system

Abstract

The system according to this embodiment aims to securely and effectively manage digital identities within the metaverse. [Solution] The system according to the embodiment comprises an avatar creation support unit, a security management unit, a privacy settings unit, an activity monitoring unit, and a multi-factor authentication unit. The avatar creation support unit supports the design of avatars according to the user's preferences. The security management unit protects the avatars generated by the avatar creation support unit. The privacy settings unit manages the scope of disclosure and access rights of personal information of accounts protected by the security management unit. The activity monitoring unit detects unauthorized access and abnormal activity to personal information managed by the privacy settings unit and notifies the user. The multi-factor authentication unit supports secure login against unauthorized access and abnormal activity detected by the activity monitoring 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, the method including: receiving a user utterance; adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character; encoding the prompt; and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the management of digital identities and the protection of personal information in the metaverse are not sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to safely and effectively manage digital identities in the metaverse.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an avatar creation support unit, a security management unit, a privacy settings unit, an activity monitoring unit, and a multi-factor authentication unit. The avatar creation support unit supports the design of avatars according to the user's preferences. The security management unit protects the avatars generated by the avatar creation support unit. The privacy settings unit manages the scope of disclosure and access rights of personal information of accounts protected by the security management unit. The activity monitoring unit detects unauthorized access and abnormal activity to personal information managed by the privacy settings unit and notifies the user. The multi-factor authentication unit supports secure login against unauthorized access and abnormal activity detected by the activity monitoring unit. [Effects of the Invention]

[0007] The system according to this embodiment can securely and effectively manage digital identities within the metaverse. [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 tagged storage is one or more non-volatile storage devices that store various programs, various parameters, and the like. 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 tagged communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), 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 includes a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by contact of an indicator (for example, a pen or a finger, etc.) by detecting the contact of the indicator. The microphone 38B receives user input by voice by detecting the voice of the user. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, a specific processing unit 290 (see FIG. 2) acquires 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) An AI agent system according to an embodiment of the present invention is a system for securely and effectively managing a user's digital identity within the metaverse. This AI agent system supports the design of avatars tailored to the user's preferences, protects the user from account hijacking and fraud, easily manages the scope of disclosure and access rights for personal information, detects unauthorized access and unusual activity, notifies the user, and supports secure login. For example, the AI ​​agent system includes an avatar creation support unit that supports the design of avatars tailored to the user's preferences. The avatar creation support unit generates avatar designs based on the user's preferences. For example, it customizes the appearance of the avatar based on the colors and styles chosen by the user. The AI ​​agent system also includes a security management unit that protects the user from account hijacking and fraud. The security management unit monitors the user's account and detects unauthorized access. For example, it detects unusual login attempts and issues a warning to the user. The AI ​​agent system also includes a privacy settings unit that easily manages the scope of disclosure and access rights for personal information. The privacy settings unit controls access to personal information based on the disclosure scope set by the user. For example, it can be set so that only specific friends can access it. Furthermore, the AI ​​agent system includes an activity monitoring unit that detects unauthorized access and abnormal activity and notifies the user. The activity monitoring unit monitors the user's account activity in real time and detects abnormal activity. For example, it detects unusual access patterns and notifies the user. The AI ​​agent system also includes a multi-factor authentication unit that supports secure login. The multi-factor authentication unit requires multiple authentication methods when the user logs in. For example, it combines password and fingerprint authentication for login. This allows the AI ​​agent system to comprehensively protect the user's digital identity. As a result, the AI ​​agent system can manage the user's digital identity securely and effectively.

[0029] The AI ​​agent system according to this embodiment includes an avatar creation support unit, a security management unit, a privacy settings unit, an activity monitoring unit, and a multi-factor authentication unit. The avatar creation support unit supports the design of an avatar that matches the user's preferences. For example, the avatar creation support unit customizes the appearance of the avatar based on the colors and styles selected by the user. The avatar creation support unit can also select clothing and accessories for the avatar based on a theme selected by the user. For example, the avatar creation support unit can set the facial expressions and posture of the avatar based on the characteristics of a character selected by the user. The security management unit protects the user from account hijacking and fraud. For example, the security management unit monitors the user's account and detects unauthorized access. For example, the security management unit can detect unusual login attempts and issue warnings to the user. For example, the security management unit can also strengthen security measures to prevent attacks on the user's account. The privacy settings unit manages the scope of disclosure and access rights of personal information. For example, the privacy settings unit controls access to personal information based on the disclosure scope set by the user. For example, the privacy settings unit can also set it so that only specific friends can access it. The privacy settings unit can, for example, disclose only specific information based on the access permissions set by the user. The activity monitoring unit detects unauthorized access and unusual activity and notifies the user. The activity monitoring unit can, for example, monitor the user's account activity in real time and detect unusual activity. The activity monitoring unit can, for example, detect unusual access patterns and notify the user. The activity monitoring unit can, for example, detect abnormal data transfer volumes and issue a warning to the user. The multi-factor authentication unit supports secure login. The multi-factor authentication unit can, for example, require the user to use multiple authentication methods when logging in. The multi-factor authentication unit can, for example, combine password and fingerprint authentication for login. The multi-factor authentication unit can, for example, combine facial recognition and a security token for login.This allows the AI ​​agent system according to the embodiment to comprehensively protect the user's digital identity.

[0030] The avatar creation support unit assists users in designing avatars that match their preferences. Specifically, it provides a function to customize the appearance of avatars based on the colors and styles chosen by the user. For example, if a user selects their favorite color, the avatar's hair color and clothing color will be automatically set to be based on that color. Users can also select clothing and accessories for their avatars based on a theme they choose. For example, if a user selects a sports theme, sportswear and related accessories will be automatically suggested for the avatar. Furthermore, the avatar's facial expressions and postures can be set based on the characteristics of the character chosen by the user. For example, if a user selects an energetic character, the avatar will be set to have a bright expression and an active posture. This allows the avatar creation support unit to easily create avatars that match the user's personality and preferences. The avatar creation support unit also provides an interface that allows users to customize their avatar designs while viewing them in real time. This allows users to intuitively create their own original avatars. In addition, the avatar creation support unit can use AI to learn the user's preferences and past selection history, enabling more personalized suggestions. For example, it can suggest new designs that the user might like based on previously selected colors and styles. This will allow the avatar creation support unit to increase user satisfaction and make the avatar creation process more enjoyable and efficient.

[0031] The Security Management Department provides functions to protect users from account hijacking and fraud. Specifically, it has a system in place to constantly monitor user accounts and detect unauthorized access. For example, the Security Management Department can detect unusual login attempts and issue warnings to users. This allows users to recognize the possibility of unauthorized access early and take appropriate measures. The Security Management Department can also strengthen security measures to prevent attacks on user accounts. For example, it can enhance account security by encouraging regular password changes and setting up security questions. Furthermore, the Security Management Department can use AI to learn patterns of unauthorized access and implement more advanced security measures. For example, by analyzing past unauthorized access data and detecting specific patterns, it can predict future attacks and take preventative measures. In this way, the Security Management Department can comprehensively protect user accounts and provide an environment where users can use services with peace of mind. It is also important for the Security Management Department to provide security education and information to users. For example, by regularly distributing security newsletters and informing users about the latest threats and countermeasures, users can raise their own security awareness. This allows the security management department to work with users to maintain account security and enhance the protection of digital identities.

[0032] The Privacy Settings section provides functions for managing the scope of disclosure and access rights for personal information. Specifically, it includes a system that controls access to personal information based on the disclosure scope set by the user. For example, users can choose who can see their profile information, and can set it so that only specific friends can access it. This allows users to properly manage their personal information and protect their privacy. The Privacy Settings section can also disclose only specific information based on the access rights set by the user. For example, a user can set their contact information to be disclosed only to specific groups. This allows users to provide only the necessary information to the appropriate parties and prevent unnecessary information leaks. Furthermore, the Privacy Settings section can optimize the user's privacy settings using AI. For example, it can analyze past setting history and user behavior patterns to suggest the optimal privacy settings. This allows users to easily set appropriate privacy settings and strengthen the protection of personal information. It is also important for the Privacy Settings section to provide users with education and information on privacy. For example, it can regularly provide privacy guidelines and best practices to help users properly manage their privacy. In this way, the Privacy Settings section can strengthen the protection of user privacy and provide an environment in which users can use the service with peace of mind.

[0033] The Activity Monitoring Unit provides functions to detect unauthorized access and abnormal activity and notify users. Specifically, it has a system that monitors user account activity in real time and detects abnormal activity. For example, it can detect unusual access patterns and notify users. This allows users to recognize abnormal activity early and take appropriate measures. The Activity Monitoring Unit can also detect abnormal data transfer volumes and issue warnings to users. For example, if data transfers exceed normal usage occur, it can issue a warning to the user and request confirmation. This allows users to detect unauthorized data transfers early and take countermeasures. Furthermore, the Activity Monitoring Unit can use AI to learn patterns of abnormal activity and perform more advanced anomaly detection. For example, by analyzing past abnormal activity data and detecting specific patterns, it can predict future abnormal activity and take countermeasures in advance. In this way, the Activity Monitoring Unit can comprehensively protect user accounts and provide an environment in which users can use the service with peace of mind. It is also important for the Activity Monitoring Unit to educate and provide information to users regarding abnormal activity. For example, by regularly distributing newsletters about unusual activity and informing users about the latest threats and countermeasures, it is possible to raise users' own security awareness. This allows the activity monitoring department to work with users to maintain account security and strengthen the protection of digital identity.

[0034] The multi-factor authentication unit provides functions to support secure logins. Specifically, it includes a system that requires users to use multiple authentication methods when logging in. For example, users can log in using a combination of password and fingerprint authentication. This allows for enhanced security by adding fingerprint authentication even when a password alone is insufficient. Furthermore, users can log in using a combination of facial recognition and a security token. This achieves a higher level of security by combining biometric authentication via facial recognition with physical authentication via a security token. In addition, the multi-factor authentication unit can utilize AI to learn user authentication patterns and detect abnormal login attempts. For example, it can detect login attempts from unusual devices or locations and prompt the user for confirmation. This allows for early detection of fraudulent logins and the implementation of countermeasures. It is also important for the multi-factor authentication unit to provide users with authentication education and information. For example, it can regularly provide authentication guidelines and best practices to help users select appropriate authentication methods. This allows the multi-factor authentication unit to raise users' security awareness and provide a secure login environment. Furthermore, the multi-factor authentication unit can flexibly offer a range of authentication method options, taking user convenience into consideration. For example, by allowing users to choose their preferred authentication method, a balance can be struck between security and convenience. This enables the multi-factor authentication system to provide users with a secure and convenient login environment, thereby strengthening the protection of their digital identity.

[0035] The avatar creation support unit can analyze a user's past avatar creation history and propose the most suitable design. For example, it can analyze the design elements of avatars previously chosen by the user and propose a new design that suits their preferences. For example, it can also propose a design that matches current trends based on the colors and styles the user has used in the past. For example, it can analyze the characteristics of avatars previously created by the user and propose a design that reflects their personality. This makes it possible to propose the most suitable design based on the user's past history. Some or all of the above processes in the avatar creation support unit may be performed using AI, for example, or without AI. For example, the avatar creation support unit can input the user's past avatar creation history data into a generating AI and have the generating AI execute the optimal design proposal.

[0036] The avatar creation support unit can customize the design of an avatar based on the user's current fashion and trends. For example, the avatar creation support unit can analyze the clothes the user is currently wearing and reflect that in the avatar design. For example, the avatar creation support unit can also suggest an avatar design that incorporates current fashion trends. For example, the avatar creation support unit can customize the avatar design based on the user's preferred brands and styles. This makes it possible to customize the design based on the user's current fashion and trends. Some or all of the above processes in the avatar creation support unit may be performed using AI, for example, or not using AI. For example, the avatar creation support unit can input the user's current fashion data into a generating AI and have the generating AI perform the design customization.

[0037] The avatar creation support unit can incorporate region-specific design elements based on the user's geographical location information when creating an avatar. For example, the avatar creation support unit can incorporate traditional clothing and accessories from the user's region into the avatar. For example, the avatar creation support unit can also reflect popular fashion styles in the user's region into the avatar. For example, the avatar creation support unit can incorporate design elements that take into account the culture and customs of the user's region into the avatar. This makes it possible to incorporate region-specific design elements based on the user's geographical location information. Some or all of the above processing in the avatar creation support unit may be performed using AI, for example, or without AI. For example, the avatar creation support unit can input the user's geographical location information data into a generating AI and have the generating AI execute the incorporation of region-specific design elements.

[0038] The avatar creation support unit can analyze the user's social media activity and suggest relevant design elements when creating an avatar. For example, the avatar creation support unit can analyze images and posts shared by the user on social media and reflect them in the avatar's design. For example, the avatar creation support unit can incorporate the styles of influencers and brands that the user follows into the avatar. For example, the avatar creation support unit can customize the avatar's design based on the user's social media activity. This makes it possible to suggest design elements based on the user's social media activity. Some or all of the above processes in the avatar creation support unit may be performed using AI, for example, or not using AI. For example, the avatar creation support unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant design elements.

[0039] The security management department can analyze past security incidents and optimize preventative measures during security management. For example, the security management department can analyze patterns in past security incidents and propose preventative measures to prevent similar incidents. For example, the security management department can analyze past attack methods against user accounts and strengthen countermeasures. For example, the security management department can optimize real-time threat detection based on data from past security incidents. This enables the optimization of preventative measures based on past security incidents. Some or all of the above processes in the security management department may be performed using AI, for example, or not using AI. For example, the security management department can input past security incident data into a generating AI and have the generating AI perform the optimization of preventative measures.

[0040] The security management department can improve the accuracy of anomaly detection by referring to the user's account activity history during security management. For example, the security management department can learn the user's normal account activity patterns and detect abnormal activity. For example, the security management department can also analyze the user's account activity history and detect abnormal login attempts. For example, the security management department can detect abnormal transactions and operations in real time based on the user's account activity history. This makes it possible to improve the accuracy of anomaly detection based on the user's account activity history. Some or all of the above processes in the security management department may be performed using AI, for example, or without AI. For example, the security management department can input user account activity history data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0041] The Security Management Department can respond to region-specific threats by considering the user's geographical location information during security management. For example, if the user is in a specific region, the Security Management Department can respond to security threats occurring in that region. For example, the Security Management Department can also propose region-specific security measures based on the user's geographical location information. For example, if the user is traveling, the Security Management Department can respond to security threats in the region they are visiting. This makes it possible to respond to region-specific threats based on the user's geographical location information. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not using AI. For example, the Security Management Department can input the user's geographical location information data into a generating AI and have the generating AI execute responses to region-specific threats.

[0042] The security management department can identify potential threats by analyzing users' social media activity during security management. For example, the security management department can analyze the content of users' social media posts to identify potential security threats. The security management department can also monitor the activity of accounts that users follow to detect security threats. For example, the security management department can analyze users' social media activity patterns to detect abnormal activity. This makes it possible to identify potential threats based on users' social media activity. Some or all of the above processes in the security management department may be performed using AI, for example, or not using AI. For example, the security management department can input user social media activity data into a generating AI and have the generating AI perform the identification of potential threats.

[0043] The privacy settings unit can suggest optimal settings by referring to past setting history when setting privacy settings. For example, the privacy settings unit can suggest optimal settings based on the privacy settings the user has previously selected. For example, the privacy settings unit can also analyze the user's past setting history and suggest privacy settings that are appropriate for the current situation. For example, the privacy settings unit can suggest optimal privacy settings by referring to settings the user has changed in the past. This makes it possible to suggest optimal privacy settings based on past setting history. Some or all of the above processing in the privacy settings unit may be performed using AI, for example, or without using AI. For example, the privacy settings unit can input the user's past setting history data into a generating AI and have the generating AI perform the task of suggesting optimal settings.

[0044] The privacy settings unit can dynamically change settings based on the user's current activity when setting privacy. For example, if the user is performing a specific activity, the privacy settings unit can suggest privacy settings that match that activity. For example, if the user is using a new device, the privacy settings unit can also suggest privacy settings that are optimal for that device. For example, if the user is in a specific location, the privacy settings unit can also suggest privacy settings that match that location. This enables dynamic changes to privacy settings based on the user's current activity. Some or all of the above processing in the privacy settings unit may be performed using AI, for example, or without AI. For example, the privacy settings unit can input the user's current activity data into a generating AI and have the generating AI perform the dynamic changes to the settings.

[0045] The privacy settings unit can address region-specific privacy requirements by considering the user's geographical location information when setting privacy settings. For example, if the user is in a specific region, the privacy settings unit can suggest settings that match the privacy requirements of that region. The privacy settings unit can also suggest region-specific privacy settings based on the user's geographical location information. For example, if the user is traveling, the privacy settings unit can suggest settings that address the privacy requirements of the region they are visiting. This makes it possible to address region-specific privacy requirements based on the user's geographical location information. Some or all of the above processing in the privacy settings unit may be performed using AI, for example, or without AI. For example, the privacy settings unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of addressing region-specific privacy requirements.

[0046] The privacy settings unit can analyze the user's social media activity and suggest optimal settings when setting privacy preferences. For example, the privacy settings unit can analyze information shared by the user on social media and optimize the privacy settings. For example, the privacy settings unit can also suggest privacy settings based on the activity of accounts the user follows. For example, the privacy settings unit can analyze the user's social media activity patterns and suggest optimal privacy settings. This makes it possible to suggest optimal privacy settings based on the user's social media activity. Some or all of the above processing in the privacy settings unit may be performed using AI, for example, or without AI. For example, the privacy settings unit can input the user's social media activity data into a generating AI and have the generating AI suggest optimal settings.

[0047] The activity monitoring unit can optimize its detection algorithm by referring to past anomalous activity data during activity monitoring. For example, the activity monitoring unit can analyze past anomalous activity data and optimize the algorithm for detecting similar anomalous activities. The activity monitoring unit can also analyze past anomalous activity against a user's account and enhance the detection algorithm. For example, the activity monitoring unit can optimize real-time anomalous activity detection based on past anomalous activity data. This enables the optimization of the detection algorithm based on past anomalous activity data. Some or all of the above processes in the activity monitoring unit may be performed using AI, for example, or without AI. For example, the activity monitoring unit can input past anomalous activity data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0048] The activity monitoring unit can improve the accuracy of anomaly detection by referring to the user's account activity history during activity monitoring. For example, the activity monitoring unit can learn the user's normal account activity patterns and detect abnormal activity. The activity monitoring unit can also analyze the user's account activity history and detect abnormal login attempts. For example, the activity monitoring unit can detect abnormal transactions and operations in real time based on the user's account activity history. This makes it possible to improve the accuracy of anomaly detection based on the user's account activity history. Some or all of the above processing in the activity monitoring unit may be performed using AI, for example, or without AI. For example, the activity monitoring unit can input the user's account activity history data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0049] The activity monitoring unit can detect region-specific abnormal activity by considering the user's geographical location information during activity monitoring. For example, if the user is in a specific region, the activity monitoring unit can detect abnormal activity occurring in that region. The activity monitoring unit can also detect region-specific abnormal activity based on the user's geographical location information. For example, if the user is traveling, the activity monitoring unit can detect abnormal activity in the region they are visiting. This makes it possible to detect region-specific abnormal activity based on the user's geographical location information. Some or all of the above processing in the activity monitoring unit may be performed using AI, for example, or without AI. For example, the activity monitoring unit can input the user's geographical location information data into a generating AI and have the generating AI perform the detection of region-specific abnormal activity.

[0050] The activity monitoring unit can analyze a user's social media activity during activity monitoring to identify potential anomalous activity. For example, the activity monitoring unit can analyze the content of a user's social media posts to identify potential anomalous activity. The activity monitoring unit can also monitor the activity of accounts that a user follows to detect anomalous activity. For example, the activity monitoring unit can analyze a user's social media activity patterns to detect anomalous activity. This makes it possible to identify potential anomalous activity based on a user's social media activity. Some or all of the above processing in the activity monitoring unit may be performed using AI, for example, or without AI. For example, the activity monitoring unit can input the user's social media activity data into a generating AI and have the generating AI perform the identification of potential anomalous activity.

[0051] The multi-factor authentication unit can propose the optimal authentication method by referring to past authentication history during multi-factor authentication. For example, the multi-factor authentication unit can propose the optimal authentication method based on authentication methods previously used by the user. For example, the multi-factor authentication unit can also analyze the user's past authentication history and propose an authentication method suited to the current situation. For example, the multi-factor authentication unit can propose the optimal authentication method by referring to authentication methods that the user has successfully used in the past. This makes it possible to propose the optimal authentication method based on past authentication history. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input the user's past authentication history data into a generating AI and have the generating AI execute the proposal of the optimal authentication method.

[0052] The multi-factor authentication unit can dynamically change the authentication method based on the user's current activity during multi-factor authentication. For example, if the user is performing a specific activity, the multi-factor authentication unit can suggest an authentication method suited to that activity. For example, if the user is using a new device, the multi-factor authentication unit can also suggest an authentication method best suited to that device. For example, if the user is in a specific location, the multi-factor authentication unit can also suggest an authentication method suited to that location. This enables dynamic changes to the authentication method based on the user's current activity. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input the user's current activity data into a generating AI and have the generating AI perform the dynamic change of the authentication method.

[0053] The multi-factor authentication unit can address region-specific authentication requirements by considering the user's geographical location information during multi-factor authentication. For example, if the user is in a specific region, the multi-factor authentication unit can propose an authentication method that matches the authentication requirements of that region. The multi-factor authentication unit can also propose a region-specific authentication method based on the user's geographical location information. For example, if the user is traveling, the multi-factor authentication unit can propose an authentication method that corresponds to the authentication requirements of the region they are visiting. This makes it possible to address region-specific authentication requirements based on the user's geographical location information. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of addressing region-specific authentication requirements.

[0054] The multi-factor authentication unit can analyze the user's social media activity during multi-factor authentication and propose the optimal authentication method. For example, the multi-factor authentication unit can analyze information shared by the user on social media and propose the optimal authentication method. For example, the multi-factor authentication unit can also propose an authentication method by referring to the activity of accounts that the user follows. For example, the multi-factor authentication unit can analyze the user's social media activity patterns and propose the optimal authentication method. This makes it possible to propose an optimal authentication method based on the user's social media activity. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input the user's social media activity data into a generating AI and have the generating AI propose an optimal authentication method.

[0055] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0056] The avatar creation support department can suggest avatar designs based on the user's hobbies and interests. For example, if a user is interested in a particular sport or music genre, design elements related to that hobby can be incorporated into the avatar. Similarly, if a user is a fan of a particular movie or TV show, designs reflecting those characters or scenes can be suggested. Furthermore, if a user is interested in a specific art style or culture, avatar designs incorporating those elements can be proposed. This allows for the creation of unique avatar designs tailored to the user's hobbies and interests.

[0057] The security management department can analyze users' past behavior patterns and dynamically update the criteria for detecting abnormal behavior. For example, if a user attempts to log in during a time when they do not normally access the system, this behavior can be deemed abnormal and a warning can be issued. Similarly, if a user accesses the system from a device they do not normally use, this behavior can be deemed abnormal and additional authentication can be requested. Furthermore, if a user performs an operation they do not normally perform, this behavior can be deemed abnormal and security measures can be strengthened. This enables the detection of abnormal behavior based on users' past behavior patterns.

[0058] The activity monitoring unit can analyze users' social media activity and identify potential security threats. For example, it can analyze information shared by users on social media and issue warnings if that information could pose a security risk. It can also monitor the activity of accounts that users follow and detect security threats. Furthermore, it can analyze users' social media activity patterns and detect unusual activity. This makes it possible to identify potential security threats based on users' social media activity.

[0059] The avatar creation support system can incorporate region-specific design elements based on the user's geographical location. For example, it can incorporate traditional clothing and accessories from the user's region into their avatar. It can also reflect popular fashion styles in the user's region. Furthermore, it can incorporate design elements that take into account the culture and customs of the user's region. This makes it possible to incorporate region-specific design elements based on the user's geographical location.

[0060] The privacy settings section can analyze a user's social media activity and suggest optimal privacy settings. For example, it can analyze information a user shares on social media and optimize privacy settings based on that information. It can also suggest privacy settings based on the activity of accounts the user follows. Furthermore, it can analyze a user's social media activity patterns and suggest optimal privacy settings. This makes it possible to suggest optimal privacy settings based on the user's social media activity.

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

[0062] Step 1: The avatar creation support unit assists in designing avatars to suit the user's preferences. For example, it customizes the avatar's appearance based on the colors and styles chosen by the user, selects clothing and accessories based on a theme, and sets facial expressions and postures based on the character's characteristics. Step 2: The Security Management Department protects the avatars generated by the Avatar Creation Support Department. For example, they protect users from account hijacking and fraud, monitor accounts to detect unauthorized access, and detect unusual login attempts to warn users. Step 3: The Privacy Settings section manages the scope of disclosure and access permissions for personal information of accounts protected by the Security Management section. For example, it controls access to personal information based on the disclosure settings set by the user, allowing access only by specific friends and disclosing only certain information. Step 4: The Activity Monitoring Unit detects unauthorized access to personal information managed by the Privacy Settings Unit and unusual activity, and notifies the user. For example, it monitors the user's account activity in real time, detects unusual activity, and notifies the user of unusual access patterns or abnormal data transfer volumes. Step 5: The multi-factor authentication unit supports secure logins in response to unauthorized access or abnormal activity detected by the activity monitoring unit. For example, it may require users to use multiple authentication methods when logging in, such as combining a password with fingerprint authentication or facial recognition with a security token.

[0063] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system for securely and effectively managing a user's digital identity within the metaverse. This AI agent system supports the design of avatars tailored to the user's preferences, protects the user from account hijacking and fraud, easily manages the scope of disclosure and access rights for personal information, detects unauthorized access and unusual activity, notifies the user, and supports secure login. For example, the AI ​​agent system includes an avatar creation support unit that supports the design of avatars tailored to the user's preferences. The avatar creation support unit generates avatar designs based on the user's preferences. For example, it customizes the appearance of the avatar based on the colors and styles chosen by the user. The AI ​​agent system also includes a security management unit that protects the user from account hijacking and fraud. The security management unit monitors the user's account and detects unauthorized access. For example, it detects unusual login attempts and issues a warning to the user. The AI ​​agent system also includes a privacy settings unit that easily manages the scope of disclosure and access rights for personal information. The privacy settings unit controls access to personal information based on the disclosure scope set by the user. For example, it can be set so that only specific friends can access it. Furthermore, the AI ​​agent system includes an activity monitoring unit that detects unauthorized access and abnormal activity and notifies the user. The activity monitoring unit monitors the user's account activity in real time and detects abnormal activity. For example, it detects unusual access patterns and notifies the user. The AI ​​agent system also includes a multi-factor authentication unit that supports secure login. The multi-factor authentication unit requires multiple authentication methods when the user logs in. For example, it combines password and fingerprint authentication for login. This allows the AI ​​agent system to comprehensively protect the user's digital identity. As a result, the AI ​​agent system can manage the user's digital identity securely and effectively.

[0064] The AI ​​agent system according to this embodiment includes an avatar creation support unit, a security management unit, a privacy settings unit, an activity monitoring unit, and a multi-factor authentication unit. The avatar creation support unit supports the design of an avatar that matches the user's preferences. For example, the avatar creation support unit customizes the appearance of the avatar based on the colors and styles selected by the user. The avatar creation support unit can also select clothing and accessories for the avatar based on a theme selected by the user. For example, the avatar creation support unit can set the facial expressions and posture of the avatar based on the characteristics of a character selected by the user. The security management unit protects the user from account hijacking and fraud. For example, the security management unit monitors the user's account and detects unauthorized access. For example, the security management unit can detect unusual login attempts and issue warnings to the user. For example, the security management unit can also strengthen security measures to prevent attacks on the user's account. The privacy settings unit manages the scope of disclosure and access rights of personal information. For example, the privacy settings unit controls access to personal information based on the disclosure scope set by the user. For example, the privacy settings unit can also set it so that only specific friends can access it. The privacy settings unit can, for example, disclose only specific information based on the access permissions set by the user. The activity monitoring unit detects unauthorized access and unusual activity and notifies the user. The activity monitoring unit can, for example, monitor the user's account activity in real time and detect unusual activity. The activity monitoring unit can, for example, detect unusual access patterns and notify the user. The activity monitoring unit can, for example, detect abnormal data transfer volumes and issue a warning to the user. The multi-factor authentication unit supports secure login. The multi-factor authentication unit can, for example, require the user to use multiple authentication methods when logging in. The multi-factor authentication unit can, for example, combine password and fingerprint authentication for login. The multi-factor authentication unit can, for example, combine facial recognition and a security token for login.This allows the AI ​​agent system according to the embodiment to comprehensively protect the user's digital identity.

[0065] The avatar creation support unit assists users in designing avatars that match their preferences. Specifically, it provides a function to customize the appearance of avatars based on the colors and styles chosen by the user. For example, if a user selects their favorite color, the avatar's hair color and clothing color will be automatically set to be based on that color. Users can also select clothing and accessories for their avatars based on a theme they choose. For example, if a user selects a sports theme, sportswear and related accessories will be automatically suggested for the avatar. Furthermore, the avatar's facial expressions and postures can be set based on the characteristics of the character chosen by the user. For example, if a user selects an energetic character, the avatar will be set to have a bright expression and an active posture. This allows the avatar creation support unit to easily create avatars that match the user's personality and preferences. The avatar creation support unit also provides an interface that allows users to customize their avatar designs while viewing them in real time. This allows users to intuitively create their own original avatars. In addition, the avatar creation support unit can use AI to learn the user's preferences and past selection history, enabling more personalized suggestions. For example, it can suggest new designs that the user might like based on previously selected colors and styles. This will allow the avatar creation support unit to increase user satisfaction and make the avatar creation process more enjoyable and efficient.

[0066] The Security Management Department provides functions to protect users from account hijacking and fraud. Specifically, it has a system in place to constantly monitor user accounts and detect unauthorized access. For example, the Security Management Department can detect unusual login attempts and issue warnings to users. This allows users to recognize the possibility of unauthorized access early and take appropriate measures. The Security Management Department can also strengthen security measures to prevent attacks on user accounts. For example, it can enhance account security by encouraging regular password changes and setting up security questions. Furthermore, the Security Management Department can use AI to learn patterns of unauthorized access and implement more advanced security measures. For example, by analyzing past unauthorized access data and detecting specific patterns, it can predict future attacks and take preventative measures. In this way, the Security Management Department can comprehensively protect user accounts and provide an environment where users can use services with peace of mind. It is also important for the Security Management Department to provide security education and information to users. For example, by regularly distributing security newsletters and informing users about the latest threats and countermeasures, users can raise their own security awareness. This allows the security management department to work with users to maintain account security and enhance the protection of digital identities.

[0067] The Privacy Settings section provides functions for managing the scope of disclosure and access rights for personal information. Specifically, it includes a system that controls access to personal information based on the disclosure scope set by the user. For example, users can choose who can see their profile information, and can set it so that only specific friends can access it. This allows users to properly manage their personal information and protect their privacy. The Privacy Settings section can also disclose only specific information based on the access rights set by the user. For example, a user can set their contact information to be disclosed only to specific groups. This allows users to provide only the necessary information to the appropriate parties and prevent unnecessary information leaks. Furthermore, the Privacy Settings section can optimize the user's privacy settings using AI. For example, it can analyze past setting history and user behavior patterns to suggest the optimal privacy settings. This allows users to easily set appropriate privacy settings and strengthen the protection of personal information. It is also important for the Privacy Settings section to provide users with education and information on privacy. For example, it can regularly provide privacy guidelines and best practices to help users properly manage their privacy. In this way, the Privacy Settings section can strengthen the protection of user privacy and provide an environment in which users can use the service with peace of mind.

[0068] The Activity Monitoring Unit provides functions to detect unauthorized access and abnormal activity and notify users. Specifically, it has a system that monitors user account activity in real time and detects abnormal activity. For example, it can detect unusual access patterns and notify users. This allows users to recognize abnormal activity early and take appropriate measures. The Activity Monitoring Unit can also detect abnormal data transfer volumes and issue warnings to users. For example, if data transfers exceed normal usage occur, it can issue a warning to the user and request confirmation. This allows users to detect unauthorized data transfers early and take countermeasures. Furthermore, the Activity Monitoring Unit can use AI to learn patterns of abnormal activity and perform more advanced anomaly detection. For example, by analyzing past abnormal activity data and detecting specific patterns, it can predict future abnormal activity and take countermeasures in advance. In this way, the Activity Monitoring Unit can comprehensively protect user accounts and provide an environment in which users can use the service with peace of mind. It is also important for the Activity Monitoring Unit to educate and provide information to users regarding abnormal activity. For example, by regularly distributing newsletters about unusual activity and informing users about the latest threats and countermeasures, it is possible to raise users' own security awareness. This allows the activity monitoring department to work with users to maintain account security and strengthen the protection of digital identity.

[0069] The multi-factor authentication unit provides functions to support secure logins. Specifically, it includes a system that requires users to use multiple authentication methods when logging in. For example, users can log in using a combination of password and fingerprint authentication. This allows for enhanced security by adding fingerprint authentication even when a password alone is insufficient. Furthermore, users can log in using a combination of facial recognition and a security token. This achieves a higher level of security by combining biometric authentication via facial recognition with physical authentication via a security token. In addition, the multi-factor authentication unit can utilize AI to learn user authentication patterns and detect abnormal login attempts. For example, it can detect login attempts from unusual devices or locations and prompt the user for confirmation. This allows for early detection of fraudulent logins and the implementation of countermeasures. It is also important for the multi-factor authentication unit to provide users with authentication education and information. For example, it can regularly provide authentication guidelines and best practices to help users select appropriate authentication methods. This allows the multi-factor authentication unit to raise users' security awareness and provide a secure login environment. Furthermore, the multi-factor authentication unit can flexibly offer a range of authentication method options, taking user convenience into consideration. For example, by allowing users to choose their preferred authentication method, a balance can be struck between security and convenience. This enables the multi-factor authentication system to provide users with a secure and convenient login environment, thereby strengthening the protection of their digital identity.

[0070] The avatar creation support unit can estimate the user's emotions and dynamically change the avatar's facial expression and posture based on the estimated emotions. For example, if the user is happy, the avatar creation support unit can change the avatar's facial expression to a smile and its posture to a relaxed one. For example, if the user is sad, the avatar creation support unit can change the avatar's facial expression to a sad one and its posture to a depressed one. For example, if the user is angry, the avatar creation support unit can change the avatar's facial expression to an angry one and its posture to a tense one. This makes it possible to change the avatar's facial expression and posture according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the avatar creation support unit may be performed using AI, for example, or without using AI. For example, the avatar creation support unit can input the user's facial expression data into a generation AI and have the generation AI perform changes to the avatar's facial expressions.

[0071] The avatar creation support unit can analyze a user's past avatar creation history and propose the most suitable design. For example, it can analyze the design elements of avatars previously chosen by the user and propose a new design that suits their preferences. For example, it can also propose a design that matches current trends based on the colors and styles the user has used in the past. For example, it can analyze the characteristics of avatars previously created by the user and propose a design that reflects their personality. This makes it possible to propose the most suitable design based on the user's past history. Some or all of the above processes in the avatar creation support unit may be performed using AI, for example, or without AI. For example, the avatar creation support unit can input the user's past avatar creation history data into a generating AI and have the generating AI execute the optimal design proposal.

[0072] The avatar creation support unit can customize the design of an avatar based on the user's current fashion and trends. For example, the avatar creation support unit can analyze the clothes the user is currently wearing and reflect that in the avatar design. For example, the avatar creation support unit can also suggest an avatar design that incorporates current fashion trends. For example, the avatar creation support unit can customize the avatar design based on the user's preferred brands and styles. This makes it possible to customize the design based on the user's current fashion and trends. Some or all of the above processes in the avatar creation support unit may be performed using AI, for example, or not using AI. For example, the avatar creation support unit can input the user's current fashion data into a generating AI and have the generating AI perform the design customization.

[0073] The avatar creation support unit can estimate the user's emotions and adjust the avatar's color scheme based on the estimated emotions. For example, if the user is relaxed, the avatar creation support unit can suggest a calm color scheme. For example, if the user is excited, the avatar creation support unit can suggest a bright and lively color scheme. For example, if the user is sad, the avatar creation support unit can suggest a gentle color scheme. This makes it possible to adjust the color scheme according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the avatar creation support unit may be performed using AI, for example, or without AI. For example, the avatar creation support unit can input the user's emotion data into a generative AI and have the generative AI perform the color scheme adjustment.

[0074] The avatar creation support unit can incorporate region-specific design elements based on the user's geographical location information when creating an avatar. For example, the avatar creation support unit can incorporate traditional clothing and accessories from the user's region into the avatar. For example, the avatar creation support unit can also reflect popular fashion styles in the user's region into the avatar. For example, the avatar creation support unit can incorporate design elements that take into account the culture and customs of the user's region into the avatar. This makes it possible to incorporate region-specific design elements based on the user's geographical location information. Some or all of the above processing in the avatar creation support unit may be performed using AI, for example, or without AI. For example, the avatar creation support unit can input the user's geographical location information data into a generating AI and have the generating AI execute the incorporation of region-specific design elements.

[0075] The avatar creation support unit can analyze the user's social media activity and suggest relevant design elements when creating an avatar. For example, the avatar creation support unit can analyze images and posts shared by the user on social media and reflect them in the avatar's design. For example, the avatar creation support unit can incorporate the styles of influencers and brands that the user follows into the avatar. For example, the avatar creation support unit can customize the avatar's design based on the user's social media activity. This makes it possible to suggest design elements based on the user's social media activity. Some or all of the above processes in the avatar creation support unit may be performed using AI, for example, or not using AI. For example, the avatar creation support unit can input the user's social media activity data into a generating AI and have the generating AI suggest relevant design elements.

[0076] The security management unit can estimate the user's emotions and adjust how security warnings are displayed based on those emotions. For example, if the user is tense, the security management unit may display security warnings in a calm tone. If the user is relaxed, the security management unit may also display security warnings with more detailed information. If the user is in a hurry, the security management unit may also display concise and quick security warnings. This allows for adjustments to how security warnings 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security management unit may be performed using AI or not. For example, the security management unit can input user emotion data into a generative AI and have the generative AI adjust how security warnings are displayed.

[0077] The security management department can analyze past security incidents and optimize preventative measures during security management. For example, the security management department can analyze patterns in past security incidents and propose preventative measures to prevent similar incidents. For example, the security management department can analyze past attack methods against user accounts and strengthen countermeasures. For example, the security management department can optimize real-time threat detection based on data from past security incidents. This enables the optimization of preventative measures based on past security incidents. Some or all of the above processes in the security management department may be performed using AI, for example, or not using AI. For example, the security management department can input past security incident data into a generating AI and have the generating AI perform the optimization of preventative measures.

[0078] The security management department can improve the accuracy of anomaly detection by referring to the user's account activity history during security management. For example, the security management department can learn the user's normal account activity patterns and detect abnormal activity. For example, the security management department can also analyze the user's account activity history and detect abnormal login attempts. For example, the security management department can detect abnormal transactions and operations in real time based on the user's account activity history. This makes it possible to improve the accuracy of anomaly detection based on the user's account activity history. Some or all of the above processes in the security management department may be performed using AI, for example, or without AI. For example, the security management department can input user account activity history data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0079] The security management department can estimate the user's emotions and prioritize security measures based on those emotions. For example, if the user is feeling anxious, the security management department will prioritize the most important security measures. If the user is relaxed, the security management department may also implement detailed security measures in stages. If the user is in a hurry, the security management department may also prioritize security measures that can be implemented quickly. This makes it possible to prioritize security measures 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 may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the security management department may be performed using AI or not. For example, the security management department can input user emotion data into a generative AI and have the generative AI determine the priority of security measures.

[0080] The Security Management Department can respond to region-specific threats by considering the user's geographical location information during security management. For example, if the user is in a specific region, the Security Management Department can respond to security threats occurring in that region. For example, the Security Management Department can also propose region-specific security measures based on the user's geographical location information. For example, if the user is traveling, the Security Management Department can respond to security threats in the region they are visiting. This makes it possible to respond to region-specific threats based on the user's geographical location information. Some or all of the above processes in the Security Management Department may be performed using AI, for example, or not using AI. For example, the Security Management Department can input the user's geographical location information data into a generating AI and have the generating AI execute responses to region-specific threats.

[0081] The security management department can identify potential threats by analyzing users' social media activity during security management. For example, the security management department can analyze the content of users' social media posts to identify potential security threats. The security management department can also monitor the activity of accounts that users follow to detect security threats. For example, the security management department can analyze users' social media activity patterns to detect abnormal activity. This makes it possible to identify potential threats based on users' social media activity. Some or all of the above processes in the security management department may be performed using AI, for example, or not using AI. For example, the security management department can input user social media activity data into a generating AI and have the generating AI perform the identification of potential threats.

[0082] The privacy settings unit can estimate the user's emotions and suggest privacy settings based on those emotions. For example, if the user is feeling anxious, the privacy settings unit may suggest strict privacy settings. If the user is relaxed, the privacy settings unit may suggest flexible privacy settings. If the user is in a hurry, the privacy settings unit may suggest easily adjustable privacy settings. This makes it possible to suggest privacy settings that are appropriate to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the privacy settings unit may be performed using AI, for example, or not using AI. For example, the privacy settings unit can input user emotion data into the generative AI and have the generative AI make suggestions for privacy settings.

[0083] The privacy settings unit can suggest optimal settings by referring to past setting history when setting privacy settings. For example, the privacy settings unit can suggest optimal settings based on the privacy settings the user has previously selected. For example, the privacy settings unit can also analyze the user's past setting history and suggest privacy settings that are appropriate for the current situation. For example, the privacy settings unit can suggest optimal privacy settings by referring to settings the user has changed in the past. This makes it possible to suggest optimal privacy settings based on past setting history. Some or all of the above processing in the privacy settings unit may be performed using AI, for example, or without using AI. For example, the privacy settings unit can input the user's past setting history data into a generating AI and have the generating AI perform the task of suggesting optimal settings.

[0084] The privacy settings unit can dynamically change settings based on the user's current activity when setting privacy. For example, if the user is performing a specific activity, the privacy settings unit can suggest privacy settings that match that activity. For example, if the user is using a new device, the privacy settings unit can also suggest privacy settings that are optimal for that device. For example, if the user is in a specific location, the privacy settings unit can also suggest privacy settings that match that location. This enables dynamic changes to privacy settings based on the user's current activity. Some or all of the above processing in the privacy settings unit may be performed using AI, for example, or without AI. For example, the privacy settings unit can input the user's current activity data into a generating AI and have the generating AI perform the dynamic changes to the settings.

[0085] The privacy settings unit can estimate the user's emotions and determine the priority of privacy settings based on the estimated emotions. For example, if the user is feeling anxious, the privacy settings unit will prioritize the most important privacy settings. For example, if the user is relaxed, the privacy settings unit may also implement detailed privacy settings in stages. For example, if the user is in a hurry, the privacy settings unit may also prioritize privacy settings that can be implemented quickly. This makes it possible to determine the priority of privacy settings according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the privacy settings unit may be performed using AI or not using AI. For example, the privacy settings unit can input user emotion data into a generative AI and have the generative AI determine the priority of privacy settings.

[0086] The privacy settings unit can address region-specific privacy requirements by considering the user's geographical location information when setting privacy settings. For example, if the user is in a specific region, the privacy settings unit can suggest settings that match the privacy requirements of that region. The privacy settings unit can also suggest region-specific privacy settings based on the user's geographical location information. For example, if the user is traveling, the privacy settings unit can suggest settings that address the privacy requirements of the region they are visiting. This makes it possible to address region-specific privacy requirements based on the user's geographical location information. Some or all of the above processing in the privacy settings unit may be performed using AI, for example, or without AI. For example, the privacy settings unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of addressing region-specific privacy requirements.

[0087] The privacy settings unit can analyze the user's social media activity and suggest optimal settings when setting privacy preferences. For example, the privacy settings unit can analyze information shared by the user on social media and optimize the privacy settings. For example, the privacy settings unit can also suggest privacy settings based on the activity of accounts the user follows. For example, the privacy settings unit can analyze the user's social media activity patterns and suggest optimal privacy settings. This makes it possible to suggest optimal privacy settings based on the user's social media activity. Some or all of the above processing in the privacy settings unit may be performed using AI, for example, or without AI. For example, the privacy settings unit can input the user's social media activity data into a generating AI and have the generating AI suggest optimal settings.

[0088] The activity monitoring unit can estimate the user's emotions and adjust the notification method for abnormal activity based on the estimated user emotions. For example, if the user is tense, the activity monitoring unit may notify the user of abnormal activity in a calm tone. If the user is relaxed, the activity monitoring unit may also notify the user of abnormal activity with detailed information. If the user is in a hurry, the activity monitoring unit may also notify the user of abnormal activity in a concise and quick manner. This makes it possible to adjust the notification method for abnormal activity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the activity monitoring unit may be performed using AI or not using AI. For example, the activity monitoring unit can input user emotion data into a generative AI and have the generative AI adjust the notification method for abnormal activity.

[0089] The activity monitoring unit can optimize its detection algorithm by referring to past anomalous activity data during activity monitoring. For example, the activity monitoring unit can analyze past anomalous activity data and optimize the algorithm for detecting similar anomalous activities. The activity monitoring unit can also analyze past anomalous activity against a user's account and enhance the detection algorithm. For example, the activity monitoring unit can optimize real-time anomalous activity detection based on past anomalous activity data. This enables the optimization of the detection algorithm based on past anomalous activity data. Some or all of the above processes in the activity monitoring unit may be performed using AI, for example, or without AI. For example, the activity monitoring unit can input past anomalous activity data into a generating AI and have the generating AI perform the optimization of the detection algorithm.

[0090] The activity monitoring unit can improve the accuracy of anomaly detection by referring to the user's account activity history during activity monitoring. For example, the activity monitoring unit can learn the user's normal account activity patterns and detect abnormal activity. The activity monitoring unit can also analyze the user's account activity history and detect abnormal login attempts. For example, the activity monitoring unit can detect abnormal transactions and operations in real time based on the user's account activity history. This makes it possible to improve the accuracy of anomaly detection based on the user's account activity history. Some or all of the above processing in the activity monitoring unit may be performed using AI, for example, or without AI. For example, the activity monitoring unit can input the user's account activity history data into a generating AI and have the generating AI perform the task of improving the accuracy of anomaly detection.

[0091] The activity monitoring unit can estimate the user's emotions and prioritize abnormal activities based on the estimated emotions. For example, if the user is feeling anxious, the activity monitoring unit will prioritize notifying the most important abnormal activities. For example, if the user is relaxed, the activity monitoring unit can also provide step-by-step notifications of detailed abnormal activities. For example, if the user is in a hurry, the activity monitoring unit can also prioritize abnormal activities that require immediate attention. This makes it possible to determine the priority of abnormal activities according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the activity monitoring unit may be performed using AI or not using AI. For example, the activity monitoring unit can input user emotion data into a generative AI and have the generative AI perform the determination of abnormal activity priorities.

[0092] The activity monitoring unit can detect region-specific abnormal activity by considering the user's geographical location information during activity monitoring. For example, if the user is in a specific region, the activity monitoring unit can detect abnormal activity occurring in that region. The activity monitoring unit can also detect region-specific abnormal activity based on the user's geographical location information. For example, if the user is traveling, the activity monitoring unit can detect abnormal activity in the region they are visiting. This makes it possible to detect region-specific abnormal activity based on the user's geographical location information. Some or all of the above processing in the activity monitoring unit may be performed using AI, for example, or without AI. For example, the activity monitoring unit can input the user's geographical location information data into a generating AI and have the generating AI perform the detection of region-specific abnormal activity.

[0093] The activity monitoring unit can analyze a user's social media activity during activity monitoring to identify potential anomalous activity. For example, the activity monitoring unit can analyze the content of a user's social media posts to identify potential anomalous activity. The activity monitoring unit can also monitor the activity of accounts that a user follows to detect anomalous activity. For example, the activity monitoring unit can analyze a user's social media activity patterns to detect anomalous activity. This makes it possible to identify potential anomalous activity based on a user's social media activity. Some or all of the above processing in the activity monitoring unit may be performed using AI, for example, or without AI. For example, the activity monitoring unit can input the user's social media activity data into a generating AI and have the generating AI perform the identification of potential anomalous activity.

[0094] The multi-factor authentication unit can estimate the user's emotions and adjust the difficulty of the authentication process based on the estimated emotions. For example, if the user is nervous, the multi-factor authentication unit can provide a simple authentication process. For example, if the user is relaxed, the multi-factor authentication unit can also provide a more detailed authentication process. For example, if the user is in a hurry, the multi-factor authentication unit can provide an authentication process that can be completed quickly. This makes it possible to adjust the difficulty of the authentication process according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input user emotion data into a generative AI and have the generative AI adjust the difficulty of the authentication process.

[0095] The multi-factor authentication unit can propose the optimal authentication method by referring to past authentication history during multi-factor authentication. For example, the multi-factor authentication unit can propose the optimal authentication method based on authentication methods previously used by the user. For example, the multi-factor authentication unit can also analyze the user's past authentication history and propose an authentication method suited to the current situation. For example, the multi-factor authentication unit can propose the optimal authentication method by referring to authentication methods that the user has successfully used in the past. This makes it possible to propose the optimal authentication method based on past authentication history. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input the user's past authentication history data into a generating AI and have the generating AI execute the proposal of the optimal authentication method.

[0096] The multi-factor authentication unit can dynamically change the authentication method based on the user's current activity during multi-factor authentication. For example, if the user is performing a specific activity, the multi-factor authentication unit can suggest an authentication method suited to that activity. For example, if the user is using a new device, the multi-factor authentication unit can also suggest an authentication method best suited to that device. For example, if the user is in a specific location, the multi-factor authentication unit can also suggest an authentication method suited to that location. This enables dynamic changes to the authentication method based on the user's current activity. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input the user's current activity data into a generating AI and have the generating AI perform the dynamic change of the authentication method.

[0097] The multi-factor authentication unit can estimate the user's emotions and determine the priority of authentication methods based on the estimated emotions. For example, if the user is feeling anxious, the multi-factor authentication unit will prioritize the simplest authentication method. If the user is relaxed, the multi-factor authentication unit can also implement more detailed authentication methods in stages. If the user is in a hurry, the multi-factor authentication unit can also prioritize authentication methods that can be implemented quickly. This makes it possible to determine the priority of authentication methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input user emotion data into a generative AI and have the generative AI determine the priority of authentication methods.

[0098] The multi-factor authentication unit can address region-specific authentication requirements by considering the user's geographical location information during multi-factor authentication. For example, if the user is in a specific region, the multi-factor authentication unit can propose an authentication method that matches the authentication requirements of that region. The multi-factor authentication unit can also propose a region-specific authentication method based on the user's geographical location information. For example, if the user is traveling, the multi-factor authentication unit can propose an authentication method that corresponds to the authentication requirements of the region they are visiting. This makes it possible to address region-specific authentication requirements based on the user's geographical location information. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input the user's geographical location information data into a generating AI and have the generating AI perform the task of addressing region-specific authentication requirements.

[0099] The multi-factor authentication unit can analyze the user's social media activity during multi-factor authentication and propose the optimal authentication method. For example, the multi-factor authentication unit can analyze information shared by the user on social media and propose the optimal authentication method. For example, the multi-factor authentication unit can also propose an authentication method by referring to the activity of accounts that the user follows. For example, the multi-factor authentication unit can analyze the user's social media activity patterns and propose the optimal authentication method. This makes it possible to propose an optimal authentication method based on the user's social media activity. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input the user's social media activity data into a generating AI and have the generating AI propose an optimal authentication method.

[0100] The multi-factor authentication unit can estimate the user's emotions and determine the priority of authentication methods based on the estimated emotions. For example, if the user is feeling anxious, the multi-factor authentication unit will prioritize the simplest authentication method. If the user is relaxed, the multi-factor authentication unit can also implement more detailed authentication methods in stages. If the user is in a hurry, the multi-factor authentication unit can also prioritize authentication methods that can be implemented quickly. This makes it possible to determine the priority of authentication methods according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the multi-factor authentication unit may be performed using AI, for example, or without AI. For example, the multi-factor authentication unit can input user emotion data into a generative AI and have the generative AI determine the priority of authentication methods.

[0101] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0102] The avatar creation support department can suggest avatar designs based on the user's hobbies and interests. For example, if a user is interested in a particular sport or music genre, design elements related to that hobby can be incorporated into the avatar. Similarly, if a user is a fan of a particular movie or TV show, designs reflecting those characters or scenes can be suggested. Furthermore, if a user is interested in a specific art style or culture, avatar designs incorporating those elements can be proposed. This allows for the creation of unique avatar designs tailored to the user's hobbies and interests.

[0103] The avatar creation support unit can estimate the user's emotions and adjust the avatar's movements in real time based on those estimated emotions. For example, if the user is happy, the avatar's movements can be made more lively and energetic. If the user is sad, the avatar's movements can be made slower and calmer. Furthermore, if the user is angry, the avatar's movements can be emphasized and made more forceful. This makes it possible to adjust the avatar's movements according to the user's emotions.

[0104] The security management department can analyze users' past behavior patterns and dynamically update the criteria for detecting abnormal behavior. For example, if a user attempts to log in during a time when they do not normally access the system, this behavior can be deemed abnormal and a warning can be issued. Similarly, if a user accesses the system from a device they do not normally use, this behavior can be deemed abnormal and additional authentication can be requested. Furthermore, if a user performs an operation they do not normally perform, this behavior can be deemed abnormal and security measures can be strengthened. This enables the detection of abnormal behavior based on users' past behavior patterns.

[0105] The privacy settings section can estimate the user's emotions and suggest changes to the privacy settings based on those emotions. For example, if the user is feeling anxious, it can suggest stricter privacy settings. If the user is relaxed, it can suggest more flexible privacy settings. Furthermore, if the user is in a hurry, it can suggest easily adjustable privacy settings. This makes it possible to change privacy settings in accordance with the user's emotions.

[0106] The activity monitoring unit can analyze users' social media activity and identify potential security threats. For example, it can analyze information shared by users on social media and issue warnings if that information could pose a security risk. It can also monitor the activity of accounts that users follow and detect security threats. Furthermore, it can analyze users' social media activity patterns and detect unusual activity. This makes it possible to identify potential security threats based on users' social media activity.

[0107] The multi-factor authentication unit can estimate the user's emotions and adjust the difficulty of the authentication process based on those emotions. For example, if the user is nervous, a simple authentication process can be provided. If the user is relaxed, a more detailed authentication process can be provided. Furthermore, if the user is in a hurry, an authentication process that can be completed quickly can be provided. This makes it possible to adjust the difficulty of the authentication process according to the user's emotions.

[0108] The avatar creation support system can incorporate region-specific design elements based on the user's geographical location. For example, it can incorporate traditional clothing and accessories from the user's region into their avatar. It can also reflect popular fashion styles in the user's region. Furthermore, it can incorporate design elements that take into account the culture and customs of the user's region. This makes it possible to incorporate region-specific design elements based on the user's geographical location.

[0109] The security management department can estimate the user's emotions and adjust how security warnings are displayed based on those estimates. For example, if the user is stressed, security warnings can be displayed in a calm tone. If the user is relaxed, security warnings with more detailed information can be displayed. Furthermore, if the user is in a hurry, concise and quick security warnings can be displayed. This allows for the adjustment of how security warnings are displayed according to the user's emotions.

[0110] The privacy settings section can analyze a user's social media activity and suggest optimal privacy settings. For example, it can analyze information a user shares on social media and optimize privacy settings based on that information. It can also suggest privacy settings based on the activity of accounts the user follows. Furthermore, it can analyze a user's social media activity patterns and suggest optimal privacy settings. This makes it possible to suggest optimal privacy settings based on the user's social media activity.

[0111] The activity monitoring unit can estimate the user's emotions and adjust the notification method for abnormal activity based on the estimated emotions. For example, if the user is stressed, the notification can be delivered in a calm tone. If the user is relaxed, the notification can include detailed information. Furthermore, if the user is in a hurry, the notification can be concise and quick. This allows for the adjustment of the abnormal activity notification method according to the user's emotions.

[0112] The following briefly describes the processing flow for example form 2.

[0113] Step 1: The avatar creation support unit assists in designing avatars to suit the user's preferences. For example, it customizes the avatar's appearance based on the colors and styles chosen by the user, selects clothing and accessories based on a theme, and sets facial expressions and postures based on the character's characteristics. Step 2: The Security Management Department protects the avatars generated by the Avatar Creation Support Department. For example, they protect users from account hijacking and fraud, monitor accounts to detect unauthorized access, and detect unusual login attempts to warn users. Step 3: The Privacy Settings section manages the scope of disclosure and access permissions for personal information of accounts protected by the Security Management section. For example, it controls access to personal information based on the disclosure settings set by the user, allowing access only by specific friends and disclosing only certain information. Step 4: The Activity Monitoring Unit detects unauthorized access to personal information managed by the Privacy Settings Unit and unusual activity, and notifies the user. For example, it monitors the user's account activity in real time, detects unusual activity, and notifies the user of unusual access patterns or abnormal data transfer volumes. Step 5: The multi-factor authentication unit supports secure logins in response to unauthorized access or abnormal activity detected by the activity monitoring unit. For example, it may require users to use multiple authentication methods when logging in, such as combining a password with fingerprint authentication or facial recognition with a security token.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the avatar creation support unit, security management unit, privacy settings unit, activity monitoring unit, and multi-factor authentication unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the avatar creation support unit is implemented by the control unit 46A of the smart device 14 and generates an avatar design based on the user's preferences. The security management unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the user's account and detects unauthorized access. The privacy settings unit is implemented by the control unit 46A of the smart device 14 and controls access to personal information based on the disclosure scope set by the user. The activity monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the user's account activity in real time and detects abnormal activity. The multi-factor authentication unit is implemented by the control unit 46A of the smart device 14 and requires multiple authentication methods when the user logs in. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0118] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements described above, including the avatar creation support unit, security management unit, privacy settings unit, activity monitoring unit, and multi-factor authentication unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the avatar creation support unit is implemented by the control unit 46A of the smart glasses 214 and generates an avatar design based on the user's preferences. The security management unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the user's account and detects unauthorized access. The privacy settings unit is implemented by the control unit 46A of the smart glasses 214 and controls access to personal information based on the disclosure scope set by the user. The activity monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the user's account activity in real time and detects abnormal activity. The multi-factor authentication unit is implemented by the control unit 46A of the smart glasses 214 and requires multiple authentication methods when the user logs in. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0134] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements described above, including the avatar creation support unit, security management unit, privacy settings unit, activity monitoring unit, and multi-factor authentication unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the avatar creation support unit is implemented by the control unit 46A of the headset terminal 314 and generates an avatar design based on the user's preferences. The security management unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the user's account and detects unauthorized access. The privacy settings unit is implemented by the control unit 46A of the headset terminal 314 and controls access to personal information based on the disclosure scope set by the user. The activity monitoring unit is implemented by the specific processing unit 290 of the data processing unit 12 and monitors the user's account activity in real time and detects abnormal activity. The multi-factor authentication unit is implemented by the control unit 46A of the headset terminal 314 and requires multiple authentication methods when the user logs in. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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).

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the avatar creation support unit, security management unit, privacy setting unit, activity monitoring unit, and multi-factor authentication unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the avatar creation support unit is implemented by the control unit 46A of the robot 414 and generates an avatar design based on the user's preferences. The security management unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the user's account and detects unauthorized access. The privacy setting unit is implemented by, for example, the control unit 46A of the robot 414 and controls access to personal information based on the disclosure scope set by the user. The activity monitoring unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and monitors the user's account activity in real time and detects abnormal activity. The multi-factor authentication unit is implemented by, for example, the control unit 46A of the robot 414 and requires multiple authentication means when the user logs in. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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."

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] (Note 1) The avatar creation support department assists users in designing avatars to suit their preferences, A security management unit protects the avatars generated by the aforementioned avatar creation support unit, The Privacy Settings Unit manages the scope of disclosure and access rights for personal information of accounts protected by the Security Management Unit, The Activity Monitoring Unit detects unauthorized access to personal information managed by the Privacy Settings Unit and notifies the user of any unusual activity. The system includes a multi-factor authentication unit that supports secure logins in response to unauthorized access or abnormal activity detected by the activity monitoring unit. A system characterized by the following features. (Note 2) The aforementioned avatar creation support unit, It estimates the user's emotions and dynamically changes the avatar's facial expressions and posture based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned avatar creation support unit, We analyze the user's past avatar creation history and provide appropriate design suggestions. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned avatar creation support unit, When creating an avatar, the design can be customized based on the user's current fashion and trends. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned avatar creation support unit, It estimates the user's emotions and adjusts the avatar's color scheme based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned avatar creation support unit, When creating an avatar, region-specific design elements are incorporated based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned avatar creation support unit, When creating an avatar, the system analyzes the user's social media activity and suggests relevant design elements. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned security management department, It estimates the user's emotions and adjusts how security warnings are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned security management department, When managing security, analyze past security incidents and implement appropriate preventive measures. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned security management department, During security management, referencing user account activity history improves the accuracy of anomaly detection. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned security management department, It estimates user sentiment and prioritizes security measures based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned security management department, During security management, respond to region-specific threats based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned security management department, During security management, analyze users' social media activity to identify potential threats. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned privacy settings unit is It estimates the user's emotions and suggests privacy settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned privacy settings unit is When you configure your privacy settings, we will refer to your past settings history to suggest appropriate settings. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned privacy settings unit is When configuring privacy settings, dynamically change settings based on the user's current activity. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned privacy settings unit is It estimates the user's emotions and prioritizes privacy settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned privacy settings unit is It estimates the user's emotions and prioritizes privacy settings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned privacy settings unit is When setting privacy preferences, the system addresses region-specific privacy requirements based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned privacy settings unit is When you configure your privacy settings, we analyze your social media activity and suggest the optimal settings for you. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned activity monitoring unit, The system estimates the user's emotions and adjusts the notification method for abnormal activity based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned activity monitoring unit, During activity monitoring, the detection algorithm is optimized by referring to past abnormal activity data. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned activity monitoring unit, During activity monitoring, we improve the accuracy of anomaly detection by referring to the user's account activity history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned activity monitoring unit, It estimates the user's emotions and prioritizes abnormal activities based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned activity monitoring unit, During activity monitoring, region-specific abnormal activity is detected based on the user's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned activity monitoring unit, During activity monitoring, we analyze users' social media activity to identify potential anomalies. The system described in Appendix 1, characterized by the features described herein. (Note 27) The multi-factor authentication unit described above is: The system estimates the user's emotions and adjusts the difficulty of the authentication process based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The multi-factor authentication unit described above is: During multi-factor authentication, the system refers to past authentication history to suggest the appropriate authentication method. The system described in Appendix 1, characterized by the features described herein. (Note 29) The multi-factor authentication unit described above is: During multi-factor authentication, the authentication method is dynamically changed based on the user's current activity status. The system described in Appendix 1, characterized by the features described herein. (Note 30) The multi-factor authentication unit described above is: The system estimates the user's emotions and prioritizes authentication methods based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The multi-factor authentication unit described above is: During multi-factor authentication, region-specific authentication requirements are addressed based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 32) The multi-factor authentication unit described above is: During multi-factor authentication, the system analyzes the user's social media activity and suggests the optimal authentication method. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0186] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The avatar creation support department assists users in designing avatars to suit their preferences, A security management unit protects the avatars generated by the aforementioned avatar creation support unit, The Privacy Settings Unit manages the scope of disclosure and access rights for personal information of accounts protected by the Security Management Unit, The Activity Monitoring Unit detects unauthorized access to personal information managed by the Privacy Settings Unit and notifies the user of any unusual activity. The system includes a multi-factor authentication unit that supports secure logins in response to unauthorized access or abnormal activity detected by the activity monitoring unit. A system characterized by the following features.

2. The aforementioned avatar creation support unit, It estimates the user's emotions and dynamically changes the avatar's facial expressions and posture based on the estimated emotions. The system according to feature 1.

3. The aforementioned avatar creation support unit, We analyze the user's past avatar creation history and provide appropriate design suggestions. The system according to feature 1.

4. The aforementioned avatar creation support unit, When creating an avatar, the design can be customized based on the user's current fashion and trends. The system according to feature 1.

5. The aforementioned avatar creation support unit, It estimates the user's emotions and adjusts the avatar's color scheme based on those emotions. The system according to feature 1.

6. The aforementioned avatar creation support unit, When creating an avatar, region-specific design elements are incorporated based on the user's geographical location. The system according to feature 1.

7. The aforementioned avatar creation support unit, When creating an avatar, the system analyzes the user's social media activity and suggests relevant design elements. The system according to feature 1.

8. The aforementioned security management department, It estimates the user's emotions and adjusts how security warnings are displayed based on those emotions. The system according to feature 1.

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