system

Abstract

The system according to this embodiment aims to provide protection and support against unauthorized use and leakage of personal information. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information related to the user's security. The analysis unit analyzes the information collected by the collection unit and determines the risk of misuse and leakage of personal information. The provision unit provides protective measures and support based on the determination results obtained by the analysis unit.

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, protection and support against unauthorized use and leakage of personal information are not sufficiently provided, and there is room for improvement.

[0005] The system according to the embodiment aims to provide protection and support against unauthorized use and leakage of personal information.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, an analysis unit, and a provision unit. The collection unit collects information related to the security of the user. The analysis unit analyzes the information collected by the collection unit and determines the risk of unauthorized use and leakage of personal information. The provision unit provides protection measures and support based on the determination result obtained by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide protection and support against misuse and leakage of personal information. [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 signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 security protection system according to an embodiment of the present invention is a system that entrusts protection and support against security issues such as unauthorized use and leakage of personal information to a generating AI. This security protection system collects information related to the user's security, the generating AI analyzes the collected information, determines the risk of unauthorized use and leakage of personal information, and provides appropriate protective measures and support. For example, the security protection system collects information such as the user's access history, information about the device being used, and the network status. Next, the generating AI analyzes the collected information and determines the risk of unauthorized use and leakage of personal information. For example, it analyzes the user's access history to determine whether unauthorized access has occurred. It also analyzes information about the device being used to determine whether the device is infected with malware. Furthermore, the generating AI provides appropriate protective measures and support based on the determination results. For example, if unauthorized access is detected, the generating AI issues a warning to the user and takes measures to block access. Also, if the device is infected with malware, the generating AI guides the user on how to remove the malware. Through this mechanism, protection and support against security issues such as unauthorized use and leakage of personal information are provided. Users can use the internet with peace of mind by receiving security measures powered by generated AI. For example, when using services such as online shopping or internet banking, generated AI monitors the risk of fraudulent use and provides necessary protective measures to ensure user safety. In this way, the security protection system can reduce security risks by collecting and analyzing user security information and providing appropriate protective measures and support.

[0029] The security protection system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information related to the user's security. For example, the collection unit collects information such as the user's access history, information about the device being used, and the network status. For example, the collection unit collects information such as what kind of device the user is using and what kind of network they are connected to. The analysis unit analyzes the information collected by the collection unit and determines the risk of unauthorized use and leakage of personal information. For example, the analysis unit analyzes the user's access history and determines whether unauthorized access has occurred. The analysis unit also analyzes information about the device being used and determines whether the device is infected with malware. The provision unit provides protective measures and support based on the determination results obtained by the analysis unit. For example, if unauthorized access is detected, the provision unit issues a warning to the user and takes measures to block access. Furthermore, if the device is infected with malware, the provision unit guides the user on how to remove the malware. In this way, the security protection system according to this embodiment can reduce security risks by collecting and analyzing information related to the user's security and providing appropriate protective measures and support.

[0030] The data collection unit collects user security information. Specifically, it collects information such as user access history, device information, and network status. For example, it collects information such as what devices users are using and what networks they are connected to. The data collection unit obtains detailed information such as the IP address, browser type, and operating system version when users access websites. It also collects hardware information and installed software versions of the devices users are using. Furthermore, regarding network status, it collects information such as the SSID and signal strength of the connected Wi-Fi, network latency, and packet loss rate. This information is collected in real time and used to assess security risks. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the data collection unit to determine the risk of misuse and leakage of personal information. Specifically, it analyzes user access history to determine whether unauthorized access is occurring. For example, if there are frequent accesses from unusual times or locations, the analysis unit will determine that the access is unauthorized. It also analyzes information about the devices being used to determine whether the devices are infected with malware. For example, if the CPU usage or memory usage of a device is abnormally high, the analysis unit will determine that the device may be infected with malware. The analysis unit uses AI to analyze this data in real time and quickly detect abnormal patterns and malicious behavior. The AI ​​uses machine learning algorithms to learn normal patterns from past data and detect abnormal patterns. It can also use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The service provider will provide protective measures and support based on the results obtained by the analysis department. Specifically, if unauthorized access is detected, the service provider will warn the user and take measures to block access. For example, if an unauthorized access attempt is made to a user's account, the service provider will immediately notify the user and prompt them to lock the account or change their password. In addition, if a device is infected with malware, the service provider will guide the user on how to remove the malware. For example, they will provide specific removal procedures and information on recommended security software for devices suspected of infection. Furthermore, the service provider will also provide education and training to raise users' security awareness. For example, by providing security knowledge such as how to identify phishing emails and how to create strong passwords, users will be able to take actions to reduce risks themselves. This allows the service provider to provide users with prompt and appropriate protective measures and support, thereby reducing security risks. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the protective measures and support they provide. For example, by reviewing the content of warning messages and notification methods based on user feedback, they can achieve more effective security measures. This allows the service provider to always offer users the latest and most optimal protections and support, minimizing security risks.

[0033] The data collection unit can collect information such as user access history, device information, and network status. For example, the data collection unit can collect user access history. For example, the data collection unit can collect information such as access date and time, and source IP address. The data collection unit can also collect information about the device being used. For example, the data collection unit can collect information such as device type and OS version. Furthermore, the data collection unit can collect network status. For example, the data collection unit can collect information such as network speed and connection status. By collecting user access history, device information, and network status, the accuracy of security risk assessment is improved. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input user access history into a generation AI and have the generation AI perform analysis of the access history.

[0034] The analysis unit can determine the risk of misuse and leakage of personal information based on the collected information. For example, the analysis unit can analyze a user's access history to determine whether unauthorized access has occurred. For example, the analysis unit can detect unauthorized access based on the access date and time and the source IP address. The analysis unit can also analyze information about the device being used to determine whether the device is infected with malware. For example, the analysis unit can determine the risk of malware infection based on the device type and OS version. By determining the risk based on the collected information, appropriate protective measures can be provided. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the risk determination.

[0035] The service provider can take measures to warn the user and block access if unauthorized access is detected. For example, if unauthorized access is detected, the service provider can warn the user. For example, the service provider can display a warning message to the user. The service provider can also take measures to block access. For example, the service provider can block the IP address of the unauthorized access source. This reduces security risks by enabling a rapid response when unauthorized access is detected. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the results of the unauthorized access detection into a generation AI and leave the generation AI to generate a warning message and perform access blocking.

[0036] The service provider can guide users on how to remove malware if their device is infected. For example, the service provider can issue a warning to the user if their device is infected with malware. For example, the service provider can display a message to the user guiding them on how to remove the malware. The service provider can also provide malware removal tools. For example, the service provider can provide the user with a download link for the malware removal tool. This reduces security risks by guiding users on appropriate measures when their device is infected with malware. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider can input the malware infection detection result into a generation AI and leave the generation AI to generate a message guiding users on how to remove the malware.

[0037] The data collection unit can analyze the user's past security incident history and select the optimal data collection method. For example, the data collection unit can prioritize the collection of specific device information based on the types of incidents the user has encountered in the past. For example, the data collection unit can analyze the time periods in which the user's past incidents occurred and focus on collecting information during those time periods. The data collection unit can also adjust the collection frequency based on the frequency of the user's past incidents. This allows for the selection of the optimal data collection method by analyzing past incident history, thereby improving the accuracy of security risk assessment. Some or all of the above processing in the data collection unit may be performed using or without a generating AI. For example, the data collection unit can input the user's past security incident history into a generating AI and have the generating AI select the optimal data collection method.

[0038] The data collection unit can filter security information based on the user's current activities and areas of interest. For example, if the user is online shopping, the data collection unit can prioritize collecting relevant security information. If the user is at work, the data collection unit can prioritize collecting security information related to their work. If the user is traveling, the data collection unit can prioritize collecting security information related to their travel destination. This allows for the efficient collection of highly relevant information by filtering information based on the user's activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the user's current activities and areas of interest into a generative AI and have the generative AI perform the filtering.

[0039] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting security information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of security information for that region. For example, if the user is traveling, the data collection unit can prioritize the collection of security information for the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of security information for the area around their home. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's geographical location information into a generation AI and have the generation AI perform the collection of highly relevant information.

[0040] The data collection unit can analyze a user's social media activity and collect relevant information when collecting security information. For example, if a user mentions a specific topic on social media, the data collection unit can collect security information related to that topic. For example, if a user participates in a specific event on social media, the data collection unit can collect security information related to that event. The data collection unit can also collect security information related to a specific location if a user checks in to that location on social media. This allows for the efficient collection of relevant security information by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the user's social media activity into a generative AI and have the generative AI collect the relevant information.

[0041] The analysis unit can improve the accuracy of risk assessment by considering the interrelationships of security information during analysis. For example, the analysis unit can improve the accuracy of risk assessment by analyzing the interrelationships between user access history and device information. For example, the analysis unit can identify high-risk patterns by analyzing the correlation between access history and device information. The analysis unit can also improve the accuracy of risk assessment by analyzing the interrelationships between network status and device information. For example, the analysis unit can identify high-risk situations by analyzing the correlation between network speed and device type. Furthermore, the analysis unit can improve the accuracy of risk assessment by analyzing the interrelationships between user access history and network status. For example, the analysis unit can identify high-risk access by analyzing the correlation between the access source IP address and network speed. This improves the accuracy of risk assessment by considering the interrelationships of security information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input interrelationship data of security information into a generation AI and have the generation AI perform the improvement of risk assessment accuracy.

[0042] The analysis unit can perform risk assessments by considering the user's attribute information during analysis. For example, the analysis unit can perform risk assessments by considering the user's age and gender. For example, the analysis unit can identify high-risk behavioral patterns based on age and gender. The analysis unit can also perform risk assessments by considering the user's occupation and position. For example, the analysis unit can identify high-risk behavioral patterns based on occupation and position. Furthermore, the analysis unit can also perform risk assessments by considering the user's place of residence and living environment. For example, the analysis unit can identify high-risk behavioral patterns based on place of residence and living environment. This makes it possible to perform more appropriate risk assessments by considering the user's attribute information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input the user's attribute information into a generation AI and have the generation AI perform the risk assessment.

[0043] The analysis unit can perform risk assessments by considering the geographical distribution of security information during analysis. For example, if the user is in a specific region, the analysis unit can perform risk assessments based on the security information of that region. For example, the analysis unit can analyze the frequency of security incidents in a specific region and identify high-risk areas. Furthermore, if the user is traveling, the analysis unit can perform risk assessments based on the security information of the travel destination. For example, the analysis unit can analyze the occurrence of security incidents in the travel destination and identify high-risk areas. In addition, if the user is at home, the analysis unit can perform risk assessments based on security information around the user's home. For example, the analysis unit can analyze the occurrence of security incidents around the user's home and identify high-risk areas. This allows for more appropriate risk assessments by considering the geographical distribution of security information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input geographical distribution data of security information into a generation AI and have the generation AI perform risk assessments.

[0044] The analysis unit can improve the accuracy of risk assessment by referring to relevant literature during analysis. For example, the analysis unit can improve the accuracy of risk assessment by referring to the latest security research papers. For example, the analysis unit can identify new risk factors based on the latest security research papers. The analysis unit can also improve the accuracy of risk assessment by referring to past security incident reports. For example, the analysis unit can identify high-risk patterns based on past security incident reports. Furthermore, the analysis unit can improve the accuracy of risk assessment by referring to the opinions of security experts. For example, the analysis unit can identify high-risk behavioral patterns based on the opinions of security experts. In this way, the accuracy of risk assessment is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data from relevant literature into a generation AI and have the generation AI perform the improvement of risk assessment accuracy.

[0045] The service provider can select the optimal method when providing protective measures and support by referring to the user's past security incident history. For example, the service provider can provide the optimal protective measures based on the types of incidents the user has encountered in the past. For example, the service provider can prioritize the provision of specific protective measures based on the types of past incidents. The service provider can also select the optimal support method by referring to the time periods when the user's past incidents occurred. For example, the service provider can prioritize the provision of specific support methods based on the time periods when the past incidents occurred. Furthermore, the service provider can also provide the optimal protective measures based on the frequency of the user's past incidents. For example, the service provider can prioritize the provision of specific protective measures based on the frequency of past incidents. This allows the service provider to provide optimal protective measures and support by referring to past incident history. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider can input the user's past security incident history into a generation AI and have the generation AI select the optimal protective measures and support methods.

[0046] The service provider can customize the means used to provide protective measures and support based on the user's current activity. For example, if the user is online shopping, the service provider can provide relevant protective measures. For example, the service provider can display a phishing scam warning to the user while they are online shopping. The service provider can also provide work-related support if the user is at work. For example, the service provider can provide work-related security information to the user while they are at work. Furthermore, if the user is traveling, the service provider can provide security information about their travel destination. For example, the service provider can provide information about security risks at their travel destination to the user while they are traveling. This allows for the provision of more appropriate protective measures and support by customizing the means according to the user's activity. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's current activity data into a generative AI and have the generative AI perform the customization of the means.

[0047] The service provider can select the optimal method when providing protective measures and support, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can provide protective measures appropriate for that region. For example, the service provider can analyze security risks in a specific region and provide protective measures appropriate for that region. Furthermore, if the user is traveling, the service provider can provide security information for their travel destination. For example, the service provider can analyze security risks in their travel destination and provide protective measures appropriate for that destination. In addition, if the user is at home, the service provider can provide security information for the area around their home. For example, the service provider can analyze security risks around their home and provide protective measures appropriate for the area around their home. This allows the service provider to provide optimal protective measures and support by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI select the optimal protective measures and support methods.

[0048] The service provider can analyze a user's social media activity and propose measures when providing safeguards and support. For example, if a user mentions a specific topic on social media, the service provider can provide safeguards related to that topic. For example, if a user participates in a specific event on social media, the service provider can provide support related to that event. The service provider can also provide safeguards related to a specific location if a user checks in to a specific location on social media. In this way, by analyzing a user's social media activity, the service provider can provide relevant safeguards and support. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input data on the user's social media activity into a generative AI and have the generative AI propose measures.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The security protection system may also include a predictive unit that learns and predicts user behavior patterns. For example, if a user tends to access a particular website during a specific time period, the predictive unit can proactively assess security risks during that time period and prepare appropriate protective measures. The predictive unit can also learn how often and where a user uses a particular device and predict security risks associated with that device and location. Furthermore, based on the user's past behavior data, the predictive unit can predict the likelihood of future security incidents and take preventative measures. In this way, the predictive unit can prevent security risks by learning and predicting user behavior patterns.

[0051] The security protection system can also include a customization section that tailors security information based on the user's hobbies and interests. For example, if a user frequently visits websites related to a particular hobby, the customization section can prioritize notifying them of security risks related to that hobby. Furthermore, if a user has specific interests, the customization section can provide security information related to those interests. In addition, the customization section can suggest security measures based on the user's hobbies and interests. This allows the customization section to provide more relevant information by tailoring security information to the user's hobbies and interests.

[0052] The security protection system may also include a predictive unit that analyzes the user's past behavior history and predicts security risks based on behavioral patterns. For example, if a user tends to access a particular website during a specific time period, the predictive unit can pre-assess the security risks during that time period and prepare appropriate protective measures. The predictive unit can also learn how often and where a user uses a particular device and predict security risks associated with that device and location. Furthermore, based on the user's past behavioral data, the predictive unit can predict the likelihood of future security incidents and take preventative measures. In this way, the predictive unit can prevent security risks by learning and predicting user behavior patterns.

[0053] The security protection system can also include a customization section that tailors security information based on the user's hobbies and interests. For example, if a user frequently visits websites related to a particular hobby, the customization section can prioritize notifying them of security risks related to that hobby. Furthermore, if a user has specific interests, the customization section can provide security information related to those interests. In addition, the customization section can suggest security measures based on the user's hobbies and interests. This allows the customization section to provide more relevant information by tailoring security information to the user's hobbies and interests.

[0054] The following briefly describes the processing flow for example form 1.

[0055] Step 1: The collection unit collects information related to the user's security. Specifically, it collects information such as the user's access history, information about the devices being used, and network status. For example, it collects information such as what devices the user is using and what networks they are connected to. Step 2: The analysis unit analyzes the information collected by the collection unit to determine the risk of misuse or leakage of personal information. Specifically, it analyzes the user's access history to determine whether unauthorized access has occurred. It also analyzes information about the device being used to determine whether the device is infected with malware. Step 3: The service provider will provide protective measures and support based on the results obtained by the analysis unit. Specifically, if unauthorized access is detected, the service provider will issue a warning to the user and take measures to block access. In addition, if the device is infected with malware, the service provider will guide the user on how to remove the malware.

[0056] (Example of form 2) The security protection system according to an embodiment of the present invention is a system that entrusts protection and support against security issues such as unauthorized use and leakage of personal information to a generating AI. This security protection system collects information related to the user's security, the generating AI analyzes the collected information, determines the risk of unauthorized use and leakage of personal information, and provides appropriate protective measures and support. For example, the security protection system collects information such as the user's access history, information about the device being used, and the network status. Next, the generating AI analyzes the collected information and determines the risk of unauthorized use and leakage of personal information. For example, it analyzes the user's access history to determine whether unauthorized access has occurred. It also analyzes information about the device being used to determine whether the device is infected with malware. Furthermore, the generating AI provides appropriate protective measures and support based on the determination results. For example, if unauthorized access is detected, the generating AI issues a warning to the user and takes measures to block access. Also, if the device is infected with malware, the generating AI guides the user on how to remove the malware. Through this mechanism, protection and support against security issues such as unauthorized use and leakage of personal information are provided. Users can use the internet with peace of mind by receiving security measures powered by generated AI. For example, when using services such as online shopping or internet banking, generated AI monitors the risk of fraudulent use and provides necessary protective measures to ensure user safety. In this way, the security protection system can reduce security risks by collecting and analyzing user security information and providing appropriate protective measures and support.

[0057] The security protection system according to this embodiment comprises a collection unit, an analysis unit, and a provision unit. The collection unit collects information related to the user's security. For example, the collection unit collects information such as the user's access history, information about the device being used, and the network status. For example, the collection unit collects information such as what kind of device the user is using and what kind of network they are connected to. The analysis unit analyzes the information collected by the collection unit and determines the risk of unauthorized use and leakage of personal information. For example, the analysis unit analyzes the user's access history and determines whether unauthorized access has occurred. The analysis unit also analyzes information about the device being used and determines whether the device is infected with malware. The provision unit provides protective measures and support based on the determination results obtained by the analysis unit. For example, if unauthorized access is detected, the provision unit issues a warning to the user and takes measures to block access. Furthermore, if the device is infected with malware, the provision unit guides the user on how to remove the malware. In this way, the security protection system according to this embodiment can reduce security risks by collecting and analyzing information related to the user's security and providing appropriate protective measures and support.

[0058] The data collection unit collects user security information. Specifically, it collects information such as user access history, device information, and network status. For example, it collects information such as what devices users are using and what networks they are connected to. The data collection unit obtains detailed information such as the IP address, browser type, and operating system version when users access websites. It also collects hardware information and installed software versions of the devices users are using. Furthermore, regarding network status, it collects information such as the SSID and signal strength of the connected Wi-Fi, network latency, and packet loss rate. This information is collected in real time and used to assess security risks. The data collection unit centrally manages this data and can collaborate with other systems and departments as needed. For example, collected data is stored on a cloud server and made accessible to the analysis unit. In addition, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions are possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0059] The analysis unit analyzes the information collected by the data collection unit to determine the risk of misuse and leakage of personal information. Specifically, it analyzes user access history to determine whether unauthorized access is occurring. For example, if there are frequent accesses from unusual times or locations, the analysis unit will determine that the access is unauthorized. It also analyzes information about the devices being used to determine whether the devices are infected with malware. For example, if the CPU usage or memory usage of a device is abnormally high, the analysis unit will determine that the device may be infected with malware. The analysis unit uses AI to analyze this data in real time and quickly detect abnormal patterns and malicious behavior. The AI ​​uses machine learning algorithms to learn normal patterns from past data and detect abnormal patterns. It can also use anomaly detection algorithms to detect patterns that are different from the norm or abnormal data and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0060] The service provider will provide protective measures and support based on the results obtained by the analysis department. Specifically, if unauthorized access is detected, the service provider will warn the user and take measures to block access. For example, if an unauthorized access attempt is made to a user's account, the service provider will immediately notify the user and prompt them to lock the account or change their password. In addition, if a device is infected with malware, the service provider will guide the user on how to remove the malware. For example, they will provide specific removal procedures and information on recommended security software for devices suspected of infection. Furthermore, the service provider will also provide education and training to raise users' security awareness. For example, by providing security knowledge such as how to identify phishing emails and how to create strong passwords, users will be able to take actions to reduce risks themselves. This allows the service provider to provide users with prompt and appropriate protective measures and support, thereby reducing security risks. Furthermore, the service provider can collect user feedback and continuously improve the accuracy and effectiveness of the protective measures and support they provide. For example, by reviewing the content of warning messages and notification methods based on user feedback, they can achieve more effective security measures. This allows the service provider to always offer users the latest and most optimal protections and support, minimizing security risks.

[0061] The data collection unit can collect information such as user access history, device information, and network status. For example, the data collection unit can collect user access history. For example, the data collection unit can collect information such as access date and time, and source IP address. The data collection unit can also collect information about the device being used. For example, the data collection unit can collect information such as device type and OS version. Furthermore, the data collection unit can collect network status. For example, the data collection unit can collect information such as network speed and connection status. By collecting user access history, device information, and network status, the accuracy of security risk assessment is improved. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input user access history into a generation AI and have the generation AI perform analysis of the access history.

[0062] The analysis unit can determine the risk of misuse and leakage of personal information based on the collected information. For example, the analysis unit can analyze a user's access history to determine whether unauthorized access has occurred. For example, the analysis unit can detect unauthorized access based on the access date and time and the source IP address. The analysis unit can also analyze information about the device being used to determine whether the device is infected with malware. For example, the analysis unit can determine the risk of malware infection based on the device type and OS version. By determining the risk based on the collected information, appropriate protective measures can be provided. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the risk determination.

[0063] The service provider can take measures to warn the user and block access if unauthorized access is detected. For example, if unauthorized access is detected, the service provider can warn the user. For example, the service provider can display a warning message to the user. The service provider can also take measures to block access. For example, the service provider can block the IP address of the unauthorized access source. This reduces security risks by enabling a rapid response when unauthorized access is detected. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the results of the unauthorized access detection into a generation AI and leave the generation AI to generate a warning message and perform access blocking.

[0064] The service provider can guide users on how to remove malware if their device is infected. For example, the service provider can issue a warning to the user if their device is infected with malware. For example, the service provider can display a message to the user guiding them on how to remove the malware. The service provider can also provide malware removal tools. For example, the service provider can provide the user with a download link for the malware removal tool. This reduces security risks by guiding users on appropriate measures when their device is infected with malware. Some or all of the above processing in the service provider may be performed using a generation AI, or not. For example, the service provider can input the malware infection detection result into a generation AI and leave the generation AI to generate a message guiding users on how to remove the malware.

[0065] The data collection unit can estimate the user's emotions and adjust the timing of security information collection based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing to reduce the user's burden. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also accelerate the collection timing to quickly acquire information if the user is relaxed. For example, the data collection unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the data collection unit can optimize the collection timing to efficiently acquire information. For example, the data collection unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows the data collection timing to be adjusted according to the user's emotions, reducing the user's burden and efficiently collecting information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the data collection unit can input user emotion data into a generation AI and have the generation AI adjust the timing of data collection.

[0066] The data collection unit can analyze the user's past security incident history and select the optimal data collection method. For example, the data collection unit can prioritize the collection of specific device information based on the types of incidents the user has encountered in the past. For example, the data collection unit can analyze the time periods in which the user's past incidents occurred and focus on collecting information during those time periods. The data collection unit can also adjust the collection frequency based on the frequency of the user's past incidents. This allows for the selection of the optimal data collection method by analyzing past incident history, thereby improving the accuracy of security risk assessment. Some or all of the above processing in the data collection unit may be performed using or without a generating AI. For example, the data collection unit can input the user's past security incident history into a generating AI and have the generating AI select the optimal data collection method.

[0067] The data collection unit can filter security information based on the user's current activities and areas of interest. For example, if the user is online shopping, the data collection unit can prioritize collecting relevant security information. If the user is at work, the data collection unit can prioritize collecting security information related to their work. If the user is traveling, the data collection unit can prioritize collecting security information related to their travel destination. This allows for the efficient collection of highly relevant information by filtering information based on the user's activities and areas of interest. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the user's current activities and areas of interest into a generative AI and have the generative AI perform the filtering.

[0068] The data collection unit can estimate the user's emotions and determine the priority of security information to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting important security information. For example, the data collection unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The data collection unit can also collect normal security information if the user is relaxed. For example, the data collection unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the data collection unit can prioritize information that can be collected quickly. For example, the data collection unit can collect the user's biometric data (heart rate or skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for the rapid collection of important information by prioritizing information according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the data collection unit can input user emotion data into a generative AI and have the generative AI determine the priority of the information.

[0069] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location when collecting security information. For example, if the user is in a specific region, the data collection unit can prioritize the collection of security information for that region. For example, if the user is traveling, the data collection unit can prioritize the collection of security information for the travel destination. Furthermore, if the user is at home, the data collection unit can prioritize the collection of security information for the area around their home. This allows for the efficient collection of highly relevant information by considering the user's geographical location. Some or all of the above processing in the data collection unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the data collection unit can input the user's geographical location information into a generation AI and have the generation AI perform the collection of highly relevant information.

[0070] The data collection unit can analyze a user's social media activity and collect relevant information when collecting security information. For example, if a user mentions a specific topic on social media, the data collection unit can collect security information related to that topic. For example, if a user participates in a specific event on social media, the data collection unit can collect security information related to that event. The data collection unit can also collect security information related to a specific location if a user checks in to that location on social media. This allows for the efficient collection of relevant security information by analyzing a user's social media activity. Some or all of the above processing in the data collection unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the data collection unit can input data on the user's social media activity into a generative AI and have the generative AI collect the relevant information.

[0071] The analysis unit can estimate the user's emotions and adjust the risk assessment criteria based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit can tighten the risk assessment criteria. For example, the analysis unit can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also return the risk assessment criteria to normal if the user is relaxed. For example, the analysis unit can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can adjust the criteria to make a quick risk assessment. For example, the analysis unit can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for more appropriate risk assessment by adjusting the risk assessment criteria according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input user emotion data into a generation AI and have the generation AI adjust the risk assessment criteria.

[0072] The analysis unit can improve the accuracy of risk assessment by considering the interrelationships of security information during analysis. For example, the analysis unit can improve the accuracy of risk assessment by analyzing the interrelationships between user access history and device information. For example, the analysis unit can identify high-risk patterns by analyzing the correlation between access history and device information. The analysis unit can also improve the accuracy of risk assessment by analyzing the interrelationships between network status and device information. For example, the analysis unit can identify high-risk situations by analyzing the correlation between network speed and device type. Furthermore, the analysis unit can improve the accuracy of risk assessment by analyzing the interrelationships between user access history and network status. For example, the analysis unit can identify high-risk access by analyzing the correlation between the access source IP address and network speed. This improves the accuracy of risk assessment by considering the interrelationships of security information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input interrelationship data of security information into a generation AI and have the generation AI perform the improvement of risk assessment accuracy.

[0073] The analysis unit can perform risk assessments by considering the user's attribute information during analysis. For example, the analysis unit can perform risk assessments by considering the user's age and gender. For example, the analysis unit can identify high-risk behavioral patterns based on age and gender. The analysis unit can also perform risk assessments by considering the user's occupation and position. For example, the analysis unit can identify high-risk behavioral patterns based on occupation and position. Furthermore, the analysis unit can also perform risk assessments by considering the user's place of residence and living environment. For example, the analysis unit can identify high-risk behavioral patterns based on place of residence and living environment. This makes it possible to perform more appropriate risk assessments by considering the user's attribute information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without using a generation AI. For example, the analysis unit can input the user's attribute information into a generation AI and have the generation AI perform the risk assessment.

[0074] The analysis unit can estimate the user's emotions and adjust the order in which risk assessment results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will display important risks first. The analysis unit can, for example, capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The analysis unit can also display risks in the normal order if the user is relaxed. The analysis unit can, for example, record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the analysis unit can also display risks requiring immediate attention first. The analysis unit can, for example, collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for quick identification of important risks by adjusting the order in which risk assessment results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the analysis unit can input user emotion data into a generation AI and have the generation AI adjust the display order of the risk assessment results.

[0075] The analysis unit can perform risk assessments by considering the geographical distribution of security information during analysis. For example, if the user is in a specific region, the analysis unit can perform risk assessments based on the security information of that region. For example, the analysis unit can analyze the frequency of security incidents in a specific region and identify high-risk areas. Furthermore, if the user is traveling, the analysis unit can perform risk assessments based on the security information of the travel destination. For example, the analysis unit can analyze the occurrence of security incidents in the travel destination and identify high-risk areas. In addition, if the user is at home, the analysis unit can perform risk assessments based on security information around the user's home. For example, the analysis unit can analyze the occurrence of security incidents around the user's home and identify high-risk areas. This allows for more appropriate risk assessments by considering the geographical distribution of security information. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input geographical distribution data of security information into a generation AI and have the generation AI perform risk assessments.

[0076] The analysis unit can improve the accuracy of risk assessment by referring to relevant literature during analysis. For example, the analysis unit can improve the accuracy of risk assessment by referring to the latest security research papers. For example, the analysis unit can identify new risk factors based on the latest security research papers. The analysis unit can also improve the accuracy of risk assessment by referring to past security incident reports. For example, the analysis unit can identify high-risk patterns based on past security incident reports. Furthermore, the analysis unit can improve the accuracy of risk assessment by referring to the opinions of security experts. For example, the analysis unit can identify high-risk behavioral patterns based on the opinions of security experts. In this way, the accuracy of risk assessment is improved by referring to relevant literature. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit can input data from relevant literature into a generation AI and have the generation AI perform the improvement of risk assessment accuracy.

[0077] The service provider can estimate the user's emotions and adjust the method of providing protective measures and support based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide protective measures including detailed explanations. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also provide standard protective measures if the user is relaxed. For example, the service provider can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the service provider can provide protective measures that allow for a quick response. For example, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for a more appropriate response by adjusting the method of providing protective measures and support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provision unit can input user emotion data into a generative AI and have the generative AI adjust the methods of providing protective measures and support.

[0078] The service provider can select the optimal method when providing protective measures and support by referring to the user's past security incident history. For example, the service provider can provide the optimal protective measures based on the types of incidents the user has encountered in the past. For example, the service provider can prioritize the provision of specific protective measures based on the types of past incidents. The service provider can also select the optimal support method by referring to the time periods when the user's past incidents occurred. For example, the service provider can prioritize the provision of specific support methods based on the time periods when the past incidents occurred. Furthermore, the service provider can also provide the optimal protective measures based on the frequency of the user's past incidents. For example, the service provider can prioritize the provision of specific protective measures based on the frequency of past incidents. This allows the service provider to provide optimal protective measures and support by referring to the past incident history. Some or all of the above processing in the service provider may be performed using a generation AI, or it may be performed without a generation AI. For example, the service provider can input the user's past security incident history into a generation AI and have the generation AI select the optimal protective measures and support methods.

[0079] The service provider can customize the means used to provide protective measures and support based on the user's current activity. For example, if the user is online shopping, the service provider can provide relevant protective measures. For example, the service provider can display a phishing scam warning to the user while they are online shopping. The service provider can also provide work-related support if the user is at work. For example, the service provider can provide work-related security information to the user while they are at work. Furthermore, if the user is traveling, the service provider can provide security information about their travel destination. For example, the service provider can provide information about security risks at their travel destination to the user while they are traveling. This allows for the provision of more appropriate protective measures and support by customizing the means according to the user's activity. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's current activity data into a generative AI and have the generative AI perform the customization of the means.

[0080] The service provider can estimate the user's emotions and determine the priority of protective measures and support based on the estimated emotions. For example, if the user is feeling anxious, the service provider will prioritize providing important protective measures. For example, the service provider can capture the user's facial expressions with a camera and estimate their emotions using an emotion estimation algorithm. The service provider can also provide standard protective measures if the user is relaxed. For example, the service provider can record the user's voice and estimate their emotions using voice analysis technology. Furthermore, if the user is in a hurry, the service provider can prioritize providing protective measures that can be responded to quickly. For example, the service provider can collect the user's biometric data (heart rate and skin electrical activity) with sensors and estimate their emotions using an emotion estimation algorithm. This allows for quick responses to important situations by determining the priority of protective measures and support according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provision unit may be performed using a generative AI, or it may be performed without using a generative AI. For example, the service provision unit can input user emotion data into a generative AI and have the generative AI determine the priority of protective measures and support.

[0081] The service provider can select the optimal method when providing protective measures and support, taking into account the user's geographical location. For example, if the user is in a specific region, the service provider can provide protective measures appropriate for that region. For example, the service provider can analyze security risks in a specific region and provide protective measures appropriate for that region. Furthermore, if the user is traveling, the service provider can provide security information for their travel destination. For example, the service provider can analyze security risks in their travel destination and provide protective measures appropriate for that destination. In addition, if the user is at home, the service provider can provide security information for the area around their home. For example, the service provider can analyze security risks around their home and provide protective measures appropriate for the area around their home. This allows the service provider to provide optimal protective measures and support by taking into account the user's geographical location. Some or all of the above processing in the service provider may be performed using a generative AI, or not. For example, the service provider can input the user's geographical location information into a generative AI and have the generative AI select the optimal protective measures and support methods.

[0082] The service provider can analyze a user's social media activity and propose measures when providing safeguards and support. For example, if a user mentions a specific topic on social media, the service provider can provide safeguards related to that topic. For example, if a user participates in a specific event on social media, the service provider can provide support related to that event. The service provider can also provide safeguards related to a specific location if a user checks in to a specific location on social media. In this way, by analyzing a user's social media activity, the service provider can provide relevant safeguards and support. Some or all of the above processing in the service provider may be performed using or without a generative AI. For example, the service provider can input data on the user's social media activity into a generative AI and have the generative AI propose measures.

[0083] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0084] The security protection system may also include a predictive unit that learns and predicts user behavior patterns. For example, if a user tends to access a particular website during a specific time period, the predictive unit can proactively assess security risks during that time period and prepare appropriate protective measures. The predictive unit can also learn how often and where a user uses a particular device and predict security risks associated with that device and location. Furthermore, based on the user's past behavior data, the predictive unit can predict the likelihood of future security incidents and take preventative measures. In this way, the predictive unit can prevent security risks by learning and predicting user behavior patterns.

[0085] The security protection system may also include a notification unit that estimates the user's emotions and adjusts the method of notifying users of security risks based on those emotions. For example, if the user is stressed, the notification unit can downplay notifications and only notify them of important risks. Conversely, if the user is relaxed, the notification unit can provide detailed risk information. Furthermore, if the user is in a hurry, the notification unit can provide concise and rapid notifications. In this way, the notification unit can reduce the burden on the user and provide appropriate risk information by adjusting the notification method according to the user's emotions.

[0086] The security protection system may also include a health monitoring unit that monitors the user's health status and adjusts the response to security risks based on that status. For example, the health monitoring unit can monitor the user's heart rate and blood pressure, and if an abnormality is detected, it can refrain from issuing security risk notifications. Furthermore, if the user is healthy, the health monitoring unit can perform normal security risk responses. Additionally, if the user is fatigued, the health monitoring unit can provide a concise and rapid response. In this way, by adjusting the response method according to the user's health status, the health monitoring unit can reduce the user's burden and provide appropriate security measures.

[0087] The security protection system can also include a customization section that tailors security information based on the user's hobbies and interests. For example, if a user frequently visits websites related to a particular hobby, the customization section can prioritize notifying them of security risks related to that hobby. Furthermore, if a user has specific interests, the customization section can provide security information related to those interests. In addition, the customization section can suggest security measures based on the user's hobbies and interests. This allows the customization section to provide more relevant information by tailoring security information to the user's hobbies and interests.

[0088] The security protection system may further include a priority determination unit that estimates the user's emotions and determines the priority of security measures based on those emotions. For example, if the user is feeling anxious, the priority determination unit can prioritize the execution of critical security measures. Conversely, if the user is relaxed, the priority determination unit can execute standard security measures. Furthermore, if the user is in a hurry, the priority determination unit can prioritize security measures that can be addressed quickly. In this way, the priority determination unit can quickly execute critical measures by determining the priority of security measures according to the user's emotions.

[0089] The security protection system may also include a predictive unit that analyzes the user's past behavior history and predicts security risks based on behavioral patterns. For example, if a user tends to access a particular website during a specific time period, the predictive unit can pre-assess the security risks during that time period and prepare appropriate protective measures. The predictive unit can also learn how often and where a user uses a particular device and predict security risks associated with that device and location. Furthermore, based on the user's past behavioral data, the predictive unit can predict the likelihood of future security incidents and take preventative measures. In this way, the predictive unit can prevent security risks by learning and predicting user behavior patterns.

[0090] The security protection system may also include a notification unit that estimates the user's emotions and adjusts the method of notifying users of security risks based on those emotions. For example, if the user is stressed, the notification unit can downplay notifications and only notify them of important risks. Conversely, if the user is relaxed, the notification unit can provide detailed risk information. Furthermore, if the user is in a hurry, the notification unit can provide concise and rapid notifications. In this way, the notification unit can reduce the burden on the user and provide appropriate risk information by adjusting the notification method according to the user's emotions.

[0091] The security protection system may also include a health monitoring unit that monitors the user's health status and adjusts the response to security risks based on that status. For example, the health monitoring unit can monitor the user's heart rate and blood pressure, and if an abnormality is detected, it can refrain from issuing security risk notifications. Furthermore, if the user is healthy, the health monitoring unit can perform normal security risk responses. Additionally, if the user is fatigued, the health monitoring unit can provide a concise and rapid response. In this way, by adjusting the response method according to the user's health status, the health monitoring unit can reduce the user's burden and provide appropriate security measures.

[0092] The security protection system can also include a customization section that tailors security information based on the user's hobbies and interests. For example, if a user frequently visits websites related to a particular hobby, the customization section can prioritize notifying them of security risks related to that hobby. Furthermore, if a user has specific interests, the customization section can provide security information related to those interests. In addition, the customization section can suggest security measures based on the user's hobbies and interests. This allows the customization section to provide more relevant information by tailoring security information to the user's hobbies and interests.

[0093] The security protection system may further include a priority determination unit that estimates the user's emotions and determines the priority of security measures based on those emotions. For example, if the user is feeling anxious, the priority determination unit can prioritize the execution of critical security measures. Conversely, if the user is relaxed, the priority determination unit can execute standard security measures. Furthermore, if the user is in a hurry, the priority determination unit can prioritize security measures that can be addressed quickly. In this way, the priority determination unit can quickly execute critical measures by determining the priority of security measures according to the user's emotions.

[0094] The following briefly describes the processing flow for example form 2.

[0095] Step 1: The collection unit collects information related to the user's security. Specifically, it collects information such as the user's access history, information about the devices being used, and network status. For example, it collects information such as what devices the user is using and what networks they are connected to. Step 2: The analysis unit analyzes the information collected by the collection unit to determine the risk of misuse or leakage of personal information. Specifically, it analyzes the user's access history to determine whether unauthorized access has occurred. It also analyzes information about the device being used to determine whether the device is infected with malware. Step 3: The service provider will provide protective measures and support based on the results obtained by the analysis unit. Specifically, if unauthorized access is detected, the service provider will issue a warning to the user and take measures to block access. In addition, if the device is infected with malware, the service provider will guide the user on how to remove the malware.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects user access history and device information using the camera 42 and communication I / F 44 of the smart device 14. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and determines the risk of misuse or leakage of personal information. The provision unit is implemented in the control unit 46A of the smart device 14, which issues a warning to the user based on the determination result and provides appropriate protective measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0100] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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).

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.).

[0112] 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.

[0113] 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.

[0114] 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.

[0115] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects user access history and device information using the camera 42 and communication I / F 44 of the smart glasses 214. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and determines the risk of misuse or leakage of personal information. The provision unit is implemented in the control unit 46A of the smart glasses 214, which issues a warning to the user based on the determination result and provides appropriate protective measures. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0116] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0117] 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.

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

[0119] The 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.

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

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

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

[0123] Figure 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.

[0124] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0126] In the 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.

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

[0128] The specific processing unit 290 transmits the result of the specific processing to the 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.

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

[0130] The data processing system 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.

[0131] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects user access history and device information using the camera 42 and communication I / F 44 of the headset terminal 314. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and determines the risk of misuse or leakage of personal information. The provision unit is implemented in the control unit 46A of the headset terminal 314, which issues a warning to the user based on the determination result and provides appropriate protective measures. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0132] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0133] 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.

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

[0135] The 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.

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

[0137] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

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

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] Each of the multiple elements described above, including the collection unit, analysis unit, and provision unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the collection unit collects user access history and device information using the camera 42 and communication I / F 44 of the robot 414. The analysis unit is implemented in the identification processing unit 290 of the data processing unit 12, which analyzes the collected information and determines the risk of misuse or leakage of personal information. The provision unit is implemented in the control unit 46A of the robot 414, which issues a warning to the user based on the determination result and provides appropriate protective measures. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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."

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] (Note 1) A collection unit that collects information about user security, An analysis unit analyzes the information collected by the aforementioned collection unit and determines the risk of misuse or leakage of personal information, The system includes a provisioning unit that provides protective measures and support based on the determination results obtained by the analysis unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is It collects information such as the user's access history, information about the device being used, and network status. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on the collected information, we assess the risk of misuse and leakage of personal information. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, If unauthorized access is detected, the user will be warned and measures will be taken to block access. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned supply unit is, If a device is infected with malware, the system will guide the user on how to remove the malware. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of security information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is Analyze the user's past security incident history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting security information, filtering is performed based on the user's current activity and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is It estimates the user's emotions and determines the priority of security information to collect based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting security information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting security information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, The system estimates user sentiment and adjusts risk assessment criteria based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, we improve the accuracy of risk assessment by considering the interrelationships of security information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, risk assessment is performed by considering user attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the order in which the risk assessment results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, risk assessment is performed considering the geographical distribution of security information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we refer to relevant literature to improve the accuracy of risk assessment. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned supply unit is, We estimate the user's emotions and adjust the protective measures and support provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, When providing protective measures and support, the system will refer to the user's past security incident history to select the most appropriate method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing protective measures and support, customize the methods based on the user's current activity level. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of protective measures and support based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, When providing protective measures and support, the optimal method will be selected considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing protective measures and support, we analyze users' social media activity and suggest appropriate approaches. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0168] 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. A collection unit that collects information about user security, An analysis unit analyzes the information collected by the aforementioned collection unit and determines the risk of misuse or leakage of personal information, The system includes a provisioning unit that provides protective measures and support based on the determination results obtained by the analysis unit. A system characterized by the following features.

2. The aforementioned collection unit is It collects information such as the user's access history, information about the device being used, and network status. The system according to feature 1.

3. The aforementioned analysis unit, Based on the collected information, we assess the risk of misuse and leakage of personal information. The system according to feature 1.

4. The aforementioned supply unit is, If unauthorized access is detected, the user will be warned and measures will be taken to block access. The system according to feature 1.

5. The aforementioned supply unit is, If a device is infected with malware, the system will guide the user on how to remove the malware. The system according to feature 1.

6. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of security information collection based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned collection unit is Analyze the user's past security incident history and select the optimal data collection method. The system according to feature 1.

8. The aforementioned collection unit is When collecting security information, filtering is performed based on the user's current activity and areas of interest. The system according to feature 1.

9. The aforementioned collection unit is It estimates the user's emotions and determines the priority of security information to collect based on the estimated user emotions. The system according to feature 1.

10. The aforementioned collection unit is When collecting security information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system according to feature 1.

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