system

Abstract

The system according to this embodiment aims to automatically analyze the content of SMS messages, linked text, and attached files to detect suspicious activity and warn the user. [Solution] The system according to the embodiment comprises an analysis unit, a detection unit, a warning unit, and a blocking unit. The analysis unit analyzes the body of the SMS, the text of the linked page, and the contents of the attached file. The detection unit detects suspicious activity based on the content analyzed by the analysis unit. The warning unit displays a warning to the user based on the suspicious activity detected by the detection unit. The blocking unit blocks the content for which the warning unit has displayed a warning, as needed.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, it is necessary to manually check the text of SMS, the text of the linked page, and the content of attached files, and there is a risk of overlooking suspicious activities.

[0005] The system according to the embodiment aims to automatically analyze the text of SMS, the text of the linked page, and the content of attached files, detect suspicious activities, and warn the user.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a detection unit, a warning unit, and a blocking unit. The analysis unit analyzes the body of the SMS, the text of the linked page, and the content of the attached file. The detection unit detects suspicious activity based on the content analyzed by the analysis unit. The warning unit displays a warning to the user based on the suspicious activity detected by the detection unit. The blocking unit blocks the content for which the warning unit has displayed a warning, as needed. [Effects of the Invention]

[0007] The system according to this embodiment can automatically analyze the content of SMS messages, linked text, and attached files to detect suspicious activity and warn the user. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

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

[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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a reception device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent according to an embodiment of the present invention is a system that automatically analyzes the content of SMS messages, linked text, and attached files, detects suspicious activity, warns the user, and blocks the content as necessary. This AI agent performs advanced text analysis utilizing natural language processing and machine learning to detect suspicious links and attached files in real time. The user is shown a warning through a user-friendly warning system, and the content is blocked as necessary. This effectively reduces the risk of phishing scams and personal information leaks. For example, the AI ​​agent analyzes the content of SMS messages, linked text, and attached files. Next, it detects suspicious activity based on the analysis results. For detected suspicious activity, it displays a warning to the user and blocks the content as necessary. This mechanism effectively reduces the risk of phishing scams and personal information leaks. Thus, the AI ​​agent can analyze the content of SMS messages, linked text, and attached files, detect suspicious activity, warn the user, and block the content as necessary.

[0029] The AI ​​agent according to this embodiment comprises an analysis unit, a detection unit, a warning unit, and a blocking unit. The analysis unit analyzes the body of an SMS, the text of a linked page, and the content of an attached file. The analysis unit analyzes the body of an SMS using, for example, natural language processing technology. The analysis unit can also analyze the text of a linked page. The analysis unit can also analyze the content of an attached file. For example, the analysis unit analyzes the body of an SMS using natural language processing technology. The analysis unit can also analyze the text of a linked page. The analysis unit can also analyze the content of an attached file. The detection unit detects suspicious activity based on the content analyzed by the analysis unit. The detection unit detects, for example, specific keywords or phrases. The detection unit can also detect specific keywords or phrases based on the analysis results. The detection unit can also detect specific keywords or phrases based on the analysis results. For example, the detection unit detects specific keywords or phrases. The detection unit can also detect specific keywords or phrases based on the analysis results. The detection unit can also detect specific keywords or phrases based on the analysis results. The warning unit displays a warning to the user based on suspicious activity detected by the detection unit. The warning unit can, for example, immediately display a warning to the user if suspicious content is detected. The warning unit can also immediately display a warning to the user if suspicious content is detected. The warning unit can also immediately display a warning to the user if suspicious content is detected. For example, the warning unit can immediately display a warning to the user if suspicious content is detected. The warning unit can also immediately display a warning to the user if suspicious content is detected. The warning unit can also immediately display a warning to the user if suspicious content is detected. The blocking unit blocks the content for which the warning unit has issued a warning, as needed. The blocking unit can, for example, provide guidelines to prevent the user from clicking on a link after receiving a warning. The blocking unit can also provide guidelines to prevent the user from clicking on a link after receiving a warning. The blocking unit can also provide guidelines to prevent the user from clicking on a link after receiving a warning.For example, the blocking section can provide guidelines to prevent users from clicking on links after receiving a warning. The blocking section can also provide guidelines to prevent users from clicking on links after receiving a warning. The blocking section can also provide guidelines to prevent users from clicking on links after receiving a warning. This allows the AI ​​agent according to the embodiment to analyze the content of the SMS message, the linked text, and the attachments, detect suspicious activity, warn the user, and block the content as necessary.

[0030] The analysis unit analyzes the content of SMS messages, linked text, and attached files. For example, the analysis unit uses natural language processing technology to analyze the content of SMS messages. Specifically, it uses natural language processing technology to understand the context and intent from the content of SMS messages and identify potential threats or suspicious content. For example, it uses a pre-trained model to detect wording that has the characteristics of spam messages or phishing scams. The analysis unit can also analyze linked text. In analyzing linked text, it follows links to obtain the content of web pages and analyzes that content. For example, it evaluates the metadata and content of a page to determine whether the linked page is a malicious site. The analysis unit can also analyze the content of attached files. In analyzing attached files, it uses analysis methods appropriate to the file type. For example, in the case of PDFs and Word documents, it uses text extraction technology to analyze the content and check for malicious scripts or suspicious links. In the case of image files, it uses image recognition technology to detect text and suspicious elements contained within the image. This allows the analysis department to comprehensively analyze the content of SMS messages, linked text, and attachments, enabling early detection of potential threats. Furthermore, the analysis department can share the analysis results with other systems and departments, and collaborate on countermeasures. For example, it can notify the security department of the analysis results and request further investigation and countermeasures. This allows the analysis department to analyze data efficiently and effectively, improving the overall security of the system.

[0031] The detection unit detects suspicious activity based on the analysis performed by the analysis unit. For example, the detection unit can detect specific keywords or phrases. Specifically, it identifies suspicious content by comparing the analysis results provided by the analysis unit with pre-configured keyword and phrase lists. For example, it can detect keywords related to phishing scams or phrases common in spam messages. The detection unit can also detect specific keywords or phrases based on the analysis results. Furthermore, the detection unit can use machine learning algorithms to detect suspicious activity based on patterns learned from past data. For example, it can use anomaly detection algorithms to detect patterns or abnormal behavior that differ from normal messages. This allows the detection unit to handle not only known threats but also new threats and unknown attack methods. The detection unit can also detect specific keywords or phrases based on the analysis results. For example, the detection unit can detect specific keywords or phrases. This allows the detection unit to quickly and accurately detect suspicious activity based on the information provided by the analysis unit, thereby strengthening the overall system security. Furthermore, the detection unit can share detection results with other systems and departments to implement coordinated countermeasures. For example, the detection results can be notified to the security department, and further investigation and countermeasures can be requested. This allows the detection unit to efficiently and effectively detect suspicious activity and improve the overall security of the system.

[0032] The warning unit displays warnings to the user based on suspicious activity detected by the detection unit. For example, if suspicious content is detected, the warning unit will immediately display a warning to the user. Specifically, it will display a pop-up notification or alert message on the user's device, warning them about the detected suspicious content. For example, if a link suspected of being a phishing scam is included, it will warn the user not to click on that link. Also, if a spam message is detected, it will warn the user not to open that message. The warning unit can also display warnings to the user immediately if suspicious content is detected. Furthermore, the warning unit can include detailed information in the warning message. For example, it can provide a specific explanation of the detected suspicious content and guidelines on what the user should do. This allows the user to understand the detected threat and take appropriate action. The warning unit can also display warnings to the user immediately if suspicious content is detected. For example, if suspicious content is detected, the warning unit will immediately display a warning to the user. The warning unit can also display warnings to the user immediately if suspicious content is detected. This allows the warning unit to provide users with prompt and appropriate warnings, preventing damage before it occurs. Furthermore, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of its warnings. For example, it can review the content and display method of warning messages based on feedback from users who have received warnings. In addition, the warning unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only pop-up notifications but also voice calls, SMS, and email. This allows the warning unit to provide users with prompt and reliable warnings, preventing damage before it occurs.

[0033] The blocking unit blocks the content for which the warning unit has displayed a warning, as needed. The blocking unit can, for example, provide guidelines to prevent the user from clicking on a link after receiving a warning. Specifically, it can also provide guidelines to prevent the user from clicking on a link after receiving a warning. For example, the blocking unit can provide guidelines to prevent the user from clicking on a link after receiving a warning. Furthermore, the blocking unit can also provide guidelines to prevent the user from clicking on a link after receiving a warning. For example, the blocking unit can provide guidelines to prevent the user from clicking on a link after receiving a warning. The blocking unit can also provide guidelines to prevent the user from clicking on a link after receiving a warning. As a result, the AI ​​agent according to the embodiment can analyze the content of the SMS message, the linked text, and the attachments, detect suspicious activity, warn the user, and block the content as needed.

[0034] The analysis unit can analyze the body of an SMS using natural language processing. The analysis unit can analyze the body of an SMS using, for example, morphological analysis. The analysis unit can also analyze the body of an SMS using grammatical analysis. The analysis unit can also analyze the body of an SMS using semantic analysis. For example, the analysis unit can analyze the body of an SMS using morphological analysis. The analysis unit can also analyze the body of an SMS using grammatical analysis. The analysis unit can also analyze the body of an SMS using semantic analysis. This allows for high-precision analysis of the body of an SMS by using natural language processing. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the body of an SMS into a generative AI, which can perform morphological analysis, grammatical analysis, and semantic analysis.

[0035] The analysis unit can analyze the text of a linked page. For example, the analysis unit can analyze the text of a linked webpage. The analysis unit can also analyze a linked PDF document. The analysis unit can also analyze a linked document file. For example, the analysis unit can analyze the text of a linked webpage. The analysis unit can also analyze a linked PDF document. The analysis unit can also analyze a linked document file. This allows suspicious links to be detected by analyzing the text of the linked page. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the text of the linked page into a generating AI, and the generating AI can analyze the text of the linked page.

[0036] The analysis unit can analyze the contents of attached files. For example, the analysis unit can analyze image files of attached files. The analysis unit can also analyze document files of attached files. The analysis unit can also analyze executable files of attached files. For example, the analysis unit can analyze image files of attached files. The analysis unit can also analyze document files of attached files. The analysis unit can also analyze executable files of attached files. By analyzing the contents of attached files, suspicious attached files can be detected. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the contents of an attached file into a generating AI, and the generating AI can analyze the contents of the attached file.

[0037] The detection unit can detect specific keywords or phrases based on the analysis results. For example, the detection unit can detect specific keywords or phrases based on a blacklist. The detection unit can also detect specific keywords or phrases using the extraction of frequently occurring words. The detection unit can also detect specific keywords or phrases using natural language processing techniques. For example, the detection unit can detect specific keywords or phrases based on a blacklist. The detection unit can also detect specific keywords or phrases using the extraction of frequently occurring words. The detection unit can also detect specific keywords or phrases using natural language processing techniques. This allows for the early detection of suspicious activity by detecting specific keywords or phrases. Some or all of the above-described processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the analysis results into a generative AI, which can then detect specific keywords or phrases.

[0038] The warning unit can immediately display a warning to the user if suspicious content is detected. The warning unit can display a warning to the user within a few seconds, for example. The warning unit can also display a warning to the user in real time. The warning unit can also display a warning to the user using a pop-up message. For example, the warning unit can display a warning to the user within a few seconds. The warning unit can also display a warning to the user in real time. The warning unit can also display a warning to the user using a pop-up message. This allows the user to be quickly alerted by displaying a warning immediately when suspicious content is detected. Some or all of the above processing in the warning unit may be performed using, for example, a generation AI, or without a generation AI. For example, if suspicious content is detected, the warning unit can have a generation AI generate a warning message and display the warning message generated by the generation AI to the user.

[0039] The blocking section can provide guidelines to prevent users from clicking links after receiving a warning. The blocking section can provide guidelines, for example, using pop-up messages. The blocking section can also provide guidelines using help documentation. The blocking section can also provide guidelines using the notification bar. For example, the blocking section can provide guidelines using pop-up messages. The blocking section can also provide guidelines using help documentation. The blocking section can also provide guidelines using the notification bar. This reduces the risk of phishing scams and personal information leaks by providing guidelines to prevent users from clicking links after receiving a warning. Some or all of the above processing in the blocking section may be performed, for example, using a generative AI, or not using a generative AI. For example, after a user receives a warning, the blocking section can have a generative AI generate guidelines and provide the user with the guidelines generated by the generative AI.

[0040] The analysis unit can improve its analysis accuracy by referring to past analysis results when analyzing the body of an SMS message, the text of a link, and the content of an attachment. For example, the analysis unit can refer to patterns of phishing scams detected in the past and analyze similar messages with high accuracy. The analysis unit can also learn from past analysis results and optimize algorithms for detecting new suspicious activity. The analysis unit can also improve the detection accuracy of specific keywords or phrases based on past analysis results. For example, the analysis unit can refer to patterns of phishing scams detected in the past and analyze similar messages with high accuracy. The analysis unit can also learn from past analysis results and optimize algorithms for detecting new suspicious activity. The analysis unit can also improve the detection accuracy of specific keywords or phrases based on past analysis results. This allows for improved analysis accuracy by referring to past analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past analysis results into a generative AI, which can then refer to the past analysis results to improve analysis accuracy.

[0041] The analysis unit can apply multilingual analysis algorithms to handle different languages ​​and dialects during analysis. For example, the analysis unit can apply analysis algorithms for major languages ​​such as English, Spanish, and Chinese. The analysis unit can also introduce algorithms for analyzing regional dialects and slang. The analysis unit can also analyze messages in different languages ​​with high accuracy using multilingual natural language processing technology. For example, the analysis unit can apply analysis algorithms for major languages ​​such as English, Spanish, and Chinese. The analysis unit can also introduce algorithms for analyzing regional dialects and slang. The analysis unit can also analyze messages in different languages ​​with high accuracy using multilingual natural language processing technology. This makes it possible to analyze messages in different languages ​​and dialects by applying multilingual analysis algorithms. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input messages in different languages ​​or dialects into a generative AI, which can then apply a multilingual analysis algorithm for analysis.

[0042] The analysis unit can prioritize analyzing region-specific suspicious activities based on the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis unit will prioritize analyzing patterns of phishing scams that frequently occur in that region. The analysis unit can also apply algorithms to detect region-specific suspicious activities based on the user's geographical location information. The analysis unit can also refer to crime data for each region and prioritize analyzing region-specific suspicious activities. For example, if the user is in a specific region, the analysis unit will prioritize analyzing patterns of phishing scams that frequently occur in that region. The analysis unit can also apply algorithms to detect region-specific suspicious activities based on the user's geographical location information. The analysis unit can also refer to crime data for each region and prioritize analyzing region-specific suspicious activities. This enables region-specific analysis by prioritizing the analysis of region-specific suspicious activities based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can input the user's geographical location information into the generating AI, which can then prioritize the analysis of suspicious activities specific to that region.

[0043] The analysis unit can analyze a user's social media activity during analysis and identify related suspicious activities. For example, the analysis unit can analyze a user's social media accounts and identify related suspicious messages. The analysis unit can also detect patterns in a user's social media activity that make them likely to be targeted by phishing scams. The analysis unit can also analyze suspicious links and attachments shared on social media. For example, the analysis unit can analyze a user's social media accounts and identify related suspicious messages. The analysis unit can also detect patterns in a user's social media activity that make them likely to be targeted by phishing scams. The analysis unit can also analyze suspicious links and attachments shared on social media. This allows the analysis unit to identify related suspicious activities by analyzing a user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI, which can then identify related suspicious activities.

[0044] The detection unit can optimize its detection algorithm by referring to past suspicious activity data during detection. For example, the detection unit can refer to patterns of phishing scams detected in the past to detect similar activities with high accuracy. The detection unit can also learn from past suspicious activity data to optimize its algorithm for detecting new suspicious activities. The detection unit can also improve the detection accuracy of specific keywords or phrases based on past suspicious activity data. For example, the detection unit can refer to patterns of phishing scams detected in the past to detect similar activities with high accuracy. The detection unit can also learn from past suspicious activity data to optimize its algorithm for detecting new suspicious activities. The detection unit can also improve the detection accuracy of specific keywords or phrases based on past suspicious activity data. In this way, by referring to past suspicious activity data, the detection algorithm can be optimized and detection accuracy can be improved. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without using generative AI. For example, the detection unit can input past suspicious activity data into the generating AI, which can then optimize the detection algorithm by referring to the past suspicious activity data.

[0045] The detection unit can apply a customized algorithm to detect suspicious activities specific to a particular industry or field during detection. For example, the detection unit can apply an algorithm to detect phishing scam patterns specific to the financial industry. The detection unit can also implement a customized algorithm to detect suspicious activities specific to the medical industry. The detection unit can also apply an algorithm to detect suspicious activities specific to the education sector. For example, the detection unit can apply an algorithm to detect phishing scam patterns specific to the financial industry. The detection unit can also implement a customized algorithm to detect suspicious activities specific to the medical industry. The detection unit can also apply an algorithm to detect suspicious activities specific to the education sector. This makes it possible to detect suspicious activities with higher accuracy by applying a customized algorithm specific to a particular industry or field. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the detection unit can input suspicious activity data specific to a particular industry or field into a generative AI, and the generative AI can apply a customized algorithm to detect suspicious activities.

[0046] The detection unit can prioritize the detection of region-specific suspicious activities based on the user's geographical location information during detection. For example, if the user is in a specific region, the detection unit will prioritize the detection of phishing scam patterns that frequently occur in that region. The detection unit can also apply algorithms to detect region-specific suspicious activities based on the user's geographical location information. The detection unit can also refer to crime data for each region and prioritize the detection of region-specific suspicious activities. For example, if the user is in a specific region, the detection unit will prioritize the detection of phishing scam patterns that frequently occur in that region. The detection unit can also apply algorithms to detect region-specific suspicious activities based on the user's geographical location information. The detection unit can also refer to crime data for each region and prioritize the detection of region-specific suspicious activities. This enables region-specific detection by prioritizing the detection of region-specific suspicious activities based on the user's geographical location information. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without using generative AI. For example, the detection unit inputs the user's geographical location information into the generating AI, which can then prioritize the detection of suspicious activity specific to that region.

[0047] The detection unit can analyze the user's social media activity and identify related suspicious activities at the time of detection. For example, the detection unit can analyze the user's social media accounts and identify related suspicious messages. The detection unit can also detect patterns from the user's social media activity that make them likely to be targeted by phishing scams. The detection unit can also analyze suspicious links and attachments shared on social media. For example, the detection unit can analyze the user's social media accounts and identify related suspicious messages. The detection unit can also detect patterns from the user's social media activity that make them likely to be targeted by phishing scams. The detection unit can also analyze suspicious links and attachments shared on social media. This allows the detection unit to identify related suspicious activities by analyzing the user's social media activity. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the detection unit can input the user's social media activity data into a generative AI, which can then identify related suspicious activities.

[0048] The warning unit can select the optimal warning method by referring to past warning history when displaying a warning. For example, the warning unit can refer to a warning method that was effective in the past and display the warning in a similar manner. The warning unit can also learn from past warning history and select the optimal warning method for the user. The warning unit can also select a warning method appropriate to a specific situation based on past warning history. For example, the warning unit can refer to a warning method that was effective in the past and display the warning in a similar manner. The warning unit can also learn from past warning history and select the optimal warning method for the user. The warning unit can also select a warning method appropriate to a specific situation based on past warning history. This allows the optimal warning method to be selected by referring to past warning history. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the warning unit can input past warning history into a generative AI, and the generative AI can refer to past warning history and select the optimal warning method.

[0049] The warning unit can generate customized warning messages based on the user's attribute information when displaying a warning. For example, the warning unit can generate an appropriate warning message based on the user's age and gender. The warning unit can also generate a highly relevant warning message based on the user's occupation and interests. The warning unit can also generate a customized warning message based on the user's past behavior history. For example, the warning unit can generate an appropriate warning message based on the user's age and gender. The warning unit can also generate a highly relevant warning message based on the user's occupation and interests. The warning unit can also generate a customized warning message based on the user's past behavior history. This makes it possible to generate more effective warnings by generating customized warning messages based on the user's attribute information. Some or all of the above processing in the warning unit may be performed using, for example, a generation AI, or without a generation AI. For example, the warning unit can input the user's attribute information into a generation AI, and the generation AI can generate a customized warning message.

[0050] The warning unit can select the optimal warning method when displaying a warning, taking into account the user's device information. For example, if the user is using a smartphone, the warning unit provides a warning method that matches the screen size. If the user is using a tablet, the warning unit can also provide a warning method optimized for a larger screen. If the user is using a smartwatch, the warning unit can also provide a concise and highly visible warning method. For example, if the user is using a smartphone, the warning unit provides a warning method that matches the screen size. If the user is using a tablet, the warning unit can also provide a warning method optimized for a larger screen. If the user is using a smartwatch, the warning unit can also provide a concise and highly visible warning method. This allows the optimal warning method to be selected by taking into account the user's device information. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the warning unit can input the user's device information into a generative AI, which can then select the optimal warning method.

[0051] The warning unit can analyze the user's social media activity and generate relevant warning messages when displaying a warning. For example, the warning unit can analyze the user's social media accounts and generate relevant warning messages. The warning unit can also detect patterns in the user's social media activity that make them susceptible to phishing scams and generate warning messages. The warning unit can also generate warning messages for suspicious links and attachments shared on social media. For example, the warning unit can analyze the user's social media accounts and generate relevant warning messages. The warning unit can also detect patterns in the user's social media activity that make them susceptible to phishing scams and generate warning messages. The warning unit can also generate warning messages for suspicious links and attachments shared on social media. This allows the warning unit to generate relevant warning messages by analyzing the user's social media activity. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the user's social media activity data into a generating AI, which can then generate relevant warning messages.

[0052] The blocking unit can select the optimal blocking method by referring to past blocking history when blocking. For example, the blocking unit can refer to a blocking method that was effective in the past and execute the block in a similar manner. The blocking unit can also learn from past blocking history and select the optimal blocking method for the user. The blocking unit can also select a blocking method appropriate to a specific situation based on past blocking history. For example, the blocking unit can refer to a blocking method that was effective in the past and execute the block in a similar manner. The blocking unit can also learn from past blocking history and select the optimal blocking method for the user. The blocking unit can also select a blocking method appropriate to a specific situation based on past blocking history. This allows the optimal blocking method to be selected by referring to past blocking history. Some or all of the above processing in the blocking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the blocking unit can input past blocking history into a generative AI, and the generative AI can refer to past blocking history and select the optimal blocking method.

[0053] The blocking unit can apply blocking methods specific to particular industries or fields when blocking. For example, the blocking unit can apply a phishing scam blocking method specific to the financial industry. The blocking unit can also implement a customized blocking method to block suspicious activity specific to the medical industry. The blocking unit can also apply a blocking method to block suspicious activity specific to the education sector. For example, the blocking unit can apply a phishing scam blocking method specific to the financial industry. The blocking unit can also implement a customized blocking method to block suspicious activity specific to the medical industry. The blocking unit can also apply a blocking method to block suspicious activity specific to the education sector. This makes it possible to block more effectively by applying blocking methods specific to particular industries or fields. Some or all of the above processing in the blocking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the blocking unit can input suspicious activity data specific to a particular industry or field into a generative AI, and the generative AI can apply a customized blocking method to block the suspicious activity.

[0054] The blocking unit can prioritize blocking region-specific suspicious activities based on the user's geographical location information. For example, if the user is in a specific region, the blocking unit will prioritize blocking phishing scam patterns that frequently occur in that region. The blocking unit can also apply algorithms that block region-specific suspicious activities based on the user's geographical location information. The blocking unit can also refer to crime data for each region and prioritize blocking region-specific suspicious activities. For example, if the user is in a specific region, the blocking unit will prioritize blocking phishing scam patterns that frequently occur in that region. The blocking unit can also apply algorithms that block region-specific suspicious activities based on the user's geographical location information. The blocking unit can also refer to crime data for each region and prioritize blocking region-specific suspicious activities. This enables region-specific blocking by prioritizing the blocking of region-specific suspicious activities based on the user's geographical location information. Some or all of the above processing in the blocking unit may be performed using, for example, generative AI, or without using generative AI. For example, the blocking unit inputs the user's geographical location information into a generating AI, which can then prioritize blocking suspicious activity specific to that region.

[0055] The blocking unit can analyze a user's social media activity and block any related suspicious activity when blocking. For example, the blocking unit can analyze a user's social media accounts and block any related suspicious messages. The blocking unit can also detect patterns in a user's social media activity that make them likely targets for phishing scams and block those. The blocking unit can also block suspicious links and attachments shared on social media. For example, the blocking unit can analyze a user's social media accounts and block any related suspicious messages. The blocking unit can also detect patterns in a user's social media activity that make them likely targets for phishing scams and block those. The blocking unit can also block suspicious links and attachments shared on social media. This allows the blocking unit to block related suspicious activity by analyzing a user's social media activity. Some or all of the above processing in the blocking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the blocking unit can input the user's social media activity data into a generative AI, which can then block any related suspicious activity.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The analysis unit can improve its analysis accuracy by referring to past analysis results. For example, it can refer to patterns of phishing scams detected in the past and analyze similar messages with high accuracy. It can also learn from past analysis results and optimize algorithms for detecting new suspicious activity. It can also improve the detection accuracy of specific keywords and phrases. In this way, analysis accuracy can be improved by referring to past analysis results.

[0058] The analysis unit can apply multilingual analysis algorithms to handle different languages ​​and dialects. For example, it can apply analysis algorithms for major languages ​​such as English, Spanish, and Chinese. It can also introduce algorithms for analyzing regional dialects and slang. Using multilingual natural language processing technology, it can analyze messages in different languages ​​with high accuracy. In this way, by applying multilingual analysis algorithms, analysis that supports different languages ​​and dialects becomes possible.

[0059] The analysis unit can prioritize the analysis of region-specific suspicious activities based on the user's geographical location information. For example, if a user is in a specific region, it can prioritize the analysis of phishing scam patterns that frequently occur in that region. It can also apply algorithms to detect region-specific suspicious activities. It can also refer to crime data for each region and prioritize the analysis of region-specific suspicious activities. As a result, by prioritizing the analysis of region-specific suspicious activities based on the user's geographical location information, region-specific analysis becomes possible.

[0060] The analysis unit can analyze a user's social media activity and identify related suspicious activity. For example, it can analyze a user's social media accounts and identify related suspicious messages. It can also detect patterns that make a user a target for phishing scams. It can also analyze suspicious links and attachments shared on social media. In this way, by analyzing a user's social media activity, it is possible to identify related suspicious activity.

[0061] The following briefly describes the processing flow for example form 1.

[0062] Step 1: The analysis unit analyzes the content of the SMS message, the linked text, and the attached files. The analysis unit can analyze the SMS message using natural language processing technology, and can also analyze the content of linked text and attached files. Step 2: The detection unit detects suspicious activity based on the analysis performed by the analysis unit. The detection unit identifies suspicious activity by detecting specific keywords or phrases. Step 3: The warning unit displays a warning to the user based on the suspicious activity detected by the detection unit. If suspicious content is detected, a warning is immediately displayed to the user. Step 4: The blocking section blocks the content that the warning section has displayed as needed. It can also provide guidelines to prevent users from clicking the link after receiving the warning.

[0063] (Example of form 2) The AI ​​agent according to an embodiment of the present invention is a system that automatically analyzes the content of SMS messages, linked text, and attached files, detects suspicious activity, warns the user, and blocks the content as necessary. This AI agent performs advanced text analysis utilizing natural language processing and machine learning to detect suspicious links and attached files in real time. The user is shown a warning through a user-friendly warning system, and the content is blocked as necessary. This effectively reduces the risk of phishing scams and personal information leaks. For example, the AI ​​agent analyzes the content of SMS messages, linked text, and attached files. Next, it detects suspicious activity based on the analysis results. For detected suspicious activity, it displays a warning to the user and blocks the content as necessary. This mechanism effectively reduces the risk of phishing scams and personal information leaks. Thus, the AI ​​agent can analyze the content of SMS messages, linked text, and attached files, detect suspicious activity, warn the user, and block the content as necessary.

[0064] The AI ​​agent according to this embodiment comprises an analysis unit, a detection unit, a warning unit, and a blocking unit. The analysis unit analyzes the body of an SMS, the text of a linked page, and the content of an attached file. The analysis unit analyzes the body of an SMS using, for example, natural language processing technology. The analysis unit can also analyze the text of a linked page. The analysis unit can also analyze the content of an attached file. For example, the analysis unit analyzes the body of an SMS using natural language processing technology. The analysis unit can also analyze the text of a linked page. The analysis unit can also analyze the content of an attached file. The detection unit detects suspicious activity based on the content analyzed by the analysis unit. The detection unit detects, for example, specific keywords or phrases. The detection unit can also detect specific keywords or phrases based on the analysis results. The detection unit can also detect specific keywords or phrases based on the analysis results. For example, the detection unit detects specific keywords or phrases. The detection unit can also detect specific keywords or phrases based on the analysis results. The detection unit can also detect specific keywords or phrases based on the analysis results. The warning unit displays a warning to the user based on suspicious activity detected by the detection unit. The warning unit can, for example, immediately display a warning to the user if suspicious content is detected. The warning unit can also immediately display a warning to the user if suspicious content is detected. The warning unit can also immediately display a warning to the user if suspicious content is detected. For example, the warning unit can immediately display a warning to the user if suspicious content is detected. The warning unit can also immediately display a warning to the user if suspicious content is detected. The warning unit can also immediately display a warning to the user if suspicious content is detected. The blocking unit blocks the content for which the warning unit has issued a warning, as needed. The blocking unit can, for example, provide guidelines to prevent the user from clicking on a link after receiving a warning. The blocking unit can also provide guidelines to prevent the user from clicking on a link after receiving a warning. The blocking unit can also provide guidelines to prevent the user from clicking on a link after receiving a warning.For example, the blocking section can provide guidelines to prevent users from clicking on links after receiving a warning. The blocking section can also provide guidelines to prevent users from clicking on links after receiving a warning. The blocking section can also provide guidelines to prevent users from clicking on links after receiving a warning. This allows the AI ​​agent according to the embodiment to analyze the content of the SMS message, the linked text, and the attachments, detect suspicious activity, warn the user, and block the content as necessary.

[0065] The analysis unit analyzes the content of SMS messages, linked text, and attached files. For example, the analysis unit uses natural language processing technology to analyze the content of SMS messages. Specifically, it uses natural language processing technology to understand the context and intent from the content of SMS messages and identify potential threats or suspicious content. For example, it uses a pre-trained model to detect wording that has the characteristics of spam messages or phishing scams. The analysis unit can also analyze linked text. In analyzing linked text, it follows links to obtain the content of web pages and analyzes that content. For example, it evaluates the metadata and content of a page to determine whether the linked page is a malicious site. The analysis unit can also analyze the content of attached files. In analyzing attached files, it uses analysis methods appropriate to the file type. For example, in the case of PDFs and Word documents, it uses text extraction technology to analyze the content and check for malicious scripts or suspicious links. In the case of image files, it uses image recognition technology to detect text and suspicious elements contained within the image. This allows the analysis department to comprehensively analyze the content of SMS messages, linked text, and attachments, enabling early detection of potential threats. Furthermore, the analysis department can share the analysis results with other systems and departments, and collaborate on countermeasures. For example, it can notify the security department of the analysis results and request further investigation and countermeasures. This allows the analysis department to analyze data efficiently and effectively, improving the overall security of the system.

[0066] The detection unit detects suspicious activity based on the analysis performed by the analysis unit. For example, the detection unit can detect specific keywords or phrases. Specifically, it identifies suspicious content by comparing the analysis results provided by the analysis unit with pre-configured keyword and phrase lists. For example, it can detect keywords related to phishing scams or phrases common in spam messages. The detection unit can also detect specific keywords or phrases based on the analysis results. Furthermore, the detection unit can use machine learning algorithms to detect suspicious activity based on patterns learned from past data. For example, it can use anomaly detection algorithms to detect patterns or abnormal behavior that differ from normal messages. This allows the detection unit to handle not only known threats but also new threats and unknown attack methods. The detection unit can also detect specific keywords or phrases based on the analysis results. For example, the detection unit can detect specific keywords or phrases. This allows the detection unit to quickly and accurately detect suspicious activity based on the information provided by the analysis unit, thereby strengthening the overall system security. Furthermore, the detection unit can share detection results with other systems and departments to implement coordinated countermeasures. For example, the detection results can be notified to the security department, and further investigation and countermeasures can be requested. This allows the detection unit to efficiently and effectively detect suspicious activity and improve the overall security of the system.

[0067] The warning unit displays warnings to the user based on suspicious activity detected by the detection unit. For example, if suspicious content is detected, the warning unit will immediately display a warning to the user. Specifically, it will display a pop-up notification or alert message on the user's device, warning them about the detected suspicious content. For example, if a link suspected of being a phishing scam is included, it will warn the user not to click on that link. Also, if a spam message is detected, it will warn the user not to open that message. The warning unit can also display warnings to the user immediately if suspicious content is detected. Furthermore, the warning unit can include detailed information in the warning message. For example, it can provide a specific explanation of the detected suspicious content and guidelines on what the user should do. This allows the user to understand the detected threat and take appropriate action. The warning unit can also display warnings to the user immediately if suspicious content is detected. For example, if suspicious content is detected, the warning unit will immediately display a warning to the user. The warning unit can also display warnings to the user immediately if suspicious content is detected. This allows the warning unit to provide users with prompt and appropriate warnings, preventing damage before it occurs. Furthermore, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of its warnings. For example, it can review the content and display method of warning messages based on feedback from users who have received warnings. In addition, the warning unit can reliably transmit information using multiple communication methods. For example, it can reliably deliver important information by using not only pop-up notifications but also voice calls, SMS, and email. This allows the warning unit to provide users with prompt and reliable warnings, preventing damage before it occurs.

[0068] The blocking unit blocks the content for which the warning unit has displayed a warning, as needed. The blocking unit can, for example, provide guidelines to prevent the user from clicking on a link after receiving a warning. Specifically, it can also provide guidelines to prevent the user from clicking on a link after receiving a warning. For example, the blocking unit can provide guidelines to prevent the user from clicking on a link after receiving a warning. Furthermore, the blocking unit can also provide guidelines to prevent the user from clicking on a link after receiving a warning. For example, the blocking unit can provide guidelines to prevent the user from clicking on a link after receiving a warning. The blocking unit can also provide guidelines to prevent the user from clicking on a link after receiving a warning. As a result, the AI ​​agent according to the embodiment can analyze the content of the SMS message, the linked text, and the attachments, detect suspicious activity, warn the user, and block the content as needed.

[0069] The analysis unit can analyze the body of an SMS using natural language processing. The analysis unit can analyze the body of an SMS using, for example, morphological analysis. The analysis unit can also analyze the body of an SMS using grammatical analysis. The analysis unit can also analyze the body of an SMS using semantic analysis. For example, the analysis unit can analyze the body of an SMS using morphological analysis. The analysis unit can also analyze the body of an SMS using grammatical analysis. The analysis unit can also analyze the body of an SMS using semantic analysis. This allows for high-precision analysis of the body of an SMS by using natural language processing. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input the body of an SMS into a generative AI, which can perform morphological analysis, grammatical analysis, and semantic analysis.

[0070] The analysis unit can analyze the text of a linked page. For example, the analysis unit can analyze the text of a linked webpage. The analysis unit can also analyze a linked PDF document. The analysis unit can also analyze a linked document file. For example, the analysis unit can analyze the text of a linked webpage. The analysis unit can also analyze a linked PDF document. The analysis unit can also analyze a linked document file. This allows suspicious links to be detected by analyzing the text of the linked page. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without a generating AI. For example, the analysis unit can input the text of the linked page into a generating AI, and the generating AI can analyze the text of the linked page.

[0071] The analysis unit can analyze the contents of attached files. For example, the analysis unit can analyze image files of attached files. The analysis unit can also analyze document files of attached files. The analysis unit can also analyze executable files of attached files. For example, the analysis unit can analyze image files of attached files. The analysis unit can also analyze document files of attached files. The analysis unit can also analyze executable files of attached files. By analyzing the contents of attached files, suspicious attached files can be detected. Some or all of the above processing in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the analysis unit can input the contents of an attached file into a generating AI, and the generating AI can analyze the contents of the attached file.

[0072] The detection unit can detect specific keywords or phrases based on the analysis results. For example, the detection unit can detect specific keywords or phrases based on a blacklist. The detection unit can also detect specific keywords or phrases using the extraction of frequently occurring words. The detection unit can also detect specific keywords or phrases using natural language processing techniques. For example, the detection unit can detect specific keywords or phrases based on a blacklist. The detection unit can also detect specific keywords or phrases using the extraction of frequently occurring words. The detection unit can also detect specific keywords or phrases using natural language processing techniques. This allows for the early detection of suspicious activity by detecting specific keywords or phrases. Some or all of the above-described processing in the detection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the detection unit can input the analysis results into a generative AI, which can then detect specific keywords or phrases.

[0073] The warning unit can immediately display a warning to the user if suspicious content is detected. The warning unit can display a warning to the user within a few seconds, for example. The warning unit can also display a warning to the user in real time. The warning unit can also display a warning to the user using a pop-up message. For example, the warning unit can display a warning to the user within a few seconds. The warning unit can also display a warning to the user in real time. The warning unit can also display a warning to the user using a pop-up message. This allows the user to be quickly alerted by displaying a warning immediately when suspicious content is detected. Some or all of the above processing in the warning unit may be performed using, for example, a generation AI, or without a generation AI. For example, if suspicious content is detected, the warning unit can have a generation AI generate a warning message and display the warning message generated by the generation AI to the user.

[0074] The blocking section can provide guidelines to prevent users from clicking links after receiving a warning. The blocking section can provide guidelines, for example, using pop-up messages. The blocking section can also provide guidelines using help documentation. The blocking section can also provide guidelines using the notification bar. For example, the blocking section can provide guidelines using pop-up messages. The blocking section can also provide guidelines using help documentation. The blocking section can also provide guidelines using the notification bar. This reduces the risk of phishing scams and personal information leaks by providing guidelines to prevent users from clicking links after receiving a warning. Some or all of the above processing in the blocking section may be performed, for example, using a generative AI, or not using a generative AI. For example, after a user receives a warning, the blocking section can have a generative AI generate guidelines and provide the user with the guidelines generated by the generative AI.

[0075] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will prioritize analyzing urgent messages. If the user is relaxed, the analysis unit can also perform analysis with normal priorities. If the user is in a hurry, the analysis unit can also adjust the priority to complete the analysis quickly. For example, if the user is feeling anxious, the analysis unit will prioritize analyzing urgent messages. If the user is relaxed, the analysis unit can also perform analysis with normal priorities. If the user is in a hurry, the analysis unit can also adjust the priority to complete the analysis quickly. This allows for more appropriate analysis by adjusting the analysis priority based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the analysis priority.

[0076] The analysis unit can improve its analysis accuracy by referring to past analysis results when analyzing the body of an SMS message, the text of a link, and the content of an attachment. For example, the analysis unit can refer to patterns of phishing scams detected in the past and analyze similar messages with high accuracy. The analysis unit can also learn from past analysis results and optimize algorithms for detecting new suspicious activity. The analysis unit can also improve the detection accuracy of specific keywords or phrases based on past analysis results. For example, the analysis unit can refer to patterns of phishing scams detected in the past and analyze similar messages with high accuracy. The analysis unit can also learn from past analysis results and optimize algorithms for detecting new suspicious activity. The analysis unit can also improve the detection accuracy of specific keywords or phrases based on past analysis results. This allows for improved analysis accuracy by referring to past analysis results. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input past analysis results into a generative AI, which can then refer to the past analysis results to improve analysis accuracy.

[0077] The analysis unit can apply multilingual analysis algorithms to handle different languages ​​and dialects during analysis. For example, the analysis unit can apply analysis algorithms for major languages ​​such as English, Spanish, and Chinese. The analysis unit can also introduce algorithms for analyzing regional dialects and slang. The analysis unit can also analyze messages in different languages ​​with high accuracy using multilingual natural language processing technology. For example, the analysis unit can apply analysis algorithms for major languages ​​such as English, Spanish, and Chinese. The analysis unit can also introduce algorithms for analyzing regional dialects and slang. The analysis unit can also analyze messages in different languages ​​with high accuracy using multilingual natural language processing technology. This makes it possible to analyze messages in different languages ​​and dialects by applying multilingual analysis algorithms. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input messages in different languages ​​or dialects into a generative AI, which can then apply a multilingual analysis algorithm for analysis.

[0078] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit can provide a concise and reassuring display method. If the user is relaxed, the analysis unit can also display detailed analysis results. If the user is in a hurry, the analysis unit can also provide a concise and to-the-point display method. For example, if the user is feeling anxious, the analysis unit can provide a concise and reassuring display method. If the user is relaxed, the analysis unit can also display detailed analysis results. If the user is in a hurry, the analysis unit can also provide a concise and to-the-point display method. By adjusting the display method of the analysis results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI. For example, the analysis unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust how the analysis results are displayed.

[0079] The analysis unit can prioritize analyzing region-specific suspicious activities based on the user's geographical location information during analysis. For example, if the user is in a specific region, the analysis unit will prioritize analyzing patterns of phishing scams that frequently occur in that region. The analysis unit can also apply algorithms to detect region-specific suspicious activities based on the user's geographical location information. The analysis unit can also refer to crime data for each region and prioritize analyzing region-specific suspicious activities. For example, if the user is in a specific region, the analysis unit will prioritize analyzing patterns of phishing scams that frequently occur in that region. The analysis unit can also apply algorithms to detect region-specific suspicious activities based on the user's geographical location information. The analysis unit can also refer to crime data for each region and prioritize analyzing region-specific suspicious activities. This enables region-specific analysis by prioritizing the analysis of region-specific suspicious activities based on the user's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI. For example, the analysis unit can input the user's geographical location information into the generating AI, which can then prioritize the analysis of suspicious activities specific to that region.

[0080] The analysis unit can analyze a user's social media activity during analysis and identify related suspicious activities. For example, the analysis unit can analyze a user's social media accounts and identify related suspicious messages. The analysis unit can also detect patterns in a user's social media activity that make them likely to be targeted by phishing scams. The analysis unit can also analyze suspicious links and attachments shared on social media. For example, the analysis unit can analyze a user's social media accounts and identify related suspicious messages. The analysis unit can also detect patterns in a user's social media activity that make them likely to be targeted by phishing scams. The analysis unit can also analyze suspicious links and attachments shared on social media. This allows the analysis unit to identify related suspicious activities by analyzing a user's social media activity. Some or all of the above processing in the analysis unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the analysis unit can input the user's social media activity data into a generative AI, which can then identify related suspicious activities.

[0081] The detection unit can estimate the user's emotions and adjust the detection criteria for suspicious activity based on the estimated emotions. For example, if the user is feeling anxious, the detection unit can tighten the detection criteria to detect suspicious activity earlier. If the user is relaxed, the detection unit can also detect suspicious activity using normal detection criteria. If the user is in a hurry, the detection unit can also adjust the detection criteria to enable rapid detection. For example, if the user is feeling anxious, the detection unit can tighten the detection criteria to detect suspicious activity earlier. If the user is relaxed, the detection unit can also detect suspicious activity using normal detection criteria. If the user is in a hurry, the detection unit can also adjust the detection criteria to enable rapid detection. This allows for more appropriate detection by adjusting the detection criteria for suspicious activity based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the detection unit may be performed using, for example, a generating AI, or without using a generating AI. For example, the detection unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the detection criteria.

[0082] The detection unit can optimize its detection algorithm by referring to past suspicious activity data during detection. For example, the detection unit can refer to patterns of phishing scams detected in the past to detect similar activities with high accuracy. The detection unit can also learn from past suspicious activity data to optimize its algorithm for detecting new suspicious activities. The detection unit can also improve the detection accuracy of specific keywords or phrases based on past suspicious activity data. For example, the detection unit can refer to patterns of phishing scams detected in the past to detect similar activities with high accuracy. The detection unit can also learn from past suspicious activity data to optimize its algorithm for detecting new suspicious activities. The detection unit can also improve the detection accuracy of specific keywords or phrases based on past suspicious activity data. In this way, by referring to past suspicious activity data, the detection algorithm can be optimized and detection accuracy can be improved. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without using generative AI. For example, the detection unit can input past suspicious activity data into the generating AI, which can then optimize the detection algorithm by referring to the past suspicious activity data.

[0083] The detection unit can apply a customized algorithm to detect suspicious activities specific to a particular industry or field during detection. For example, the detection unit can apply an algorithm to detect phishing scam patterns specific to the financial industry. The detection unit can also implement a customized algorithm to detect suspicious activities specific to the medical industry. The detection unit can also apply an algorithm to detect suspicious activities specific to the education sector. For example, the detection unit can apply an algorithm to detect phishing scam patterns specific to the financial industry. The detection unit can also implement a customized algorithm to detect suspicious activities specific to the medical industry. The detection unit can also apply an algorithm to detect suspicious activities specific to the education sector. This makes it possible to detect suspicious activities with higher accuracy by applying a customized algorithm specific to a particular industry or field. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the detection unit can input suspicious activity data specific to a particular industry or field into a generative AI, and the generative AI can apply a customized algorithm to detect suspicious activities.

[0084] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated user emotions. For example, if the user is feeling anxious, the detection unit provides a concise and reassuring display method. If the user is relaxed, the detection unit can also display detailed detection results. If the user is in a hurry, the detection unit can also provide a concise and to-the-point display method. For example, if the user is feeling anxious, the detection unit provides a concise and reassuring display method. If the user is relaxed, the detection unit can also display detailed detection results. If the user is in a hurry, the detection unit can also provide a concise and to-the-point display method. By adjusting the display method of the detection results based on the user's emotions, it becomes possible to provide a display that is easy for the user to understand. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the detection unit may be performed using a generative AI, for example, or without a generative AI. For example, the detection unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust how the detection results are displayed.

[0085] The detection unit can prioritize the detection of region-specific suspicious activities based on the user's geographical location information during detection. For example, if the user is in a specific region, the detection unit will prioritize the detection of phishing scam patterns that frequently occur in that region. The detection unit can also apply algorithms to detect region-specific suspicious activities based on the user's geographical location information. The detection unit can also refer to crime data for each region and prioritize the detection of region-specific suspicious activities. For example, if the user is in a specific region, the detection unit will prioritize the detection of phishing scam patterns that frequently occur in that region. The detection unit can also apply algorithms to detect region-specific suspicious activities based on the user's geographical location information. The detection unit can also refer to crime data for each region and prioritize the detection of region-specific suspicious activities. This enables region-specific detection by prioritizing the detection of region-specific suspicious activities based on the user's geographical location information. Some or all of the above processing in the detection unit may be performed using, for example, generative AI, or without using generative AI. For example, the detection unit inputs the user's geographical location information into the generating AI, which can then prioritize the detection of suspicious activity specific to that region.

[0086] The detection unit can analyze the user's social media activity and identify related suspicious activities at the time of detection. For example, the detection unit can analyze the user's social media accounts and identify related suspicious messages. The detection unit can also detect patterns from the user's social media activity that make them likely to be targeted by phishing scams. The detection unit can also analyze suspicious links and attachments shared on social media. For example, the detection unit can analyze the user's social media accounts and identify related suspicious messages. The detection unit can also detect patterns from the user's social media activity that make them likely to be targeted by phishing scams. The detection unit can also analyze suspicious links and attachments shared on social media. This allows the detection unit to identify related suspicious activities by analyzing the user's social media activity. Some or all of the above processing in the detection unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the detection unit can input the user's social media activity data into a generative AI, which can then identify related suspicious activities.

[0087] The warning unit can estimate the user's emotions and adjust the way the warning is expressed based on the estimated emotions. For example, if the user is feeling anxious, the warning unit will display a warning in a reassuring way. If the user is relaxed, the warning unit can also display a detailed warning message. If the user is in a hurry, the warning unit can also display a concise and quick warning message. For example, if the user is feeling anxious, the warning unit will display a warning in a reassuring way. If the user is relaxed, the warning unit can also display a detailed warning message. If the user is in a hurry, the warning unit can also display a concise and quick warning message. This makes it possible to provide warnings that are easy for the user to understand by adjusting the way the warning is expressed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the warning unit may be performed using, for example, generative AI, or not using generative AI. For example, the warning unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the way the warning is expressed.

[0088] The warning unit can select the optimal warning method by referring to past warning history when displaying a warning. For example, the warning unit can refer to a warning method that was effective in the past and display the warning in a similar manner. The warning unit can also learn from past warning history and select the optimal warning method for the user. The warning unit can also select a warning method appropriate to a specific situation based on past warning history. For example, the warning unit can refer to a warning method that was effective in the past and display the warning in a similar manner. The warning unit can also learn from past warning history and select the optimal warning method for the user. The warning unit can also select a warning method appropriate to a specific situation based on past warning history. This allows the optimal warning method to be selected by referring to past warning history. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without using a generative AI. For example, the warning unit can input past warning history into a generative AI, and the generative AI can refer to past warning history and select the optimal warning method.

[0089] The warning unit can generate customized warning messages based on the user's attribute information when displaying a warning. For example, the warning unit can generate an appropriate warning message based on the user's age and gender. The warning unit can also generate a highly relevant warning message based on the user's occupation and interests. The warning unit can also generate a customized warning message based on the user's past behavior history. For example, the warning unit can generate an appropriate warning message based on the user's age and gender. The warning unit can also generate a highly relevant warning message based on the user's occupation and interests. The warning unit can also generate a customized warning message based on the user's past behavior history. This makes it possible to generate more effective warnings by generating customized warning messages based on the user's attribute information. Some or all of the above processing in the warning unit may be performed using, for example, a generation AI, or without a generation AI. For example, the warning unit can input the user's attribute information into a generation AI, and the generation AI can generate a customized warning message.

[0090] The alert unit can estimate the user's emotions and determine the priority of alerts based on the estimated emotions. For example, if the user is feeling anxious, the alert unit will prioritize displaying urgent alerts. If the user is relaxed, the alert unit can also display alerts with normal priority. If the user is in a hurry, the alert unit can also prioritize displaying alerts that require immediate attention. For example, if the user is feeling anxious, the alert unit will prioritize displaying urgent alerts. If the user is relaxed, the alert unit can also display alerts with normal priority. If the user is in a hurry, the alert unit can also prioritize displaying alerts that require immediate attention. This allows for more appropriate alerts by prioritizing alerts based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the alert unit may be performed using, for example, generative AI, or not using generative AI. For example, the warning unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of warnings.

[0091] The warning unit can select the optimal warning method when displaying a warning, taking into account the user's device information. For example, if the user is using a smartphone, the warning unit provides a warning method that matches the screen size. If the user is using a tablet, the warning unit can also provide a warning method optimized for a larger screen. If the user is using a smartwatch, the warning unit can also provide a concise and highly visible warning method. For example, if the user is using a smartphone, the warning unit provides a warning method that matches the screen size. If the user is using a tablet, the warning unit can also provide a warning method optimized for a larger screen. If the user is using a smartwatch, the warning unit can also provide a concise and highly visible warning method. This allows the optimal warning method to be selected by taking into account the user's device information. Some or all of the above processing in the warning unit may be performed using, for example, a generative AI, or without a generative AI. For example, the warning unit can input the user's device information into a generative AI, which can then select the optimal warning method.

[0092] The warning unit can analyze the user's social media activity and generate relevant warning messages when displaying a warning. For example, the warning unit can analyze the user's social media accounts and generate relevant warning messages. The warning unit can also detect patterns in the user's social media activity that make them susceptible to phishing scams and generate warning messages. The warning unit can also generate warning messages for suspicious links and attachments shared on social media. For example, the warning unit can analyze the user's social media accounts and generate relevant warning messages. The warning unit can also detect patterns in the user's social media activity that make them susceptible to phishing scams and generate warning messages. The warning unit can also generate warning messages for suspicious links and attachments shared on social media. This allows the warning unit to generate relevant warning messages by analyzing the user's social media activity. Some or all of the above processing in the warning unit may be performed using, for example, a generating AI, or without a generating AI. For example, the warning unit can input the user's social media activity data into a generating AI, which can then generate relevant warning messages.

[0093] The blocking unit can estimate the user's emotions and adjust the blocking method based on the estimated emotions. For example, if the user is feeling anxious, the blocking unit can immediately block to provide reassurance. If the user is relaxed, the blocking unit can also apply the normal blocking method. If the user is in a hurry, the blocking unit can quickly block to minimize user interaction. For example, if the user is feeling anxious, the blocking unit can immediately block to provide reassurance. If the user is relaxed, the blocking unit can also apply the normal blocking method. If the user is in a hurry, the blocking unit can also quickly block to minimize user interaction. This allows for more appropriate blocking by adjusting the blocking method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the blocking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the blocking unit can input user emotion data into a generating AI, which can then estimate the emotion and adjust the blocking method.

[0094] The blocking unit can select the optimal blocking method by referring to past blocking history when blocking. For example, the blocking unit can refer to a blocking method that was effective in the past and execute the block in a similar manner. The blocking unit can also learn from past blocking history and select the optimal blocking method for the user. The blocking unit can also select a blocking method appropriate to a specific situation based on past blocking history. For example, the blocking unit can refer to a blocking method that was effective in the past and execute the block in a similar manner. The blocking unit can also learn from past blocking history and select the optimal blocking method for the user. The blocking unit can also select a blocking method appropriate to a specific situation based on past blocking history. This allows the optimal blocking method to be selected by referring to past blocking history. Some or all of the above processing in the blocking unit may be performed using, for example, a generative AI, or without a generative AI. For example, the blocking unit can input past blocking history into a generative AI, and the generative AI can refer to past blocking history and select the optimal blocking method.

[0095] The blocking unit can apply blocking methods specific to particular industries or fields when blocking. For example, the blocking unit can apply a phishing scam blocking method specific to the financial industry. The blocking unit can also implement a customized blocking method to block suspicious activity specific to the medical industry. The blocking unit can also apply a blocking method to block suspicious activity specific to the education sector. For example, the blocking unit can apply a phishing scam blocking method specific to the financial industry. The blocking unit can also implement a customized blocking method to block suspicious activity specific to the medical industry. The blocking unit can also apply a blocking method to block suspicious activity specific to the education sector. This makes it possible to block more effectively by applying blocking methods specific to particular industries or fields. Some or all of the above processing in the blocking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the blocking unit can input suspicious activity data specific to a particular industry or field into a generative AI, and the generative AI can apply a customized blocking method to block the suspicious activity.

[0096] The blocking unit can estimate the user's emotions and determine the priority of blocks based on the estimated emotions. For example, if the user is feeling anxious, the blocking unit will prioritize executing blocks with high urgency. If the user is relaxed, the blocking unit can also execute blocks with normal priority. If the user is in a hurry, the blocking unit can also prioritize executing blocks that require a quick response. For example, if the user is feeling anxious, the blocking unit will prioritize executing blocks with high urgency. If the user is relaxed, the blocking unit can also execute blocks with normal priority. If the user is in a hurry, the blocking unit can also prioritize executing blocks that require a quick response. This allows for more appropriate blocks by determining the priority of blocks based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the blocking unit may be performed using, for example, generative AI, or not using generative AI. For example, the block unit can input user emotion data into a generating AI, which can then estimate the emotion and determine the priority of the blocks.

[0097] The blocking unit can prioritize blocking region-specific suspicious activities based on the user's geographical location information. For example, if the user is in a specific region, the blocking unit will prioritize blocking phishing scam patterns that frequently occur in that region. The blocking unit can also apply algorithms that block region-specific suspicious activities based on the user's geographical location information. The blocking unit can also refer to crime data for each region and prioritize blocking region-specific suspicious activities. For example, if the user is in a specific region, the blocking unit will prioritize blocking phishing scam patterns that frequently occur in that region. The blocking unit can also apply algorithms that block region-specific suspicious activities based on the user's geographical location information. The blocking unit can also refer to crime data for each region and prioritize blocking region-specific suspicious activities. This enables region-specific blocking by prioritizing the blocking of region-specific suspicious activities based on the user's geographical location information. Some or all of the above processing in the blocking unit may be performed using, for example, generative AI, or without using generative AI. For example, the blocking unit inputs the user's geographical location information into a generating AI, which can then prioritize blocking suspicious activity specific to that region.

[0098] The blocking unit can analyze a user's social media activity and block any related suspicious activity when blocking. For example, the blocking unit can analyze a user's social media accounts and block any related suspicious messages. The blocking unit can also detect patterns in a user's social media activity that make them likely targets for phishing scams and block those. The blocking unit can also block suspicious links and attachments shared on social media. For example, the blocking unit can analyze a user's social media accounts and block any related suspicious messages. The blocking unit can also detect patterns in a user's social media activity that make them likely targets for phishing scams and block those. The blocking unit can also block suspicious links and attachments shared on social media. This allows the blocking unit to block related suspicious activity by analyzing a user's social media activity. Some or all of the above processing in the blocking unit may be performed using, for example, a generative AI, or not using a generative AI. For example, the blocking unit can input the user's social media activity data into a generative AI, which can then block any related suspicious activity.

[0099] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0100] The analysis unit can estimate the user's emotions and adjust the analysis priority based on those emotions. For example, if the user is feeling anxious, it can prioritize analyzing urgent messages. If the user is relaxed, it can perform analysis with the normal priority. If the user is in a hurry, it can adjust the priority to complete the analysis quickly. By adjusting the analysis priority based on the user's emotions, more appropriate analysis becomes possible.

[0101] The analysis unit can improve its analysis accuracy by referring to past analysis results. For example, it can refer to patterns of phishing scams detected in the past and analyze similar messages with high accuracy. It can also learn from past analysis results and optimize algorithms for detecting new suspicious activity. It can also improve the detection accuracy of specific keywords and phrases. In this way, analysis accuracy can be improved by referring to past analysis results.

[0102] The analysis unit can apply multilingual analysis algorithms to handle different languages ​​and dialects. For example, it can apply analysis algorithms for major languages ​​such as English, Spanish, and Chinese. It can also introduce algorithms for analyzing regional dialects and slang. Using multilingual natural language processing technology, it can analyze messages in different languages ​​with high accuracy. In this way, by applying multilingual analysis algorithms, analysis that supports different languages ​​and dialects becomes possible.

[0103] The analysis unit can prioritize the analysis of region-specific suspicious activities based on the user's geographical location information. For example, if a user is in a specific region, it can prioritize the analysis of phishing scam patterns that frequently occur in that region. It can also apply algorithms to detect region-specific suspicious activities. It can also refer to crime data for each region and prioritize the analysis of region-specific suspicious activities. As a result, by prioritizing the analysis of region-specific suspicious activities based on the user's geographical location information, region-specific analysis becomes possible.

[0104] The analysis unit can analyze a user's social media activity and identify related suspicious activity. For example, it can analyze a user's social media accounts and identify related suspicious messages. It can also detect patterns that make a user a target for phishing scams. It can also analyze suspicious links and attachments shared on social media. In this way, by analyzing a user's social media activity, it is possible to identify related suspicious activity.

[0105] The detection unit can estimate the user's emotions and adjust the detection criteria for suspicious activity based on those emotions. For example, if the user is feeling anxious, the detection criteria can be tightened to detect suspicious activity earlier. If the user is relaxed, the normal detection criteria can still be used to detect suspicious activity. If the user is in a hurry, the detection criteria can be adjusted to allow for quicker detection. By adjusting the detection criteria for suspicious activity based on the user's emotions, more appropriate detection becomes possible.

[0106] The warning unit can estimate the user's emotions and adjust the way the warning is presented based on those emotions. For example, if the user is feeling anxious, the warning can be displayed in a reassuring manner. If the user is relaxed, a detailed warning message can be displayed. If the user is in a hurry, a concise and quick warning message can be displayed. By adjusting the way the warning is presented based on the user's emotions, it becomes possible to provide warnings that are easy for the user to understand.

[0107] The warning system can estimate the user's emotions and prioritize warnings based on those emotions. For example, if the user is feeling anxious, urgent warnings can be displayed first. If the user is relaxed, warnings can be displayed with normal priority. If the user is in a hurry, warnings requiring immediate attention can be displayed first. This allows for more appropriate warnings by prioritizing warnings based on the user's emotions.

[0108] The blocking function can estimate the user's emotions and adjust the blocking method based on those estimates. For example, if the user is feeling anxious, it can immediately block to provide reassurance. If the user is relaxed, the normal blocking method can be applied. If the user is in a hurry, it can quickly block to minimize user interaction. This allows for more appropriate blocking by adjusting the blocking method based on the user's emotions.

[0109] The blocking function can estimate the user's emotions and determine the priority of blocks based on those emotions. For example, if the user is feeling anxious, it can prioritize urgent blocks. If the user is relaxed, blocks can be executed with normal priority. If the user is in a hurry, blocks requiring a quick response can be prioritized. This allows for more appropriate blocking by prioritizing blocks based on the user's emotions.

[0110] The following briefly describes the processing flow for example form 2.

[0111] Step 1: The analysis unit analyzes the content of the SMS message, the linked text, and the attached files. The analysis unit can analyze the SMS message using natural language processing technology, and can also analyze the content of linked text and attached files. Step 2: The detection unit detects suspicious activity based on the analysis performed by the analysis unit. The detection unit identifies suspicious activity by detecting specific keywords or phrases. Step 3: The warning unit displays a warning to the user based on the suspicious activity detected by the detection unit. If suspicious content is detected, a warning is immediately displayed to the user. Step 4: The blocking section blocks the content that the warning section has displayed as needed. It can also provide guidelines to prevent users from clicking the link after receiving the warning.

[0112] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0113] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0114] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0115] Each of the multiple elements described above, including the analysis unit, detection unit, warning unit, and blocking unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart device 14 and analyzes the content of the SMS message, the linked text, and the attachments. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects suspicious activity based on the analysis results. The warning unit is implemented by the control unit 46A of the smart device 14 and displays a warning to the user when suspicious content is detected. The blocking unit is implemented by the identification processing unit 290 of the data processing unit 12 and blocks the content as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0116] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0117] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0118] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0119] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0120] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0122] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0123] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0124] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0125] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0126] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0127] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0128] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0129] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0130] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0131] Each of the multiple elements described above, including the analysis unit, detection unit, warning unit, and blocking unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 and analyzes the content of the SMS message, the linked text, and the attachments. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects suspicious activity based on the analysis results. The warning unit is implemented by the control unit 46A of the smart glasses 214 and displays a warning to the user when suspicious content is detected. The blocking unit is implemented by the identification processing unit 290 of the data processing unit 12 and blocks the content as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0132] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0133] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0134] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0135] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0136] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0138] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0139] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0140] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0141] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0142] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0143] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0144] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0145] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0146] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0147] Each of the multiple elements described above, including the analysis unit, detection unit, warning unit, and blocking unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 and analyzes the content of the SMS message, the linked text, and the attached files. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects suspicious activity based on the analysis results. The warning unit is implemented by the control unit 46A of the headset terminal 314 and displays a warning to the user when suspicious content is detected. The blocking unit is implemented by the identification processing unit 290 of the data processing unit 12 and blocks the content as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0148] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0149] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0150] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0151] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0152] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0153] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0154] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0155] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0156] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0157] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0158] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0159] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0160] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0161] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0162] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0163] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0164] Each of the multiple elements described above, including the analysis unit, detection unit, warning unit, and blocking unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the analysis unit is implemented by the processor 46 of the robot 414 and analyzes the content of the SMS message, the linked text, and the attachments. The detection unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects suspicious activity based on the analysis results. The warning unit is implemented by the control unit 46A of the robot 414 and displays a warning to the user when suspicious content is detected. The blocking unit is implemented by the identification processing unit 290 of the data processing unit 12 and blocks the content as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0165] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0166] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0167] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0168] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0169] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0170] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0171] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0172] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0173] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0174] 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.

[0175] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0176] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0177] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0178] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0179] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0180] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0181] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0182] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0183] (Note 1) The analysis unit analyzes the content of the SMS message, the text of the linked page, and the content of the attached file. A detection unit that detects suspicious activity based on the content analyzed by the aforementioned analysis unit, A warning unit that displays a warning to the user based on the suspicious activity detected by the detection unit, The system includes a blocking unit that blocks the content of the warning displayed by the warning unit as needed. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Analyze the text of an SMS message using natural language processing. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Analyze the text at the link. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit, Analyze the contents of the attached file. The system described in Appendix 1, characterized by the features described herein. (Note 5) The detection unit is Detect specific keywords and phrases based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned warning unit is If suspicious content is detected, a warning will be displayed to the user immediately. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned block section is Provide guidelines to prevent users from clicking links after receiving a warning. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, When analyzing the content of SMS messages, linked text, and attached files, we improve analysis accuracy by referring to past analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, During analysis, a multilingual analysis algorithm is applied to handle different languages ​​and dialects. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During analysis, the system prioritizes analyzing suspicious activity specific to a particular region, based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During the analysis, the user's social media activity is analyzed to identify any related suspicious activity. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit is It estimates the user's emotions and adjusts the detection criteria for suspicious activity based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit is When detection occurs, the detection algorithm is optimized by referring to past suspicious activity data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit is During detection, a customized algorithm is applied to detect suspicious activity specific to a particular industry or field. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit is It estimates the user's emotions and adjusts how the detection results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The detection unit is During detection, the system prioritizes detecting suspicious activity specific to a given region based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit is Upon detection, the system analyzes the user's social media activity and identifies related suspicious activity. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warnings are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is When a warning is displayed, the system will refer to past warning history to select the most appropriate warning method. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is When a warning is displayed, a customized warning message is generated based on the user's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is The system estimates the user's emotions and prioritizes warnings based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned warning unit is When displaying a warning, the system selects the most appropriate warning method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned warning unit is When a warning is displayed, the system analyzes the user's social media activity and generates relevant warning messages. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned block section is It estimates the user's emotions and adjusts the blocking method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned block section is When blocking, the system refers to past blocking history to select the most appropriate blocking method. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned block section is When blocking, apply a blocking method specific to a particular industry or field. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned block section is It estimates the user's emotions and determines the priority of blocking based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned block section is When blocking, the system prioritizes blocking suspicious activity specific to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned block section is When blocking, the system analyzes the user's social media activity and blocks any related suspicious activity. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0184] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis unit analyzes the content of the SMS message, the text of the linked page, and the content of the attached file. A detection unit that detects suspicious activity based on the content analyzed by the aforementioned analysis unit, A warning unit that displays a warning to the user based on the suspicious activity detected by the detection unit, The system includes a blocking unit that blocks the content of the warning displayed by the warning unit as needed. A system characterized by the following features.

2. The aforementioned analysis unit, Analyze the text of an SMS message using natural language processing. The system according to feature 1.

3. The aforementioned analysis unit, Analyze the text at the link. The system according to feature 1.

4. The aforementioned analysis unit, Analyze the contents of the attached file. The system according to feature 1.

5. The detection unit is Detect specific keywords and phrases based on the analysis results. The system according to feature 1.

6. The aforementioned warning unit is If suspicious content is detected, a warning will be displayed to the user immediately. The system according to feature 1.

7. The aforementioned block section is Provide guidelines to prevent users from clicking links after receiving a warning. The system according to feature 1.

8. The aforementioned analysis unit, It estimates the user's emotions and adjusts the analysis priority based on the estimated user emotions. The system according to feature 1.

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