System

Abstract

An object of a system according to an embodiment is to effectively detect and analyze a new type of malware.SOLUTION: A system includes a generation AI, a detection unit, and an analyzing unit. The generation AI processes a large amount of AI. The detection unit identifies an abnormal pattern or behavior in data processed by the generation AI. The analysis unit analyzes the abnormal pattern or behavior specified by the detection unit in detail.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 technology has had the problem that signature-based antivirus software has difficulty detecting new types of malware.

[0005] The system according to the embodiment aims to effectively detect and analyze new types of malware. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation AI, a detection unit, and an analysis unit. The generation AI processes large amounts of data using the generation AI. The detection unit identifies abnormal patterns or behaviors from the data processed by the generation AI. The analysis unit performs detailed analysis of the abnormal patterns or behaviors identified by the detection unit. [Effects of the Invention]

[0007] The system according to the embodiment can effectively detect and analyze new types of malware. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 system according to an embodiment of the present invention utilizes generative AI technology to detect and analyze malware. This system uses generative AI to detect unknown malware and malicious programs and perform detailed analysis of the malware. This enables the security system to provide effective defense against new types of malware that cannot be detected by conventional signature-based antivirus software.

[0029] A security system according to an embodiment includes a generation AI, a detection unit, and an analysis unit. The generation AI processes large amounts of data. For example, the generation AI analyzes system log data and network traffic data to identify anomalous patterns and behaviors. The generation AI can also detect new types of malware based on real-time data. The detection unit identifies anomalous patterns and behaviors from the data processed by the generation AI. For example, the detection unit detects files and processes that behave differently from normal programs. The detection unit can also detect anomalous network traffic that occurs only during specific time periods and unauthorized access to specific files. The analysis unit performs detailed analysis of the anomalous patterns and behaviors identified by the detection unit. For example, the analysis unit analyzes the code of detected malware to identify its behavior and purpose. The analysis unit can also perform detailed analysis based on malware behavior data. This enables the security system according to an embodiment to provide effective defense against new types of malware that cannot be detected by conventional signature-based antivirus software.

[0030] The detection unit can refer to past similar cases for abnormal patterns and build a feedback loop to improve detection accuracy. For example, the detection unit compares an abnormal pattern detected by the generation AI with past similar cases and builds a feedback loop. For example, it compares it with patterns of malware detected in the past to improve detection accuracy. The detection unit can also refer to past detection logs and a database of known malware to reevaluate detection results and retrain the model. This improves detection accuracy by building a feedback loop to refer to past similar cases and improve detection accuracy.

[0031] The detection unit can cooperate with other security systems to mutually verify anomalous patterns, thereby increasing the reliability of the detection results. For example, the detection unit cooperates with other security systems, such as firewalls and IDS / IPS, to mutually verify anomalous patterns detected by the generation AI. This increases the reliability of the detection results. The detection unit can also perform cross-checks and cross-references to verify anomalous patterns. This improves the reliability of the detection results by cooperating with other security systems to mutually verify.

[0032] The detection unit can link anomaly detection by the generation AI with a physical security system to realize comprehensive security measures. For example, the detection unit can link anomaly detection by the generation AI with a surveillance camera to strengthen physical security measures. For example, when abnormal network traffic is detected, the detection unit can check the surveillance camera footage. The detection unit can also link with an access control system to strengthen physical access control when abnormal access is detected. In this way, comprehensive security measures can be realized by linking anomaly detection by the generation AI with a physical security system.

[0033] The detection unit can apply the anomalous patterns detected by the generation AI to security systems in other industries, thereby increasing the versatility of anomaly detection. For example, the detection unit can apply the anomalous patterns detected by the generation AI to security systems in the financial industry, thereby increasing the versatility of anomaly detection. For example, it can be used to detect fraudulent transactions. The detection unit can also be applied to security systems in the medical industry, thereby increasing the versatility of anomaly detection. For example, it can detect unauthorized access to medical data. In this way, the anomalous patterns detected by the generation AI can be applied to security systems in other industries, thereby increasing the versatility of anomaly detection.

[0034] The analysis unit can improve the accuracy of analysis by comparing the malware code analyzed by the generation AI with the code of other malware and extracting common features and patterns. For example, the analysis unit compares the malware code analyzed by the generation AI with past malware code and extracts common features and patterns. For example, it analyzes the similarity of specific code segments and functions. The analysis unit can also refer to a database of known malware and analyze code similarities and matches of behavioral patterns. This improves the accuracy of analysis by comparing malware code and extracting common features and patterns.

[0035] The analysis unit can reproduce the behavior of malware analyzed by the generation AI in a simulation environment and evaluate its actual impact. The analysis unit, for example, builds a system that reproduces the behavior of malware analyzed by the generation AI in a simulation environment and evaluates its actual impact. For example, it executes malware in a virtual environment and observes its impact. The analysis unit can also use a sandbox environment to reproduce the behavior of malware and evaluate the impact on the system and data loss. This improves the accuracy of the analysis by reproducing the behavior of malware in a simulation environment and evaluating its actual impact.

[0036] The analysis unit can link the malware analysis results from the generative AI with other security systems to build comprehensive defense measures. For example, the analysis unit can link the malware analysis results from the generative AI with a firewall to build comprehensive defense measures. For example, the analysis unit can automatically update firewall rules based on the analysis results. The analysis unit can also link with IDS / IPS to block abnormal traffic based on the malware analysis results. In this way, comprehensive defense measures can be built by linking the malware analysis results from the generative AI with other security systems.

[0037] The analysis unit can apply the characteristics of malware analyzed by the generation AI to security measures in other industries to strengthen security. For example, the analysis unit can apply the characteristics of malware analyzed by the generation AI to security measures for IoT devices to strengthen security. For example, to prevent attacks on the firmware of IoT devices. The analysis unit can also apply the characteristics of malware analyzed by the generation AI to security measures for smart homes to strengthen security. For example, to prevent unauthorized access to smart home devices. In this way, security can be strengthened by applying the characteristics of malware analyzed by the generation AI to security measures in other industries.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The security system also includes a prediction unit. The prediction unit can predict future attacks based on the abnormal patterns detected by the generation AI. For example, it can analyze past attack data and identify attacker behavior patterns. The prediction unit can also analyze the frequency and time periods of abnormal network traffic to predict the next attack that is likely to occur. This allows the security system to take measures in advance against future attacks.

[0040] The security system further includes an education unit. The education unit can provide users with educational content to raise their security awareness. For example, the education unit can provide information on the latest malware techniques and countermeasures. The education unit can also provide a platform where users can learn through security quizzes and simulations. This improves users' security awareness and strengthens the overall defense capabilities of the system.

[0041] The security system further includes a reporting unit. The reporting unit can report the abnormal patterns and analysis results detected by the generation AI to the user in an easy-to-understand manner. For example, it can generate a detailed report of the anomaly detection and provide it to the user via email or a dashboard. The reporting unit can also visually display the results of the anomaly detection in graphs and charts, allowing the user to intuitively understand them. This allows the user to quickly understand the results of the anomaly detection and take appropriate measures.

[0042] The security system also includes a prevention unit. The prevention unit can propose preventive measures based on the abnormal patterns detected by the generative AI. For example, changing certain network settings can reduce the risk of an attack. The prevention unit can also provide users with regular security checklists to detect system vulnerabilities in advance. This allows the security system to provide preventive measures to prevent attacks before they occur.

[0043] The security system further includes an automatic repair unit. The automatic repair unit can automatically repair the system based on the abnormal patterns detected by the generation AI. For example, it can automatically isolate abnormal files to maintain normal system operation. The automatic repair unit can also automatically block abnormal network traffic to ensure system security. This allows the security system to respond quickly after detecting an abnormality and maintain system stability.

[0044] The security system also includes a recovery unit. The recovery unit can assist in system recovery based on the abnormal patterns detected by the generation AI. For example, when an abnormal file is detected, it can restore a normal file from a backup. The recovery unit can also automatically reset the system settings and return the system to a normal state. This enables the security system to support rapid recovery after detecting an abnormality and ensure continuous system operation.

[0045] The processing flow of the first embodiment will be briefly explained below.

[0046] Step 1: Generative AI processes large amounts of data. For example, it analyzes system log data and network traffic data to identify abnormal patterns and behaviors. Generative AI can also detect new malware strains based on real-time data. Step 2: The detection unit identifies abnormal patterns and behaviors from the data processed by the generative AI. For example, the detection unit detects files or processes that behave differently from normal programs. The detection unit can also detect abnormal network traffic that occurs only during certain times of the day or unauthorized access to specific files. Step 3: The analysis unit performs detailed analysis of the abnormal patterns and behaviors identified by the detection unit. For example, the analysis unit analyzes the code of the detected malware to identify its behavior and purpose. The analysis unit can also perform detailed analysis based on malware behavior data.

[0047] (Example 2) A security system according to an embodiment of the present invention utilizes generative AI technology to detect and analyze malware. This system uses generative AI to detect unknown malware and malicious programs and perform detailed analysis of the malware. This enables the security system to provide effective defense against new types of malware that cannot be detected by conventional signature-based antivirus software.

[0048] A security system according to an embodiment includes a generation AI, a detection unit, and an analysis unit. The generation AI processes large amounts of data. For example, the generation AI analyzes system log data and network traffic data to identify anomalous patterns and behaviors. The generation AI can also detect new types of malware based on real-time data. The detection unit identifies anomalous patterns and behaviors from the data processed by the generation AI. For example, the detection unit detects files and processes that behave differently from normal programs. The detection unit can also detect anomalous network traffic that occurs only during specific time periods and unauthorized access to specific files. The analysis unit performs detailed analysis of the anomalous patterns and behaviors identified by the detection unit. For example, the analysis unit analyzes the code of detected malware to identify its behavior and purpose. The analysis unit can also perform detailed analysis based on malware behavior data. This enables the security system according to an embodiment to provide effective defense against new types of malware that cannot be detected by conventional signature-based antivirus software.

[0049] The detection unit can refer to past similar cases for abnormal patterns and build a feedback loop to improve detection accuracy. For example, the detection unit compares an abnormal pattern detected by the generation AI with past similar cases and builds a feedback loop. For example, it compares it with patterns of malware detected in the past to improve detection accuracy. The detection unit can also refer to past detection logs and a database of known malware to reevaluate detection results and retrain the model. This improves detection accuracy by building a feedback loop to refer to past similar cases and improve detection accuracy.

[0050] The detection unit can cooperate with other security systems to mutually verify anomalous patterns, thereby increasing the reliability of the detection results. For example, the detection unit cooperates with other security systems, such as firewalls and IDS / IPS, to mutually verify anomalous patterns detected by the generation AI. This increases the reliability of the detection results. The detection unit can also perform cross-checks and cross-references to verify anomalous patterns. This improves the reliability of the detection results by cooperating with other security systems to mutually verify.

[0051] The detection unit uses the emotion estimation function to analyze the user's emotion data and prioritizes detecting anomalies at times when the user feels anxious. The detection unit, for example, uses the emotion estimation function to analyze the user's emotion data in real time and prioritizes detecting anomalies at times when the user feels anxious. For example, the detection unit analyzes the user's facial expressions and voice. The detection unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze the emotion using an emotion estimation algorithm. In this way, analyzing the user's emotion data and prioritizing detecting anomalies at times when the user feels anxious increases the user's sense of security.

[0052] The detection unit can link anomaly detection by the generation AI with a physical security system to realize comprehensive security measures. For example, the detection unit can link anomaly detection by the generation AI with a surveillance camera to strengthen physical security measures. For example, when abnormal network traffic is detected, the detection unit can check the surveillance camera footage. The detection unit can also link with an access control system to strengthen physical access control when abnormal access is detected. In this way, comprehensive security measures can be realized by linking anomaly detection by the generation AI with a physical security system.

[0053] The detection unit can apply the anomalous patterns detected by the generation AI to security systems in other industries, thereby increasing the versatility of anomaly detection. For example, the detection unit can apply the anomalous patterns detected by the generation AI to security systems in the financial industry, thereby increasing the versatility of anomaly detection. For example, it can be used to detect fraudulent transactions. The detection unit can also be applied to security systems in the medical industry, thereby increasing the versatility of anomaly detection. For example, it can detect unauthorized access to medical data. In this way, the anomalous patterns detected by the generation AI can be applied to security systems in other industries, thereby increasing the versatility of anomaly detection.

[0054] The detection unit can use the emotion estimation function to monitor in real time how the user feels in response to an anomaly detection and optimize the method of notifying the user of the detection result. The detection unit, for example, uses the emotion estimation function to build a system that monitors in real time how the user feels in response to an anomaly detection. For example, the detection unit analyzes the user's facial expression and voice. The detection unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze the user's emotion using an emotion estimation algorithm. This allows the user's emotion to be monitored in real time and the method of notifying the detection result to be optimized, thereby increasing the user's sense of security.

[0055] The analysis unit can improve the accuracy of analysis by comparing the malware code analyzed by the generation AI with the code of other malware and extracting common features and patterns. For example, the analysis unit compares the malware code analyzed by the generation AI with past malware code and extracts common features and patterns. For example, it analyzes the similarity of specific code segments and functions. The analysis unit can also refer to a database of known malware and analyze code similarities and matches of behavioral patterns. This improves the accuracy of analysis by comparing malware code and extracting common features and patterns.

[0056] The analysis unit can reproduce the behavior of malware analyzed by the generation AI in a simulation environment and evaluate its actual impact. The analysis unit, for example, builds a system that reproduces the behavior of malware analyzed by the generation AI in a simulation environment and evaluates its actual impact. For example, it executes malware in a virtual environment and observes its impact. The analysis unit can also use a sandbox environment to reproduce the behavior of malware and evaluate the impact on the system and data loss. This improves the accuracy of the analysis by reproducing the behavior of malware in a simulation environment and evaluating its actual impact.

[0057] The analysis unit can use the emotion estimation function to analyze how a user feels about malware analysis results and improve the method for reporting the analysis results. The analysis unit, for example, uses the emotion estimation function to build a system that analyzes how a user feels about malware analysis results. For example, the analysis unit analyzes the user's facial expressions and voice. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and analyze the user's emotions using an emotion estimation algorithm. This allows for a deeper understanding of the user by analyzing the user's emotions and improving the method for reporting the analysis results.

[0058] The analysis unit can link the malware analysis results from the generative AI with other security systems to build comprehensive defense measures. For example, the analysis unit can link the malware analysis results from the generative AI with a firewall to build comprehensive defense measures. For example, the analysis unit can automatically update firewall rules based on the analysis results. The analysis unit can also link with IDS / IPS to block abnormal traffic based on the malware analysis results. In this way, comprehensive defense measures can be built by linking the malware analysis results from the generative AI with other security systems.

[0059] The analysis unit can apply the characteristics of malware analyzed by the generation AI to security measures in other industries to strengthen security. For example, the analysis unit can apply the characteristics of malware analyzed by the generation AI to security measures for IoT devices to strengthen security. For example, to prevent attacks on the firmware of IoT devices. The analysis unit can also apply the characteristics of malware analyzed by the generation AI to security measures for smart homes to strengthen security. For example, to prevent unauthorized access to smart home devices. In this way, security can be strengthened by applying the characteristics of malware analyzed by the generation AI to security measures in other industries.

[0060] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0061] The security system also includes a prediction unit. The prediction unit can predict future attacks based on the abnormal patterns detected by the generation AI. For example, it can analyze past attack data and identify attacker behavior patterns. The prediction unit can also analyze the frequency and time periods of abnormal network traffic to predict the next attack that is likely to occur. This allows the security system to take measures in advance against future attacks.

[0062] The security system further includes an education unit. The education unit can provide users with educational content to raise their security awareness. For example, the education unit can provide information on the latest malware techniques and countermeasures. The education unit can also provide a platform where users can learn through security quizzes and simulations. This improves users' security awareness and strengthens the overall defense capabilities of the system.

[0063] The security system further includes a reporting unit. The reporting unit can report the abnormal patterns and analysis results detected by the generation AI to the user in an easy-to-understand manner. For example, it can generate a detailed report of the anomaly detection and provide it to the user via email or a dashboard. The reporting unit can also visually display the results of the anomaly detection in graphs and charts, allowing the user to intuitively understand them. This allows the user to quickly understand the results of the anomaly detection and take appropriate measures.

[0064] The security system can further use emotion estimation to monitor a user's stress level and provide alerts to encourage relaxation when stress levels rise. For example, sensors can be used to collect the user's heart rate and electrodermal activity to estimate the user's stress level. The emotion estimation function can also provide relaxing music or guided meditations if it determines that the user is feeling stressed. This reduces the user's stress and improves the security system's user experience.

[0065] The security system can also use emotion estimation to analyze how the user feels about detected anomalies and optimize the notification method for the detection results. For example, it can analyze the user's facial expressions and voice. It can also collect the user's biometric data (heart rate and electrodermal activity) with sensors and analyze their emotions using emotion estimation algorithms. This allows the system to monitor the user's emotions in real time and optimize the notification method for detection results, thereby increasing the user's sense of security.

[0066] Security systems can further use emotion estimation capabilities to analyze how users feel about malware analysis results and improve the reporting method for analysis results. For example, by analyzing the user's facial expressions and voice. Also, sensors can collect the user's biometric data (heart rate and electrodermal activity) and use emotion estimation algorithms to analyze their emotions. This allows for a deeper understanding of the user by analyzing their emotions and improving the reporting method for analysis results.

[0067] The security system can also use emotion estimation functions to prioritize anomaly detection at times when the user feels anxious. For example, it can analyze the user's facial expressions and voice. It can also collect the user's biometric data (heart rate and electrodermal activity) with sensors and analyze their emotions using emotion estimation algorithms. This allows the system to analyze the user's emotional data and prioritize anomaly detection at times when the user feels anxious, thereby increasing the user's sense of security.

[0068] The security system also includes a prevention unit. The prevention unit can propose preventive measures based on the abnormal patterns detected by the generative AI. For example, changing certain network settings can reduce the risk of an attack. The prevention unit can also provide users with regular security checklists to detect system vulnerabilities in advance. This allows the security system to provide preventive measures to prevent attacks before they occur.

[0069] The security system further includes an automatic repair unit. The automatic repair unit can automatically repair the system based on the abnormal patterns detected by the generation AI. For example, it can automatically isolate abnormal files to maintain normal system operation. The automatic repair unit can also automatically block abnormal network traffic to ensure system security. This allows the security system to respond quickly after detecting an abnormality and maintain system stability.

[0070] The security system also includes a recovery unit. The recovery unit can assist in system recovery based on the abnormal patterns detected by the generation AI. For example, when an abnormal file is detected, it can restore a normal file from a backup. The recovery unit can also automatically reset the system settings and return the system to a normal state. This enables the security system to support rapid recovery after detecting an abnormality and ensure continuous system operation.

[0071] The processing flow of the second embodiment will be briefly explained below.

[0072] Step 1: Generative AI processes large amounts of data. For example, it analyzes system log data and network traffic data to identify abnormal patterns and behaviors. Generative AI can also detect new malware strains based on real-time data. Step 2: The detection unit identifies abnormal patterns and behaviors from the data processed by the generative AI. For example, the detection unit detects files or processes that behave differently from normal programs. The detection unit can also detect abnormal network traffic that occurs only during certain times of the day or unauthorized access to specific files. Step 3: The analysis unit performs detailed analysis of the abnormal patterns and behaviors identified by the detection unit. For example, the analysis unit analyzes the code of the detected malware to identify its behavior and purpose. The analysis unit can also perform detailed analysis based on malware behavior data.

[0073] 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.

[0074] 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.

[0075] 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.

[0076] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0077] 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.

[0078] 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.

[0079] 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.

[0080] 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.

[0081] 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).

[0082] 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.

[0083] 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.

[0084] 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.

[0085] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0086] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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).

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0101] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0117] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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."

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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]

[0140] 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. Generative AI processes large amounts of data using generative AI, a detection unit that identifies abnormal patterns or behaviors from the data processed by the generation AI; an analysis unit that analyzes in detail the abnormal patterns and behaviors identified by the detection unit. A system characterized by:

2. The detection unit For the abnormal patterns, a feedback loop is created to improve detection accuracy by referring to similar cases from the past.

2. The system of claim 1.

3. The detection unit Anomaly detection by the generative AI will be linked with physical security systems to achieve comprehensive security measures.

2. The system of claim 1.

4. The analysis unit The malware code analyzed by the generative AI is compared with the code of other malware, and common characteristics and patterns are extracted to improve the accuracy of the analysis.

2. The system of claim 1.

5. The detection unit Analyzes user emotional data and prioritizes detecting anomalies when the user feels anxious 2. The system of claim 1.

6. The analysis unit Analyzing how users feel about malware analysis results and improving how they report said results 2. The system of claim 1.

7. The analysis unit The malware analysis results from the generative AI will be linked with other security systems to build comprehensive defense measures.

2. The system of claim 1.

8. The analysis unit The characteristics of malware analyzed by the AI ​​will be applied to security measures in other industries to strengthen security.

2. The system of claim 1.

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