requires a quick response by the entire team. This allows multiple participants to work together simultaneously to respond, thereby training teamwork.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The scenario generation unit uses a generation AI to generate a real-time scenario. For example, the generation AI generates a scenario for a security incident training. The input to the generation AI is a prompt that includes the training objectives and basic settings of the scenario, and the generation AI generates a scenario based on the prompt. Step 2: The randomization unit adds randomness to the scenarios generated by the scenario generation unit. For example, by taking advantage of the fact that the output of the generation AI is random, the scenario content is designed to be different each time. This forces training participants to constantly adapt to new situations, improving their ability to respond to real security incidents. Step 3: The feedback evaluation unit conducts training based on the scenarios generated by the randomization unit and evaluates the results. For example, the generation AI analyzes the training results and evaluates the participants' responses. This evaluation is based on how the participants handled the scenarios, and provides feedback on areas for improvement and merit.

[0051] (Example 2) A security incident training system according to an embodiment of the present invention utilizes generative AI to provide real-time scenarios. This system improves training and response capabilities by simulating unpredictable situations using the random output of generative AI. This allows the security incident training system to simulate situations that are close to actual security incidents, improving the response capabilities of training participants.

[0052] A security incident training system according to an embodiment includes a scenario generation unit, a randomization unit, and a feedback evaluation unit. The scenario generation unit generates real-time scenarios using a generation AI. For example, the generation AI generates scenarios for security incident training. The generation AI receives inputs from prompts including the training objectives and basic scenario settings, and the generation AI generates scenarios based on the prompts. The randomization unit adds randomness to the scenarios generated by the scenario generation unit. For example, by utilizing the random nature of the generation AI's output, the scenario content is designed to be different each time. This requires training participants to constantly adapt to new situations, thereby improving their ability to respond to actual security incidents. The feedback evaluation unit conducts training based on the scenarios generated by the randomization unit and evaluates the results. For example, the generation AI analyzes the training results and evaluates the participants' responses. This evaluation is based on how the participants responded to the scenarios, and areas for improvement and merit are provided as feedback. As a result, the security incident training system according to an embodiment provides unpredictable scenarios in real time, thereby improving the training participants' ability to respond.

[0053] The scenario generation unit can add realistic elements based on past security incident data. For example, the generation AI in the scenario generation unit analyzes past security incident data and generates realistic scenarios based on that data. For example, specific cases of unauthorized access that occurred in the past can be incorporated into the scenario. The generation AI can also generate scenarios related to specific industries or companies based on past security incident data. For example, it can recreate a data leak incident that occurred in the financial industry. The generation AI can also generate complex scenarios that combine multiple elements based on past security incident data. For example, it can create a scenario in which a phishing attack and internal fraud occur simultaneously. This makes it possible to provide realistic scenarios based on past security incident data and create a more realistic training environment.

[0054] The scenario generation unit can reflect the participant's past training history and customize the scenario to strengthen individual weaknesses. In the scenario generation unit, for example, the generation AI analyzes the participant's past training history and generates a scenario to strengthen individual weaknesses. For example, it retrains attack methods that were not properly addressed in the past. The generation AI also generates scenarios to strengthen specific skills based on the participant's past training history. For example, it creates a scenario to compensate for weaknesses in password management. The generation AI also generates scenarios according to the participant's individual progress based on the participant's past training history. For example, it provides a basic scenario for beginners and an advanced scenario for advanced participants. This makes it possible to customize the scenario to strengthen individual weaknesses based on the participant's past training history.

[0055] The scenario generation unit can use the emotion estimation function to monitor the stress levels of participants in real time and generate a scenario in which stress increases. The scenario generation unit, for example, uses the emotion estimation function to monitor the stress levels of participants in real time and generate a scenario in which stress increases. For example, it provides a scenario with a time limit. It also uses the emotion estimation function to adjust the difficulty of the scenario according to the stress levels of participants. For example, it increases the complexity of the scenario as stress increases. It also uses the emotion estimation function to generate a scenario that strengthens psychological coping skills based on the stress levels of participants. For example, it provides a scenario that requires an emergency response. In this way, it is possible to monitor the stress levels of participants in real time and train psychological coping skills.

[0056] The scenario generation unit can generate scenarios that combine physical security elements. For example, the generation AI in the scenario generation unit generates scenarios that combine physical security elements. For example, it simultaneously provides an office intrusion simulation and a data leak scenario. The generation AI also generates scenarios that include physical security elements, enabling comprehensive security training. For example, it creates a scenario in which a physical intrusion and a cyber attack occur simultaneously. The generation AI also generates scenarios that reproduce actual office environments based on physical security elements. For example, it simulates intrusion routes based on the office layout. This makes it possible to provide scenarios that combine physical security elements and enable comprehensive security training.

[0057] The scenario generation unit can generate scenarios specialized for different industries. In the scenario generation unit, for example, the generation AI generates scenarios specialized for different industries. For example, it provides a patient data leak scenario for the medical industry, and a fraudulent transaction scenario for the financial industry. The generation AI also generates scenarios that address security risks for each industry. For example, it creates a production line shutdown scenario for the manufacturing industry, and a student data leak scenario for the education industry. The generation AI also generates industry-specific scenarios to address security risks for each industry. For example, it provides an infrastructure attack scenario for the energy industry. This makes it possible to provide scenarios specialized for different industries and train to address security risks for each industry.

[0058] The scenario generation unit can use the emotion estimation function to dynamically change the scenario based on the emotional reactions of the participants. The scenario generation unit, for example, uses the emotion estimation function to dynamically change the scenario based on the emotional reactions of the participants. For example, the difficulty of the scenario is adjusted when a participant feels stressed. The emotion estimation function is also used to analyze the emotional reactions of the participants in real time and dynamically change the scenario. For example, the scenario is made more complex when a participant is excited. The emotion estimation function is also used to optimize the scenario based on the emotional reactions of the participants and provide more effective training. For example, the scenario is made more difficult when a participant is relaxed. In this way, the scenario can be dynamically changed based on the emotional reactions of the participants and provide more effective training.

[0059] The randomness imparting unit can combine multiple random elements. For example, the randomness imparting unit uses a generation AI to generate a scenario that combines multiple random elements. For example, it provides a scenario that combines multiple attack methods that occur simultaneously. The generation AI also combines randomly selected elements to generate unpredictable scenarios. For example, it creates a scenario in which a phishing attack and a malware infection occur simultaneously. The generation AI also generates complex scenarios based on multiple random elements. For example, it provides a scenario in which internal fraud and external attacks work together. In this way, by combining multiple random elements, it is possible to provide more complex and unpredictable scenarios.

[0060] The randomization unit can incorporate external data that changes in real time. For example, the generation AI in the randomization unit generates a scenario that incorporates network traffic data that changes in real time. For example, it provides a scenario that responds to a sudden increase in traffic. The generation AI also generates a scenario based on weather information that changes in real time. For example, it simulates physical security risks associated with changes in weather. The generation AI also generates dynamic scenarios based on external data that changes in real time. For example, it provides a scenario that is linked to network anomaly detection. This makes it possible to provide dynamic scenarios by incorporating external data that changes in real time.

[0061] The randomization unit can use the emotion estimation function to adjust the difficulty of the scenario according to the emotional state of the participant. The randomization unit, for example, uses the emotion estimation function to adjust the difficulty of the scenario according to the emotional state of the participant. For example, if the participant is relaxed, the scenario is made more difficult. The emotion estimation function is also used to analyze the emotional state of the participant in real time and dynamically change the difficulty of the scenario. For example, if the participant is feeling stressed, the scenario is made easier. The emotion estimation function is also used to optimize the difficulty of the scenario based on the emotional state of the participant, always providing an appropriate challenge. For example, if the participant is excited, the scenario is made more complex. In this way, the difficulty of the scenario can be adjusted according to the emotional state of the participant, always providing an appropriate challenge.

[0062] The randomization unit can reflect security risks from different cultures and regions. In the randomization unit, for example, the generation AI generates scenarios that reflect the security risks from different cultures and regions. For example, it simulates cyber attacks that are likely to occur in a specific region. In addition, to enable training from a global perspective, the generation AI generates scenarios that combine security risks from different cultures and regions. For example, it reproduces international cyber attacks. In addition, the generation AI generates region-specific scenarios based on security risks from different cultures and regions. For example, it reproduces security risks in a specific country or region. This allows training from a global perspective to be realized by reflecting security risks from different cultures and regions.

[0063] The randomization unit can support a multiplayer mode in which multiple participants work together simultaneously to respond. For example, the randomization unit generates a scenario in which a generation AI generates a scenario in which multiple participants work together simultaneously to respond. For example, it provides a cyber attack scenario that requires a team response. The generation AI also generates a scenario compatible with the multiplayer mode to train teamwork. For example, it creates a scenario in which participants with multiple roles work together to respond. The generation AI also provides training aimed at strengthening teamwork based on a scenario in which multiple participants work together simultaneously. For example, it provides a scenario that requires a quick response by the entire team. This allows multiple participants to work together simultaneously to respond, thereby training teamwork.

[0064] The randomization unit can use the emotion estimation function to change the progression of the scenario in real time based on the emotional reactions of the participants. The randomization unit, for example, uses the emotion estimation function to change the progression of the scenario in real time based on the emotional reactions of the participants. For example, the development of the scenario is adjusted if the participant feels stressed. The emotion estimation function is also used to analyze the emotional reactions of the participants in real time and dynamically change the progression of the scenario. For example, the scenario is made more complex if the participant is excited. The emotion estimation function is also used to optimize the progression of the scenario based on the emotional reactions of the participants to provide more interactive training. For example, the scenario is made more difficult if the participant is relaxed. In this way, the progression of the scenario can be changed in real time based on the emotional reactions of the participants to provide more interactive training.

[0065] The feedback evaluation unit can analyze participants' action logs in detail and suggest specific areas for improvement. In the feedback evaluation unit, for example, the generation AI analyzes participants' action logs in detail and suggests specific areas for improvement. For example, it points out delayed responses and incorrect judgments. In addition, a system can be built in which the generation AI suggests specific areas for improvement based on participants' action logs. For example, it points out specific operational errors and errors in judgment and suggests improvement measures. In addition, the generation AI analyzes participants' action logs and provides feedback on specific areas for improvement. For example, it evaluates the speed and accuracy of responses and suggests areas for improvement. In this way, the effectiveness of training can be increased by analyzing participants' action logs in detail and suggesting specific areas for improvement.

[0066] The feedback evaluation unit can compare and evaluate with other participants and clearly indicate the relative skill level. For example, when the generation AI evaluates the training results, the feedback evaluation unit compares and evaluates with other participants. For example, it displays the overall ranking or score. Furthermore, a system can be constructed in which the generation AI clearly indicates the relative skill level based on the comparison and evaluation with other participants. For example, it displays the average score or the top percentage. Furthermore, the generation AI compares and evaluates with other participants based on the training results and provides feedback on the relative skill level. For example, it clearly indicates strengths and weaknesses in specific skills. In this way, by comparing and evaluating with other participants and clearly indicating the relative skill level, it is possible to improve the motivation of participants.

[0067] The feedback evaluation unit can use the emotion estimation function to reflect emotional changes during training in the evaluation. The feedback evaluation unit, for example, uses the emotion estimation function to reflect emotional changes during training in the evaluation. For example, psychological coping ability is evaluated based on changes in stress levels. In addition, a system is constructed using the emotion estimation function to analyze emotional changes during training and reflect the results in the evaluation. For example, emotional stability and fluctuations are added to the evaluation criteria. In addition, the emotion estimation function is used to evaluate psychological coping ability based on emotional changes during training. For example, calmness and judgment in an emergency are evaluated. In this way, psychological coping ability can also be evaluated by reflecting emotional changes during training in the evaluation.

[0068] The feedback evaluation unit can compare performance across different training scenarios and evaluate overall skill improvement. For example, the generation AI compares performance across different training scenarios and evaluates overall skill improvement. For example, it compares response speed and accuracy across multiple scenarios. In addition, a system is constructed in which the generation AI compares and evaluates performance across different scenarios based on the training results. For example, the score in each scenario is reflected in the overall evaluation. In addition, the generation AI analyzes performance across different training scenarios and evaluates overall skill improvement. For example, it evaluates the degree of improvement of a specific skill set. This makes it possible to compare performance across different training scenarios and evaluate overall skill improvement.

[0069] The feedback evaluation unit can provide a visualized report and present areas for improvement in a visually easy-to-understand format. In the feedback evaluation unit, for example, the generation AI visualizes the training results and provides a report that is visually easy to understand. For example, areas for improvement are shown using graphs and charts. In addition, a system is constructed in which the generation AI generates visualized reports based on the training results. For example, response time and accuracy are visually displayed. In addition, the generation AI visualizes the training results and presents areas for improvement in a visually easy-to-understand format. For example, weaknesses are highlighted using a heat map. This makes it possible to provide a visualized report and present areas for improvement in a visually easy-to-understand format.

[0070] The feedback evaluation unit can use the emotion estimation function to provide individualized feedback based on emotional reactions during training. The feedback evaluation unit, for example, uses the emotion estimation function to provide individualized feedback based on emotional reactions during training. For example, it provides advice on stress management. Furthermore, the emotion estimation function is used to build a system that analyzes emotional reactions during training and provides individualized feedback. For example, it evaluates emotional stability and suggests improvement measures. Furthermore, the emotion estimation function is used to provide individualized feedback based on emotional reactions during training to promote emotional growth. For example, it evaluates calmness in an emergency and suggests ways to deal with it. In this way, it is possible to provide individualized feedback based on emotional reactions during training and promote emotional growth.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The security incident training system can further include a voice recognition unit. The voice recognition unit can analyze the voice instructions of the training participants in real time and dynamically change the progress of the scenario. For example, if a participant issues a specific instruction, the development of the scenario can be changed. The voice recognition unit can also adjust the difficulty of the scenario based on the participant's voice instructions. For example, if a participant issues an instruction for an emergency response, the voice recognition unit can increase the complexity of the scenario. The voice recognition unit can also provide feedback based on the participant's voice instructions. For example, the voice recognition unit can evaluate whether the participant's instructions were appropriate and suggest areas for improvement. This makes it possible to dynamically change the scenario and provide feedback using voice recognition.

[0073] The security incident training system can further include a biometric authentication unit. The biometric authentication unit can perform individual authentication using the biometric information of training participants. For example, fingerprint or facial recognition can be used to confirm the identity of participants. The biometric authentication unit can also set situations in which authentication is required in specific scenarios during training. For example, fingerprint authentication can be required when accessing important data. The biometric authentication unit can also suggest areas for improvement in the authentication process based on the training results. For example, it can evaluate the time and accuracy required for authentication and propose improvement measures. This makes it possible to train authentication processes that utilize biometric information.

[0074] The security incident training system can also be equipped with a virtual reality (VR) section. The VR section can provide training participants with a more realistic training environment. For example, a virtual office environment can be recreated using VR goggles. The VR section can also dynamically change the virtual environment depending on the training scenario. For example, it can simulate responses in the virtual environment when a cyberattack occurs. The VR section can also evaluate actions in the virtual environment based on the training results and provide feedback. For example, it can evaluate the speed and accuracy of responses in the virtual environment and suggest areas for improvement. This makes it possible to create a realistic training environment and provide feedback using virtual reality.

[0075] The security incident training system can further use the emotion estimation function to adjust the progress of the scenario based on the emotional state of the participants. For example, if a participant is feeling stressed, the difficulty of the scenario can be adjusted. The emotion estimation function can also be used to analyze the emotional state of the participants in real time and dynamically change the development of the scenario. For example, if a participant is excited, the scenario can be made more complex. The emotion estimation function can also be used to provide feedback based on the emotional state of the participants. For example, advice on stress management can be provided. This makes it possible to dynamically change the scenario and provide feedback based on the emotional state.

[0076] The security incident training system can further use an emotion estimation function to change the progression of the scenario in real time based on the emotional reactions of the participants. For example, the development of the scenario can be adjusted if the participant feels stressed. The emotion estimation function can also be used to analyze the emotional reactions of the participants in real time and dynamically change the progression of the scenario. For example, the scenario can be made more complex if the participant is excited. The emotion estimation function can also be used to optimize the progression of the scenario based on the emotional reactions of the participants, allowing for more interactive training. For example, the scenario can be made more difficult if the participant is relaxed. In this way, the progression of the scenario can be changed in real time based on the emotional reactions of the participants, allowing for more interactive training.

[0077] The security incident training system can further include a data analysis unit. The data analysis unit can analyze data collected during the training and evaluate the effectiveness of the training. For example, it can analyze the speed and accuracy of participants' responses and evaluate the effectiveness of the training. The data analysis unit can also suggest specific areas for improvement based on the training results. For example, it can point out specific operational errors or errors in judgment and propose improvement measures. The data analysis unit can also optimize training scenarios based on data collected during the training. For example, it can provide scenarios that are appropriate for the skill level of the participants. This enables effective training evaluation and feedback using data analysis.

[0078] The security incident training system can further use an emotion estimation function to adjust the difficulty of the scenario based on the emotional state of the participants. For example, the scenario can be made more difficult if the participant is relaxed. The emotion estimation function can also be used to analyze the emotional state of the participants in real time and dynamically change the difficulty of the scenario. For example, the scenario can be made easier if the participant is feeling stressed. The emotion estimation function can also be used to optimize the difficulty of the scenario based on the emotional state of the participants, always providing an appropriate challenge. For example, the scenario can be made more complex if the participant is excited. In this way, the difficulty of the scenario can be adjusted according to the emotional state of the participants, always providing an appropriate challenge.

[0079] The security incident training system can also generate scenarios that reflect the security risks of different cultures and regions. For example, it can simulate cyberattacks that are likely to occur in a particular region. In addition, to enable training from a global perspective, the generative AI can generate scenarios that combine security risks from different cultures and regions. For example, it can reproduce international cyberattacks. The generative AI can also generate region-specific scenarios based on security risks from different cultures and regions. For example, it can reproduce security risks in a specific country or region. This allows training to be conducted from a global perspective, reflecting the security risks of different cultures and regions.

[0080] The security incident training system can also support a multiplayer mode in which multiple participants work together simultaneously to respond. For example, it can provide a cyberattack scenario that requires a team response. The generation AI can also generate scenarios compatible with the multiplayer mode to train teamwork. For example, it can create a scenario in which participants with multiple roles work together to respond. The generation AI can also provide training aimed at strengthening teamwork based on a scenario in which multiple participants work simultaneously. For example, it can provide a scenario that requires a quick response by the entire team. This allows multiple participants to work together simultaneously to respond, thereby training teamwork.

[0081] The security incident training system can further use an emotion estimation function to reflect emotional changes during training in the evaluation. For example, psychological coping ability can be evaluated based on changes in stress levels. The emotion estimation function can also be used to build a system that analyzes emotional changes during training and reflects them in the evaluation. For example, emotional stability and fluctuations can be added to the evaluation criteria. The emotion estimation function can also be used to evaluate psychological coping ability based on emotional changes during training. For example, calmness and judgment in an emergency can be evaluated. In this way, psychological coping ability can also be evaluated by reflecting emotional changes during training in the evaluation.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The scenario generation unit uses a generation AI to generate a real-time scenario. For example, the generation AI generates a scenario for a security incident training. The input to the generation AI is a prompt that includes the training objectives and basic settings of the scenario, and the generation AI generates a scenario based on the prompt. Step 2: The randomization unit adds randomness to the scenarios generated by the scenario generation unit. For example, by taking advantage of the fact that the output of the generation AI is random, the scenario content is designed to be different each time. This forces training participants to constantly adapt to new situations, improving their ability to respond to real security incidents. Step 3: The feedback evaluation unit conducts training based on the scenarios generated by the randomization unit and evaluates the results. For example, the generation AI analyzes the training results and evaluates the participants' responses. This evaluation is based on how the participants handled the scenarios, and provides feedback on areas for improvement and merit.

[0084] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0085] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0086] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0090] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0091] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0092] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0093] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0094] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0099] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0104] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0105] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0106] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0107] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0108] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

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

[0114] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

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

[0116] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0120] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0121] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0122] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0123] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0124] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0125] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0130] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0133] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0134] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0135] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0136] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0139] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0140] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0141] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0142] 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.

[0143] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0144] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0145] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0146] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0147] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0148] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0149] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0150] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0151] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a scenario generation unit that generates a real-time scenario using a generation AI; a randomness imparting unit that imparts randomness to the scenario generated by the scenario generation unit; a feedback evaluation unit that performs training based on the scenario generated by the randomization unit and evaluates the results of the training. A system characterized by:

2. The scenario generation unit Adding realistic elements based on past security incident data 2. The system of claim 1.

3. The scenario generation unit Reflecting participants' past training history, the training is customized to strengthen individual weaknesses.

2. The system of claim 1.

4. The scenario generation unit Monitor participants' stress levels in real time and generate stressful scenarios 2. The system of claim 1.

5. The scenario generation unit Generate scenarios that combine physical security elements 2. The system of claim 1.

6. The scenario generation unit Generate scenarios specific to different industries 2. The system of claim 1.

7. The scenario generation unit Dynamically change the scenario based on participants' emotional responses 2. The system of claim 1.

8. The randomness imparting unit Combining multiple random elements 2. The system of claim 1.

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