system

Abstract

The system according to the embodiment aims to detect problematic content on the Internet at an early stage and deal with it appropriately. [Solution] A system according to an embodiment includes a monitoring unit, a deletion unit, a support unit, and a provision unit. The monitoring unit monitors information on the Internet. The deletion unit deletes problematic content detected by the monitoring unit. The support unit provides psychological support to victims based on the information deleted by the deletion unit. The provision unit provides digital security information based on the support provided by the support unit.

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the 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] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies have not adequately detected problematic content on the Internet early and dealt with it appropriately, and there is room for improvement.

[0005] The system according to the embodiment aims to detect problematic content on the Internet at an early stage and deal with it appropriately. [Means for solving the problem]

[0006] The system according to the embodiment includes a monitoring unit, a deletion unit, a support unit, and a providing unit. The monitoring unit monitors information on the Internet. The deletion unit deletes problematic content detected by the monitoring unit. The support unit provides psychological support to victims based on the information deleted by the deletion unit. The providing unit provides digital security information based on the support provided by the support unit. [Effects of the Invention]

[0007] The system according to the embodiment can detect problematic content on the Internet at an early stage and deal with it appropriately. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol 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 including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

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

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

[0028] (Example 1) A digital tattoo countermeasure system according to an embodiment of the present invention monitors online information, analyzes specific keywords and phrases to detect digital tattoo-related issues early, removes problematic content, provides psychological support to victims, and provides digital security information. This system uses AI to monitor online information and analyze specific keywords and phrases to detect digital tattoo-related issues early. It then automates the process for removing the detected problematic content. Furthermore, the AI ​​provides counseling services to provide psychological support to victims. For example, it provides a chatbot that victims can consult with, offering appropriate advice and support. It also provides information on digital security, enabling users to take measures to protect their information. However, because the ultimate ethical decision is left to humans, countermeasures against digital tattoo issues rely on the collaboration of technology and humans. For example, specific keywords and phrases that the AI ​​analyzes include "bullying," "slander," and "personal information leaks." This identifies problematic content. If the AI ​​detects problematic content, the removal process begins by first reviewing the content and determining whether it meets the removal criteria. If removal is determined necessary, the removal process is automatically initiated. Furthermore, AI can provide psychological support to victims by providing counseling services. For example, it can use a chatbot to answer victims' questions and provide specific advice and support. It can also refer them to specialists if necessary. AI can also provide digital security information by suggesting specific measures to users. For example, it can provide specific methods for users to protect their information, such as strengthening passwords and setting up two-factor authentication. In this way, systems using AI can address the digital tattoo problem. However, since the final ethical decision is left to humans, cooperation between technology and humans is essential. This allows the digital tattoo countermeasure system to provide a comprehensive system for addressing the digital tattoo problem.

[0029] A digital tattoo countermeasure system according to an embodiment includes a monitoring unit, a deletion unit, a support unit, and a providing unit. The monitoring unit monitors information on the Internet. Examples of information on the Internet include, but are not limited to, social media posts, blog posts, and news articles. The monitoring unit, for example, analyzes specific keywords and phrases to detect problems related to digital tattoos early on. For example, the monitoring unit sets keywords such as "bullying," "slander," and "personal information leaks" and detects content containing these keywords. The deletion unit deletes problematic content detected by the monitoring unit. For example, the deletion unit checks the content of the detected content and determines whether it meets deletion criteria. If deletion is determined to be necessary, the deletion unit automatically initiates the deletion procedure. For example, the deletion unit can automate the deletion procedure using an AI algorithm. The support unit provides psychological support to victims based on the information deleted by the deletion unit. For example, the support unit uses a chatbot to provide specific advice and support to victims. For example, the support unit provides a chatbot that victims can consult with and provides appropriate advice and support. The support unit may also refer the user to an expert as needed. The provision unit provides digital security information based on the support provided by the support unit. The provision unit may provide the user with digital security information, such as strengthening passwords or setting up two-step authentication. For example, the provision unit may suggest specific methods for the user to protect their information. As a result, the digital tattoo countermeasure system according to the embodiment can provide a comprehensive system for addressing the digital tattoo issue.

[0030] The monitoring unit can analyze predetermined keywords or phrases to detect problems related to digital tattoos early. Examples of predetermined keywords or phrases include, but are not limited to, specific offensive words, personal information, and words related to specific incidents. The monitoring unit can set keywords such as "bullying," "slander," and "personal information leaks" and detect content containing these keywords. For example, the monitoring unit can use AI to monitor information on the Internet and analyze specific keywords and phrases to detect problems related to digital tattoos early. Thus, analyzing specific keywords and phrases can detect problems related to digital tattoos early. Some or all of the above-described processing by the monitoring unit can be performed using AI, or can be performed without AI. For example, the monitoring unit can detect problematic content early using an AI model for detecting content containing specific keywords or phrases.

[0031] The removal unit can automate a procedure for removing the detected problematic content. Specific methods for automating the procedure include, but are not limited to, the use of an AI algorithm or a specific rule-based system. For example, the removal unit can check the details of the detected content and determine whether it meets removal criteria. If it is determined that removal is necessary, the removal unit automatically initiates the removal procedure. For example, the removal unit can automate the removal procedure using an AI algorithm. This enables a rapid response by automatically removing the problematic content. Some or all of the above-described processing in the removal unit can be performed using, for example, AI, or can be performed without using AI. For example, the removal unit can automate the removal procedure using an AI model for removing the detected problematic content.

[0032] The support department can use a chatbot to respond to victims' consultations and provide specific advice and support. Specific chatbot functions include, but are not limited to, the use of natural language processing technology, 24-hour availability, and automated responses to specific questions. For example, the support department may provide a chatbot that victims can consult with and provide appropriate advice and support. For example, the support department may provide a chatbot that victims can consult with and provide appropriate advice and support. The support department may also refer victims to experts as needed. This allows victims to quickly seek advice and receive appropriate advice and support. Some or all of the above-described processing in the support department may be performed using, for example, AI, or may be performed without AI. For example, the support department may respond to victims' consultations using an AI model that analyzes the content of the victim's consultation and provides appropriate advice and support.

[0033] The support unit can refer a victim to a specialist based on predetermined conditions. Predetermined conditions include, but are not limited to, the severity of the harm, the type of consultation content, and the victim's wishes. The support unit can refer a victim to a specialist based on, for example, the severity of the harm. For example, the support unit prioritizes referral to a specialist when the severity of the harm is high. The support unit can also refer a victim to a specialist based on the type of consultation content. For example, the support unit can refer a victim to a specialist for a specific consultation content. Furthermore, the support unit can also refer a victim to a specialist based on the victim's wishes. For example, the support unit can refer a victim to a specialist of the victim's choice. This allows the victim to receive specialized support. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without AI. For example, the support unit can analyze the severity of the harm and the type of consultation content and use an AI model for referral to a specialist.

[0034] The providing unit can provide the user with digital security information, such as password strengthening and two-step authentication setup. Specific content of the digital security information includes, but is not limited to, password management, two-step authentication, and phishing countermeasures. For example, the providing unit can suggest password strengthening to the user. For example, the providing unit can provide the user with a method for creating a strong password. The providing unit can also suggest two-step authentication setup to the user. For example, the providing unit can provide the user with a method for setting up two-step authentication. Furthermore, the providing unit can suggest phishing countermeasures to the user. For example, the providing unit can provide the user with a method for identifying phishing emails. This allows the user to take specific measures to protect their information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide the digital security information using an AI model for analyzing the user's security status and providing appropriate digital security information.

[0035] The monitoring unit can change the monitoring intensity based on a predetermined time period or event. Examples of predetermined time periods or events include, but are not limited to, late night hours, specific holidays, and event times. The monitoring unit increases the monitoring intensity during times when bullying is likely to occur, such as at night or on weekends. For example, the monitoring unit increases the monitoring intensity during late night hours to detect problems early. The monitoring unit can also increase the monitoring intensity during school events or exam periods. For example, the monitoring unit increases the monitoring intensity during exam periods to prevent bullying and slander. The monitoring unit can also adjust the monitoring intensity during holidays or long vacations. For example, the monitoring unit adjusts the monitoring intensity during holidays or long vacations to prevent problems from occurring. This enables more effective monitoring by adjusting the monitoring intensity according to specific time periods or events. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can change the monitoring intensity using an AI model for adjusting the monitoring intensity based on specific time periods or events.

[0036] The monitoring unit can analyze a user's past posting history and prioritize monitoring high-risk posts. High-risk posts include, but are not limited to, posts that have been problematic in the past and posts containing specific keywords. For example, the monitoring unit prioritizes monitoring posts by users who have made problematic posts in the past. For example, the monitoring unit may focus on monitoring posts by users who have frequently posted bullying or defamatory content in the past. The monitoring unit can also prioritize monitoring posts containing high-risk keywords from the past posting history. For example, the monitoring unit may analyze the past posting history and detect posts containing high-risk keywords. This prioritizes monitoring high-risk posts, enabling early detection of problems. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can analyze a user's past posting history and prioritize monitoring high-risk posts using an AI model for priority monitoring of high-risk posts.

[0037] The monitoring unit can prioritize monitoring posts in a predetermined area by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if bullying is occurring frequently in a specific area, the monitoring unit prioritizes monitoring posts in that area. For example, the monitoring unit prioritizes monitoring posts in a specific area to detect problems early. The monitoring unit can also focus on monitoring posts in high-risk areas based on the user's geographical location information. For example, the monitoring unit prioritizes monitoring posts in high-risk areas to prevent problems from occurring. Furthermore, the monitoring unit can determine monitoring targets by taking into account the bullying incidence rate in each area. For example, the monitoring unit analyzes the bullying incidence rate in each area and prioritizes monitoring posts in high-risk areas. This allows for rapid response to problems in each area by prioritizing monitoring posts in specific areas. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can prioritize monitoring posts in a specific region using an AI model that takes into account the user's geographic location information and prioritizes monitoring posts in a specific region.

[0038] The monitoring unit can analyze the user's social media activity and prioritize monitoring related posts. Specific methods for analyzing social media activity include, but are not limited to, posting frequency, number of followers, and use of specific hashtags. The monitoring unit, for example, prioritizes monitoring posts from the user's social media activity that pose a high risk of bullying or defamation. For example, the monitoring unit can analyze the user's posting frequency and number of followers to detect high-risk posts. The monitoring unit can also analyze the user's followers and friendships to detect high-risk posts. For example, the monitoring unit can analyze the user's followers and friendships to detect high-risk posts. Furthermore, the monitoring unit can prioritize monitoring high-risk posts based on the user's past social media activity. For example, the monitoring unit can analyze the user's past social media activity to detect high-risk posts. In this way, high-risk posts can be prioritized by analyzing the user's social media activity. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can analyze a user's social media activity and prioritize monitoring relevant posts using an AI model for prioritizing monitoring relevant posts.

[0039] The deletion unit can adjust the urgency of deletion based on the impact of the content. Specific methods for evaluating the impact of content include, but are not limited to, the number of views, the number of shares, and the number of comments. For example, the deletion unit prioritizes deletion of content with high impact. For example, the deletion unit prioritizes deletion of content with high views or shares. The deletion unit can also perform a normal deletion procedure on content with low impact. For example, the deletion unit can perform a normal deletion procedure on content with low views or shares. Furthermore, the deletion unit can analyze the impact of content and determine the deletion priority according to the urgency. For example, the deletion unit analyzes the impact of content and determines the deletion priority according to the urgency. This enables more effective deletion by adjusting the urgency of deletion according to the impact of content. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without AI. For example, the deletion unit can adjust the urgency of deletion using an AI model for evaluating the impact of content.

[0040] The deletion unit can apply different deletion algorithms depending on the type of content. Examples of content types include, but are not limited to, text, images, and videos. For example, the deletion unit can apply a rapid deletion algorithm to bullying or defamatory content. For example, the deletion unit can apply an algorithm for quickly deleting bullying or defamatory content. The deletion unit can also apply a special deletion algorithm to content that leaks personal information. For example, the deletion unit can apply a special algorithm for deleting content that leaks personal information. Furthermore, the deletion unit can select an optimal deletion algorithm depending on the type of content. For example, the deletion unit selects and applies an optimal deletion algorithm depending on the type of content. This enables more effective deletion by applying the optimal deletion algorithm depending on the type of content. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can apply a deletion algorithm using an AI model for applying different deletion algorithms depending on the type of content.

[0041] The deletion unit may perform deletion by taking into consideration attribute information, such as the age, gender, and place of residence, of the poster of the content. Specific types of attribute information include, but are not limited to, age, gender, and place of residence. For example, the deletion unit may apply a special deletion procedure if the poster is a minor. For example, the deletion unit may apply a special procedure for deleting content by a minor poster. Furthermore, if the poster has a specific attribute, the deletion unit may also perform a deletion procedure according to the attribute. For example, the deletion unit may apply a procedure for deleting content by a poster with a specific attribute. Furthermore, the deletion unit may analyze the poster's attribute information and select an optimal deletion procedure. For example, the deletion unit may analyze the poster's attribute information and select and apply an optimal deletion procedure. This enables more effective deletion by performing an optimal deletion procedure according to the poster's attribute information. Some or all of the above-described processing by the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit may perform deletion using an AI model for deleting content by taking into consideration the poster's attribute information.

[0042] The deletion unit can adjust the deletion criteria by referring to relevant legal regulations. Specific content of legal regulations includes, but is not limited to, specific laws, regulations, guidelines, etc. The deletion unit, for example, sets the deletion criteria based on legal regulations. For example, the deletion unit sets and applies the deletion criteria based on specific laws and regulations. The deletion unit can also update the deletion criteria in response to changes in legal regulations. For example, the deletion unit updates and applies the deletion criteria in response to changes in legal regulations. Furthermore, the deletion unit can also apply optimal deletion criteria by referring to legal regulations. For example, the deletion unit applies optimal deletion criteria by referring to specific laws and regulations. This enables legally appropriate deletion by setting the deletion criteria based on legal regulations. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can adjust the deletion criteria using an AI model for adjusting the deletion criteria by referring to relevant legal regulations.

[0043] The support unit can provide optimal advice by referring to the victim's past consultation history. Specific criteria for optimal advice include, but are not limited to, the content of the past consultation, the victim's situation, and the opinion of an expert. The support unit can provide optimal advice based on, for example, the victim's past consultation history. For example, the support unit can analyze the content of the victim's past consultation and provide optimal advice. The support unit can also analyze the content of the victim's past consultation and provide appropriate support. For example, the support unit can refer to the victim's past consultation history and provide appropriate support. Furthermore, the support unit can refer to the victim's past consultation history and refer the victim to an expert. For example, the support unit can refer the victim to an expert based on the victim's past consultation history. This enables more effective support by providing optimal advice based on the victim's past consultation history. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide optimal advice by referring to the victim's past consultation history and using an AI model for providing optimal advice.

[0044] The support unit can customize the support method based on the victim's current psychological state. Specific methods for evaluating the current psychological state include, but are not limited to, psychological tests, counseling results, and self-reports. For example, if the victim is feeling stressed, the support unit can provide a support method to help the victim relax. For example, the support unit can analyze the results of the victim's psychological test and provide a support method to help the victim relax. The support unit can also provide a support method to help the victim relax based on the results of the victim's counseling. For example, the support unit can analyze the results of the victim's counseling and provide a support method to help the victim relax. Furthermore, the support unit can also provide a support method to help the victim relax based on the victim's self-report. For example, the support unit can analyze the victim's self-report and provide a support method to help the victim relax. This enables more effective support by customizing the support method according to the victim's current psychological state. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can customize the support method using an AI model for evaluating the victim's current psychological state.

[0045] The support unit can select a predetermined support method by taking into account the victim's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. The support unit can provide an optimal support method based on the victim's geographical location information. For example, the support unit can analyze the victim's geographical location information and provide the optimal support method. The support unit can also refer the victim to a specialist by taking into account the victim's geographical location information. For example, the support unit can refer the victim to a specialist based on the victim's geographical location information. The support unit can also provide appropriate support content by referring to the victim's geographical location information. For example, the support unit can analyze the victim's geographical location information and provide appropriate support content. This enables more effective support by providing the optimal support method based on the victim's geographical location information. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without AI. For example, the support unit can select a support method using an AI model for selecting the optimal support method by taking into account the victim's geographical location information.

[0046] The support department can analyze the victim's social media activity and propose support measures. Specific methods for analyzing social media activity include, but are not limited to, posting frequency, number of followers, and use of specific hashtags. The support department can propose optimal support measures based on the victim's social media activity. For example, the support department can analyze the victim's posting frequency and number of followers to propose optimal support measures. The support department can also analyze the victim's social media activity and provide appropriate support. For example, the support department can analyze the victim's social media activity and provide appropriate support. Furthermore, the support department can refer the victim to an expert based on the victim's social media activity. For example, the support department can refer the victim to an expert based on the victim's social media activity. This enables more effective support by proposing optimal support measures based on the victim's social media activity. Some or all of the above-described processing in the support department can be performed using, for example, AI, or without AI. For example, the support department can propose support measures using an AI model for analyzing the victim's social media activity and proposing support measures.

[0047] The providing unit can provide optimal information by referring to the user's past security history. Specific content of the security history includes, but is not limited to, past security settings and security incident history. The providing unit can provide optimal security information based on the user's past security history. For example, the providing unit can analyze the user's past security settings and provide optimal security information. The providing unit can also propose appropriate security measures based on the user's past security incident history. For example, the providing unit can analyze the user's past security incident history and propose appropriate security measures. Furthermore, the providing unit can refer the user to an expert by referring to the user's past security history. For example, the providing unit can refer the user to an expert based on the user's past security history. This enables more effective security measures by providing optimal information based on the user's past security history. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide optimal information by referring to the user's past security history using an AI model for providing optimal information.

[0048] The providing unit can customize the content of the information based on the user's current security situation. Specific methods for evaluating the current security situation include, but are not limited to, current security settings and recent security incidents. The providing unit, for example, provides optimal security information based on the user's current security situation. For example, the providing unit analyzes the user's current security settings and provides optimal security information. The providing unit can also suggest appropriate security measures based on the user's recent security incidents. For example, the providing unit analyzes the user's recent security incidents and suggests appropriate security measures. Furthermore, the providing unit can refer the user to an expert based on the user's current security situation. For example, the providing unit can refer the user to an expert based on the user's current security situation. This enables more effective security measures by customizing the content of the information according to the user's current security situation. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can customize the content of the information using an AI model for evaluating the user's current security situation.

[0049] The providing unit can provide predetermined security information taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. The providing unit can provide security information for each region based on the user's geographical location information. For example, the providing unit can analyze the user's geographical location information and provide security information for each region. The providing unit can also propose security measures for a specific region based on the user's geographical location information. For example, the providing unit can propose security measures for a specific region based on the user's geographical location information. Furthermore, the providing unit can refer the user to security experts for each region by referring to the user's geographical location information. For example, the providing unit can refer the user to security experts for each region based on the user's geographical location information. This enables more effective security measures by providing optimal security information based on the user's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide security information using an AI model for providing optimal security information taking into account the user's geographical location information.

[0050] The providing unit can adjust the content of the security information by analyzing the user's social media activity. Specific methods for analyzing social media activity include, but are not limited to, posting frequency, number of followers, and use of specific hashtags. The providing unit can provide optimal security information based on the user's social media activity. For example, the providing unit can analyze the user's posting frequency and number of followers to provide optimal security information. The providing unit can also analyze the user's social media activity and suggest appropriate security measures. For example, the providing unit can analyze the user's social media activity and suggest appropriate security measures. Furthermore, the providing unit can refer the user to a security expert by referring to the user's social media activity. For example, the providing unit can refer the user to a security expert based on the user's social media activity. This enables more effective information provision by adjusting the content of the security information based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can adjust the content of the security information using an AI model for analyzing the user's social media activity and adjusting the content of the security information.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The deletion unit can adjust the urgency of deletion based on the impact of the content. For example, it prioritizes deletion of content with a high impact. The deletion unit prioritizes deletion of content with a high number of views or shares. The deletion unit can also perform normal deletion procedures for content with a low impact. Furthermore, the deletion unit can analyze the impact of content and determine the priority of deletion according to the urgency. This allows for more effective deletion by adjusting the urgency of deletion according to the impact of content.

[0053] The monitoring unit can analyze a user's past posting history and prioritize monitoring high-risk posts. For example, it prioritizes monitoring posts by users who have made problematic posts in the past. The monitoring unit focuses on monitoring posts by users who have made many bullying or defamatory posts in the past. The monitoring unit can also prioritize monitoring posts that include high-risk keywords from the past posting history. This makes it possible to detect problems early by prioritizing high-risk posts.

[0054] The support department can provide optimal advice by referencing the victim's past consultation history. For example, optimal advice can be provided based on the content of the victim's past consultations. The support department can analyze the content of the victim's past consultations and provide optimal advice. The support department can also analyze the content of the victim's past consultations and provide appropriate support. Furthermore, the support department can refer the victim to a specialist by referencing the victim's past consultation history. This makes it possible to provide more effective support by providing optimal advice based on the victim's past consultation history.

[0055] The providing unit can provide optimal information by referring to the user's past security history. For example, optimal security information is provided based on the user's past security settings. The providing unit analyzes the user's past security settings and provides optimal security information. The providing unit can also suggest appropriate security measures based on the user's past security incident history. Furthermore, the providing unit can refer the user to an expert by referring to the user's past security history. This allows for more effective security measures by providing optimal information based on the user's past security history.

[0056] The monitoring unit can prioritize monitoring posts in predetermined areas by taking into account the user's geographical location information. For example, if bullying is occurring frequently in a specific area, the monitoring unit will prioritize monitoring posts in that area. The monitoring unit prioritizes monitoring posts in specific areas to detect problems early. The monitoring unit can also focus on monitoring posts in high-risk areas based on the user's geographical location information. Furthermore, the monitoring unit can determine monitoring targets by taking into account the incidence of bullying in each area. This allows for rapid response to problems in each area by prioritizing monitoring posts in specific areas.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The monitoring unit monitors online information, including social media posts, blog posts, and news articles. The monitoring unit analyzes specific keywords and phrases to detect problems related to digital tattoos early. For example, it sets keywords such as "bullying," "defamation," and "personal information leaks" and detects content containing these keywords. Step 2: The removal unit removes the problematic content detected by the monitoring unit. The removal unit checks the content of the detected content and determines whether it meets the removal criteria. If it determines that removal is necessary, the removal unit automatically initiates the removal procedure. For example, the removal procedure can be automated using an AI algorithm. Step 3: The Support Unit provides psychological support to victims based on the information deleted by the Removal Unit. The Support Unit uses a chatbot to respond to victims' inquiries and provide specific advice and support. If necessary, they can also refer them to specialists. Step 4: The provision unit provides digital security information based on the support provided by the support unit. The provision unit provides the user with digital security information such as strengthening passwords and setting up two-step authentication, thereby suggesting specific methods for the user to protect their information.

[0059] (Example 2) A digital tattoo countermeasure system according to an embodiment of the present invention monitors online information, analyzes specific keywords and phrases to detect digital tattoo-related issues early, removes problematic content, provides psychological support to victims, and provides digital security information. This system uses AI to monitor online information and analyze specific keywords and phrases to detect digital tattoo-related issues early. It then automates the process for removing the detected problematic content. Furthermore, the AI ​​provides counseling services to provide psychological support to victims. For example, it provides a chatbot that victims can consult with, offering appropriate advice and support. It also provides information on digital security, enabling users to take measures to protect their information. However, because the ultimate ethical decision is left to humans, countermeasures against digital tattoo issues rely on the collaboration of technology and humans. For example, specific keywords and phrases that the AI ​​analyzes include "bullying," "slander," and "personal information leaks." This identifies problematic content. If the AI ​​detects problematic content, the removal process begins by first reviewing the content and determining whether it meets the removal criteria. If removal is determined necessary, the removal process is automatically initiated. Furthermore, AI can provide psychological support to victims by providing counseling services. For example, it can use a chatbot to answer victims' questions and provide specific advice and support. It can also refer them to specialists if necessary. AI can also provide digital security information by suggesting specific measures to users. For example, it can provide specific methods for users to protect their information, such as strengthening passwords and setting up two-factor authentication. In this way, systems using AI can address the digital tattoo problem. However, since the final ethical decision is left to humans, cooperation between technology and humans is essential. This allows the digital tattoo countermeasure system to provide a comprehensive system for addressing the digital tattoo problem.

[0060] A digital tattoo countermeasure system according to an embodiment includes a monitoring unit, a deletion unit, a support unit, and a providing unit. The monitoring unit monitors information on the Internet. Examples of information on the Internet include, but are not limited to, social media posts, blog posts, and news articles. The monitoring unit, for example, analyzes specific keywords and phrases to detect problems related to digital tattoos early on. For example, the monitoring unit sets keywords such as "bullying," "slander," and "personal information leaks" and detects content containing these keywords. The deletion unit deletes problematic content detected by the monitoring unit. For example, the deletion unit checks the content of the detected content and determines whether it meets deletion criteria. If deletion is determined to be necessary, the deletion unit automatically initiates the deletion procedure. For example, the deletion unit can automate the deletion procedure using an AI algorithm. The support unit provides psychological support to victims based on the information deleted by the deletion unit. For example, the support unit uses a chatbot to provide specific advice and support to victims. For example, the support unit provides a chatbot that victims can consult with and provides appropriate advice and support. The support unit may also refer the user to an expert as needed. The provision unit provides digital security information based on the support provided by the support unit. The provision unit may provide the user with digital security information, such as strengthening passwords or setting up two-step authentication. For example, the provision unit may suggest specific methods for the user to protect their information. As a result, the digital tattoo countermeasure system according to the embodiment can provide a comprehensive system for addressing the digital tattoo issue.

[0061] The monitoring unit can analyze predetermined keywords or phrases to detect problems related to digital tattoos early. Examples of predetermined keywords or phrases include, but are not limited to, specific offensive words, personal information, and words related to specific incidents. The monitoring unit can set keywords such as "bullying," "slander," and "personal information leaks" and detect content containing these keywords. For example, the monitoring unit can use AI to monitor information on the Internet and analyze specific keywords and phrases to detect problems related to digital tattoos early. Thus, analyzing specific keywords and phrases can detect problems related to digital tattoos early. Some or all of the above-described processing by the monitoring unit can be performed using AI, or can be performed without AI. For example, the monitoring unit can detect problematic content early using an AI model for detecting content containing specific keywords or phrases.

[0062] The removal unit can automate a procedure for removing the detected problematic content. Specific methods for automating the procedure include, but are not limited to, the use of an AI algorithm or a specific rule-based system. For example, the removal unit can check the details of the detected content and determine whether it meets removal criteria. If it is determined that removal is necessary, the removal unit automatically initiates the removal procedure. For example, the removal unit can automate the removal procedure using an AI algorithm. This enables a rapid response by automatically removing the problematic content. Some or all of the above-described processing in the removal unit can be performed using, for example, AI, or can be performed without using AI. For example, the removal unit can automate the removal procedure using an AI model for removing the detected problematic content.

[0063] The support department can use a chatbot to respond to victims' consultations and provide specific advice and support. Specific chatbot functions include, but are not limited to, the use of natural language processing technology, 24-hour availability, and automated responses to specific questions. For example, the support department may provide a chatbot that victims can consult with and provide appropriate advice and support. For example, the support department may provide a chatbot that victims can consult with and provide appropriate advice and support. The support department may also refer victims to experts as needed. This allows victims to quickly seek advice and receive appropriate advice and support. Some or all of the above-described processing in the support department may be performed using, for example, AI, or may be performed without AI. For example, the support department may respond to victims' consultations using an AI model that analyzes the content of the victim's consultation and provides appropriate advice and support.

[0064] The support unit can refer a victim to a specialist based on predetermined conditions. Predetermined conditions include, but are not limited to, the severity of the harm, the type of consultation content, and the victim's wishes. The support unit can refer a victim to a specialist based on, for example, the severity of the harm. For example, the support unit prioritizes referral to a specialist when the severity of the harm is high. The support unit can also refer a victim to a specialist based on the type of consultation content. For example, the support unit can refer a victim to a specialist for a specific consultation content. Furthermore, the support unit can also refer a victim to a specialist based on the victim's wishes. For example, the support unit can refer a victim to a specialist of the victim's choice. This allows the victim to receive specialized support. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without AI. For example, the support unit can analyze the severity of the harm and the type of consultation content and use an AI model for referral to a specialist.

[0065] The providing unit can provide the user with digital security information, such as password strengthening and two-step authentication setup. Specific content of the digital security information includes, but is not limited to, password management, two-step authentication, and phishing countermeasures. For example, the providing unit can suggest password strengthening to the user. For example, the providing unit can provide the user with a method for creating a strong password. The providing unit can also suggest two-step authentication setup to the user. For example, the providing unit can provide the user with a method for setting up two-step authentication. Furthermore, the providing unit can suggest phishing countermeasures to the user. For example, the providing unit can provide the user with a method for identifying phishing emails. This allows the user to take specific measures to protect their information. Some or all of the above-described processing in the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide the digital security information using an AI model for analyzing the user's security status and providing appropriate digital security information.

[0066] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. Specific methods for estimating the user's emotions include, but are not limited to, text analysis, facial expression recognition, and voice analysis. For example, if the user is feeling stressed, the monitoring unit increases the monitoring frequency to detect problems early. For example, the monitoring unit can analyze the user's text messages to detect signs of stress. The monitoring unit can also capture the user's facial expressions with a camera and estimate the user's emotions using facial expression recognition technology. For example, the monitoring unit can analyze changes in the user's facial expressions to determine whether the user is feeling stressed. Furthermore, the monitoring unit can record the user's voice and estimate the user's emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the user's voice to determine whether the user is feeling stressed. This enables more effective monitoring by adjusting the monitoring frequency according to the user's emotions. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can adjust the frequency of monitoring using an AI model to estimate the user's emotions.

[0067] The monitoring unit can change the monitoring intensity based on a predetermined time period or event. Examples of predetermined time periods or events include, but are not limited to, late night hours, specific holidays, and event times. The monitoring unit increases the monitoring intensity during times when bullying is likely to occur, such as at night or on weekends. For example, the monitoring unit increases the monitoring intensity during late night hours to detect problems early. The monitoring unit can also increase the monitoring intensity during school events or exam periods. For example, the monitoring unit increases the monitoring intensity during exam periods to prevent bullying and slander. The monitoring unit can also adjust the monitoring intensity during holidays or long vacations. For example, the monitoring unit adjusts the monitoring intensity during holidays or long vacations to prevent problems from occurring. This enables more effective monitoring by adjusting the monitoring intensity according to specific time periods or events. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without AI. For example, the monitoring unit can change the monitoring intensity using an AI model for adjusting the monitoring intensity based on specific time periods or events.

[0068] The monitoring unit can analyze a user's past posting history and prioritize monitoring high-risk posts. High-risk posts include, but are not limited to, posts that have been problematic in the past and posts containing specific keywords. For example, the monitoring unit prioritizes monitoring posts by users who have made problematic posts in the past. For example, the monitoring unit may focus on monitoring posts by users who have frequently posted bullying or defamatory content in the past. The monitoring unit can also prioritize monitoring posts containing high-risk keywords from the past posting history. For example, the monitoring unit may analyze the past posting history and detect posts containing high-risk keywords. This prioritizes monitoring high-risk posts, enabling early detection of problems. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can analyze a user's past posting history and prioritize monitoring high-risk posts using an AI model for priority monitoring of high-risk posts.

[0069] The monitoring unit can estimate a user's emotions and prioritize monitoring targets based on the estimated user emotions. Specific methods for estimating a user's emotions include, but are not limited to, text analysis, facial expression recognition, and voice analysis. For example, if a user is feeling stressed, the monitoring unit prioritizes monitoring of the user's posts. For example, the monitoring unit can analyze the user's text messages to detect signs of stress. The monitoring unit can also capture the user's facial expressions with a camera and estimate the user's emotions using facial expression recognition technology. For example, the monitoring unit can analyze changes in the user's facial expressions to determine whether the user is feeling stressed. Furthermore, the monitoring unit can record the user's voice and estimate the user's emotions using voice analysis technology. For example, the monitoring unit can analyze the tone and speed of the user's voice to determine whether the user is feeling stressed. This enables more effective monitoring by prioritizing monitoring targets based on the user's emotions. Some or all of the above-described processing in the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can use an AI model to estimate a user's emotions to prioritize monitoring targets.

[0070] The monitoring unit can prioritize monitoring posts in a predetermined area by taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. For example, if bullying is occurring frequently in a specific area, the monitoring unit can prioritize monitoring posts in that area. For example, the monitoring unit can prioritize monitoring posts in a specific area to detect problems early. The monitoring unit can also focus on monitoring posts in high-risk areas based on the user's geographical location information. For example, the monitoring unit can prioritize monitoring posts in high-risk areas to prevent problems from occurring. Furthermore, the monitoring unit can determine monitoring targets by taking into account the bullying incidence rate in each area. For example, the monitoring unit can analyze the bullying incidence rate in each area and prioritize monitoring posts in high-risk areas. By prioritizing monitoring posts in specific areas, problems in each area can be addressed quickly. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or without AI. For example, the monitoring unit can prioritize monitoring posts in a specific region using an AI model that takes into account the user's geographic location information to prioritize monitoring posts in a specific region.

[0071] The monitoring unit can analyze the user's social media activity and prioritize monitoring related posts. Specific methods for analyzing social media activity include, but are not limited to, posting frequency, number of followers, and use of specific hashtags. The monitoring unit, for example, prioritizes monitoring posts that pose a high risk of bullying or defamation from the user's social media activity. For example, the monitoring unit can analyze the user's posting frequency and number of followers to detect high-risk posts. The monitoring unit can also analyze the user's followers and friendships to detect high-risk posts. For example, the monitoring unit can analyze the user's followers and friendships to detect high-risk posts. Furthermore, the monitoring unit can prioritize monitoring high-risk posts based on the user's past social media activity. For example, the monitoring unit can analyze the user's past social media activity to detect high-risk posts. In this way, high-risk posts can be prioritized by analyzing the user's social media activity. Some or all of the above-described processing by the monitoring unit may be performed using, for example, AI, or may be performed without using AI. For example, the monitoring unit can analyze a user's social media activity and prioritize monitoring relevant posts using an AI model for prioritizing monitoring relevant posts.

[0072] The deletion unit can estimate a user's emotions and determine deletion priorities based on the estimated user emotions. Specific methods for estimating a user's emotions include, but are not limited to, text analysis, facial expression recognition, and voice analysis. For example, if a user is feeling stressed, the deletion unit prioritizes deleting the user's posts. For example, the deletion unit can analyze the user's text messages to detect signs of stress. The deletion unit can also capture the user's facial expressions with a camera and estimate the user's emotions using facial expression recognition technology. For example, the deletion unit can analyze changes in the user's facial expressions to determine whether the user is feeling stressed. Furthermore, the deletion unit can record the user's voice and estimate the user's emotions using voice analysis technology. For example, the deletion unit can analyze the tone and speed of the user's voice to determine whether the user is feeling stressed. This enables more effective deletion by determining deletion priorities based on the user's emotions. Some or all of the above-described processing by the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can use an AI model for estimating user emotions to determine deletion priorities.

[0073] The deletion unit can adjust the urgency of deletion based on the impact of the content. Specific methods for evaluating the impact of content include, but are not limited to, the number of views, the number of shares, and the number of comments. For example, the deletion unit prioritizes deletion of content with high impact. For example, the deletion unit prioritizes deletion of content with high views or shares. The deletion unit can also perform a normal deletion procedure on content with low impact. For example, the deletion unit can perform a normal deletion procedure on content with low views or shares. Furthermore, the deletion unit can analyze the impact of content and determine the deletion priority according to the urgency. For example, the deletion unit analyzes the impact of content and determines the deletion priority according to the urgency. This enables more effective deletion by adjusting the urgency of deletion according to the impact of content. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without AI. For example, the deletion unit can adjust the urgency of deletion using an AI model for evaluating the impact of content.

[0074] The deletion unit can apply different deletion algorithms depending on the type of content. Examples of content types include, but are not limited to, text, images, and videos. For example, the deletion unit can apply a rapid deletion algorithm to bullying or defamatory content. For example, the deletion unit can apply an algorithm for quickly deleting bullying or defamatory content. The deletion unit can also apply a special deletion algorithm to content that leaks personal information. For example, the deletion unit can apply a special algorithm for deleting content that leaks personal information. Furthermore, the deletion unit can select an optimal deletion algorithm depending on the type of content. For example, the deletion unit selects and applies an optimal deletion algorithm depending on the type of content. This enables more effective deletion by applying the optimal deletion algorithm depending on the type of content. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can apply a deletion algorithm using an AI model for applying different deletion algorithms depending on the type of content.

[0075] The deletion unit can estimate the user's emotions and adjust the timing of deletion based on the estimated user emotions. Specific methods for estimating the user's emotions include, but are not limited to, text analysis, facial expression recognition, and voice analysis. The deletion unit can quickly delete messages when the user is feeling stressed. For example, the deletion unit can analyze the user's text messages, detect signs of stress, and quickly delete messages. The deletion unit can also capture the user's facial expressions with a camera, estimate the user's emotions using facial expression recognition technology, and quickly delete messages. For example, the deletion unit can analyze changes in the user's facial expressions, determine whether the user is feeling stressed, and quickly delete messages. The deletion unit can also record the user's voice, estimate the user's emotions using voice analysis technology, and quickly delete messages. For example, the deletion unit can analyze the tone and speed of the user's voice, determine whether the user is feeling stressed, and quickly delete messages. This allows for more effective deletion by adjusting the timing of deletion according to the user's emotions. Some or all of the above-described processing by the deletion unit can be performed using, for example, AI, or without AI. For example, the deletion unit can adjust the timing of deletion using an AI model for estimating the user's emotions.

[0076] The deletion unit may perform deletion by taking into consideration attribute information, such as the age, gender, and place of residence, of the poster of the content. Specific types of attribute information include, but are not limited to, age, gender, and place of residence. For example, the deletion unit may apply a special deletion procedure if the poster is a minor. For example, the deletion unit may apply a special procedure for deleting content by a minor poster. Furthermore, if the poster has a specific attribute, the deletion unit may also perform a deletion procedure according to the attribute. For example, the deletion unit may apply a procedure for deleting content by a poster with a specific attribute. Furthermore, the deletion unit may analyze the poster's attribute information and select an optimal deletion procedure. For example, the deletion unit may analyze the poster's attribute information and select and apply an optimal deletion procedure. This enables more effective deletion by performing an optimal deletion procedure according to the poster's attribute information. Some or all of the above-described processing by the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit may perform deletion using an AI model for deleting content by taking into consideration the poster's attribute information.

[0077] The deletion unit can adjust the deletion criteria by referring to relevant legal regulations. Specific content of legal regulations includes, but is not limited to, specific laws, regulations, guidelines, etc. The deletion unit, for example, sets the deletion criteria based on legal regulations. For example, the deletion unit sets and applies the deletion criteria based on specific laws and regulations. The deletion unit can also update the deletion criteria in response to changes in legal regulations. For example, the deletion unit updates and applies the deletion criteria in response to changes in legal regulations. Furthermore, the deletion unit can also apply optimal deletion criteria by referring to legal regulations. For example, the deletion unit applies optimal deletion criteria by referring to specific laws and regulations. This enables legally appropriate deletion by setting the deletion criteria based on legal regulations. Some or all of the above-described processing in the deletion unit may be performed using, for example, AI, or may be performed without using AI. For example, the deletion unit can adjust the deletion criteria using an AI model for adjusting the deletion criteria by referring to relevant legal regulations.

[0078] The support unit can estimate the user's emotions and adjust the content of the support based on the estimated user emotions. Specific methods for estimating the user's emotions include, but are not limited to, text analysis, facial expression recognition, and voice analysis. For example, if the user is feeling stressed, the support unit can provide relaxation advice. For example, the support unit can analyze the user's text messages, detect signs of stress, and provide relaxation advice. The support unit can also capture the user's facial expressions with a camera, estimate the user's emotions using facial expression recognition technology, and provide relaxation advice. For example, the support unit can analyze changes in the user's facial expressions, determine whether the user is feeling stressed, and provide relaxation advice. Furthermore, the support unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide relaxation advice. For example, the support unit can analyze the tone and speed of the user's voice, determine whether the user is feeling stressed, and provide relaxation advice. This enables more effective support by adjusting the content of the support according to the user's emotions. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can adjust the content of support using an AI model for estimating the user's emotions.

[0079] The support unit can provide optimal advice by referring to the victim's past consultation history. Specific criteria for optimal advice include, but are not limited to, the content of the past consultation, the victim's situation, and the opinion of an expert. The support unit can provide optimal advice based on, for example, the victim's past consultation history. For example, the support unit can analyze the content of the victim's past consultation and provide optimal advice. The support unit can also analyze the content of the victim's past consultation and provide appropriate support. For example, the support unit can refer to the victim's past consultation history and provide appropriate support. Furthermore, the support unit can refer to the victim's past consultation history and refer the victim to an expert. For example, the support unit can refer the victim to an expert based on the victim's past consultation history. This enables more effective support by providing optimal advice based on the victim's past consultation history. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can provide optimal advice by referring to the victim's past consultation history and using an AI model for providing optimal advice.

[0080] The support unit can customize the support method based on the victim's current psychological state. Specific methods for evaluating the current psychological state include, but are not limited to, psychological tests, counseling results, and self-reports. For example, if the victim is feeling stressed, the support unit can provide a support method to help the victim relax. For example, the support unit can analyze the results of the victim's psychological test and provide a support method to help the victim relax. The support unit can also provide a support method to help the victim relax based on the results of the victim's counseling. For example, the support unit can analyze the results of the victim's counseling and provide a support method to help the victim relax. Furthermore, the support unit can also provide a support method to help the victim relax based on the victim's self-report. For example, the support unit can analyze the victim's self-report and provide a support method to help the victim relax. This enables more effective support by customizing the support method according to the victim's current psychological state. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without AI. For example, the support unit can customize the support method using an AI model for evaluating the victim's current psychological state.

[0081] The support unit can estimate a user's emotions and determine a priority of support based on the estimated user emotions. Specific methods for estimating a user's emotions include, but are not limited to, text analysis, facial expression recognition, and voice analysis. For example, if a user is feeling stressed, the support unit prioritizes support for that user. For example, the support unit analyzes the user's text messages to detect signs of stress and prioritizes support for that user. The support unit can also capture the user's facial expression with a camera, estimate the emotion using facial expression recognition technology, and prioritize support for that user. For example, the support unit can analyze changes in the user's facial expression to determine whether the user is feeling stressed and prioritize support for that user. Furthermore, the support unit can record the user's voice, estimate the emotion using voice analysis technology, and prioritize support for that user. For example, the support unit can analyze the tone and speed of the user's voice to determine whether the user is feeling stressed and prioritize support for that user. This enables more effective support by determining the priority of support according to the user's emotions. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit may determine the priority of support using an AI model for estimating the user's emotions.

[0082] The support unit can select a predetermined support method by taking into account the victim's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. The support unit can provide an optimal support method based on the victim's geographical location information. For example, the support unit can analyze the victim's geographical location information and provide the optimal support method. The support unit can also refer the victim to a specialist by taking into account the victim's geographical location information. For example, the support unit can refer the victim to a specialist based on the victim's geographical location information. The support unit can also provide appropriate support content by referring to the victim's geographical location information. For example, the support unit can analyze the victim's geographical location information and provide appropriate support content. This enables more effective support by providing the optimal support method based on the victim's geographical location information. Some or all of the above-described processing in the support unit can be performed using, for example, AI, or can be performed without AI. For example, the support unit can select a support method using an AI model for selecting the optimal support method by taking into account the victim's geographical location information.

[0083] The support department can analyze the victim's social media activity and propose support measures. Specific methods for analyzing social media activity include, but are not limited to, posting frequency, number of followers, and use of specific hashtags. The support department can propose optimal support measures based on the victim's social media activity. For example, the support department can analyze the victim's posting frequency and number of followers to propose optimal support measures. The support department can also analyze the victim's social media activity and provide appropriate support. For example, the support department can analyze the victim's social media activity and provide appropriate support. Furthermore, the support department can refer the victim to an expert based on the victim's social media activity. For example, the support department can refer the victim to an expert based on the victim's social media activity. This enables more effective support by proposing optimal support measures based on the victim's social media activity. Some or all of the above-described processing in the support department can be performed using, for example, AI, or without AI. For example, the support department can propose support measures using an AI model for analyzing the victim's social media activity and proposing support measures.

[0084] The providing unit can estimate the user's emotions and adjust the method of providing security information based on the estimated user emotions. Specific methods for estimating the user's emotions include, but are not limited to, text analysis, facial expression recognition, and voice analysis. For example, if the user is feeling stressed, the providing unit provides concise and easy-to-understand security information. For example, the providing unit can analyze the user's text messages, detect signs of stress, and provide concise and easy-to-understand security information. The providing unit can also capture the user's facial expressions with a camera, estimate the user's emotions using facial expression recognition technology, and provide concise and easy-to-understand security information. For example, the providing unit can analyze changes in the user's facial expressions, determine whether the user is feeling stressed, and provide concise and easy-to-understand security information. Furthermore, the providing unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide concise and easy-to-understand security information. For example, the providing unit can analyze the tone and speed of the user's voice, determine whether the user is feeling stressed, and provide concise and easy-to-understand security information. This allows for more effective information provision by adjusting the method of providing security information according to the user's emotions. Some or all of the above-described processing by the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may use an AI model for estimating a user's emotions to adjust the way security information is provided.

[0085] The providing unit can provide optimal information by referring to the user's past security history. Specific content of the security history includes, but is not limited to, past security settings and security incident history. The providing unit can provide optimal security information based on the user's past security history. For example, the providing unit can analyze the user's past security settings and provide optimal security information. The providing unit can also propose appropriate security measures based on the user's past security incident history. For example, the providing unit can analyze the user's past security incident history and propose appropriate security measures. Furthermore, the providing unit can refer the user to an expert by referring to the user's past security history. For example, the providing unit can refer the user to an expert based on the user's past security history. This enables more effective security measures by providing optimal information based on the user's past security history. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide optimal information by referring to the user's past security history using an AI model for providing optimal information.

[0086] The providing unit can customize the content of the information based on the user's current security status. Specific methods for evaluating the current security status include, but are not limited to, current security settings and recent security incidents. The providing unit, for example, provides optimal security information based on the user's current security status. For example, the providing unit analyzes the user's current security settings and provides optimal security information. The providing unit can also suggest appropriate security measures based on the user's recent security incidents. For example, the providing unit analyzes the user's recent security incidents and suggests appropriate security measures. Furthermore, the providing unit can refer the user to an expert based on the user's current security status. For example, the providing unit can refer the user to an expert based on the user's current security status. This enables more effective security measures by customizing the content of the information according to the user's current security status. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without AI. For example, the providing unit can customize the content of the information using an AI model for evaluating the user's current security status.

[0087] The providing unit can estimate the user's emotions and prioritize security information based on the estimated user emotions. Specific methods for estimating the user's emotions include, but are not limited to, text analysis, facial expression recognition, and voice analysis. For example, the providing unit can prioritize providing important security information when the user is feeling stressed. For example, the providing unit can analyze the user's text messages, detect signs of stress, and prioritize providing important security information. The providing unit can also capture the user's facial expressions with a camera, estimate the user's emotions using facial expression recognition technology, and prioritize providing important security information. For example, the providing unit can analyze changes in the user's facial expressions, determine whether the user is feeling stressed, and prioritize providing important security information. Furthermore, the providing unit can record the user's voice, estimate the user's emotions using voice analysis technology, and prioritize providing important security information. For example, the providing unit can analyze the user's tone and speed of voice, determine whether the user is feeling stressed, and prioritize providing important security information. This enables more effective information provision by prioritizing security information according to the user's emotions. Some or all of the above-described processing in the providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the providing unit may determine the priority of security information using an AI model for estimating user emotions.

[0088] The providing unit can provide predetermined security information taking into account the user's geographical location information. Specific methods for acquiring geographical location information include, but are not limited to, GPS data, IP addresses, and location information services. The providing unit can provide security information for each region based on the user's geographical location information. For example, the providing unit can analyze the user's geographical location information and provide security information for each region. The providing unit can also propose security measures for a specific region based on the user's geographical location information. For example, the providing unit can propose security measures for a specific region based on the user's geographical location information. Furthermore, the providing unit can refer to security experts for each region by referring to the user's geographical location information. For example, the providing unit can refer to security experts for each region based on the user's geographical location information. This enables more effective security measures by providing optimal security information based on the user's geographical location information. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can provide security information using an AI model for providing optimal security information taking into account the user's geographical location information.

[0089] The providing unit can adjust the content of the security information by analyzing the user's social media activity. Specific methods for analyzing social media activity include, but are not limited to, posting frequency, number of followers, and use of specific hashtags. The providing unit can provide optimal security information based on the user's social media activity. For example, the providing unit can analyze the user's posting frequency and number of followers to provide optimal security information. The providing unit can also analyze the user's social media activity and suggest appropriate security measures. For example, the providing unit can analyze the user's social media activity and suggest appropriate security measures. Furthermore, the providing unit can refer the user to a security expert by referring to the user's social media activity. For example, the providing unit can refer the user to a security expert based on the user's social media activity. This enables more effective information provision by adjusting the content of the security information based on the user's social media activity. Some or all of the above-described processing by the providing unit can be performed using, for example, AI, or can be performed without using AI. For example, the providing unit can adjust the content of the security information using an AI model for analyzing the user's social media activity and adjusting the content of the security information. === Hard Collateral 1-1 === Each of the multiple elements, including the monitoring unit, deletion unit, support unit, and provision unit, described above, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the monitoring unit monitors information on the Internet using the camera 42 and microphone 38B of the smart device 14 and analyzes specific keywords and phrases using the control unit 46A. The deletion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically deletes problematic content. The support unit is realized, for example, by the control unit 46A of the smart device 14 and provides consultation to victims using a chatbot. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides digital security information. === Hard Collateral 1-2 === Each of the multiple elements, including the monitoring unit, deletion unit, support unit, and provision unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the monitoring unit monitors information on the Internet using the camera 42 and microphone 238 of the smart glasses 214 and analyzes specific keywords and phrases using the control unit 46A. The deletion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically deletes problematic content. The support unit is realized, for example, by the control unit 46A of the smart glasses 214 and provides consultation to victims using a chatbot. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides digital security information. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned monitoring unit, deletion unit, support unit, and provision unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the monitoring unit monitors information on the Internet using the camera 42 and microphone 238 of the headset-type terminal 314 and analyzes specific keywords and phrases using the control unit 46A. The deletion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically deletes problematic content. The support unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and uses a chatbot to provide consultation to victims. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides digital security information. === Hard Collateral 1-4 === Each of the multiple elements including the monitoring unit, deletion unit, support unit, and provision unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the monitoring unit monitors information on the Internet using the camera 42 and microphone 238 of the robot 414 and analyzes specific keywords and phrases using the control unit 46A. The deletion unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and automatically deletes problematic content. The support unit is realized, for example, by the control unit 46A of the robot 414 and provides consultation to victims using a chatbot. The provision unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides digital security information.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The monitoring unit can estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions. For example, if the user is feeling stressed, the monitoring frequency can be increased to detect problems early. The monitoring unit can analyze the user's text messages to detect signs of stress. The monitoring unit can also capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. Furthermore, the monitoring unit can record the user's voice and estimate the emotions using voice analysis technology. This allows for more effective monitoring by adjusting the monitoring frequency according to the user's emotions.

[0092] The deletion unit can adjust the urgency of deletion based on the impact of the content. For example, it prioritizes deletion of content with a high impact. The deletion unit prioritizes deletion of content with a high number of views or shares. The deletion unit can also perform normal deletion procedures for content with a low impact. Furthermore, the deletion unit can analyze the impact of content and determine the priority of deletion according to the urgency. This allows for more effective deletion by adjusting the urgency of deletion according to the impact of content.

[0093] The support unit can estimate the user's emotions and adjust the content of the support based on the estimated user emotions. For example, if the user is feeling stressed, the support unit can provide relaxation advice. The support unit can analyze the user's text messages, detect signs of stress, and provide relaxation advice. The support unit can also capture the user's facial expressions with a camera, estimate the user's emotions using facial expression recognition technology, and provide relaxation advice. Furthermore, the support unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide relaxation advice. This allows for more effective support by adjusting the content of the support according to the user's emotions.

[0094] The providing unit can estimate the user's emotions and adjust the method of providing security information based on the estimated user emotions. For example, if the user is feeling stressed, concise and easy-to-understand security information is provided. The providing unit can analyze the user's text messages, detect signs of stress, and provide concise and easy-to-understand security information. The providing unit can also capture the user's facial expressions with a camera, estimate the user's emotions using facial expression recognition technology, and provide concise and easy-to-understand security information. Furthermore, the providing unit can record the user's voice, estimate the user's emotions using voice analysis technology, and provide concise and easy-to-understand security information. This allows for more effective information provision by adjusting the method of providing security information according to the user's emotions.

[0095] The monitoring unit can analyze a user's past posting history and prioritize monitoring high-risk posts. For example, it prioritizes monitoring posts by users who have made problematic posts in the past. The monitoring unit focuses on monitoring posts by users who have made many bullying or defamatory posts in the past. The monitoring unit can also prioritize monitoring posts that include high-risk keywords from the past posting history. This makes it possible to detect problems early by prioritizing high-risk posts.

[0096] The deletion unit can estimate a user's emotions and determine a deletion priority based on the estimated user emotions. For example, if a user is feeling stressed, the deletion unit prioritizes deleting posts from that user. The deletion unit can analyze the user's text messages to detect signs of stress. The deletion unit can also capture the user's facial expressions with a camera and estimate the emotions using facial expression recognition technology. Furthermore, the deletion unit can record the user's voice and estimate the emotions using voice analysis technology. This enables more effective deletion by determining the deletion priority according to the user's emotions.

[0097] The support department can provide optimal advice by referencing the victim's past consultation history. For example, optimal advice can be provided based on the content of the victim's past consultations. The support department can analyze the content of the victim's past consultations and provide optimal advice. The support department can also analyze the content of the victim's past consultations and provide appropriate support. Furthermore, the support department can refer the victim to a specialist by referencing the victim's past consultation history. This makes it possible to provide more effective support by providing optimal advice based on the victim's past consultation history.

[0098] The providing unit can provide optimal information by referring to the user's past security history. For example, optimal security information is provided based on the user's past security settings. The providing unit analyzes the user's past security settings and provides optimal security information. The providing unit can also suggest appropriate security measures based on the user's past security incident history. Furthermore, the providing unit can refer the user to an expert by referring to the user's past security history. This allows for more effective security measures by providing optimal information based on the user's past security history.

[0099] The providing unit can estimate the user's emotions and determine the priority of security information based on the estimated user emotions. For example, if the user is feeling stressed, important security information is provided preferentially. The providing unit can analyze the user's text messages, detect signs of stress, and provide important security information preferentially. The providing unit can also capture the user's facial expressions with a camera, estimate the emotions using facial expression recognition technology, and provide important security information preferentially. Furthermore, the providing unit can record the user's voice, estimate the emotions using voice analysis technology, and provide important security information preferentially. This enables more effective information provision by determining the priority of security information according to the user's emotions.

[0100] The monitoring unit can prioritize monitoring posts in predetermined areas by taking into account the user's geographical location information. For example, if bullying is occurring frequently in a specific area, the monitoring unit will prioritize monitoring posts in that area. The monitoring unit prioritizes monitoring posts in specific areas to detect problems early. The monitoring unit can also focus on monitoring posts in high-risk areas based on the user's geographical location information. Furthermore, the monitoring unit can determine monitoring targets by taking into account the incidence of bullying in each area. This allows for rapid response to problems in each area by prioritizing monitoring posts in specific areas.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The monitoring unit monitors online information, including social media posts, blog posts, and news articles. The monitoring unit analyzes specific keywords and phrases to detect problems related to digital tattoos early. For example, it sets keywords such as "bullying," "defamation," and "personal information leaks" and detects content containing these keywords. Step 2: The removal unit removes the problematic content detected by the monitoring unit. The removal unit checks the content of the detected content and determines whether it meets the removal criteria. If it determines that removal is necessary, the removal unit automatically initiates the removal procedure. For example, the removal procedure can be automated using an AI algorithm. Step 3: The Support Unit provides psychological support to victims based on the information deleted by the Removal Unit. The Support Unit uses a chatbot to respond to victims' inquiries and provide specific advice and support. If necessary, they can also refer them to specialists. Step 4: The provision unit provides digital security information based on the support provided by the support unit. The provision unit provides the user with digital security information such as strengthening passwords and setting up two-step authentication, thereby suggesting specific methods for the user to protect their information.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0110] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0111] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0112] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0113] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0114] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0115] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0116] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0117] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0118] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0119] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0120] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0132] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0134] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0142] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0143] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0144] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0145] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0146] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0147] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0148] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0149] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0150] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0151] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0152] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0153] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, 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), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0157] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0158] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0159] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0160] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0161] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0162] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0163] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0164] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0165] 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.

[0166] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0167] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0168] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0169] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0170] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0171] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0172] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0173] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0174] [Explanation of symbols]

[0175] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a monitoring unit that monitors information on the Internet; a deletion unit that deletes problematic content detected by the monitoring unit; a support unit that provides psychological support to the victim based on the information deleted by the deletion unit; a providing unit that provides digital security information based on the support provided by the support unit. A system characterized by:

2. The monitoring unit Analyzing predefined keywords or phrases to detect problems related to digital tattoos early 2. The system of claim 1.

3. The deletion unit Automate procedures for removing problematic content that is detected 2. The system of claim 1.

4. The support unit Chatbots are used to respond to victims' inquiries and provide specific advice and support 2. The system of claim 1.

5. The support unit Providing referrals to specialists based on predefined criteria 2. The system of claim 1.

6. The providing unit Providing users with digital security information, such as strengthening passwords and setting up two-factor authentication 2. The system of claim 1.

7. The monitoring unit Estimate the user's emotions and adjust the monitoring frequency based on the estimated user emotions.

2. The system of claim 1.

8. The monitoring unit Vary monitoring intensity based on predefined times or events 2. The system of claim 1.

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