system

Abstract

The system according to this embodiment aims to detect the risks of excessive sharing of personal information and phishing scams on social networking services and to provide users with appropriate warnings and advice. [Solution] The system according to the embodiment comprises an analysis unit, a warning unit, a detection unit, and an advice unit. The analysis unit analyzes the content of SNS posts and detects excessive sharing of personal information and risks. The warning unit provides warnings based on the risks detected by the analysis unit. The detection unit analyzes SNS messages and links and detects phishing scams. The warning unit provides warnings based on the phishing scams detected by the detection unit. The advice unit analyzes the user's SNS usage patterns, generates individual risk profiles, and provides advice.

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 and includes 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 conventional technology, it has not been sufficiently done to effectively detect the excessive sharing of personal information on SNS and the risk of phishing fraud, and to provide appropriate warnings and advice to users, and there is room for improvement.

[0005] The system according to the embodiment aims to detect the excessive sharing of personal information on SNS and the risk of phishing fraud, and to provide appropriate warnings and advice to users.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a warning unit, a detection unit, and an advice unit. The analysis unit analyzes the content of SNS posts and detects excessive sharing of personal information and risks. The warning unit provides warnings based on the risks detected by the analysis unit. The detection unit analyzes SNS messages and links and detects phishing scams. The warning unit provides warnings based on the phishing scams detected by the detection unit. The advice unit analyzes the user's SNS usage patterns, generates individual risk profiles, and provides advice. [Effects of the Invention]

[0007] The system according to this embodiment can detect the risks of excessive sharing of personal information and phishing scams on social networking services, and provide users with appropriate warnings and advice. [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, and the like. The communication I / F controls communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[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 (Interface) 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 receiving 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 receiving 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-powered security assistant according to an embodiment of the present invention is a system targeting young people who regularly use social networking services (SNS) and value online self-expression. This system addresses issues such as excessive sharing of personal information on SNS, vulnerability to phishing scams, difficulty in managing online reputation, and cyberbullying. The AI-powered security assistant provides functions such as automatic privacy checks of SNS posts, real-time phishing detection, 24-hour support via an AI chatbot, online reputation management support, and cyberbullying detection. For example, the AI-powered security assistant analyzes the content of SNS posts to detect excessive sharing of personal information and risks. The AI-powered security assistant analyzes SNS messages and links to detect phishing scams. The AI-powered security assistant analyzes the user's SNS usage patterns, generates an individual risk profile, and provides advice. The AI-powered security assistant analyzes the user's SNS posts and comments from others to generate advice for managing online reputation. The AI-powered security assistant analyzes messages and comments on SNS to detect signs of cyberbullying and propose countermeasures. The AI-powered security assistant automatically generates engaging mini-games for young people, providing an opportunity to learn about social media security in a fun way. The AI ​​chatbot, trained to understand the language used by young people, provides 24 / 7 support for social media security questions. In this way, the AI-powered security assistant offers security solutions optimized for young people's social media usage habits, providing flexible and effective protection against ever-evolving threats on social media. This allows the AI-powered security assistant to ensure the safety of young people using social media and support their online self-expression.

[0029] The AI-powered security assistant according to this embodiment comprises an analysis unit, a warning unit, a detection unit, and an advice unit. The analysis unit analyzes the content of SNS posts and detects excessive sharing of personal information and risks. The analysis unit, for example, uses a generation AI to analyze the content of posts and identify exposure of personal information and risks. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the content of posts and detect exposure of personal information. The analysis unit can also use image recognition technology to detect personal information from images included in posts. For example, the generation AI identifies personal information such as faces and addresses in images and assesses the risk. Furthermore, the analysis unit can also use speech recognition technology to detect personal information from voice posts. For example, the generation AI analyzes voice data and detects exposure of personal information. The warning unit provides warnings based on the risks detected by the analysis unit. The warning unit, for example, uses a generation AI to assess risks and generate appropriate warnings. For example, the generation AI adjusts the content of the warning according to the severity of the risk and notifies the user. The warning unit can also provide warnings through methods such as pop-up notifications, email notifications, and in-app notifications. For example, the warning unit displays an immediate warning using a pop-up notification. The detection unit analyzes SNS messages and links to detect phishing scams. The detection unit analyzes the content of messages and links using, for example, a generative AI to identify signs of phishing scams. For example, the generative AI uses text generation AI to analyze the content of messages and assess the risk of phishing scams. The detection unit can also analyze the URL of a link to identify a phishing site. For example, the generative AI analyzes the structure and domain information of the URL to assess the risk of the phishing site. The advice unit analyzes the user's SNS usage patterns and generates individual risk profiles to provide advice. The advice unit analyzes the user's behavior history using, for example, a generative AI to generate a risk profile. For example, the generative AI creates a risk profile based on data such as the user's posts, usage time, and number of followers. The advice unit can also use generative AI to provide advice on individual risks.For example, the generating AI proposes specific countermeasures and preventative measures based on the user's risk profile. This allows the AI-powered security assistant according to this embodiment to ensure the safety of young people using social media and support their online self-expression.

[0030] The analysis department analyzes the content of social media posts to detect excessive sharing of personal information and associated risks. Specifically, it uses generative AI to analyze post content and identify exposure of personal information and associated risks. The generative AI uses text generation AI (e.g., LLM) to analyze post content and detect exposure of personal information. For example, it identifies personal information such as names, addresses, phone numbers, and email addresses contained in text posted by users and assesses the risk of this information being made public. The generative AI can also understand the context of the post content and detect cases where personal information is unintentionally disclosed or could be used by malicious third parties. Furthermore, the analysis department can also use image recognition technology to detect personal information from images included in posts. For example, the generative AI can identify personal information such as faces, addresses, and license plates in images and assess the risks. It can also analyze text within images to determine whether personal information is included. In addition, the analysis department can use speech recognition technology to detect personal information from audio posts. For example, the generative AI analyzes audio data and detects exposure of personal information such as names, addresses, and phone numbers. It can understand the context of the audio data and detect cases where personal information is unintentionally disclosed or could be used by malicious third parties. This allows the analysis department to comprehensively analyze the content of posts in all formats—text, images, and audio—and quickly and accurately detect the risk of personal information exposure.

[0031] The warning unit provides warnings based on risks detected by the analysis unit. Specifically, it uses a generation AI to assess risks and generate appropriate warnings. The generation AI adjusts the content of the warning according to the severity of the risk and notifies the user. For example, if the exposure of personal information poses a significant risk, the warning unit will immediately issue a strong warning to prompt the user to take action. On the other hand, if the risk is relatively low, it can issue a milder warning to draw attention. The warning unit can provide warnings through methods such as pop-up notifications, email notifications, and in-app notifications. For example, a pop-up notification can be used to display a warning immediately, allowing the user to address the risk right away. It is also possible to provide detailed warning content using email notifications, allowing the user to review it later. Furthermore, in-app notifications allow users to receive warnings while using the app. This enables the warning unit to provide users with warnings at the appropriate time and to respond quickly to risks. In addition, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of the warning content. For example, it can analyze how users reacted to warnings and optimize the content and method of the warnings. The warning unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the warning system to provide users with quick and reliable warnings, minimizing risks.

[0032] The detection unit analyzes SNS messages and links to detect phishing scams. Specifically, it uses generative AI to analyze the content of messages and links and identify signs of phishing scams. The generative AI uses text generation AI to analyze the content of messages and assess the risk of phishing scams. For example, it can analyze the wording in messages and the structure of links to identify typical patterns and methods of phishing scams. The generative AI can also analyze the sender of messages and the domain information of links to assess their reliability. For example, it can determine whether the sender's email address and the domain of the link are trustworthy and assess the risk of phishing scams. Furthermore, the detection unit can analyze the URL of links to identify phishing sites. The generative AI analyzes the structure and domain information of URLs to assess the risk of phishing sites. For example, if a URL is spoofed or an untrustworthy domain is used, the generative AI will detect this and issue a warning to the user. In addition, the detection unit continuously monitors the content of messages and links to respond to new phishing scam methods. For example, if a new phishing scam method is discovered, the generative AI will learn the pattern and improve the accuracy of future detections. This allows the detection unit to quickly and accurately detect phishing scams in social media messages and links, thereby protecting users.

[0033] The advisory department analyzes users' social media usage patterns, generates individual risk profiles, and provides advice. Specifically, it uses a generative AI to analyze users' behavioral history and generate risk profiles. The generative AI creates risk profiles based on data such as the user's posts, usage time, and number of followers. For example, if a user frequently discloses personal information or engages in high-risk behavior during specific time periods, the generative AI detects this and reflects it in the risk profile. The advisory department can also use the generative AI to provide advice on individual risks. Based on the user's risk profile, the generative AI proposes specific countermeasures and preventative measures. For example, it can encourage users to refrain from disclosing personal information or advise them to avoid using social media during specific time periods. Furthermore, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it analyzes the results of users following the advice and optimizes the content and methods of the advice. The advisory department can also regularly update user risk profiles to respond to the latest situations. This allows the advisory department to always provide users with appropriate advice based on the latest information, minimizing risks. Furthermore, the advisory department can continuously monitor users' SNS usage patterns and respond quickly if new risks arise. This allows the advisory department to ensure user safety and support online self-expression.

[0034] The analysis unit can improve the accuracy of risk detection by referring to past posting history when analyzing posted content. For example, the analysis unit can use a generative AI to analyze a user's past posting history and identify patterns of frequently shared personal information. The analysis unit can also use the generative AI to identify times and situations in which specific risks are likely to occur based on past posting history. Furthermore, the analysis unit can use the generative AI to rate posts containing specific keywords or phrases as having a higher risk based on past posting history. This improves the accuracy of risk detection by referring to past posting history. Past posting history includes, but is not limited to, the date and time of posting, the content of the post, and the type of post. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input past posting history data into the generative AI and have the generative AI perform the task of improving the accuracy of risk detection.

[0035] The analysis unit can apply different analysis algorithms depending on the category of the post when analyzing the content of the post. For example, the analysis unit can use image recognition technology to detect the exposure of personal information in photo posts using a generation AI. The analysis unit can also use natural language processing technology to detect risky keywords in text posts using a generation AI. Furthermore, the analysis unit can use speech recognition technology to detect the leakage of personal information in video posts using a generation AI. This improves the accuracy of risk detection by applying analysis algorithms appropriate to the category of the post. Post categories include, but are not limited to, text posts, image posts, and video posts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input post category data into a generation AI and have the generation AI execute the application of a category-appropriate analysis algorithm.

[0036] The analysis unit can detect risks by considering the user's geographical location information when analyzing posted content. For example, the analysis unit can use a generative AI to detect risks that are likely to occur in a specific area based on the user's current location. The analysis unit can also use a generative AI to analyze the user's past location information and evaluate risks at specific locations. Furthermore, the analysis unit can use a generative AI to predict risks at travel destinations based on the user's location information. This improves the accuracy of risk detection by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location data into a generative AI and have the generative AI perform risk detection.

[0037] The analysis unit can analyze a user's social media activity and detect related risks when analyzing posted content. For example, the analysis unit can use generative AI to analyze posts from a user's followers and friends to identify risky relationships. The analysis unit can also use generative AI to analyze a user's past social media activity and assess risks based on specific behavioral patterns. Furthermore, the analysis unit can use generative AI to analyze a user's interactions on social media and detect risky comments and messages. This improves the accuracy of detecting related risks by analyzing the user's social media activity. Social media activity includes, but is not limited to, posting frequency, number of followers, and number of likes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input social media activity data into generative AI and have the generative AI perform risk detection.

[0038] The warning unit can adjust the level of detail of a warning based on the severity of the risk when a warning is issued. For example, the warning unit's generating AI can provide a detailed explanation and countermeasures for high-risk warnings. The warning unit can also have the generating AI provide a concise explanation and basic countermeasures for medium-risk warnings. Furthermore, the warning unit can have the generating AI display only a simple reminder for low-risk warnings. This ensures that appropriate warnings are provided by adjusting the level of detail of the warning based on the severity of the risk. The severity of a risk includes, but is not limited to, the frequency and impact of the risk. Some or all of the processing described above in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input risk severity data into the generating AI and have the generating AI adjust the level of detail of the warning.

[0039] The warning unit can apply different warning algorithms depending on the risk category when a warning is issued. For example, the warning unit can display a warning for phishing scams that explains specific fraudulent methods and countermeasures. The warning unit can also display a warning for personal data breaches that explains how to delete information and preventative measures. Furthermore, the warning unit can display a warning for cyberbullying that provides information on how to cope if victimized and the resources available for support. This ensures that appropriate warnings are provided by applying warning algorithms appropriate to the risk category. Risk categories include, but are not limited to, privacy risks, security risks, and phishing risks. Some or all of the processing described above in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input risk category data into the generating AI and have the generating AI apply the warning algorithm.

[0040] The warning unit can adjust the display order of warnings based on when the risk occurred. For example, the warning unit can prioritize displaying warnings for risks that have occurred most recently, generated by the AI. The warning unit can also display risks that have occurred in the past in chronological order, generated by the AI. Furthermore, the warning unit can predict risks that may occur in the future and display warnings for them. This ensures that warnings are provided at the appropriate time by adjusting the display order of warnings based on when the risk occurred. The timing of the risk occurrence includes, but is not limited to, the date and time, duration, etc., when the risk occurred. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input risk occurrence timing data into the generating AI and have the generating AI adjust the display order of warnings.

[0041] The warning unit can adjust the content of a warning based on the relevance of the risk. For example, the warning unit's generating AI can present relevant past cases in response to a phishing scam warning. The warning unit can also have the generating AI suggest relevant security measures in response to a personal data breach warning. Furthermore, the warning unit can have the generating AI provide relevant support resources in response to a cyberbullying warning. This ensures that appropriate warnings are provided by adjusting the content of the warning based on the relevance of the risk. The relevance of the risk includes, but is not limited to, the cause of the risk and the scope of its impact. Some or all of the processing described above in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input risk relevance data into the generating AI and have the generating AI adjust the content of the warning.

[0042] The detection unit can improve detection accuracy by referring to past message history when detecting phishing scams. For example, the detection unit's generating AI analyzes the user's past message history to identify phishing scam patterns. The detection unit can also have the generating AI assess the risk of specific senders or links based on past message history. Furthermore, the detection unit can have the generating AI assess the risk of messages containing specific keywords or phrases based on past message history. This improves the accuracy of phishing scam detection by referring to past message history. Past message history includes, but is not limited to, the date and time a message was sent, the sender, and the content. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past message history data into the generating AI and have the generating AI perform the improvement of detection accuracy.

[0043] The detection unit can apply different detection algorithms depending on the message category when detecting phishing scams. For example, the detection unit can have the generating AI apply an algorithm that detects specific fraudulent techniques to financial-related messages. The detection unit can also have the generating AI apply an algorithm that detects specific fraudulent techniques to social media-related messages. Furthermore, the detection unit can have the generating AI apply an algorithm that detects specific fraudulent techniques to shopping-related messages. This improves the accuracy of phishing scam detection by applying detection algorithms according to the message category. Message categories include, but are not limited to, text messages, image messages, and link messages. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input message category data into the generating AI and cause the generating AI to apply a detection algorithm according to the category.

[0044] The detection unit can perform phishing scam detection while taking into account the user's geographical location information. For example, the detection unit's generating AI can detect phishing scams that are likely to occur in a specific area based on the user's current location. The detection unit can also have the generating AI analyze the user's past location information and evaluate phishing scams in specific locations. Furthermore, the detection unit can have the generating AI predict phishing scams in a travel destination based on the user's location information. This improves the accuracy of phishing scam detection by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical location data into the generating AI and have the generating AI perform phishing scam detection.

[0045] The detection unit can analyze a user's social media activity and detect related scams when detecting phishing scams. For example, the detection unit can use a generative AI to analyze messages from the user's followers and friends to identify certain relationships related to phishing scams. The detection unit can also use a generative AI to analyze a user's past social media activity and evaluate phishing scams based on specific behavioral patterns. Furthermore, the detection unit can use a generative AI to analyze a user's interactions on social media and detect comments and messages that may be related to phishing scams. This improves the accuracy of detecting related phishing scams by analyzing the user's social media activity. Social media activity includes, but is not limited to, posting frequency, number of followers, and number of likes. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input social media activity data into a generative AI and have the generative AI perform phishing scam detection.

[0046] The advice unit can improve the accuracy of the risk profile by referring to the user's past SNS usage patterns when providing advice. For example, the advice unit's generating AI can analyze the user's past SNS usage patterns and identify behaviors that are likely to cause specific risks. The advice unit can also have the generating AI evaluate the risk at specific times or situations based on past SNS usage patterns. Furthermore, the advice unit can have the generating AI rate posts containing specific keywords or phrases as having a higher risk based on past SNS usage patterns. This improves the accuracy of the risk profile by referring to past SNS usage patterns. Past SNS usage patterns include, but are not limited to, posting frequency, usage time, and usage purpose. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input past SNS usage pattern data into the generating AI and have the generating AI perform the task of improving the accuracy of the risk profile.

[0047] The advice unit can apply different advice algorithms depending on the user's SNS usage category when providing advice. For example, the advice unit's generating AI can use image recognition technology to provide advice to prevent the exposure of personal information in photo posts. The advice unit can also use natural language processing technology to provide advice to avoid risky keywords in text posts. Furthermore, the advice unit can use speech recognition technology to provide advice to prevent the leakage of personal information in video posts. By applying an advice algorithm according to the SNS usage category, appropriate advice is provided. SNS usage categories include, but are not limited to, personal use, business use, and hobby use. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input SNS usage category data into the generating AI and have the generating AI apply an advice algorithm according to the category.

[0048] The advice unit can adjust the display order of advice based on the user's SNS usage period. For example, the advice unit can prioritize providing advice on risks that have recently occurred, generated by the AI. The advice unit can also provide advice on risks that have occurred in the past in chronological order. Furthermore, the advice unit can predict and provide advice on risks that may occur in the future, generated by the AI. By adjusting the display order of advice based on the SNS usage period, advice is provided at the appropriate time. The SNS usage period includes, but is not limited to, the start date and time of use, frequency of use, and duration of use. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input SNS usage period data into the generating AI and have the generating AI adjust the display order of advice.

[0049] The advice unit can analyze the user's social media activity and provide relevant advice when giving advice. For example, the advice unit can use a generative AI to analyze posts from the user's followers and friends and provide advice on risky relationships. The advice unit can also use a generative AI to analyze the user's past social media activity and provide advice based on specific behavioral patterns. Furthermore, the advice unit can use a generative AI to analyze the user's interactions on social media and provide advice on risky comments and messages. In this way, relevant advice is provided by analyzing social media activity. Social media activity includes, but is not limited to, posting frequency, number of followers, and number of likes. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input social media activity data into a generative AI and have the generative AI perform the provision of advice.

[0050] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0051] A security assistant powered by AI generation can analyze a user's social media usage patterns and generate risk profiles that take into account the user's interests and preferences. For example, if a user has a particular hobby or interest, the AI ​​can identify risks related to that hobby or interest and provide appropriate advice. If a user belongs to a specific community, the AI ​​can also assess the risks specific to that community and suggest countermeasures. Furthermore, if a user participates in a particular event or campaign, the AI ​​can predict risks associated with that event or campaign and provide advance warnings. This enables the generation of risk profiles based on the user's interests and preferences, providing more personalized security measures.

[0052] The AI-powered security assistant can predict times when specific risks are more likely to occur based on a user's social media usage history and provide warnings. For example, if a user frequently uses social media at night, the AI ​​can identify risks that are more likely to occur at night and display a warning. Furthermore, if a user frequently uses social media on specific days of the week, the AI ​​can assess the risks associated with those days and suggest countermeasures. Additionally, if a user frequently uses social media during specific events or holidays, the AI ​​can predict risks associated with those events or holidays and provide advance warnings. This enables risk prediction and warnings based on the user's social media usage history, providing more effective security measures.

[0053] A security assistant powered by generative AI can analyze a user's social media usage patterns and predict and warn about behaviors that are likely to cause specific risks. For example, if a user frequently shares their location, the generative AI can identify the risks associated with location sharing and display a warning. If a user has many followers, the generative AI can also assess the risks associated with interactions with followers and suggest countermeasures. Furthermore, if a user frequently follows new accounts, the generative AI can predict the risks associated with those new accounts and provide a warning. This enables risk prediction and warnings based on the user's social media usage patterns, providing more effective security measures.

[0054] The AI-powered security assistant can predict and warn users about the types of posts that are likely to pose specific risks based on their social media usage history. For example, if a user has frequently posted personal information in the past, the AI ​​can identify the risks associated with such posts and display a warning. Furthermore, if a user has previously posted content containing specific keywords, the AI ​​can assess the risks associated with those keywords and suggest countermeasures. Additionally, if a user has previously posted specific images or videos, the AI ​​can predict the risks associated with that content and provide a proactive warning. This enables risk prediction and warnings based on the user's social media usage history, providing more effective security measures.

[0055] A security assistant powered by AI generation can analyze a user's social media usage patterns and identify followers who are likely to pose specific risks, providing warnings. For example, if a user has many followers, the AI ​​can identify risky accounts among them and display a warning. Furthermore, if a user frequently adds new followers, the AI ​​can assess the risks associated with those new followers and suggest countermeasures. Additionally, if a user frequently interacts with specific followers, the AI ​​can predict the risks associated with those followers and provide proactive warnings. This enables risk prediction and warnings based on the user's social media usage patterns, providing more effective security measures.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analysis department analyzes the content of social media posts to detect excessive sharing of personal information and associated risks. For example, it uses generative AI to analyze post content and identify exposure of personal information and associated risks. The generative AI uses text generation AI (e.g., LLM) to analyze post content and detect exposure of personal information. It can also use image recognition technology to detect personal information from images included in posts. The generative AI identifies personal information such as faces and addresses in images and assesses the risks. Furthermore, it can also use speech recognition technology to detect personal information from audio posts. The generative AI analyzes audio data and detects exposure of personal information. Step 2: The warning unit provides warnings based on the risks detected by the analysis unit. For example, it uses a generation AI to assess the risks and generate appropriate warnings. The generation AI adjusts the content of the warning according to the severity of the risk and notifies the user. The warning unit can provide warnings through methods such as pop-up notifications, email notifications, and in-app notifications. For example, it can display a warning immediately using a pop-up notification. Step 3: The detection unit analyzes SNS messages and links to detect phishing scams. For example, it uses a generation AI to analyze the content of messages and links and identify signs of phishing scams. The generation AI uses text generation AI to analyze the content of messages and assess the risk of phishing scams. It can also analyze the URL of a link to identify a phishing site. The generation AI analyzes the structure and domain information of the URL to assess the risk of the phishing site. Step 4: The warning unit provides a warning based on the phishing scam detected by the detection unit. For example, it uses a generation AI to assess the risk and generate an appropriate warning. The generation AI adjusts the content of the warning according to the severity of the risk and notifies the user. The warning unit can provide warnings through methods such as pop-up notifications, email notifications, and in-app notifications. For example, it can display a warning immediately using a pop-up notification. Step 5: The advice department analyzes the user's SNS usage patterns, generates individual risk profiles, and provides advice. For example, it uses a generative AI to analyze the user's behavioral history and generate a risk profile. The generative AI creates the risk profile based on data such as the user's posts, usage time, and number of followers. It can also use the generative AI to provide advice on individual risks. Based on the user's risk profile, the generative AI proposes specific countermeasures and preventive measures.

[0058] (Example of form 2) The AI-powered security assistant according to an embodiment of the present invention is a system targeting young people who regularly use social networking services (SNS) and value online self-expression. This system addresses issues such as excessive sharing of personal information on SNS, vulnerability to phishing scams, difficulty in managing online reputation, and cyberbullying. The AI-powered security assistant provides functions such as automatic privacy checks of SNS posts, real-time phishing detection, 24-hour support via an AI chatbot, online reputation management support, and cyberbullying detection. For example, the AI-powered security assistant analyzes the content of SNS posts to detect excessive sharing of personal information and risks. The AI-powered security assistant analyzes SNS messages and links to detect phishing scams. The AI-powered security assistant analyzes the user's SNS usage patterns, generates an individual risk profile, and provides advice. The AI-powered security assistant analyzes the user's SNS posts and comments from others to generate advice for managing online reputation. The AI-powered security assistant analyzes messages and comments on SNS to detect signs of cyberbullying and propose countermeasures. The AI-powered security assistant automatically generates engaging mini-games for young people, providing an opportunity to learn about social media security in a fun way. The AI ​​chatbot, trained to understand the language used by young people, provides 24 / 7 support for social media security questions. In this way, the AI-powered security assistant offers security solutions optimized for young people's social media usage habits, providing flexible and effective protection against ever-evolving threats on social media. This allows the AI-powered security assistant to ensure the safety of young people using social media and support their online self-expression.

[0059] The AI-powered security assistant according to this embodiment comprises an analysis unit, a warning unit, a detection unit, and an advice unit. The analysis unit analyzes the content of SNS posts and detects excessive sharing of personal information and risks. The analysis unit, for example, uses a generation AI to analyze the content of posts and identify exposure of personal information and risks. For example, the generation AI uses a text generation AI (e.g., LLM) to analyze the content of posts and detect exposure of personal information. The analysis unit can also use image recognition technology to detect personal information from images included in posts. For example, the generation AI identifies personal information such as faces and addresses in images and assesses the risk. Furthermore, the analysis unit can also use speech recognition technology to detect personal information from voice posts. For example, the generation AI analyzes voice data and detects exposure of personal information. The warning unit provides warnings based on the risks detected by the analysis unit. The warning unit, for example, uses a generation AI to assess risks and generate appropriate warnings. For example, the generation AI adjusts the content of the warning according to the severity of the risk and notifies the user. The warning unit can also provide warnings through methods such as pop-up notifications, email notifications, and in-app notifications. For example, the warning unit displays an immediate warning using a pop-up notification. The detection unit analyzes SNS messages and links to detect phishing scams. The detection unit analyzes the content of messages and links using, for example, a generative AI to identify signs of phishing scams. For example, the generative AI uses text generation AI to analyze the content of messages and assess the risk of phishing scams. The detection unit can also analyze the URL of a link to identify a phishing site. For example, the generative AI analyzes the structure and domain information of the URL to assess the risk of the phishing site. The advice unit analyzes the user's SNS usage patterns and generates individual risk profiles to provide advice. The advice unit analyzes the user's behavior history using, for example, a generative AI to generate a risk profile. For example, the generative AI creates a risk profile based on data such as the user's posts, usage time, and number of followers. The advice unit can also use generative AI to provide advice on individual risks.For example, the generating AI proposes specific countermeasures and preventative measures based on the user's risk profile. This allows the AI-powered security assistant according to this embodiment to ensure the safety of young people using social media and support their online self-expression.

[0060] The analysis department analyzes the content of social media posts to detect excessive sharing of personal information and associated risks. Specifically, it uses generative AI to analyze post content and identify exposure of personal information and associated risks. The generative AI uses text generation AI (e.g., LLM) to analyze post content and detect exposure of personal information. For example, it identifies personal information such as names, addresses, phone numbers, and email addresses contained in text posted by users and assesses the risk of this information being made public. The generative AI can also understand the context of the post content and detect cases where personal information is unintentionally disclosed or could be used by malicious third parties. Furthermore, the analysis department can also use image recognition technology to detect personal information from images included in posts. For example, the generative AI can identify personal information such as faces, addresses, and license plates in images and assess the risks. It can also analyze text within images to determine whether personal information is included. In addition, the analysis department can use speech recognition technology to detect personal information from audio posts. For example, the generative AI analyzes audio data and detects exposure of personal information such as names, addresses, and phone numbers. It can understand the context of the audio data and detect cases where personal information is unintentionally disclosed or could be used by malicious third parties. This allows the analysis department to comprehensively analyze the content of posts in all formats—text, images, and audio—and quickly and accurately detect the risk of personal information exposure.

[0061] The warning unit provides warnings based on risks detected by the analysis unit. Specifically, it uses a generation AI to assess risks and generate appropriate warnings. The generation AI adjusts the content of the warning according to the severity of the risk and notifies the user. For example, if the exposure of personal information poses a significant risk, the warning unit will immediately issue a strong warning to prompt the user to take action. On the other hand, if the risk is relatively low, it can issue a milder warning to draw attention. The warning unit can provide warnings through methods such as pop-up notifications, email notifications, and in-app notifications. For example, a pop-up notification can be used to display a warning immediately, allowing the user to address the risk right away. It is also possible to provide detailed warning content using email notifications, allowing the user to review it later. Furthermore, in-app notifications allow users to receive warnings while using the app. This enables the warning unit to provide users with warnings at the appropriate time and to respond quickly to risks. In addition, the warning unit can collect user feedback and continuously improve the accuracy and effectiveness of the warning content. For example, it can analyze how users reacted to warnings and optimize the content and method of the warnings. The warning unit can also reliably transmit information using multiple communication methods. For example, important information can be reliably delivered not only through smartphone notifications, but also through voice calls, SMS, and email. This allows the warning system to provide users with quick and reliable warnings, minimizing risks.

[0062] The detection unit analyzes SNS messages and links to detect phishing scams. Specifically, it uses generative AI to analyze the content of messages and links and identify signs of phishing scams. The generative AI uses text generation AI to analyze the content of messages and assess the risk of phishing scams. For example, it can analyze the wording in messages and the structure of links to identify typical patterns and methods of phishing scams. The generative AI can also analyze the sender of messages and the domain information of links to assess their reliability. For example, it can determine whether the sender's email address and the domain of the link are trustworthy and assess the risk of phishing scams. Furthermore, the detection unit can analyze the URL of links to identify phishing sites. The generative AI analyzes the structure and domain information of URLs to assess the risk of phishing sites. For example, if a URL is spoofed or an untrustworthy domain is used, the generative AI will detect this and issue a warning to the user. In addition, the detection unit continuously monitors the content of messages and links to respond to new phishing scam methods. For example, if a new phishing scam method is discovered, the generative AI will learn the pattern and improve the accuracy of future detections. This allows the detection unit to quickly and accurately detect phishing scams in social media messages and links, thereby protecting users.

[0063] The advisory department analyzes users' social media usage patterns, generates individual risk profiles, and provides advice. Specifically, it uses a generative AI to analyze users' behavioral history and generate risk profiles. The generative AI creates risk profiles based on data such as the user's posts, usage time, and number of followers. For example, if a user frequently discloses personal information or engages in high-risk behavior during specific time periods, the generative AI detects this and reflects it in the risk profile. The advisory department can also use the generative AI to provide advice on individual risks. Based on the user's risk profile, the generative AI proposes specific countermeasures and preventative measures. For example, it can encourage users to refrain from disclosing personal information or advise them to avoid using social media during specific time periods. Furthermore, the advisory department can collect user feedback and continuously improve the accuracy and effectiveness of its advice. For example, it analyzes the results of users following the advice and optimizes the content and methods of the advice. The advisory department can also regularly update user risk profiles to respond to the latest situations. This allows the advisory department to always provide users with appropriate advice based on the latest information, minimizing risks. Furthermore, the advisory department can continuously monitor users' SNS usage patterns and respond quickly if new risks arise. This allows the advisory department to ensure user safety and support online self-expression.

[0064] The analysis unit can estimate the user's emotions and adjust the analysis method of the posted content based on the estimated user emotions. For example, if the user is stressed, the analysis unit's generative AI can perform a concise analysis and highlight only the important risks. If the user is relaxed, the analysis unit's generative AI can perform a detailed analysis and provide background information on the risks. Furthermore, if the user is excited, the analysis unit's generative AI can present the analysis results using visually easy-to-understand graphs and charts. This allows for more appropriate risk detection by adjusting the analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or 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 processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI and have the generative AI perform adjustments to the analysis method based on emotions.

[0065] The analysis unit can improve the accuracy of risk detection by referring to past posting history when analyzing posted content. For example, the analysis unit can use a generative AI to analyze a user's past posting history and identify patterns of frequently shared personal information. The analysis unit can also use the generative AI to identify times and situations in which specific risks are likely to occur based on past posting history. Furthermore, the analysis unit can use the generative AI to rate posts containing specific keywords or phrases as having a higher risk based on past posting history. This improves the accuracy of risk detection by referring to past posting history. Past posting history includes, but is not limited to, the date and time of posting, the content of the post, and the type of post. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input past posting history data into the generative AI and have the generative AI perform the task of improving the accuracy of risk detection.

[0066] The analysis unit can apply different analysis algorithms depending on the category of the post when analyzing the content of the post. For example, the analysis unit can use image recognition technology to detect the exposure of personal information in photo posts using a generation AI. The analysis unit can also use natural language processing technology to detect risky keywords in text posts using a generation AI. Furthermore, the analysis unit can use speech recognition technology to detect the leakage of personal information in video posts using a generation AI. This improves the accuracy of risk detection by applying analysis algorithms appropriate to the category of the post. Post categories include, but are not limited to, text posts, image posts, and video posts. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input post category data into a generation AI and have the generation AI execute the application of a category-appropriate analysis algorithm.

[0067] 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 nervous, the generation AI can provide a simple and highly visible display method. If the user is relaxed, the generation AI can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can provide a concise display method. By adjusting the display method of the analysis results according to the user's emotions, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit can input user emotion data into the generation AI and have the generation AI adjust the display method based on the emotions.

[0068] The analysis unit can detect risks by considering the user's geographical location information when analyzing posted content. For example, the analysis unit can use a generative AI to detect risks that are likely to occur in a specific area based on the user's current location. The analysis unit can also use a generative AI to analyze the user's past location information and evaluate risks at specific locations. Furthermore, the analysis unit can use a generative AI to predict risks at travel destinations based on the user's location information. This improves the accuracy of risk detection by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input geographical location data into a generative AI and have the generative AI perform risk detection.

[0069] The analysis unit can analyze a user's social media activity and detect related risks when analyzing posted content. For example, the analysis unit can use generative AI to analyze posts from a user's followers and friends to identify risky relationships. The analysis unit can also use generative AI to analyze a user's past social media activity and assess risks based on specific behavioral patterns. Furthermore, the analysis unit can use generative AI to analyze a user's interactions on social media and detect risky comments and messages. This improves the accuracy of detecting related risks by analyzing the user's social media activity. Social media activity includes, but is not limited to, posting frequency, number of followers, and number of likes. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input social media activity data into generative AI and have the generative AI perform risk detection.

[0070] 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 tense, the generating AI can display a warning in a calm tone. If the user is relaxed, the generating AI can also display a warning with a detailed explanation. Furthermore, if the user is in a hurry, the generating AI can display a concise and quick warning. This provides more appropriate warnings by adjusting the way the warning is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generating AI. The generating 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 AI or not using AI. For example, the warning unit can input user emotion data into the generating AI and have the generating AI adjust the way the warning is expressed based on the emotion.

[0071] The warning unit can adjust the level of detail of a warning based on the severity of the risk when a warning is issued. For example, the warning unit's generating AI can provide a detailed explanation and countermeasures for high-risk warnings. The warning unit can also have the generating AI provide a concise explanation and basic countermeasures for medium-risk warnings. Furthermore, the warning unit can have the generating AI display only a simple reminder for low-risk warnings. This ensures that appropriate warnings are provided by adjusting the level of detail of the warning based on the severity of the risk. The severity of a risk includes, but is not limited to, the frequency and impact of the risk. Some or all of the processing described above in the warning unit may be performed using AI, for example, or without AI. For example, the warning unit can input risk severity data into the generating AI and have the generating AI adjust the level of detail of the warning.

[0072] The warning unit can apply different warning algorithms depending on the risk category when a warning is issued. For example, the warning unit can display a warning for phishing scams that explains specific fraudulent methods and countermeasures. The warning unit can also display a warning for personal data breaches that explains how to delete information and preventative measures. Furthermore, the warning unit can display a warning for cyberbullying that provides information on how to cope if victimized and the resources available for support. This ensures that appropriate warnings are provided by applying warning algorithms appropriate to the risk category. Risk categories include, but are not limited to, privacy risks, security risks, and phishing risks. Some or all of the processing described above in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input risk category data into the generating AI and have the generating AI apply the warning algorithm.

[0073] The warning unit can estimate the user's emotions and determine the priority of warnings based on the estimated emotions. For example, if the user is stressed, the generation AI can prioritize displaying the most important warnings. If the user is relaxed, the generation AI can also display all warnings sequentially. Furthermore, if the user is in a hurry, the generation AI can prioritize displaying warnings that require immediate attention. This ensures that important warnings are provided preferentially by prioritizing warnings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generation AI. The generation 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 AI or not. For example, the warning unit can input user emotion data into the generation AI and have the generation AI determine the priority of warnings based on emotions.

[0074] The warning unit can adjust the display order of warnings based on when the risk occurred. For example, the warning unit can prioritize displaying warnings for risks that have occurred most recently, generated by the AI. The warning unit can also display risks that have occurred in the past in chronological order, generated by the AI. Furthermore, the warning unit can predict risks that may occur in the future and display warnings for them. This ensures that warnings are provided at the appropriate time by adjusting the display order of warnings based on when the risk occurred. The timing of the risk occurrence includes, but is not limited to, the date and time, duration, etc., when the risk occurred. Some or all of the above processing in the warning unit may be performed using, for example, AI, or not using AI. For example, the warning unit can input risk occurrence timing data into the generating AI and have the generating AI adjust the display order of warnings.

[0075] The warning unit can adjust the content of a warning based on the relevance of the risk. For example, the warning unit's generating AI can present relevant past cases in response to a phishing scam warning. The warning unit can also have the generating AI suggest relevant security measures in response to a personal data breach warning. Furthermore, the warning unit can have the generating AI provide relevant support resources in response to a cyberbullying warning. This ensures that appropriate warnings are provided by adjusting the content of the warning based on the relevance of the risk. The relevance of the risk includes, but is not limited to, the cause of the risk and the scope of its impact. Some or all of the processing described above in the warning unit may be performed using AI, for example, or not using AI. For example, the warning unit can input risk relevance data into the generating AI and have the generating AI adjust the content of the warning.

[0076] The detection unit can estimate the user's emotions and adjust the phishing scam detection method based on the estimated user emotions. For example, if the user is tense, the generation AI can quickly detect phishing scams and display a warning immediately. If the user is relaxed, the generation AI can provide detailed detection results and explain the background information of the risks. Furthermore, if the user is excited, the generation AI can present the detection results using visually easy-to-understand graphs and charts. This allows for appropriate detection by adjusting the phishing scam detection method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation 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 detection unit may be performed using AI or not using AI. For example, the detection unit can input user emotion data into the generation AI and have the generation AI perform emotion-based adjustments to the detection method.

[0077] The detection unit can improve detection accuracy by referring to past message history when detecting phishing scams. For example, the detection unit's generating AI analyzes the user's past message history to identify phishing scam patterns. The detection unit can also have the generating AI assess the risk of specific senders or links based on past message history. Furthermore, the detection unit can have the generating AI assess the risk of messages containing specific keywords or phrases based on past message history. This improves the accuracy of phishing scam detection by referring to past message history. Past message history includes, but is not limited to, the date and time a message was sent, the sender, and the content. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input past message history data into the generating AI and have the generating AI perform the improvement of detection accuracy.

[0078] The detection unit can apply different detection algorithms depending on the message category when detecting phishing scams. For example, the detection unit can have the generating AI apply an algorithm that detects specific fraudulent techniques to financial-related messages. The detection unit can also have the generating AI apply an algorithm that detects specific fraudulent techniques to social media-related messages. Furthermore, the detection unit can have the generating AI apply an algorithm that detects specific fraudulent techniques to shopping-related messages. This improves the accuracy of phishing scam detection by applying detection algorithms according to the message category. Message categories include, but are not limited to, text messages, image messages, and link messages. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input message category data into the generating AI and cause the generating AI to apply a detection algorithm according to the category.

[0079] The detection unit can estimate the user's emotions and adjust the display method of the detection results based on the estimated emotions. For example, if the user is tense, the generation AI can provide a simple and highly visible display method. If the user is relaxed, the generation AI can also provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the generation AI can provide a concise display method. This allows for the provision of appropriate information by adjusting the display method of the detection results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation 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 detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input user emotion data into the generation AI and have the generation AI adjust the display method based on the emotions.

[0080] The detection unit can perform phishing scam detection while taking into account the user's geographical location information. For example, the detection unit's generating AI can detect phishing scams that are likely to occur in a specific area based on the user's current location. The detection unit can also have the generating AI analyze the user's past location information and evaluate phishing scams in specific locations. Furthermore, the detection unit can have the generating AI predict phishing scams in a travel destination based on the user's location information. This improves the accuracy of phishing scam detection by considering the user's geographical location information. Geographical location information includes, but is not limited to, GPS data and IP addresses. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input geographical location data into the generating AI and have the generating AI perform phishing scam detection.

[0081] The detection unit can analyze a user's social media activity and detect related scams when detecting phishing scams. For example, the detection unit can use a generative AI to analyze messages from the user's followers and friends to identify certain relationships related to phishing scams. The detection unit can also use a generative AI to analyze a user's past social media activity and evaluate phishing scams based on specific behavioral patterns. Furthermore, the detection unit can use a generative AI to analyze a user's interactions on social media and detect comments and messages that may be related to phishing scams. This improves the accuracy of detecting related phishing scams by analyzing the user's social media activity. Social media activity includes, but is not limited to, posting frequency, number of followers, and number of likes. Some or all of the above processing in the detection unit may be performed using AI, for example, or without AI. For example, the detection unit can input social media activity data into a generative AI and have the generative AI perform phishing scam detection.

[0082] The advice unit can estimate the user's emotions and adjust the way advice is expressed based on those emotions. For example, if the user is nervous, the generating AI can provide advice in a calm tone. If the user is relaxed, the generating AI can provide advice with more detailed explanations. Furthermore, if the user is in a hurry, the generating AI can provide concise and quick advice. This allows for more appropriate advice to be provided by adjusting the way advice is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into the generating AI and have the generating AI adjust the way advice is expressed based on those emotions.

[0083] The advice unit can improve the accuracy of the risk profile by referring to the user's past SNS usage patterns when providing advice. For example, the advice unit's generating AI can analyze the user's past SNS usage patterns and identify behaviors that are likely to cause specific risks. The advice unit can also have the generating AI evaluate the risk at specific times or situations based on past SNS usage patterns. Furthermore, the advice unit can have the generating AI rate posts containing specific keywords or phrases as having a higher risk based on past SNS usage patterns. This improves the accuracy of the risk profile by referring to past SNS usage patterns. Past SNS usage patterns include, but are not limited to, posting frequency, usage time, and usage purpose. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input past SNS usage pattern data into the generating AI and have the generating AI perform the task of improving the accuracy of the risk profile.

[0084] The advice unit can apply different advice algorithms depending on the user's SNS usage category when providing advice. For example, the advice unit's generating AI can use image recognition technology to provide advice to prevent the exposure of personal information in photo posts. The advice unit can also use natural language processing technology to provide advice to avoid risky keywords in text posts. Furthermore, the advice unit can use speech recognition technology to provide advice to prevent the leakage of personal information in video posts. By applying an advice algorithm according to the SNS usage category, appropriate advice is provided. SNS usage categories include, but are not limited to, personal use, business use, and hobby use. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input SNS usage category data into the generating AI and have the generating AI apply an advice algorithm according to the category.

[0085] The advice unit can estimate the user's emotions and prioritize advice based on those emotions. For example, if the user is stressed, the advice unit's generating AI will prioritize providing the most important advice. If the user is relaxed, the advice unit's generating AI can also provide all advice sequentially. Furthermore, if the user is in a hurry, the advice unit's generating AI can prioritize providing advice that requires immediate attention. This ensures that important advice is provided preferentially by prioritizing advice according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating 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 advice unit may be performed using AI or not. For example, the advice unit can input user emotion data into the generating AI and have the generating AI determine the priority of advice based on emotions.

[0086] The advice unit can adjust the display order of advice based on the user's SNS usage period. For example, the advice unit can prioritize providing advice on risks that have recently occurred, generated by the AI. The advice unit can also provide advice on risks that have occurred in the past in chronological order. Furthermore, the advice unit can predict and provide advice on risks that may occur in the future, generated by the AI. By adjusting the display order of advice based on the SNS usage period, advice is provided at the appropriate time. The SNS usage period includes, but is not limited to, the start date and time of use, frequency of use, and duration of use. Some or all of the above processing in the advice unit may be performed using AI, for example, or without AI. For example, the advice unit can input SNS usage period data into the generating AI and have the generating AI adjust the display order of advice.

[0087] The advice unit can analyze the user's social media activity and provide relevant advice when giving advice. For example, the advice unit can use a generative AI to analyze posts from the user's followers and friends and provide advice on risky relationships. The advice unit can also use a generative AI to analyze the user's past social media activity and provide advice based on specific behavioral patterns. Furthermore, the advice unit can use a generative AI to analyze the user's interactions on social media and provide advice on risky comments and messages. In this way, relevant advice is provided by analyzing social media activity. Social media activity includes, but is not limited to, posting frequency, number of followers, and number of likes. Some or all of the above processing in the advice unit may be performed using, for example, AI, or not using AI. For example, the advice unit can input social media activity data into a generative AI and have the generative AI perform the provision of advice.

[0088] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0089] A security assistant powered by AI generation can analyze a user's social media usage patterns and generate risk profiles that take into account the user's interests and preferences. For example, if a user has a particular hobby or interest, the AI ​​can identify risks related to that hobby or interest and provide appropriate advice. If a user belongs to a specific community, the AI ​​can also assess the risks specific to that community and suggest countermeasures. Furthermore, if a user participates in a particular event or campaign, the AI ​​can predict risks associated with that event or campaign and provide advance warnings. This enables the generation of risk profiles based on the user's interests and preferences, providing more personalized security measures.

[0090] The AI-powered security assistant can estimate a user's emotions and advise on the best timing for social media posts based on those emotions. For example, if a user is feeling stressed, the AI ​​might advise against posting and recommend posting when the user is relaxed. If a user is excited, the AI ​​might suggest waiting until they calm down before posting. Furthermore, if a user is sad, the AI ​​might recommend posting positive content and advise against reflecting negative emotions. This allows for appropriate posting timing advice tailored to the user's emotions, potentially preventing problems on social media.

[0091] The AI-powered security assistant can predict times when specific risks are more likely to occur based on a user's social media usage history and provide warnings. For example, if a user frequently uses social media at night, the AI ​​can identify risks that are more likely to occur at night and display a warning. Furthermore, if a user frequently uses social media on specific days of the week, the AI ​​can assess the risks associated with those days and suggest countermeasures. Additionally, if a user frequently uses social media during specific events or holidays, the AI ​​can predict risks associated with those events or holidays and provide advance warnings. This enables risk prediction and warnings based on the user's social media usage history, providing more effective security measures.

[0092] A security assistant powered by generative AI can estimate a user's emotions and manage their social media interactions based on those emotions. For example, if a user is angry, the generative AI will advise them to refrain from making aggressive comments or messages. If a user is happy, the generative AI can recommend positive interactions and suggest building good relationships with others. Furthermore, if a user is feeling anxious, the generative AI can advise them to send a message seeking support and recommend interactions that will help them feel more secure. This enables appropriate management of social media interactions based on the user's emotions, helping to prevent online troubles.

[0093] A security assistant powered by generative AI can analyze a user's social media usage patterns and predict and warn about behaviors that are likely to cause specific risks. For example, if a user frequently shares their location, the generative AI can identify the risks associated with location sharing and display a warning. If a user has many followers, the generative AI can also assess the risks associated with interactions with followers and suggest countermeasures. Furthermore, if a user frequently follows new accounts, the generative AI can predict the risks associated with those new accounts and provide a warning. This enables risk prediction and warnings based on the user's social media usage patterns, providing more effective security measures.

[0094] A generative AI-powered security assistant can estimate a user's emotions and advise on their privacy settings on social media based on those emotions. For example, if a user is feeling anxious, the generative AI will advise strengthening their privacy settings to provide reassurance. If a user is relaxed, the generative AI can suggest reviewing their privacy settings and advise maintaining appropriate settings. Furthermore, if a user is agitated, the generative AI can advise against changing their privacy settings and recommend making changes when they are calm. This enables appropriate privacy setting advice tailored to the user's emotions, improving security on social media.

[0095] The AI-powered security assistant can predict and warn users about the types of posts that are likely to pose specific risks based on their social media usage history. For example, if a user has frequently posted personal information in the past, the AI ​​can identify the risks associated with such posts and display a warning. Furthermore, if a user has previously posted content containing specific keywords, the AI ​​can assess the risks associated with those keywords and suggest countermeasures. Additionally, if a user has previously posted specific images or videos, the AI ​​can predict the risks associated with that content and provide a proactive warning. This enables risk prediction and warnings based on the user's social media usage history, providing more effective security measures.

[0096] A security assistant powered by generative AI can estimate a user's emotions and manage their social media friendships based on those emotions. For example, if a user is feeling lonely, the generative AI will offer advice on making new friends and suggest expanding their social circle. If a user is feeling stressed, the generative AI can recommend communication with trusted friends and offer advice on how to get support. Furthermore, if a user is feeling happy, the generative AI can recommend positive interactions and offer advice on strengthening friendships. This enables appropriate friendship management tailored to the user's emotions, fostering positive relationships on social media.

[0097] A security assistant powered by AI generation can analyze a user's social media usage patterns and identify followers who are likely to pose specific risks, providing warnings. For example, if a user has many followers, the AI ​​can identify risky accounts among them and display a warning. Furthermore, if a user frequently adds new followers, the AI ​​can assess the risks associated with those new followers and suggest countermeasures. Additionally, if a user frequently interacts with specific followers, the AI ​​can predict the risks associated with those followers and provide proactive warnings. This enables risk prediction and warnings based on the user's social media usage patterns, providing more effective security measures.

[0098] The AI-powered security assistant can estimate a user's emotions and filter content on social media based on those estimates. For example, if a user is feeling anxious, the AI ​​will filter out content that could potentially cause anxiety and avoid displaying it. If a user is relaxed, the AI ​​can prioritize displaying positive content to help them maintain that relaxation. Furthermore, if a user is agitated, the AI ​​can display calming content to soothe them. This enables appropriate content filtering tailored to the user's emotions, providing a comfortable environment for using social media.

[0099] The following briefly describes the processing flow for example form 2.

[0100] Step 1: The analysis department analyzes the content of social media posts to detect excessive sharing of personal information and associated risks. For example, it uses generative AI to analyze post content and identify exposure of personal information and associated risks. The generative AI uses text generation AI (e.g., LLM) to analyze post content and detect exposure of personal information. It can also use image recognition technology to detect personal information from images included in posts. The generative AI identifies personal information such as faces and addresses in images and assesses the risks. Furthermore, it can also use speech recognition technology to detect personal information from audio posts. The generative AI analyzes audio data and detects exposure of personal information. Step 2: The warning unit provides warnings based on the risks detected by the analysis unit. For example, it uses a generation AI to assess the risks and generate appropriate warnings. The generation AI adjusts the content of the warning according to the severity of the risk and notifies the user. The warning unit can provide warnings through methods such as pop-up notifications, email notifications, and in-app notifications. For example, it can display a warning immediately using a pop-up notification. Step 3: The detection unit analyzes SNS messages and links to detect phishing scams. For example, it uses a generation AI to analyze the content of messages and links and identify signs of phishing scams. The generation AI uses text generation AI to analyze the content of messages and assess the risk of phishing scams. It can also analyze the URL of a link to identify a phishing site. The generation AI analyzes the structure and domain information of the URL to assess the risk of the phishing site. Step 4: The warning unit provides a warning based on the phishing scam detected by the detection unit. For example, it uses a generation AI to assess the risk and generate an appropriate warning. The generation AI adjusts the content of the warning according to the severity of the risk and notifies the user. The warning unit can provide warnings through methods such as pop-up notifications, email notifications, and in-app notifications. For example, it can display a warning immediately using a pop-up notification. Step 5: The advice department analyzes the user's SNS usage patterns, generates individual risk profiles, and provides advice. For example, it uses a generative AI to analyze the user's behavioral history and generate a risk profile. The generative AI creates the risk profile based on data such as the user's posts, usage time, and number of followers. It can also use the generative AI to provide advice on individual risks. Based on the user's risk profile, the generative AI proposes specific countermeasures and preventive measures.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] Each of the multiple elements described above, including the analysis unit, warning unit, detection unit, and advice 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 control unit 46A of the smart device 14, which analyzes the content of SNS posts and identifies exposure of personal information and risks. The warning unit is implemented by the identification processing unit 290 of the data processing unit 12, which assesses risks and generates appropriate warnings. The detection unit is implemented by the control unit 46A of the smart device 14, which analyzes the content of SNS messages and links and identifies signs of phishing scams. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's behavior history, generates a risk profile, and provides advice. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0105] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] Each of the multiple elements described above, including the analysis unit, warning unit, detection unit, and advice 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 control unit 46A of the smart glasses 214, which analyzes the content of SNS posts and identifies exposure of personal information and risks. The warning unit is implemented by the identification processing unit 290 of the data processing unit 12, which assesses risks and generates appropriate warnings. The detection unit is implemented by the control unit 46A of the smart glasses 214, which analyzes the content of SNS messages and links and identifies signs of phishing scams. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's behavior history, generates a risk profile, and provides advice. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0121] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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).

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] Each of the multiple elements described above, including the analysis unit, warning unit, detection unit, and advice 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 control unit 46A of the headset terminal 314, which analyzes the content of SNS posts and identifies exposure of personal information and risks. The warning unit is implemented by the identification processing unit 290 of the data processing unit 12, which assesses risks and generates appropriate warnings. The detection unit is implemented by the control unit 46A of the headset terminal 314, which analyzes the content of SNS messages and links and identifies signs of phishing scams. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's behavior history, generates a risk profile, and provides advice. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0137] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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).

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.).

[0150] 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.

[0151] 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.

[0152] 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.

[0153] Each of the multiple elements described above, including the analysis unit, warning unit, detection unit, and advice 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 control unit 46A of the robot 414, which analyzes the content of SNS posts and identifies exposure of personal information and risks. The warning unit is implemented by the identification processing unit 290 of the data processing unit 12, which assesses risks and generates appropriate warnings. The detection unit is implemented by the control unit 46A of the robot 414, which analyzes the content of SNS messages and links and identifies signs of phishing scams. The advice unit is implemented by the identification processing unit 290 of the data processing unit 12, which analyzes the user's behavior history, generates a risk profile, and provides advice. The correspondence between each unit and the device or control unit is not limited to the examples described above and can be modified in various ways.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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."

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] (Note 1) The analysis department analyzes the content of social media posts to detect excessive sharing of personal information and related risks, A warning unit provides a warning based on the risk detected by the aforementioned analysis unit, A detection unit that analyzes SNS messages and links to detect phishing scams, A warning unit provides a warning based on the phishing scam detected by the detection unit, It includes an advisory unit that analyzes users' SNS usage patterns, generates individual risk profiles, and provides advice. A system characterized by the following features. (Note 2) The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method of post content based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit is When analyzing post content, we improve the accuracy of risk detection by referring to past posting history. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned analysis unit is When analyzing the content of posts, different analysis algorithms are applied depending on the category of the post. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is When analyzing posted content, risk detection is performed by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is When analyzing posted content, we analyze users' social media activity and detect related risks. The system described in Appendix 1, characterized by the features described herein. (Note 8) 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 9) The aforementioned warning unit is When a warning is issued, adjust the level of detail in the warning based on the severity of the risk. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned warning unit is When a warning is issued, different warning algorithms are applied depending on the risk category. The system described in Appendix 1, characterized by the features described herein. (Note 11) 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 12) The aforementioned warning unit is When a warning is issued, the order in which the warnings are displayed is adjusted based on when the risk occurred. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned warning unit is When issuing a warning, adjust the content of the warning based on the relevance of the risk. The system described in Appendix 1, characterized by the features described herein. (Note 14) The detection unit, We estimate user sentiment and adjust phishing scam detection methods based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 15) The detection unit, When detecting phishing scams, we improve detection accuracy by referring to past message history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The detection unit, When detecting phishing scams, different detection algorithms are applied depending on the message category. The system described in Appendix 1, characterized by the features described herein. (Note 17) The detection unit, 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, When detecting phishing scams, the system takes the user's geographical location into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The detection unit, When detecting phishing scams, the system analyzes the user's social media activity to identify related scams. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned advice section, It estimates the user's emotions and adjusts the way advice is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned advice section, When providing advice, we improve the accuracy of the risk profile by referring to the user's past SNS usage patterns. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned advice section, When providing advice, different advice algorithms are applied depending on the user's SNS usage category. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned advice section, It estimates the user's emotions and determines the priority of advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned advice section, When providing advice, the display order of the advice is adjusted based on when the user was using social media. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned advice section, When providing advice, we analyze the user's social media activity and offer relevant advice. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0173] 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 department analyzes the content of social media posts to detect excessive sharing of personal information and related risks, A warning unit provides a warning based on the risk detected by the aforementioned analysis unit, A detection unit that analyzes SNS messages and links to detect phishing scams, It includes an advisory unit that analyzes users' SNS usage patterns, generates individual risk profiles, and provides advice. A system characterized by the following features.

2. The aforementioned analysis unit is We estimate user sentiment and adjust the analysis method of post content based on the estimated user sentiment. The system according to feature 1.

3. The aforementioned analysis unit is When analyzing post content, we improve the accuracy of risk detection by referring to past posting history. The system according to feature 1.

4. The aforementioned analysis unit is When analyzing the content of posts, different analysis algorithms are applied depending on the category of the post. The system according to feature 1.

5. The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system according to feature 1.

6. The aforementioned analysis unit is When analyzing posted content, risk detection is performed by considering the user's geographical location. The system according to feature 1.

7. The aforementioned analysis unit is When analyzing posted content, we analyze users' social media activity and detect related risks. The system according to feature 1.

8. 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 according to feature 1.

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