system

Abstract

The system according to this embodiment aims to efficiently detect and appropriately address crime-related information contained in user-submitted content. [Solution] The system according to the embodiment comprises a monitoring unit, an analysis unit, a warning unit, a deletion unit, and a notification unit. The monitoring unit monitors the content of posts. The analysis unit analyzes the content of posts monitored by the monitoring unit. The warning unit displays a warning based on the results of the analysis performed by the analysis unit. The deletion unit deletes posts if the warning from the warning unit is ignored. The notification unit notifies the administrator of information about posts deleted by the deletion 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, including steps of 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, there is a problem that it is difficult to efficiently detect information related to crimes contained in user-posted content and appropriately handle it.

[0005] The system according to the embodiment aims to efficiently detect information related to crimes contained in user-posted content and appropriately handle it.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a monitoring unit, an analysis unit, a warning unit, a deletion unit, and a notification unit. The monitoring unit monitors the content of posts. The analysis unit analyzes the content of posts monitored by the monitoring unit. The warning unit displays a warning based on the results of the analysis performed by the analysis unit. The deletion unit deletes posts if the warning from the warning unit is ignored. The notification unit notifies the administrator of information about posts deleted by the deletion unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently detect crime-related information contained in user-submitted content and take appropriate action. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The monitoring system according to an embodiment of the present invention is a system in which AI monitors user posts on social networking services (SNS) and chat applications, detecting posts that may lead to criminal activity and issuing warnings or automatically deleting them. When a user posts on an SNS or chat application, the monitoring system uses AI to monitor the content of the post in real time. Next, the AI ​​analyzes the post content and detects keywords and phrases that may lead to criminal activity. For example, if keywords such as "illegal part-time jobs," "wire transfer fraud," "phishing scams," or "selling dangerous drugs" are included, the AI ​​will determine that the post is dangerous. If the AI ​​detects a dangerous post, it will display a warning to the user. For example, a warning message such as "This post may be related to criminal activity. Do you wish to continue posting?" will be displayed. If the user ignores the warning and continues posting, the AI ​​will automatically delete the post. Furthermore, if the AI ​​detects a dangerous post, it will notify the administrator of the information. The administrator can review the post content detected by the AI ​​and take additional measures as necessary. For example, this could include temporarily suspending the user account or reporting to law enforcement. This mechanism can prevent criminal activity on SNS and chat applications. Users can use the platform with peace of mind, and a deterrent effect on criminal activity is expected. For example, if a user posts "looking for illegal part-time jobs" on social media, the AI ​​will detect the post and display a warning message. If the user ignores the warning and continues posting, the AI ​​will automatically delete the post and notify the administrator. The administrator will then temporarily suspend the user's account and, if necessary, report the incident to law enforcement. In this way, AI can be used to effectively prevent criminal activity on social media and chat apps. As a result, the monitoring system can proactively prevent criminal activity on social media and chat apps, providing users with a safe environment.

[0029] The monitoring system according to the embodiment comprises a monitoring unit, an analysis unit, a warning unit, a deletion unit, and a notification unit. The monitoring unit monitors the content that users post to social networking services (SNS) or chat applications. The monitoring unit can, for example, monitor the content of posts in real time. The monitoring unit can also periodically scan the content of posts. For example, the monitoring unit scans the content of posts at a fixed time each day to detect posts that may lead to criminal activity. The analysis unit analyzes the content of posts monitored by the monitoring unit. The analysis unit can, for example, analyze the content of posts using text analysis technology. The analysis unit can also analyze the content of posts using image analysis technology. For example, the analysis unit analyzes images included in the content of posts to detect elements that may lead to criminal activity. The warning unit displays a warning based on the results of the analysis performed by the analysis unit. The warning unit can, for example, display a pop-up message to warn the user. The warning unit can also send an email notification to warn the user. For example, the warning unit displays a warning message such as, "This post may be related to criminal activity. Do you want to continue posting?" The deletion unit deletes the post if the warning from the warning unit is ignored. The deletion unit can, for example, automatically delete posts after a certain period of time has elapsed. The deletion unit can also delete posts if certain conditions are met. For example, the deletion unit deletes posts if a user ignores a warning and continues posting. The notification unit notifies the administrator of information about posts deleted by the deletion unit. The notification unit can, for example, notify the administrator by sending an email notification. It can also notify the administrator by displaying a dashboard notification. For example, the notification unit notifies the administrator of the content and reason for the deleted post. As a result, the monitoring system according to this embodiment can prevent criminal activity on social networking services and chat applications, providing an environment where users can use the services with peace of mind.

[0030] The monitoring unit monitors the content that users post on social media and chat applications. For example, the monitoring unit can monitor posts in real time. Specifically, the monitoring unit uses the APIs of social media and chat applications to retrieve user posts in real time and store them in a database. This makes it possible to monitor the content the moment a post is made. The monitoring unit can also periodically scan posts. For example, the monitoring unit scans posts at a set time each day to detect posts that may lead to criminal activity. Periodic scans are effective for re-evaluating past posts, checking whether they contain specific keywords or phrases. Furthermore, the monitoring unit can use natural language processing technology to understand the context of posts and detect potential risks. For example, by analyzing not only whether specific keywords are included, but also in what context those keywords are used, more accurate monitoring becomes possible. As a result, the monitoring unit can efficiently and effectively monitor user posts and detect early signs of criminal activity.

[0031] The analysis unit analyzes the content of posts monitored by the monitoring unit. The analysis unit can analyze the content of posts using, for example, text analysis technology. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the meaning of the content of posts and detect elements that may lead to criminal activity. For example, it can extract specific keywords or phrases contained in the content of posts and evaluate whether they are related to criminal activity. The analysis unit can also analyze the content of posts using image analysis technology. For example, the analysis unit analyzes images contained in the content of posts and detect elements that may lead to criminal activity. Image analysis includes technology that uses machine learning algorithms to recognize specific objects or scenes within images. For example, it can detect images that contain weapons, drugs, or violent scenes. Furthermore, the analysis unit can also analyze the content of voice messages using voice analysis technology. Voice analysis includes a process of converting voice to text using speech recognition technology and then analyzing that text. This allows the analysis unit to perform comprehensive analysis on all media formats—text, images, and voice—and detect signs of criminal activity.

[0032] The warning unit displays warnings based on the results analyzed by the analysis unit. For example, the warning unit can warn users by displaying pop-up messages. Specifically, when a user attempts to make a post, the warning unit displays a warning message on the screen informing them that the post may be related to criminal activity. The warning unit can also warn users by sending email notifications. For example, the warning unit may display a warning message such as, "This post may be related to criminal activity. Do you want to continue posting?" Furthermore, the warning unit can display individual warning messages to specific users based on their past activity history. For example, it can display a stronger warning message to users who have ignored warnings and continued posting in the past. This allows the warning unit to quickly provide appropriate warnings to users and deter criminal activity.

[0033] The deletion unit deletes posts if warnings from the warning unit are ignored. The deletion unit can, for example, automatically delete posts after a certain period of time has elapsed. Specifically, if a user does not modify a post within a certain time after a warning message is displayed, the deletion unit will automatically delete the post. The deletion unit can also delete posts if certain conditions are met. For example, the deletion unit will delete a post if the user ignores the warning and continues to post. Furthermore, the deletion unit can also provide an interface for administrators to manually delete posts. This allows administrators to quickly delete specific posts. The deletion unit records the content and reason for deletion of deleted posts for later reference. This allows the deletion unit to effectively delete inappropriate posts by users and support the healthy operation of social networking services and chat applications.

[0034] The notification unit notifies administrators of posts deleted by the deletion unit. For example, the notification unit can send email notifications to administrators. Specifically, it sends an email to administrators detailing the content and reason for the deleted post. The notification unit can also display dashboard notifications to administrators. For example, it can display a list of deleted posts on the administrator's dashboard, allowing them to check the reason for deletion and the date and time of deletion. Furthermore, the notification unit can also provide information about users associated with the deleted posts. This allows administrators to identify whether a particular user is repeatedly making inappropriate posts. The notification unit also provides a function to customize notification content, ensuring administrators receive the information they need. This enables the notification unit to provide administrators with timely and accurate information, supporting the healthy operation of social networking services and chat applications.

[0035] The analysis unit can detect keywords and phrases that may lead to criminal activity. For example, the analysis unit can detect keywords using a specific word list. It can also detect phrases using natural language processing techniques. For example, the analysis unit can detect keywords such as "illegal part-time jobs" and "wire transfer fraud" contained in the posted content. By detecting keywords and phrases that may lead to criminal activity, criminal acts can be prevented. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the posted content into AI, and the AI ​​can detect keywords and phrases.

[0036] The warning unit can display a warning message such as, "This post may be related to a crime. Do you wish to continue posting?" The warning unit can warn the user, for example, by displaying a pop-up message. It can also warn the user by sending an email notification. For example, the warning unit can display a warning message based on the content of the post. This can deter criminal activity by displaying a warning message to the user. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the results analyzed by the analysis unit into the AI, and the AI ​​can generate a warning message.

[0037] The deletion unit can automatically delete posts if a warning is ignored. For example, the deletion unit can automatically delete posts after a certain period of time has elapsed. Furthermore, the deletion unit can delete posts if certain conditions are met. For example, the deletion unit deletes posts if the user ignores a warning and continues posting. This prevents criminal activity by automatically deleting posts when warnings are ignored. Some or all of the above-described processes in the deletion unit may be performed using AI or not. For example, the deletion unit can input to the AI ​​that the warning unit ignored the warning, and the AI ​​can then delete the post.

[0038] The notification unit can notify administrators of information about deleted posts. For example, the notification unit can notify administrators by sending email notifications. Alternatively, the notification unit can notify administrators by displaying dashboard notifications. For example, the notification unit can notify administrators of the content and reason for deletion of deleted posts. This allows administrators to take appropriate action by notifying them of information about deleted posts. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input information about posts deleted by the deletion unit into the AI, and the AI ​​can generate notification content.

[0039] The monitoring unit can analyze a user's past posting history during monitoring and detect specific patterns. For example, if a user has a history of making crime-related posts, the monitoring unit can increase the intensity of monitoring. The monitoring unit can also analyze the frequency of specific keywords from the user's posting history and detect abnormal patterns. Furthermore, based on the user's posting history, if there are many crime-related posts during a particular time period, the monitoring unit can strengthen monitoring during that time period. In this way, by analyzing a user's past posting history, specific patterns can be detected and criminal activity can be prevented. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's past posting history into AI, which can then detect specific patterns.

[0040] The monitoring unit can apply different monitoring algorithms depending on the category of the posted content during monitoring. For example, the monitoring unit can apply a monitoring algorithm using a specific keyword list to posts related to illegal part-time jobs. It can also apply a specific phrase detection algorithm to posts related to wire fraud. Furthermore, it can apply a monitoring algorithm including image analysis to posts related to the buying and selling of dangerous drugs. This allows for more effective monitoring by applying different monitoring algorithms depending on the category of the posted content. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the category of the posted content into the AI, which can then select an appropriate monitoring algorithm.

[0041] The monitoring unit can prioritize monitoring highly relevant posts by considering the user's geographical location information during monitoring. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring posts related to crimes occurring in that area. Furthermore, based on the user's location information, the monitoring unit can prioritize monitoring posts related to crimes occurring in the vicinity. Additionally, if the user is on the move, the monitoring unit can prioritize monitoring posts related to crimes in the area they are currently traveling to. This allows for the prioritization of highly relevant posts by considering the user's geographical location information, thereby preventing criminal activity. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the user's geographical location information into the AI, which can then select highly relevant posts.

[0042] The monitoring unit can analyze a user's social media activity and monitor relevant posts during monitoring. For example, if a user belongs to a specific group, the monitoring unit will prioritize monitoring posts within that group. The monitoring unit can also analyze the content of posts by the user's followers and friends and monitor relevant posts. Furthermore, if a user frequently uses a specific hashtag, the monitoring unit can prioritize monitoring posts related to that hashtag. In this way, by analyzing a user's social media activity, relevant posts can be monitored and criminal activity can be prevented. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's social media activity data into AI, which can then select relevant posts.

[0043] The analysis unit can adjust the level of detail of its analysis based on the importance of the posted content. For example, it can perform a detailed analysis on high-importance posts to thoroughly examine the possibility of criminal activity. Conversely, it can perform a standard analysis on low-importance posts to avoid allocating excessive resources. Furthermore, the analysis unit can automatically evaluate the importance of the posted content and set an appropriate level of detail. This allows for efficient resource use and helps prevent criminal activity by adjusting the level of detail based on the importance of the posted content. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the posted content into the AI, which can then adjust the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the posted content during analysis. For example, the analysis unit can apply an analysis algorithm using a specific keyword list to posts related to illegal part-time jobs. It can also apply a specific phrase detection algorithm to posts related to wire fraud. Furthermore, it can apply an analysis algorithm including image analysis to posts related to the buying and selling of dangerous drugs. This allows for more effective analysis by applying different analysis algorithms depending on the category of the posted content. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the category of the posted content into the AI, which can then select an appropriate analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the submission date of the posts during the analysis process. For example, the analysis unit can prioritize the analysis of recently posted content and respond in real time. The analysis unit can also periodically analyze past posts to prevent overlooking anything. Furthermore, the analysis unit can prioritize the analysis of content posted during specific time periods to prevent criminal activity during those times. This real-time response is possible by determining the priority of analysis based on the submission date of the posts. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the submission date of the posts into the AI, which can then determine the priority of analysis.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the posts during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant posts to quickly detect the possibility of a crime. Furthermore, the analysis unit can postpone the analysis of less relevant posts, allowing for efficient use of resources. In addition, the analysis unit can automatically evaluate the relevance of the post content and set an appropriate analysis order. This allows for efficient resource use and helps prevent criminal activity by adjusting the analysis order based on the relevance of the posts. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input the relevance of the post content into the AI, which can then adjust the analysis order.

[0047] The warning unit can adjust the level of detail of a warning based on the importance of the post content when issuing a warning. For example, the warning unit can display a detailed warning message for high-importance posts to draw the user's attention. Conversely, it can display a concise warning message for low-importance posts to avoid excessive warnings. Furthermore, the warning unit can automatically evaluate the importance of the post content and set an appropriate level of warning detail. This allows for efficient use of resources and helps prevent criminal activity by adjusting the level of warning detail based on the importance of the post content. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the importance of the post content into the AI, which can then adjust the level of warning detail.

[0048] The warning unit can display different warning messages depending on the category of the posted content when a warning is issued. For example, the warning unit can display a specific warning message for posts related to illegal part-time jobs. It can also display a specific warning message for posts related to wire fraud. Furthermore, it can display a specific warning message for posts related to the buying and selling of dangerous drugs. This allows for more effective warnings by displaying different warning messages depending on the category of the posted content. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the category of the posted content into the AI, and the AI ​​can generate an appropriate warning message.

[0049] The warning unit can prioritize warnings based on when the post was submitted. For example, it can prioritize displaying warning messages for recently posted content. It can also periodically display warning messages for older posts to prevent them from being overlooked. Furthermore, it can prioritize displaying warning messages for content posted within a specific time period. This allows for real-time response by prioritizing warnings based on when the post was submitted. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the submission date of the posts into the AI, which can then determine the priority of the warnings.

[0050] The warning unit can adjust the order of warnings based on the relevance of the posts when issuing a warning. For example, the warning unit can prioritize displaying warning messages for highly relevant posts. It can also postpone less relevant posts, allowing for efficient use of resources. Furthermore, the warning unit can automatically evaluate the relevance of post content and set an appropriate warning order. This allows for efficient use of resources and prevention of criminal activity by adjusting the order of warnings based on the relevance of posts. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the relevance of post content into AI, which can then adjust the order of warnings.

[0051] The deletion unit can determine the priority of deletions based on the importance of the content of the posts. For example, the deletion unit can prioritize the deletion of high-importance posts to quickly eliminate the possibility of criminal activity. It can also postpone the deletion of low-importance posts, allowing for efficient use of resources. Furthermore, the deletion unit can automatically evaluate the importance of the content of the posts and set appropriate deletion priorities. This allows for efficient use of resources and prevention of criminal activity by determining the deletion priority based on the importance of the content of the posts. Some or all of the above processes in the deletion unit may be performed using AI or not. For example, the deletion unit can input the importance of the content of the posts into the AI, which can then determine the deletion priority.

[0052] The deletion unit can apply different deletion algorithms depending on the category of the posted content when deleting it. For example, the deletion unit can apply a deletion algorithm using a specific keyword list to posts related to illegal part-time jobs. It can also apply a specific phrase detection algorithm to posts related to wire fraud. Furthermore, it can apply a deletion algorithm including image analysis to posts related to the buying and selling of dangerous drugs. This allows for more effective deletion by applying different deletion algorithms depending on the category of the posted content. Some or all of the above-described processes in the deletion unit may be performed using AI or not. For example, the deletion unit can input the category of the posted content into the AI, which can then select an appropriate deletion algorithm.

[0053] The deletion unit can determine the priority of deletions based on the submission date of the posts. For example, the deletion unit can prioritize the deletion of recently posted content, enabling real-time action. It can also periodically delete past posts to prevent overlooking any. Furthermore, the deletion unit can prioritize the deletion of content posted during specific time periods, preventing criminal activity during those times. This real-time response is possible by determining the priority of deletions based on the submission date of the posts. Some or all of the above processes in the deletion unit may be performed using AI, or not. For example, the deletion unit can input the submission date of posts into the AI, which can then determine the priority of deletions.

[0054] The deletion unit can adjust the order of deletion based on the relevance of the posts. For example, the deletion unit can prioritize the deletion of highly relevant posts to quickly eliminate the possibility of criminal activity. It can also postpone the deletion of less relevant posts, allowing for efficient use of resources. Furthermore, the deletion unit can automatically evaluate the relevance of the post content and set an appropriate deletion order. This allows for efficient use of resources and prevention of criminal activity by adjusting the deletion order based on the relevance of the posts. Some or all of the above processing in the deletion unit may be performed using AI or not. For example, the deletion unit can input the relevance of the post content into the AI, which can then adjust the deletion order.

[0055] The notification unit can adjust the level of detail of notifications based on the importance of the posted content. For example, it can provide detailed notifications for high-importance posts to draw the administrator's attention. Conversely, it can provide concise notifications for low-importance posts to avoid excessive notifications. Furthermore, the notification unit can automatically evaluate the importance of the posted content and set an appropriate level of detail. This allows for efficient use of resources and prevention of criminal activity by adjusting the level of detail of notifications based on the importance of the posted content. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the importance of the posted content into the AI, which can then adjust the level of detail of the notification.

[0056] The notification unit can display different notification messages depending on the category of the posted content when a notification is sent. For example, the notification unit can display a specific notification message for posts related to illegal part-time jobs. It can also display a specific notification message for posts related to wire fraud. Furthermore, it can display a specific notification message for posts related to the buying and selling of dangerous drugs. By displaying different notification messages depending on the category of the posted content, more effective notifications can be made. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the category of the posted content into the AI, and the AI ​​can generate an appropriate notification message.

[0057] The notification unit can determine the priority of notifications based on when the posts were submitted. For example, the notification unit can prioritize notifications for recently posted content to enable real-time responses. It can also periodically notify users of past posts to prevent them from being overlooked. Furthermore, the notification unit can prioritize notifications for content posted during specific time periods to prevent criminal activity during those times. This enables real-time responses by determining the priority of notifications based on when the posts were submitted. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the submission dates of posts into the AI, which can then determine the priority of notifications.

[0058] The notification unit can adjust the order of notifications based on the relevance of the posts when sending notifications. For example, the notification unit can prioritize notifications for highly relevant posts, allowing administrators to respond quickly. It can also postpone notifications for less relevant posts, enabling efficient use of resources. Furthermore, the notification unit can automatically evaluate the relevance of post content and set an appropriate notification order. By adjusting the order of notifications based on the relevance of posts, resources can be used efficiently, and criminal activity can be prevented. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the relevance of post content into AI, which can then adjust the order of notifications.

[0059] The notification unit can suggest the most suitable notification method when sending a notification, taking into account the administrator's past response history. For example, the notification unit may prioritize suggesting notification methods that the administrator has responded to quickly in the past. Furthermore, the notification unit can automatically select the most suitable notification method based on the administrator's past response history. In addition, the notification unit can suggest new notification methods based on notification methods that the administrator has effectively used in the past. This allows for the suggestion of the most suitable notification method and effective responses by taking into account the administrator's past response history. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the administrator's past response history into AI, which can then suggest the most suitable notification method.

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

[0061] The monitoring system may further include a provisioning unit that estimates the user's interests and preferences based on the user's posts and provides relevant information. For example, if the user shows interest in a particular topic, the provisioning unit can provide news articles or blog posts related to that topic. The provisioning unit can also display advertisements related to a particular product or service if the user shows interest in that product or service. Furthermore, if the user shows interest in a particular event, the provisioning unit can provide information related to that event. This improves user satisfaction by providing relevant information based on the user's interests. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the user's posts into an AI, which can then estimate the user's interests and preferences and provide relevant information.

[0062] The monitoring system may further include a learning support unit that estimates the user's learning needs based on the user's posts and provides appropriate learning resources. For example, if a user wants to learn a specific skill, the learning support unit can provide online courses and materials related to that skill. If a user is studying for a specific exam, the learning support unit can provide past exam questions and reference books related to that exam. Furthermore, if a user is interested in a particular field, the learning support unit can provide articles and videos related to that field. This allows the system to support the user's learning by providing appropriate resources based on their learning needs. Some or all of the above processing in the learning support unit may be performed using AI or not. For example, the learning support unit can input the user's posts into an AI, which can then estimate the user's learning needs and provide appropriate resources.

[0063] The monitoring system may further include a product recommendation unit that estimates the user's purchasing intent based on the user's posts and provides relevant products. For example, if a user shows interest in a particular product, the product recommendation unit can provide products related to that product. It can also provide products related to a particular brand if the user shows interest in that brand. Furthermore, if the user shows interest in products in a particular category, the product recommendation unit can provide products related to that category. This improves the user's purchasing experience by providing relevant products based on the user's purchasing intent. Some or all of the above processing in the product recommendation unit may be performed using AI or not. For example, the product recommendation unit can input the user's posts into an AI, which can then estimate the user's purchasing intent and provide relevant products.

[0064] The monitoring system may also include a community referral section that estimates the user's hobbies and preferences based on the user's posts and introduces relevant communities. For example, if a user shows interest in a particular hobby, the community referral section can introduce online communities related to that hobby. It can also introduce forums and groups related to a particular topic if the user shows interest in that topic. Furthermore, if a user shows interest in a particular event, the community referral section can introduce communities related to that event. This expands the user's opportunities for interaction by introducing relevant communities based on their hobbies and preferences. Some or all of the above processing in the community referral section may be performed using AI or not. For example, the community referral section can input the user's posts into an AI, which can estimate the user's hobbies and preferences and introduce relevant communities.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The monitoring unit monitors the content that users post on social media and chat apps. The monitoring unit can monitor posts in real time and can also scan posts periodically. For example, it can scan posts at a set time each day to detect posts that may lead to criminal activity. Step 2: The analysis unit analyzes the content of posts monitored by the monitoring unit. The analysis unit uses text analysis and image analysis technologies to analyze the content of posts and detect elements that may lead to criminal activity. Step 3: The warning unit displays a warning based on the results analyzed by the analysis unit. The warning unit warns the user using pop-up messages or email notifications. For example, it displays a warning message such as, "This post may be related to a crime. Do you want to continue posting?" Step 4: The deletion section deletes the post if the warning from the warning section is ignored. The deletion section can automatically delete the post after a certain period of time, or it can delete the post if certain conditions are met. For example, it can delete the post if the user ignores the warning and continues to post. Step 5: The notification unit notifies the administrator of information about posts deleted by the deletion unit. The notification unit notifies the administrator using email notifications or dashboard notifications. For example, it notifies the administrator of the content of the deleted post and the reason for its deletion.

[0067] (Example of form 2) The monitoring system according to an embodiment of the present invention is a system in which AI monitors user posts on social networking services (SNS) and chat applications, detecting posts that may lead to criminal activity and issuing warnings or automatically deleting them. When a user posts on an SNS or chat application, the monitoring system uses AI to monitor the content of the post in real time. Next, the AI ​​analyzes the post content and detects keywords and phrases that may lead to criminal activity. For example, if keywords such as "illegal part-time jobs," "wire transfer fraud," "phishing scams," or "selling dangerous drugs" are included, the AI ​​will determine that the post is dangerous. If the AI ​​detects a dangerous post, it will display a warning to the user. For example, a warning message such as "This post may be related to criminal activity. Do you wish to continue posting?" will be displayed. If the user ignores the warning and continues posting, the AI ​​will automatically delete the post. Furthermore, if the AI ​​detects a dangerous post, it will notify the administrator of the information. The administrator can review the post content detected by the AI ​​and take additional measures as necessary. For example, this could include temporarily suspending the user account or reporting to law enforcement. This mechanism can prevent criminal activity on SNS and chat applications. Users can use the platform with peace of mind, and a deterrent effect on criminal activity is expected. For example, if a user posts "looking for illegal part-time jobs" on social media, the AI ​​will detect the post and display a warning message. If the user ignores the warning and continues posting, the AI ​​will automatically delete the post and notify the administrator. The administrator will then temporarily suspend the user's account and, if necessary, report the incident to law enforcement. In this way, AI can be used to effectively prevent criminal activity on social media and chat apps. As a result, the monitoring system can proactively prevent criminal activity on social media and chat apps, providing users with a safe environment.

[0068] The monitoring system according to the embodiment comprises a monitoring unit, an analysis unit, a warning unit, a deletion unit, and a notification unit. The monitoring unit monitors the content that users post to social networking services (SNS) or chat applications. The monitoring unit can, for example, monitor the content of posts in real time. The monitoring unit can also periodically scan the content of posts. For example, the monitoring unit scans the content of posts at a fixed time each day to detect posts that may lead to criminal activity. The analysis unit analyzes the content of posts monitored by the monitoring unit. The analysis unit can, for example, analyze the content of posts using text analysis technology. The analysis unit can also analyze the content of posts using image analysis technology. For example, the analysis unit analyzes images included in the content of posts to detect elements that may lead to criminal activity. The warning unit displays a warning based on the results of the analysis performed by the analysis unit. The warning unit can, for example, display a pop-up message to warn the user. The warning unit can also send an email notification to warn the user. For example, the warning unit displays a warning message such as, "This post may be related to criminal activity. Do you want to continue posting?" The deletion unit deletes the post if the warning from the warning unit is ignored. The deletion unit can, for example, automatically delete posts after a certain period of time has elapsed. The deletion unit can also delete posts if certain conditions are met. For example, the deletion unit deletes posts if a user ignores a warning and continues posting. The notification unit notifies the administrator of information about posts deleted by the deletion unit. The notification unit can, for example, notify the administrator by sending an email notification. It can also notify the administrator by displaying a dashboard notification. For example, the notification unit notifies the administrator of the content and reason for the deleted post. As a result, the monitoring system according to this embodiment can prevent criminal activity on social networking services and chat applications, providing an environment where users can use the services with peace of mind.

[0069] The monitoring unit monitors the content that users post on social media and chat applications. For example, the monitoring unit can monitor posts in real time. Specifically, the monitoring unit uses the APIs of social media and chat applications to retrieve user posts in real time and store them in a database. This makes it possible to monitor the content the moment a post is made. The monitoring unit can also periodically scan posts. For example, the monitoring unit scans posts at a set time each day to detect posts that may lead to criminal activity. Periodic scans are effective for re-evaluating past posts, checking whether they contain specific keywords or phrases. Furthermore, the monitoring unit can use natural language processing technology to understand the context of posts and detect potential risks. For example, by analyzing not only whether specific keywords are included, but also in what context those keywords are used, more accurate monitoring becomes possible. As a result, the monitoring unit can efficiently and effectively monitor user posts and detect early signs of criminal activity.

[0070] The analysis unit analyzes the content of posts monitored by the monitoring unit. The analysis unit can analyze the content of posts using, for example, text analysis technology. Specifically, the analysis unit uses natural language processing (NLP) technology to analyze the meaning of the content of posts and detect elements that may lead to criminal activity. For example, it can extract specific keywords or phrases contained in the content of posts and evaluate whether they are related to criminal activity. The analysis unit can also analyze the content of posts using image analysis technology. For example, the analysis unit analyzes images contained in the content of posts and detect elements that may lead to criminal activity. Image analysis includes technology that uses machine learning algorithms to recognize specific objects or scenes within images. For example, it can detect images that contain weapons, drugs, or violent scenes. Furthermore, the analysis unit can also analyze the content of voice messages using voice analysis technology. Voice analysis includes a process of converting voice to text using speech recognition technology and then analyzing that text. This allows the analysis unit to perform comprehensive analysis on all media formats—text, images, and voice—and detect signs of criminal activity.

[0071] The warning unit displays warnings based on the results analyzed by the analysis unit. For example, the warning unit can warn users by displaying pop-up messages. Specifically, when a user attempts to make a post, the warning unit displays a warning message on the screen informing them that the post may be related to criminal activity. The warning unit can also warn users by sending email notifications. For example, the warning unit may display a warning message such as, "This post may be related to criminal activity. Do you want to continue posting?" Furthermore, the warning unit can display individual warning messages to specific users based on their past activity history. For example, it can display a stronger warning message to users who have ignored warnings and continued posting in the past. This allows the warning unit to quickly provide appropriate warnings to users and deter criminal activity.

[0072] The deletion unit deletes posts if warnings from the warning unit are ignored. The deletion unit can, for example, automatically delete posts after a certain period of time has elapsed. Specifically, if a user does not modify a post within a certain time after a warning message is displayed, the deletion unit will automatically delete the post. The deletion unit can also delete posts if certain conditions are met. For example, the deletion unit will delete a post if the user ignores the warning and continues to post. Furthermore, the deletion unit can also provide an interface for administrators to manually delete posts. This allows administrators to quickly delete specific posts. The deletion unit records the content and reason for deletion of deleted posts for later reference. This allows the deletion unit to effectively delete inappropriate posts by users and support the healthy operation of social networking services and chat applications.

[0073] The notification unit notifies administrators of posts deleted by the deletion unit. For example, the notification unit can send email notifications to administrators. Specifically, it sends an email to administrators detailing the content and reason for the deleted post. The notification unit can also display dashboard notifications to administrators. For example, it can display a list of deleted posts on the administrator's dashboard, allowing them to check the reason for deletion and the date and time of deletion. Furthermore, the notification unit can also provide information about users associated with the deleted posts. This allows administrators to identify whether a particular user is repeatedly making inappropriate posts. The notification unit also provides a function to customize notification content, ensuring administrators receive the information they need. This enables the notification unit to provide administrators with timely and accurate information, supporting the healthy operation of social networking services and chat applications.

[0074] The analysis unit can detect keywords and phrases that may lead to criminal activity. For example, the analysis unit can detect keywords using a specific word list. It can also detect phrases using natural language processing techniques. For example, the analysis unit can detect keywords such as "illegal part-time jobs" and "wire transfer fraud" contained in the posted content. By detecting keywords and phrases that may lead to criminal activity, criminal acts can be prevented. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not be performed using AI. For example, the analysis unit can input the posted content into AI, and the AI ​​can detect keywords and phrases.

[0075] The warning unit can display a warning message such as, "This post may be related to a crime. Do you wish to continue posting?" The warning unit can warn the user, for example, by displaying a pop-up message. It can also warn the user by sending an email notification. For example, the warning unit can display a warning message based on the content of the post. This can deter criminal activity by displaying a warning message to the user. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the results analyzed by the analysis unit into the AI, and the AI ​​can generate a warning message.

[0076] The deletion unit can automatically delete posts if a warning is ignored. For example, the deletion unit can automatically delete posts after a certain period of time has elapsed. Furthermore, the deletion unit can delete posts if certain conditions are met. For example, the deletion unit deletes posts if the user ignores a warning and continues posting. This prevents criminal activity by automatically deleting posts when warnings are ignored. Some or all of the above-described processes in the deletion unit may be performed using AI or not. For example, the deletion unit can input to the AI ​​that the warning unit ignored the warning, and the AI ​​can then delete the post.

[0077] The notification unit can notify administrators of information about deleted posts. For example, the notification unit can notify administrators by sending email notifications. Alternatively, the notification unit can notify administrators by displaying dashboard notifications. For example, the notification unit can notify administrators of the content and reason for deletion of deleted posts. This allows administrators to take appropriate action by notifying them of information about deleted posts. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input information about posts deleted by the deletion unit into the AI, and the AI ​​can generate notification content.

[0078] The monitoring unit can estimate the user's emotions and adjust the monitoring intensity based on the estimated emotions. For example, if the user is excited, the monitoring unit can increase the monitoring intensity and check the posted content more strictly. Conversely, if the user is relaxed, the monitoring unit can maintain the monitoring intensity at a normal level, avoiding excessive monitoring. Furthermore, if the user is stressed, the monitoring unit can moderately adjust the monitoring intensity to reduce the user's burden. This allows for more appropriate monitoring by adjusting the monitoring intensity 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 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 monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI, which can estimate the emotions and adjust the monitoring intensity.

[0079] The monitoring unit can analyze a user's past posting history during monitoring and detect specific patterns. For example, if a user has a history of making crime-related posts, the monitoring unit can increase the intensity of monitoring. The monitoring unit can also analyze the frequency of specific keywords from the user's posting history and detect abnormal patterns. Furthermore, based on the user's posting history, if there are many crime-related posts during a particular time period, the monitoring unit can strengthen monitoring during that time period. In this way, by analyzing a user's past posting history, specific patterns can be detected and criminal activity can be prevented. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's past posting history into AI, which can then detect specific patterns.

[0080] The monitoring unit can apply different monitoring algorithms depending on the category of the posted content during monitoring. For example, the monitoring unit can apply a monitoring algorithm using a specific keyword list to posts related to illegal part-time jobs. It can also apply a specific phrase detection algorithm to posts related to wire fraud. Furthermore, it can apply a monitoring algorithm including image analysis to posts related to the buying and selling of dangerous drugs. This allows for more effective monitoring by applying different monitoring algorithms depending on the category of the posted content. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the category of the posted content into the AI, which can then select an appropriate monitoring algorithm.

[0081] The monitoring unit can estimate the user's emotions and determine monitoring priorities based on the estimated emotions. For example, if the user is excited, the monitoring unit can increase the monitoring priority and respond immediately. If the user is relaxed, the monitoring unit can maintain the normal monitoring priority. Furthermore, if the user is stressed, the monitoring unit can appropriately adjust the monitoring priority to reduce the user's burden. This enables more appropriate monitoring by determining monitoring priorities 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 monitoring unit may be performed using AI or not. For example, the monitoring unit can input user emotion data into a generative AI, which can estimate the emotions and determine monitoring priorities.

[0082] The monitoring unit can prioritize monitoring highly relevant posts by considering the user's geographical location information during monitoring. For example, if the user is in a specific area, the monitoring unit will prioritize monitoring posts related to crimes occurring in that area. Furthermore, based on the user's location information, the monitoring unit can prioritize monitoring posts related to crimes occurring in the vicinity. Additionally, if the user is on the move, the monitoring unit can prioritize monitoring posts related to crimes in the area they are currently traveling to. This allows for the prioritization of highly relevant posts by considering the user's geographical location information, thereby preventing criminal activity. Some or all of the above processing in the monitoring unit may be performed using AI, or not. For example, the monitoring unit can input the user's geographical location information into the AI, which can then select highly relevant posts.

[0083] The monitoring unit can analyze a user's social media activity and monitor relevant posts during monitoring. For example, if a user belongs to a specific group, the monitoring unit will prioritize monitoring posts within that group. The monitoring unit can also analyze the content of posts by the user's followers and friends and monitor relevant posts. Furthermore, if a user frequently uses a specific hashtag, the monitoring unit can prioritize monitoring posts related to that hashtag. In this way, by analyzing a user's social media activity, relevant posts can be monitored and criminal activity can be prevented. Some or all of the above processing in the monitoring unit may be performed using AI or not. For example, the monitoring unit can input the user's social media activity data into AI, which can then select relevant posts.

[0084] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can increase the accuracy of the analysis and perform a more detailed analysis. If the user is relaxed, the analysis unit can maintain normal analysis accuracy. Furthermore, if the user is stressed, the analysis unit can appropriately adjust the accuracy of the analysis to reduce the user's burden. By adjusting the accuracy of the analysis according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit may be performed using AI or not using AI. For example, the analysis unit can input user emotion data into a generative AI, which can estimate emotions and adjust the accuracy of the analysis.

[0085] The analysis unit can adjust the level of detail of its analysis based on the importance of the posted content. For example, it can perform a detailed analysis on high-importance posts to thoroughly examine the possibility of criminal activity. Conversely, it can perform a standard analysis on low-importance posts to avoid allocating excessive resources. Furthermore, the analysis unit can automatically evaluate the importance of the posted content and set an appropriate level of detail. This allows for efficient resource use and helps prevent criminal activity by adjusting the level of detail based on the importance of the posted content. Some or all of the above processes in the analysis unit may be performed using AI or not. For example, the analysis unit can input the importance of the posted content into the AI, which can then adjust the level of detail of the analysis.

[0086] The analysis unit can apply different analysis algorithms depending on the category of the posted content during analysis. For example, the analysis unit can apply an analysis algorithm using a specific keyword list to posts related to illegal part-time jobs. It can also apply a specific phrase detection algorithm to posts related to wire fraud. Furthermore, it can apply an analysis algorithm including image analysis to posts related to the buying and selling of dangerous drugs. This allows for more effective analysis by applying different analysis algorithms depending on the category of the posted content. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the category of the posted content into the AI, which can then select an appropriate analysis algorithm.

[0087] The analysis unit can estimate the user's emotions and determine the priority of analysis based on the estimated emotions. For example, if the user is excited, the analysis unit can increase the priority of the analysis and respond immediately. If the user is relaxed, the analysis unit can maintain the normal analysis priority. Furthermore, if the user is stressed, the analysis unit can appropriately adjust the analysis priority to reduce the user's burden. This allows for more appropriate analysis by determining the analysis priority according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into the generative AI, which can estimate the emotions and determine the analysis priority.

[0088] The analysis unit can determine the priority of analysis based on the submission date of the posts during the analysis process. For example, the analysis unit can prioritize the analysis of recently posted content and respond in real time. The analysis unit can also periodically analyze past posts to prevent overlooking anything. Furthermore, the analysis unit can prioritize the analysis of content posted during specific time periods to prevent criminal activity during those times. This real-time response is possible by determining the priority of analysis based on the submission date of the posts. Some or all of the above processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can input the submission date of the posts into the AI, which can then determine the priority of analysis.

[0089] The analysis unit can adjust the order of analysis based on the relevance of the posts during the analysis process. For example, the analysis unit can prioritize the analysis of highly relevant posts to quickly detect the possibility of a crime. Furthermore, the analysis unit can postpone the analysis of less relevant posts, allowing for efficient use of resources. In addition, the analysis unit can automatically evaluate the relevance of the post content and set an appropriate analysis order. This allows for efficient resource use and helps prevent criminal activity by adjusting the analysis order based on the relevance of the posts. Some or all of the above-described processes in the analysis unit may be performed using AI, or they may not. For example, the analysis unit can input the relevance of the post content into the AI, which can then adjust the analysis order.

[0090] The warning unit can estimate the user's emotions and adjust the way the warning message is expressed based on the estimated emotions. For example, if the user is excited, the warning unit can display the warning message in a calm tone. If the user is relaxed, the warning unit can display the warning message in a normal tone. Furthermore, if the user is stressed, the warning unit can display the warning message in a gentle tone. By adjusting the way the warning message is expressed according to the user's emotions, more effective warnings can be provided. 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 warning unit may be performed using AI or not. For example, the warning unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the way the warning message is expressed.

[0091] The warning unit can adjust the level of detail of a warning based on the importance of the post content when issuing a warning. For example, the warning unit can display a detailed warning message for high-importance posts to draw the user's attention. Conversely, it can display a concise warning message for low-importance posts to avoid excessive warnings. Furthermore, the warning unit can automatically evaluate the importance of the post content and set an appropriate level of warning detail. This allows for efficient use of resources and helps prevent criminal activity by adjusting the level of warning detail based on the importance of the post content. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the importance of the post content into the AI, which can then adjust the level of warning detail.

[0092] The warning unit can display different warning messages depending on the category of the posted content when a warning is issued. For example, the warning unit can display a specific warning message for posts related to illegal part-time jobs. It can also display a specific warning message for posts related to wire fraud. Furthermore, it can display a specific warning message for posts related to the buying and selling of dangerous drugs. This allows for more effective warnings by displaying different warning messages depending on the category of the posted content. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the category of the posted content into the AI, and the AI ​​can generate an appropriate warning message.

[0093] The warning unit can estimate the user's emotions and adjust the length of the warning message based on the estimated emotions. For example, if the user is excited, the warning unit can display a short, concise warning message. If the user is relaxed, it can display a warning message of normal length. Furthermore, if the user is stressed, it can display a simple and gentle warning message. By adjusting the length of the warning message according to the user's emotions, more effective warnings can be provided. Emotion estimation is achieved using an emotion estimation function, such as 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 warning unit may be performed using AI or not. For example, the warning unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the length of the warning message.

[0094] The warning unit can prioritize warnings based on when the post was submitted. For example, it can prioritize displaying warning messages for recently posted content. It can also periodically display warning messages for older posts to prevent them from being overlooked. Furthermore, it can prioritize displaying warning messages for content posted within a specific time period. This allows for real-time response by prioritizing warnings based on when the post was submitted. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the submission date of the posts into the AI, which can then determine the priority of the warnings.

[0095] The warning unit can adjust the order of warnings based on the relevance of the posts when issuing a warning. For example, the warning unit can prioritize displaying warning messages for highly relevant posts. It can also postpone less relevant posts, allowing for efficient use of resources. Furthermore, the warning unit can automatically evaluate the relevance of post content and set an appropriate warning order. This allows for efficient use of resources and prevention of criminal activity by adjusting the order of warnings based on the relevance of posts. Some or all of the above processing in the warning unit may be performed using AI or not. For example, the warning unit can input the relevance of post content into AI, which can then adjust the order of warnings.

[0096] The deletion unit can estimate the user's emotions and adjust the timing of deletion based on the estimated emotions. For example, if the user is excited, the deletion unit can immediately delete the post. If the user is relaxed, the deletion unit can maintain the normal deletion timing. Furthermore, if the user is stressed, the deletion unit can appropriately adjust the deletion timing to reduce the user's burden. This allows for more appropriate deletion by adjusting the deletion timing 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 deletion unit may be performed using AI or not. For example, the deletion unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the deletion timing.

[0097] The deletion unit can determine the priority of deletions based on the importance of the content of the posts. For example, the deletion unit can prioritize the deletion of high-importance posts to quickly eliminate the possibility of criminal activity. It can also postpone the deletion of low-importance posts, allowing for efficient use of resources. Furthermore, the deletion unit can automatically evaluate the importance of the content of the posts and set appropriate deletion priorities. This allows for efficient use of resources and prevention of criminal activity by determining the deletion priority based on the importance of the content of the posts. Some or all of the above processes in the deletion unit may be performed using AI or not. For example, the deletion unit can input the importance of the content of the posts into the AI, which can then determine the deletion priority.

[0098] The deletion unit can apply different deletion algorithms depending on the category of the posted content when deleting it. For example, the deletion unit can apply a deletion algorithm using a specific keyword list to posts related to illegal part-time jobs. It can also apply a specific phrase detection algorithm to posts related to wire fraud. Furthermore, it can apply a deletion algorithm including image analysis to posts related to the buying and selling of dangerous drugs. This allows for more effective deletion by applying different deletion algorithms depending on the category of the posted content. Some or all of the above-described processes in the deletion unit may be performed using AI or not. For example, the deletion unit can input the category of the posted content into the AI, which can then select an appropriate deletion algorithm.

[0099] The deletion unit can estimate the user's emotions and determine the deletion priority based on the estimated emotions. For example, if the user is excited, the deletion unit will increase the deletion priority and respond immediately. If the user is relaxed, the deletion unit can maintain the normal deletion priority. Furthermore, if the user is stressed, the deletion unit can appropriately adjust the deletion priority to reduce the user's burden. This allows for more appropriate deletion by determining the deletion priority 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 deletion unit may be performed using AI or not. For example, the deletion unit can input user emotion data into the generative AI, which can estimate the emotions and determine the deletion priority.

[0100] The deletion unit can determine the priority of deletions based on the submission date of the posts. For example, the deletion unit can prioritize the deletion of recently posted content, enabling real-time action. It can also periodically delete past posts to prevent overlooking any. Furthermore, the deletion unit can prioritize the deletion of content posted during specific time periods, preventing criminal activity during those times. This real-time response is possible by determining the priority of deletions based on the submission date of the posts. Some or all of the above processes in the deletion unit may be performed using AI, or not. For example, the deletion unit can input the submission date of posts into the AI, which can then determine the priority of deletions.

[0101] The deletion unit can adjust the order of deletion based on the relevance of the posts. For example, the deletion unit can prioritize the deletion of highly relevant posts to quickly eliminate the possibility of criminal activity. It can also postpone the deletion of less relevant posts, allowing for efficient use of resources. Furthermore, the deletion unit can automatically evaluate the relevance of the post content and set an appropriate deletion order. This allows for efficient use of resources and prevention of criminal activity by adjusting the deletion order based on the relevance of the posts. Some or all of the above processing in the deletion unit may be performed using AI or not. For example, the deletion unit can input the relevance of the post content into the AI, which can then adjust the deletion order.

[0102] The notification unit can estimate the user's emotions and adjust the content of the notification based on the estimated emotions. For example, if the user is excited, the notification unit can convey the notification content in calm language. If the user is relaxed, the notification unit can convey the notification content in normal language. Furthermore, if the user is stressed, the notification unit can convey the notification content in gentle language. By adjusting the content of the notification according to the user's emotions, more appropriate notifications become possible. 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 notification unit may be performed using AI or not using AI. For example, the notification unit can input user emotion data into the generative AI, which can estimate the emotions and adjust the content of the notification.

[0103] The notification unit can adjust the level of detail of notifications based on the importance of the posted content. For example, it can provide detailed notifications for high-importance posts to draw the administrator's attention. Conversely, it can provide concise notifications for low-importance posts to avoid excessive notifications. Furthermore, the notification unit can automatically evaluate the importance of the posted content and set an appropriate level of detail. This allows for efficient use of resources and prevention of criminal activity by adjusting the level of detail of notifications based on the importance of the posted content. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the importance of the posted content into the AI, which can then adjust the level of detail of the notification.

[0104] The notification unit can display different notification messages depending on the category of the posted content when a notification is sent. For example, the notification unit can display a specific notification message for posts related to illegal part-time jobs. It can also display a specific notification message for posts related to wire fraud. Furthermore, it can display a specific notification message for posts related to the buying and selling of dangerous drugs. By displaying different notification messages depending on the category of the posted content, more effective notifications can be made. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the category of the posted content into the AI, and the AI ​​can generate an appropriate notification message.

[0105] The notification unit can estimate the user's emotions and determine notification priorities based on the estimated emotions. For example, if the user is excited, the notification unit can increase the notification priority and respond immediately. If the user is relaxed, the notification unit can maintain the normal notification priority. Furthermore, if the user is stressed, the notification unit can appropriately adjust the notification priority to reduce the user's burden. This allows for more appropriate notifications by determining notification priorities 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 notification unit may be performed using AI or not. For example, the notification unit can input user emotion data into a generative AI, which can estimate the emotions and determine notification priorities.

[0106] The notification unit can determine the priority of notifications based on when the posts were submitted. For example, the notification unit can prioritize notifications for recently posted content to enable real-time responses. It can also periodically notify users of past posts to prevent them from being overlooked. Furthermore, the notification unit can prioritize notifications for content posted during specific time periods to prevent criminal activity during those times. This enables real-time responses by determining the priority of notifications based on when the posts were submitted. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the submission dates of posts into the AI, which can then determine the priority of notifications.

[0107] The notification unit can adjust the order of notifications based on the relevance of the posts when sending notifications. For example, the notification unit can prioritize notifications for highly relevant posts, allowing administrators to respond quickly. It can also postpone notifications for less relevant posts, enabling efficient use of resources. Furthermore, the notification unit can automatically evaluate the relevance of post content and set an appropriate notification order. By adjusting the order of notifications based on the relevance of posts, resources can be used efficiently, and criminal activity can be prevented. Some or all of the above processing in the notification unit may be performed using AI or not. For example, the notification unit can input the relevance of post content into AI, which can then adjust the order of notifications.

[0108] The notification unit can suggest the most suitable notification method when sending a notification, taking into account the administrator's past response history. For example, the notification unit may prioritize suggesting notification methods that the administrator has responded to quickly in the past. Furthermore, the notification unit can automatically select the most suitable notification method based on the administrator's past response history. In addition, the notification unit can suggest new notification methods based on notification methods that the administrator has effectively used in the past. This allows for the suggestion of the most suitable notification method and effective responses by taking into account the administrator's past response history. Some or all of the above processing in the notification unit may be performed using AI, or not. For example, the notification unit can input the administrator's past response history into AI, which can then suggest the most suitable notification method.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The monitoring system may further include a provisioning unit that estimates the user's interests and preferences based on the user's posts and provides relevant information. For example, if the user shows interest in a particular topic, the provisioning unit can provide news articles or blog posts related to that topic. The provisioning unit can also display advertisements related to a particular product or service if the user shows interest in that product or service. Furthermore, if the user shows interest in a particular event, the provisioning unit can provide information related to that event. This improves user satisfaction by providing relevant information based on the user's interests. Some or all of the above processing in the provisioning unit may be performed using AI or not. For example, the provisioning unit can input the user's posts into an AI, which can then estimate the user's interests and preferences and provide relevant information.

[0111] The monitoring system may further include a health management unit that estimates the user's health status based on the user's posts and provides health-related advice. For example, if the user is feeling stressed, the health management unit can provide advice on how to relax. It can also provide advice on how to rest if the user is tired. Furthermore, the health management unit can provide advice on how to live a healthy life. This allows the system to support the user's health by providing appropriate advice based on their health status. Some or all of the above-described processes in the health management unit may be performed using AI or not. For example, the health management unit can input the user's posts into an AI, which can then estimate the user's health status and provide appropriate advice.

[0112] The monitoring system may further include a learning support unit that estimates the user's learning needs based on the user's posts and provides appropriate learning resources. For example, if a user wants to learn a specific skill, the learning support unit can provide online courses and materials related to that skill. If a user is studying for a specific exam, the learning support unit can provide past exam questions and reference books related to that exam. Furthermore, if a user is interested in a particular field, the learning support unit can provide articles and videos related to that field. This allows the system to support the user's learning by providing appropriate resources based on their learning needs. Some or all of the above processing in the learning support unit may be performed using AI or not. For example, the learning support unit can input the user's posts into an AI, which can then estimate the user's learning needs and provide appropriate resources.

[0113] The monitoring system may further include a music recommendation unit that estimates the user's emotions based on the content of the user's posts and provides appropriate music based on the estimated emotions. For example, if the user is sad, the music recommendation unit can provide relaxing music to soothe their mood. If the user is excited, the music recommendation unit can provide energetic music. Furthermore, if the user is relaxed, the music recommendation unit can provide relaxing music. In this way, the system can support the user's mood by providing appropriate music based on their emotions. Some or all of the above processing in the music recommendation unit may be performed using AI or not. For example, the music recommendation unit can input the user's posts into an AI, which can estimate the user's emotions and provide appropriate music.

[0114] The monitoring system may further include a product recommendation unit that estimates the user's purchasing intent based on the user's posts and provides relevant products. For example, if a user shows interest in a particular product, the product recommendation unit can provide products related to that product. It can also provide products related to a particular brand if the user shows interest in that brand. Furthermore, if the user shows interest in products in a particular category, the product recommendation unit can provide products related to that category. This improves the user's purchasing experience by providing relevant products based on the user's purchasing intent. Some or all of the above processing in the product recommendation unit may be performed using AI or not. For example, the product recommendation unit can input the user's posts into an AI, which can then estimate the user's purchasing intent and provide relevant products.

[0115] The monitoring system may further include a feedback unit that estimates the user's emotions based on the content of the user's posts and provides appropriate feedback based on the estimated emotions. For example, if the user is feeling anxious, the feedback unit can provide reassuring feedback. If the user is happy, the feedback unit can provide feedback that shares that happiness. Furthermore, if the user is angry, the feedback unit can provide feedback to help them calm down. In this way, the system can support the user's emotions by providing appropriate feedback based on their feelings. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's posts into an AI, which can estimate the user's emotions and provide appropriate feedback.

[0116] The monitoring system may further include an exercise suggestion unit that estimates the user's emotions based on the user's posts and suggests appropriate exercises based on the estimated emotions. For example, if the user is feeling stressed, the exercise suggestion unit may suggest yoga or meditation to help them relax. If the user is feeling energetic, the exercise suggestion unit may suggest running or dancing. Furthermore, if the user is tired, the exercise suggestion unit may suggest light stretching or walking. In this way, the system can support the user's health by suggesting appropriate exercises based on their emotions. Some or all of the above processing in the exercise suggestion unit may be performed using AI or not. For example, the exercise suggestion unit may input the user's posts into an AI, which may estimate the user's emotions and suggest appropriate exercises.

[0117] The monitoring system may further include a relaxation suggestion unit that estimates the user's emotions based on the user's posts and suggests appropriate relaxation methods based on the estimated emotions. For example, if the user is feeling stressed, the relaxation suggestion unit may suggest deep breathing or meditation. If the user is tired, the relaxation suggestion unit may suggest relaxing music or aromatherapy. Furthermore, if the user is feeling anxious, the relaxation suggestion unit may suggest relaxing massage or a warm bath. In this way, the system can reduce the user's stress by suggesting appropriate relaxation methods based on the user's emotions. Some or all of the above processing in the relaxation suggestion unit may be performed using AI or not. For example, the relaxation suggestion unit may input the user's posts into an AI, which may estimate the user's emotions and suggest appropriate relaxation methods.

[0118] The monitoring system may also include a reading recommendation section that estimates the user's emotions based on their posts and provides appropriate reading lists based on those emotions. For example, if the user wants to relax, the reading recommendation section can provide relaxing novels or essays. If the user wants to learn, it can provide specialized books or textbooks that will help with their studies. Furthermore, if the user is looking for entertainment, it can provide interesting comics or magazines. This improves the user's reading experience by providing appropriate reading lists based on their emotions. Some or all of the above processing in the reading recommendation section may be performed using AI or not. For example, the reading recommendation section could input the user's posts into an AI, which could then estimate the user's emotions and provide appropriate reading lists.

[0119] The monitoring system may also include a community referral section that estimates the user's hobbies and preferences based on the user's posts and introduces relevant communities. For example, if a user shows interest in a particular hobby, the community referral section can introduce online communities related to that hobby. It can also introduce forums and groups related to a particular topic if the user shows interest in that topic. Furthermore, if a user shows interest in a particular event, the community referral section can introduce communities related to that event. This expands the user's opportunities for interaction by introducing relevant communities based on their hobbies and preferences. Some or all of the above processing in the community referral section may be performed using AI or not. For example, the community referral section can input the user's posts into an AI, which can estimate the user's hobbies and preferences and introduce relevant communities.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The monitoring unit monitors the content that users post on social media and chat apps. The monitoring unit can monitor posts in real time and can also scan posts periodically. For example, it can scan posts at a set time each day to detect posts that may lead to criminal activity. Step 2: The analysis unit analyzes the content of posts monitored by the monitoring unit. The analysis unit uses text analysis and image analysis technologies to analyze the content of posts and detect elements that may lead to criminal activity. Step 3: The warning unit displays a warning based on the results analyzed by the analysis unit. The warning unit warns the user using pop-up messages or email notifications. For example, it displays a warning message such as, "This post may be related to a crime. Do you want to continue posting?" Step 4: The deletion section deletes the post if the warning from the warning section is ignored. The deletion section can automatically delete the post after a certain period of time, or it can delete the post if certain conditions are met. For example, it can delete the post if the user ignores the warning and continues to post. Step 5: The notification unit notifies the administrator of information about posts deleted by the deletion unit. The notification unit notifies the administrator using email notifications or dashboard notifications. For example, it notifies the administrator of the content of the deleted post and the reason for its deletion.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the monitoring unit, analysis unit, warning unit, deletion unit, and notification unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors the content posted by the user to SNS or chat applications in real time. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the posted content using text analysis technology and image analysis technology. The warning unit is implemented by the control unit 46A of the smart device 14 and displays a warning to the user through a pop-up message or email notification. The deletion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically deletes the post if the warning is ignored. The notification unit is implemented by the control unit 46A of the smart device 14 and notifies the administrator of information about the deleted post. 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.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the monitoring unit, analysis unit, warning unit, deletion unit, and notification unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors the content posted by the user to SNS or chat applications in real time. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and analyzes the posted content using text analysis technology and image analysis technology. The warning unit is implemented by the control unit 46A of the smart glasses 214 and displays a warning to the user through a pop-up message or email notification. The deletion unit is implemented by the identification processing unit 290 of the data processing unit 12 and automatically deletes the post if the warning is ignored. The notification unit is implemented by the control unit 46A of the smart glasses 214 and notifies the administrator of information about the deleted post. 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.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.).

[0154] 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.

[0155] 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.

[0156] 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.

[0157] Each of the multiple elements described above, including the monitoring unit, analysis unit, warning unit, deletion unit, and notification unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors the content posted by the user to SNS or chat applications in real time. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the posted content using text analysis technology and image analysis technology. The warning unit is implemented by the control unit 46A of the headset terminal 314 and displays a warning to the user through a pop-up message or email notification. The deletion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically deletes the post if the warning is ignored. The notification unit is implemented by the control unit 46A of the headset terminal 314 and notifies the administrator of information about the deleted post. 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.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0163] 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).

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the monitoring unit, analysis unit, warning unit, deletion unit, and notification unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and monitors the content posted by the user to SNS or chat applications in real time. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the posted content using text analysis technology and image analysis technology. The warning unit is implemented by the control unit 46A of the robot 414 and displays a warning to the user through a pop-up message or email notification. The deletion unit is implemented by the specific processing unit 290 of the data processing unit 12 and automatically deletes the post if the warning is ignored. The notification unit is implemented by the control unit 46A of the robot 414 and notifies the administrator of information about the deleted post. 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) A monitoring department that monitors the content of posts, An analysis unit analyzes the posted content monitored by the aforementioned monitoring unit, A warning unit that displays a warning based on the results of the analysis performed by the aforementioned analysis unit, A deletion unit that deletes a post if the warning from the aforementioned warning unit is ignored, The system includes a notification unit that notifies the administrator of information about posts deleted by the deletion unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Detects keywords and phrases that may lead to criminal activity. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned warning unit is A warning message such as "This post may be related to a crime. Do you want to continue posting?" is displayed. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned deletion section is, Automatically delete posts if warnings are ignored. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned notification unit, Notify the administrator about the deleted post. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring intensity based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, During monitoring, the system analyzes the user's past posting history and detects specific patterns. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, During monitoring, different monitoring algorithms are applied depending on the category of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned monitoring unit, During monitoring, the system prioritizes monitoring of highly relevant posts by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned monitoring unit, During monitoring, the system analyzes users' social media activity and monitors relevant posts. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the content of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the posted content. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The system estimates the user's emotions and determines the priority of analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During analysis, the priority of analyses is determined based on the submission date of the submissions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned warning unit is The system estimates the user's emotions and adjusts the way warning messages are presented based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is When issuing a warning, adjust the level of detail based on the importance of the post content. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is When a warning is issued, different warning messages will be displayed depending on the category of the post content. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is It estimates the user's emotions and adjusts the length of the warning message based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned warning unit is When issuing a warning, the priority of the warning will be determined based on when the submission was made. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned warning unit is When issuing warnings, the order of warnings will be adjusted based on the relevance of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned deletion section is, It estimates the user's emotions and adjusts the timing of deletion based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned deletion section is, When deleting, the priority of deletion is determined based on the importance of the post's content. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned deletion section is, When deleting content, different deletion algorithms are applied depending on the category of the post. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned deletion section is, It estimates user sentiment and determines deletion priorities based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned deletion section is, When deleting content, the priority of deletion is determined based on when the post was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned deletion section is, When deleting posts, the order of deletion will be adjusted based on the relevance of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned notification unit, When sending notifications, adjust the level of detail based on the importance of the post content. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned notification unit, When a notification is sent, different notification messages will be displayed depending on the category of the post. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned notification unit, It estimates the user's emotions and prioritizes notifications based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned notification unit, When sending notifications, we will prioritize them based on when the submission was received. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned notification unit, When sending notifications, the order of notifications will be adjusted based on the relevance of the posts. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned notification unit, When sending notifications, we will suggest the most suitable notification method based on the administrator's past response history. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring department that monitors the content of posts, An analysis unit analyzes the posted content monitored by the aforementioned monitoring unit, A warning unit that displays a warning based on the results of the analysis performed by the aforementioned analysis unit, A deletion unit that deletes a post if the warning from the aforementioned warning unit is ignored, The system includes a notification unit that notifies the administrator of information about posts deleted by the deletion unit. A system characterized by the following features.

2. The aforementioned analysis unit, Detects keywords and phrases that may lead to criminal activity. The system according to feature 1.

3. The aforementioned warning unit is Display a warning message The system according to feature 1.

4. The aforementioned deletion section is, Automatically delete posts if warnings are ignored. The system according to feature 1.

5. The aforementioned notification unit, Notify the administrator about the deleted post. The system according to feature 1.

6. The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring intensity based on the estimated user emotions. The system according to feature 1.

7. The aforementioned monitoring unit, During monitoring, the system analyzes the user's past posting history and detects specific patterns. The system according to feature 1.

8. The aforementioned monitoring unit, During monitoring, different monitoring algorithms are applied depending on the category of the posted content. The system according to feature 1.

9. The aforementioned monitoring unit, It estimates user sentiment and determines monitoring priorities based on the estimated user sentiment. The system according to feature 1.

10. The aforementioned monitoring unit, During monitoring, the system prioritizes monitoring of highly relevant posts by considering the user's geographical location. The system according to feature 1.

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