system

Abstract

An object of a system according to an embodiment is to efficiently detect illegal content and sites on the Internet and take appropriate measures.SOLUTION: A system includes an information collection unit, an analysis unit, and a notification unit. The information collection unit collects information on the Internet. The analysis unit analyzes the information collected by the information collection unit and determines illegality. The notification unit notifies the police, the provider, or the SNS operator of the highly illegal information identified by the analysis unit.SELECTED DRAWING: Figure 1

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 had the problem of making it difficult to efficiently detect illegal content and sites on the Internet and take appropriate measures.

[0005] The system according to the embodiment aims to efficiently detect illegal content and sites on the Internet and take appropriate measures. [Means for solving the problem]

[0006] The system according to the embodiment includes an information collection unit, an analysis unit, and a notification unit. The information collection unit collects information on the Internet. The analysis unit analyzes the information collected by the information collection unit and determines whether the information is illegal. The notification unit notifies the police, a provider, or a social networking service operator of information that is highly illegal and that has been identified by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently detect illegal content and sites on the Internet and take appropriate measures. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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) The Internet police system according to an embodiment of the present invention uses a generation AI to analyze information on the Internet, determine whether it is illegal, and notify the police, Internet service providers, and social networking service operators. This allows the Internet police system to efficiently identify illegal content and posts on the Internet and quickly deal with them.

[0029] An internet police system according to an embodiment includes an information collection unit, an analysis unit, and a notification unit. The information collection unit collects information on the internet. For example, the information collection unit collects information from websites using a web crawler. The information collection unit can also collect information posted on social media platforms using an API. The information collection unit can also collect information based on specific keywords or hashtags. For example, the information collection unit collects information based on keywords related to the sale of illegal drugs. The analysis unit analyzes the collected information and determines whether it is illegal. For example, the analysis unit analyzes the collected information using a generation AI to identify highly illegal content and posts. The analysis unit can also determine whether the generation AI is illegal using a pre-finished model. The analysis unit can also detect posts containing illegal drug sales or defamatory comments. For example, the analysis unit detects posts containing content such as "illegal drug sales" or "defamatory comments" using the generation AI. The notification unit notifies the police, internet service providers, and social media operators of the highly illegal information identified by the analysis unit. For example, the notification unit automatically sends the highly illegal information identified by the generation AI to the police. The notification unit can also transmit highly illegal information identified by the generation AI to an internet service provider or a social networking service provider. The notification unit can also notify the police, an internet service provider, or a social networking service provider of highly illegal information identified by the generation AI. This allows the internet police system according to the embodiment to efficiently identify illegal content and posts on the internet and deal with them promptly. For example, notifying the police of highly illegal information identified by the generation AI can lead to prompt investigations and the arrest of criminals. Furthermore, notifying the internet service provider or a social networking service provider of highly illegal information identified by the generation AI can enable prompt deletion or account suspension.

[0030] The information collection unit can collect information based on specific keywords and hashtags. The information collection unit, for example, collects information based on specific keywords and hashtags. For example, the information collection unit collects information based on keywords related to the sale of illegal drugs. The information collection unit can also collect information based on specific hashtags. For example, the information collection unit collects information based on hashtags that are trending on social media. In this way, by collecting information based on specific keywords and hashtags, highly illegal information can be efficiently identified.

[0031] The analysis unit can detect posts containing illegal drug sales or defamatory comments. The analysis unit, for example, detects posts containing illegal drug sales. For example, the analysis unit detects posts in which the generation AI includes content such as "illegal drug sales." The analysis unit can also detect posts containing defamatory comments. For example, the analysis unit detects posts in which the generation AI includes content such as "defamatory comments." The analysis unit can also detect posts containing illegal drug sales or defamatory comments. For example, the analysis unit detects posts in which the generation AI includes content such as "illegal drug sales" or "defamatory comments." This makes it possible to quickly identify illegal activities on the Internet by detecting posts containing illegal drug sales or defamatory comments.

[0032] The notification unit can transmit highly illegal information to the police. The notification unit, for example, transmits highly illegal information to the police. For example, the notification unit automatically transmits highly illegal information identified by the generation AI to the police. The notification unit can also transmit highly illegal information identified by the generation AI to the police. In this way, transmitting highly illegal information to the police can lead to swift investigations and the arrest of criminals.

[0033] The notification unit can transmit highly illegal information to the provider or SNS operator. The notification unit, for example, transmits highly illegal information to the provider. For example, the notification unit automatically transmits highly illegal information identified by the generation AI to the provider. The notification unit can also transmit highly illegal information identified by the generation AI to the SNS operator. For example, the notification unit automatically transmits highly illegal information identified by the generation AI to the SNS operator. In this way, by transmitting highly illegal information to the provider or SNS operator, it becomes possible to quickly delete it or suspend the account.

[0034] The information collection unit can add geographical information to the information it collects and analyze trends in illegality by region. The information collection unit, for example, adds geographical information to the information it collects. For example, the information collection unit adds geographical information using IP addresses or GPS data. The information collection unit can also analyze trends in illegality by region. For example, the information collection unit analyzes trends in illegal activities in a specific region. The information collection unit can also analyze trends in illegality by region based on geographical information. For example, the information collection unit analyzes trends in illegal activities in a specific region. By adding geographical information, trends in illegality by region can be analyzed and effective countermeasures can be taken.

[0035] The information collection unit analyzes fluctuations in illegality by time of day or day of the week for the information it collects, and can identify illegal acts that are concentrated during specific time periods. The information collection unit, for example, analyzes fluctuations in illegality by time of day or day of the week for the information it collects. For example, the information collection unit analyzes fluctuations in illegality by time of day or day of the week for the information collected by the generation AI. The information collection unit can also identify illegal acts that are concentrated during specific time periods. For example, the information collection unit identifies illegal acts that are concentrated during the late night hours. The information collection unit can also analyze fluctuations in illegality by time of day or day of the week. For example, the information collection unit identifies illegal acts that are concentrated on specific days of the week. In this way, by analyzing fluctuations in illegality by time of day or day of the week, it is possible to identify illegal acts that are concentrated during specific time periods and take effective measures.

[0036] The information collection unit simultaneously collects multimodal information, including image or audio data, from the information to be collected, and can detect illegality from visual or audio information as well. The information collection unit, for example, simultaneously collects multimodal information, including image and audio data, from the information to be collected. For example, the information collection unit uses image recognition technology to detect illegality from the information collected by the generation AI. The information collection unit can also detect illegality using audio recognition technology. For example, the information collection unit analyzes illegal images and videos. The information collection unit can also analyze illegal audio data. For example, the information collection unit analyzes illegal audio data. In this way, by simultaneously collecting multimodal information, including image and audio data, illegality can be detected from visual and audio information as well.

[0037] The analysis unit can incorporate the opinions of experts from different industries or fields into the collected information to improve the accuracy of determining illegality. The analysis unit, for example, incorporates the opinions of experts from different industries or fields into the collected information. For example, the analysis unit incorporates the opinions of legal experts into the information collected by the generative AI to determine illegality. The analysis unit can also incorporate the opinions of experts from different industries or fields. For example, the analysis unit incorporates the opinions of medical experts to determine illegality. The analysis unit can also incorporate the opinions of experts from different industries or fields. For example, the analysis unit incorporates the opinions of financial experts to determine illegality. In this way, by incorporating the opinions of experts from different industries or fields, the accuracy of determining illegality can be improved.

[0038] The analysis unit can compare the collected information with a database of past illegal acts and identify highly similar patterns. The analysis unit, for example, compares the collected information with a database of past illegal acts. For example, the analysis unit compares the information analyzed by the generation AI with a database of past illegal acts and identify highly similar patterns. The analysis unit can also compare the information with a database of past illegal acts. For example, the analysis unit identifies posts that match past illegal acts. The analysis unit can also identify highly similar patterns. For example, the analysis unit identifies highly similar patterns using a pattern matching algorithm. In this way, by comparing the information with a database of past illegal acts, highly similar patterns can be identified and highly illegal information can be efficiently identified.

[0039] The analysis unit takes into account the illegality standards of different languages ​​or cultural spheres for the collected information and can determine illegality from a global perspective. The analysis unit, for example, takes into account the illegality standards of different languages ​​or cultural spheres for the collected information. For example, the analysis unit analyzes the information analyzed by the generation AI by taking into account the illegality standards of different languages ​​or cultural spheres. The analysis unit can also determine illegality based on the laws and regulations of each country. For example, the analysis unit determines illegality based on the laws of each country. The analysis unit can also determine illegality by taking cultural background into account. For example, the analysis unit determines illegality based on cultural background. In this way, by taking into account the illegality standards of different languages ​​and cultural spheres, illegality can be determined from a global perspective.

[0040] The analysis unit can convert the collected information into visual notes or mind maps to make it easier to understand visually. The analysis unit, for example, converts the collected information into visual notes or mind maps. For example, the analysis unit converts the information analyzed by the generation AI into visual notes and displays it visually. The analysis unit can also show important points with diagrams or icons. For example, the analysis unit shows important information with diagrams. The analysis unit can also convert it into a mind map. For example, the analysis unit converts the information into a mind map to make it easier to understand visually. In this way, converting it into a visual note or mind map makes it easier to understand visually.

[0041] The analysis unit can integrate the collected information with different datasets to gain new insights. The analysis unit, for example, integrates the collected information with different datasets. For example, the analysis unit integrates the information analyzed by the generative AI with patent data to analyze technological trends and the competitive situation. The analysis unit can also integrate with market data. For example, the analysis unit can integrate with market data to gain new insights. The analysis unit can also integrate with different datasets. For example, the analysis unit can integrate with patent data and market data to gain new insights. In this way, new insights can be gained by integrating with different datasets.

[0042] The notification unit can compare the identified highly illegal content with past investigative data, thereby improving the efficiency of the investigation. The notification unit, for example, compares the identified highly illegal content with past investigative data. For example, the notification unit compares the highly illegal content identified by the generation AI with past investigative data, thereby improving the efficiency of the investigation. The notification unit can also identify content that matches the results of past investigations. For example, the notification unit identifies content that matches the results of past investigations. This can improve the efficiency of the investigation by comparing it with past investigative data.

[0043] The notification unit can add geographical information to the identified highly illegal content and analyze crime trends by region. The notification unit, for example, adds geographical information to the identified highly illegal content. For example, the notification unit adds geographical information to the highly illegal content identified by the generation AI and analyzes crime trends by region. The notification unit can also identify crime trends in a specific region. For example, the notification unit identifies crime trends in a specific region. By adding geographical information, crime trends by region can be analyzed.

[0044] The notification unit can strengthen cooperation with different law enforcement agencies regarding the identified highly illegal content, thereby taking international crime prevention measures. The notification unit, for example, strengthens cooperation with different law enforcement agencies regarding the identified highly illegal content. For example, the notification unit can strengthen cooperation with different law enforcement agencies regarding the identified highly illegal content identified by the generation AI, thereby taking international crime prevention measures. The notification unit can also be useful in identifying international criminal organizations. For example, the notification unit can be useful in identifying international criminal organizations. This can strengthen cooperation with different law enforcement agencies, thereby taking international crime prevention measures.

[0045] The notification unit can automate the police investigation process for the identified highly illegal content, enabling a rapid response. The notification unit, for example, automates the police investigation process for the identified highly illegal content. For example, the notification unit can automate the police investigation process for the highly illegal content identified by the generation AI, enabling a rapid response. The notification unit can also build a system that automates the investigation procedure. For example, the notification unit builds a system that automates the investigation procedure. This makes it possible to automate the police investigation process, enabling a rapid response.

[0046] The notification unit can compare the identified highly illegal content with past response data, thereby improving response efficiency. The notification unit, for example, compares the identified highly illegal content with past response data. For example, the notification unit compares the highly illegal content identified by the generation AI with past response data, thereby improving response efficiency. The notification unit can also identify content that matches past response results. For example, the notification unit identifies content that matches past response results. This can improve response efficiency by comparing it with past response data.

[0047] The notification unit can strengthen information sharing between different platforms for the identified highly illegal content and take a unified response. The notification unit, for example, strengthens information sharing between different platforms for the identified highly illegal content. For example, the notification unit strengthens information sharing between different platforms for the highly illegal content identified by the generation AI and takes a unified response. The notification unit can also share information across multiple SNS platforms. For example, the notification unit shares information across multiple SNS platforms. This strengthens information sharing between different platforms and allows a unified response.

[0048] The notification unit can incorporate the opinions of experts in different industries or fields regarding the identified highly illegal content, thereby improving the accuracy of the response. The notification unit, for example, incorporates the opinions of experts in different industries or fields regarding the identified highly illegal content. For example, the notification unit incorporates the opinions of legal experts regarding highly illegal content identified by the generation AI. The notification unit can also incorporate the opinions of experts in different industries or fields. For example, the notification unit incorporates the opinions of medical experts regarding the response. The notification unit can also incorporate the opinions of experts in different industries or fields. For example, the notification unit incorporates the opinions of financial experts regarding the response. In this way, by incorporating the opinions of experts in different industries or fields, the accuracy of the response can be improved.

[0049] The notification unit can automate the response process of the provider or the SNS operator for the identified highly illegal content, enabling a rapid response. The notification unit, for example, automates the response process of the provider or the SNS operator for the identified highly illegal content. For example, the notification unit can automate the response process of the provider or the SNS operator for the identified highly illegal content, enabling a rapid response. The notification unit can also build a system that automates the response procedure. For example, the notification unit builds a system that automates the response procedure. This can automate the response process of the provider or the SNS operator, enabling a rapid response.

[0050] The analysis unit can compare the identified highly illegal content with past deletion data, thereby improving the efficiency of deletion. The analysis unit, for example, compares the identified highly illegal content with past deletion data. For example, the analysis unit compares the highly illegal content identified by the generation AI with past deletion data, thereby improving the efficiency of deletion. The analysis unit can also identify content that matches past deletion results. For example, the analysis unit identifies content that matches past deletion results. By comparing with past deletion data, the efficiency of deletion can be improved.

[0051] The analysis unit can strengthen information sharing between different platforms for the identified highly illegal content and perform unified deletion. The analysis unit, for example, strengthens information sharing between different platforms for the identified highly illegal content. For example, the analysis unit strengthens information sharing between different platforms for the highly illegal content identified by the generation AI and performs unified deletion. The analysis unit can also share information across multiple SNS platforms. For example, the analysis unit shares information across multiple SNS platforms. This strengthens information sharing between different platforms and allows unified deletion.

[0052] The analysis unit can incorporate the opinions of experts from different industries or fields into the identified highly illegal content, thereby improving the accuracy of removal. The analysis unit, for example, incorporates the opinions of experts from different industries or fields into the identified highly illegal content. For example, the analysis unit incorporates the opinions of legal experts into the highly illegal content identified by the generation AI and deletes it. The analysis unit can also incorporate the opinions of experts from different industries or fields. For example, the analysis unit incorporates the opinions of medical experts and deletes the content. The analysis unit can also incorporate the opinions of experts from different industries or fields. For example, the analysis unit incorporates the opinions of financial experts and deletes the content. In this way, by incorporating the opinions of experts from different industries or fields, the accuracy of removal can be improved.

[0053] The analysis unit can automate the removal process for the identified highly illegal content, enabling a rapid response. The analysis unit, for example, automates the removal process for identified highly illegal content. For example, the analysis unit can automate the removal process for highly illegal content identified by the generation AI, enabling a rapid response. The analysis unit can also build a system that automates the removal procedure. For example, the analysis unit builds a system that automates the removal procedure. By automating the removal process, a rapid response can be enabled.

[0054] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0055] The Internet police system may further include a behavior analysis unit that analyzes a user's behavioral history. The behavior analysis unit may, for example, analyze a user's past posting history and browsing history to detect signs of illegal activity. The behavior analysis unit may also assess the risk of illegal activity based on specific behavioral patterns. For example, the behavior analysis unit may identify users who frequently view illegal content and issue a warning. The behavior analysis unit may also add users who are at high risk of illegal activity to a watch list based on the user's behavioral history. In this way, by analyzing a user's behavioral history, signs of illegal activity can be detected early and effective countermeasures can be taken.

[0056] The Internet police system may further include a multilingual analysis unit that takes into account the illegality standards of different languages ​​and cultural spheres. The multilingual analysis unit, for example, analyzes collected information taking into account the illegality standards of different languages ​​and cultural spheres. The multilingual analysis unit may also determine illegality based on the laws and regulations of each country. For example, the multilingual analysis unit may determine illegality based on the laws of each country. The multilingual analysis unit may also determine illegality by taking cultural background into account. In this way, by taking into account the illegality standards of different languages ​​and cultural spheres, illegality can be determined from a global perspective.

[0057] The Internet police system may further include a visualization unit that converts the collected information into a visual note or a mind map. The visualization unit, for example, converts the collected information into a visual note and visually displays it. The visualization unit may also indicate important points with diagrams or icons. For example, the visualization unit may indicate important information with a diagram. The visualization unit may also convert the information into a mind map to make it easier to understand visually. By converting the information into a visual note or a mind map, it is possible to make it easier to understand visually.

[0058] The Internet Police system may further include a data integration unit that integrates with different data sets. For example, the data integration unit may integrate the collected information with patent data to analyze technological trends and competitive situations. The data integration unit may also integrate with market data. For example, the data integration unit may integrate with market data to gain new insights. The data integration unit may also integrate with different data sets. This allows new insights to be gained by integrating with different data sets.

[0059] The Internet police system may further include an investigation efficiency improvement unit that compares the identified highly illegal information with past investigation data to improve the efficiency of the investigation. The investigation efficiency improvement unit, for example, compares the identified highly illegal information with past investigation data to improve the efficiency of the investigation. The investigation efficiency improvement unit may also identify information that matches past investigation results. This allows the efficiency of the investigation to be improved by comparing it with past investigation data.

[0060] The Internet police system may further include an information sharing unit that strengthens information sharing between different platforms in response to identified highly illegal information and takes a unified response. For example, the information sharing unit strengthens information sharing between different platforms in response to identified highly illegal information and takes a unified response. The information sharing unit may also share information across multiple social media platforms. This strengthens information sharing between different platforms, enabling a unified response.

[0061] The processing flow of the first embodiment will be briefly explained below.

[0062] Step 1: The information gathering unit collects information from the internet. For example, it can use a web crawler to gather information from websites, or it can use an API to gather information from social media posts. It can also gather information based on specific keywords or hashtags, such as keywords related to the sale of illegal drugs. Step 2: The analysis unit analyzes the collected information and determines whether it is illegal. For example, the generative AI can analyze the collected information and identify highly illegal content and posts. The generative AI can also use pre-finished models to determine illegality, detecting posts containing illegal drug sales or defamatory comments. Step 3: The notification unit notifies the police, internet service providers, and social media operators of any highly illegal information identified by the analysis unit. For example, the generation AI can automatically send highly illegal information identified to the police, and then to the internet service providers and social media operators. This allows for swift investigation, deletion, and account suspension.

[0063] (Example 2) The Internet police system according to an embodiment of the present invention uses a generation AI to analyze information on the Internet, determine whether it is illegal, and notify the police, Internet service providers, and social networking service operators. This allows the Internet police system to efficiently identify illegal content and posts on the Internet and quickly deal with them.

[0064] An internet police system according to an embodiment includes an information collection unit, an analysis unit, and a notification unit. The information collection unit collects information on the internet. For example, the information collection unit collects information from websites using a web crawler. The information collection unit can also collect information posted on social media platforms using an API. The information collection unit can also collect information based on specific keywords or hashtags. For example, the information collection unit collects information based on keywords related to the sale of illegal drugs. The analysis unit analyzes the collected information and determines whether it is illegal. For example, the analysis unit analyzes the collected information using a generation AI to identify highly illegal content and posts. The analysis unit can also determine whether the generation AI is illegal using a pre-finished model. The analysis unit can also detect posts containing illegal drug sales or defamatory comments. For example, the analysis unit detects posts containing content such as "illegal drug sales" or "defamatory comments" using the generation AI. The notification unit notifies the police, internet service providers, and social media operators of the highly illegal information identified by the analysis unit. For example, the notification unit automatically sends the highly illegal information identified by the generation AI to the police. The notification unit can also transmit highly illegal information identified by the generation AI to an internet service provider or a social networking service provider. The notification unit can also notify the police, an internet service provider, or a social networking service provider of highly illegal information identified by the generation AI. This allows the internet police system according to the embodiment to efficiently identify illegal content and posts on the internet and deal with them promptly. For example, notifying the police of highly illegal information identified by the generation AI can lead to prompt investigations and the arrest of criminals. Furthermore, notifying the internet service provider or a social networking service provider of highly illegal information identified by the generation AI can enable prompt deletion or account suspension.

[0065] The information collection unit can collect information based on specific keywords and hashtags. The information collection unit, for example, collects information based on specific keywords and hashtags. For example, the information collection unit collects information based on keywords related to the sale of illegal drugs. The information collection unit can also collect information based on specific hashtags. For example, the information collection unit collects information based on hashtags that are trending on social media. In this way, by collecting information based on specific keywords and hashtags, highly illegal information can be efficiently identified.

[0066] The analysis unit can detect posts containing illegal drug sales or defamatory comments. The analysis unit, for example, detects posts containing illegal drug sales. For example, the analysis unit detects posts in which the generation AI includes content such as "illegal drug sales." The analysis unit can also detect posts containing defamatory comments. For example, the analysis unit detects posts in which the generation AI includes content such as "defamatory comments." The analysis unit can also detect posts containing illegal drug sales or defamatory comments. For example, the analysis unit detects posts in which the generation AI includes content such as "illegal drug sales" or "defamatory comments." This makes it possible to quickly identify illegal activities on the Internet by detecting posts containing illegal drug sales or defamatory comments.

[0067] The notification unit can transmit highly illegal information to the police. The notification unit, for example, transmits highly illegal information to the police. For example, the notification unit automatically transmits highly illegal information identified by the generation AI to the police. The notification unit can also transmit highly illegal information identified by the generation AI to the police. In this way, transmitting highly illegal information to the police can lead to swift investigations and the arrest of criminals.

[0068] The notification unit can transmit highly illegal information to the provider or SNS operator. The notification unit, for example, transmits highly illegal information to the provider. For example, the notification unit automatically transmits highly illegal information identified by the generation AI to the provider. The notification unit can also transmit highly illegal information identified by the generation AI to the SNS operator. For example, the notification unit automatically transmits highly illegal information identified by the generation AI to the SNS operator. In this way, by transmitting highly illegal information to the provider or SNS operator, it becomes possible to quickly delete it or suspend the account.

[0069] The analysis unit performs sentiment analysis on the collected information and can filter it based on the intensity and type of sentiment. The analysis unit, for example, performs sentiment analysis on the collected information. For example, the analysis unit performs sentiment analysis on the information collected by the generation AI and filters it based on the sentiment score. The analysis unit can also filter based on the intensity and type of sentiment. For example, the analysis unit prioritizes analyzing posts with strong negative sentiment. The analysis unit can also filter based on the intensity and type of sentiment. For example, the analysis unit excludes posts with strong positive sentiment. In this way, by performing sentiment analysis, it is possible to filter information based on the intensity and type of sentiment and efficiently identify highly illegal information.

[0070] The information collection unit can add geographical information to the information it collects and analyze trends in illegality by region. The information collection unit, for example, adds geographical information to the information it collects. For example, the information collection unit adds geographical information using IP addresses or GPS data. The information collection unit can also analyze trends in illegality by region. For example, the information collection unit analyzes trends in illegal activities in a specific region. The information collection unit can also analyze trends in illegality by region based on geographical information. For example, the information collection unit analyzes trends in illegal activities in a specific region. By adding geographical information, trends in illegality by region can be analyzed and effective countermeasures can be taken.

[0071] The information collection unit analyzes fluctuations in illegality by time of day or day of the week for the information it collects, and can identify illegal acts that are concentrated during specific time periods. The information collection unit, for example, analyzes fluctuations in illegality by time of day or day of the week for the information it collects. For example, the information collection unit analyzes fluctuations in illegality by time of day or day of the week for the information collected by the generation AI. The information collection unit can also identify illegal acts that are concentrated during specific time periods. For example, the information collection unit identifies illegal acts that are concentrated during the late night hours. The information collection unit can also analyze fluctuations in illegality by time of day or day of the week. For example, the information collection unit identifies illegal acts that are concentrated on specific days of the week. In this way, by analyzing fluctuations in illegality by time of day or day of the week, it is possible to identify illegal acts that are concentrated during specific time periods and take effective measures.

[0072] The information collection unit simultaneously collects multimodal information, including image or audio data, from the information to be collected, and can detect illegality from visual or audio information as well. The information collection unit, for example, simultaneously collects multimodal information, including image and audio data, from the information to be collected. For example, the information collection unit uses image recognition technology to detect illegality from the information collected by the generation AI. The information collection unit can also detect illegality using audio recognition technology. For example, the information collection unit analyzes illegal images and videos. The information collection unit can also analyze illegal audio data. For example, the information collection unit analyzes illegal audio data. In this way, by simultaneously collecting multimodal information, including image and audio data, illegality can be detected from visual and audio information as well.

[0073] The analysis unit can incorporate the opinions of experts from different industries or fields into the collected information to improve the accuracy of determining illegality. The analysis unit, for example, incorporates the opinions of experts from different industries or fields into the collected information. For example, the analysis unit incorporates the opinions of legal experts into the information collected by the generative AI to determine illegality. The analysis unit can also incorporate the opinions of experts from different industries or fields. For example, the analysis unit incorporates the opinions of medical experts to determine illegality. The analysis unit can also incorporate the opinions of experts from different industries or fields. For example, the analysis unit incorporates the opinions of financial experts to determine illegality. In this way, by incorporating the opinions of experts from different industries or fields, the accuracy of determining illegality can be improved.

[0074] The analysis unit can compare the collected information with a database of past illegal acts and identify highly similar patterns. The analysis unit, for example, compares the collected information with a database of past illegal acts. For example, the analysis unit compares the information analyzed by the generation AI with a database of past illegal acts and identify highly similar patterns. The analysis unit can also compare the information with a database of past illegal acts. For example, the analysis unit identifies posts that match past illegal acts. The analysis unit can also identify highly similar patterns. For example, the analysis unit identifies highly similar patterns using a pattern matching algorithm. In this way, by comparing the information with a database of past illegal acts, highly similar patterns can be identified and highly illegal information can be efficiently identified.

[0075] The analysis unit takes into account the illegality standards of different languages ​​or cultural spheres for the collected information and can determine illegality from a global perspective. The analysis unit, for example, takes into account the illegality standards of different languages ​​or cultural spheres for the collected information. For example, the analysis unit analyzes the information analyzed by the generation AI by taking into account the illegality standards of different languages ​​or cultural spheres. The analysis unit can also determine illegality based on the laws and regulations of each country. For example, the analysis unit determines illegality based on the laws of each country. The analysis unit can also determine illegality by taking cultural background into account. For example, the analysis unit determines illegality based on cultural background. In this way, by taking into account the illegality standards of different languages ​​and cultural spheres, illegality can be determined from a global perspective.

[0076] The analysis unit can convert the collected information into visual notes or mind maps to make it easier to understand visually. The analysis unit, for example, converts the collected information into visual notes or mind maps. For example, the analysis unit converts the information analyzed by the generation AI into visual notes and displays it visually. The analysis unit can also show important points with diagrams or icons. For example, the analysis unit shows important information with diagrams. The analysis unit can also convert it into a mind map. For example, the analysis unit converts the information into a mind map to make it easier to understand visually. In this way, converting it into a visual note or mind map makes it easier to understand visually.

[0077] The analysis unit can integrate the collected information with different datasets to gain new insights. The analysis unit, for example, integrates the collected information with different datasets. For example, the analysis unit integrates the information analyzed by the generative AI with patent data to analyze technological trends and the competitive situation. The analysis unit can also integrate with market data. For example, the analysis unit can integrate with market data to gain new insights. The analysis unit can also integrate with different datasets. For example, the analysis unit can integrate with patent data and market data to gain new insights. In this way, new insights can be gained by integrating with different datasets.

[0078] The analysis unit uses an emotion estimation function to collect the user's emotional reactions to the collected information, and can improve the accuracy of the analysis based on that. The analysis unit, for example, uses the emotion estimation function to collect the user's emotional reactions to the information analyzed by the generation AI. The analysis unit can also improve the accuracy of the analysis based on the emotional reactions. For example, the analysis unit prioritizes analyzing information with a large number of positive reactions. The analysis unit can also improve the accuracy of the analysis based on the emotional reactions. For example, the analysis unit excludes information with a large number of negative reactions. In this way, the emotion estimation function can be used to collect the user's emotional reactions, and the accuracy of the analysis can be improved based on that.

[0079] The notification unit can use an emotion estimation function to set priorities for the identified highly illegal content to which the police should respond quickly. The notification unit, for example, uses the emotion estimation function for the identified highly illegal content. For example, the notification unit uses the emotion estimation function to set priorities for the highly illegal content identified by the generation AI. The notification unit can also prioritize notifying the police of content with a high emotion score. For example, the notification unit prioritizes notifying the police of content with a high emotion score. In this way, by using the emotion estimation function, it is possible to set priorities for the police to respond quickly.

[0080] The notification unit can compare the identified highly illegal content with past investigative data, thereby improving the efficiency of the investigation. The notification unit, for example, compares the identified highly illegal content with past investigative data. For example, the notification unit compares the highly illegal content identified by the generation AI with past investigative data, thereby improving the efficiency of the investigation. The notification unit can also identify content that matches the results of past investigations. For example, the notification unit identifies content that matches the results of past investigations. This can improve the efficiency of the investigation by comparing it with past investigative data.

[0081] The notification unit can add geographical information to the identified highly illegal content and analyze crime trends by region. The notification unit, for example, adds geographical information to the identified highly illegal content. For example, the notification unit adds geographical information to the highly illegal content identified by the generation AI and analyzes crime trends by region. The notification unit can also identify crime trends in a specific region. For example, the notification unit identifies crime trends in a specific region. By adding geographical information, crime trends by region can be analyzed.

[0082] The notification unit can strengthen cooperation with different law enforcement agencies regarding the identified highly illegal content, thereby taking international crime prevention measures. The notification unit, for example, strengthens cooperation with different law enforcement agencies regarding the identified highly illegal content. For example, the notification unit can strengthen cooperation with different law enforcement agencies regarding the identified highly illegal content identified by the generation AI, thereby taking international crime prevention measures. The notification unit can also be useful in identifying international criminal organizations. For example, the notification unit can be useful in identifying international criminal organizations. This can strengthen cooperation with different law enforcement agencies, thereby taking international crime prevention measures.

[0083] The notification unit can automate the police investigation process for the identified highly illegal content, enabling a rapid response. The notification unit, for example, automates the police investigation process for the identified highly illegal content. For example, the notification unit can automate the police investigation process for the highly illegal content identified by the generation AI, enabling a rapid response. The notification unit can also build a system that automates the investigation procedure. For example, the notification unit builds a system that automates the investigation procedure. This makes it possible to automate the police investigation process, enabling a rapid response.

[0084] The notification unit can use an emotion estimation function for the identified highly illegal content to monitor the emotional state of the police officer and perform the stress management. The notification unit, for example, uses the emotion estimation function for the identified highly illegal content. For example, the notification unit uses the emotion estimation function for the identified highly illegal content to monitor the emotional state of the police officer for the highly illegal content identified by the generation AI. The notification unit can also measure stress levels and take appropriate measures. For example, the notification unit measures stress levels and takes appropriate measures. In this way, by using the emotion estimation function, the emotional state of the police officer can be monitored and stress management can be performed.

[0085] The notification unit can compare the identified highly illegal content with past response data, thereby improving response efficiency. The notification unit, for example, compares the identified highly illegal content with past response data. For example, the notification unit compares the highly illegal content identified by the generation AI with past response data, thereby improving response efficiency. The notification unit can also identify content that matches past response results. For example, the notification unit identifies content that matches past response results. This can improve response efficiency by comparing it with past response data.

[0086] The notification unit can strengthen information sharing between different platforms for the identified highly illegal content and take a unified response. The notification unit, for example, strengthens information sharing between different platforms for the identified highly illegal content. For example, the notification unit strengthens information sharing between different platforms for the highly illegal content identified by the generation AI and takes a unified response. The notification unit can also share information across multiple SNS platforms. For example, the notification unit shares information across multiple SNS platforms. This strengthens information sharing between different platforms and allows a unified response.

[0087] The notification unit can incorporate the opinions of experts in different industries or fields regarding the identified highly illegal content, thereby improving the accuracy of the response. The notification unit, for example, incorporates the opinions of experts in different industries or fields regarding the identified highly illegal content. For example, the notification unit incorporates the opinions of legal experts regarding highly illegal content identified by the generation AI. The notification unit can also incorporate the opinions of experts in different industries or fields. For example, the notification unit incorporates the opinions of medical experts regarding the response. The notification unit can also incorporate the opinions of experts in different industries or fields. For example, the notification unit incorporates the opinions of financial experts regarding the response. In this way, by incorporating the opinions of experts in different industries or fields, the accuracy of the response can be improved.

[0088] The notification unit can automate the response process of the provider or the SNS operator for the identified highly illegal content, enabling a rapid response. The notification unit, for example, automates the response process of the provider or the SNS operator for the identified highly illegal content. For example, the notification unit can automate the response process of the provider or the SNS operator for the identified highly illegal content, enabling a rapid response. The notification unit can also build a system that automates the response procedure. For example, the notification unit builds a system that automates the response procedure. This can automate the response process of the provider or the SNS operator, enabling a rapid response.

[0089] The notification unit can use an emotion estimation function for the identified highly illegal content to monitor the emotional state of the person in charge of the provider or the SNS operator, thereby performing the stress management. The notification unit, for example, uses the emotion estimation function for the identified highly illegal content. For example, the notification unit uses the emotion estimation function for the identified highly illegal content to monitor the emotional state of the person in charge of the provider or the SNS operator, regarding the highly illegal content identified by the generation AI. The notification unit can also measure stress levels and take appropriate measures. For example, the notification unit measures stress levels and takes appropriate measures. In this way, by using the emotion estimation function, the emotional state of the person in charge of the provider or the SNS operator can be monitored, thereby performing stress management.

[0090] The analysis unit can use the emotion estimation function to collect the user's emotional responses to the identified highly illegal content, and evaluate the impact of the illegal content. The analysis unit, for example, uses the emotion estimation function to collect the user's emotional responses to the identified highly illegal content. For example, the analysis unit uses the emotion estimation function to collect the user's emotional responses to the highly illegal content identified by the generation AI. The analysis unit can also evaluate the impact of the illegal content based on the emotional responses. For example, the analysis unit prioritizes deleting content with many negative emotional responses. The analysis unit can also evaluate the impact of the illegal content based on the emotional responses. For example, the analysis unit prioritizes deleting content with few positive emotional responses. In this way, by using the emotion estimation function, the user's emotional responses can be collected and the impact of the illegal content can be evaluated.

[0091] The analysis unit can compare the identified highly illegal content with past deletion data, thereby improving the efficiency of deletion. The analysis unit, for example, compares the identified highly illegal content with past deletion data. For example, the analysis unit compares the highly illegal content identified by the generation AI with past deletion data, thereby improving the efficiency of deletion. The analysis unit can also identify content that matches past deletion results. For example, the analysis unit identifies content that matches past deletion results. By comparing with past deletion data, the efficiency of deletion can be improved.

[0092] The analysis unit can strengthen information sharing between different platforms for the identified highly illegal content and perform unified deletion. The analysis unit, for example, strengthens information sharing between different platforms for the identified highly illegal content. For example, the analysis unit strengthens information sharing between different platforms for the highly illegal content identified by the generation AI and performs unified deletion. The analysis unit can also share information across multiple SNS platforms. For example, the analysis unit shares information across multiple SNS platforms. This strengthens information sharing between different platforms and allows unified deletion.

[0093] The analysis unit can incorporate the opinions of experts from different industries or fields into the identified highly illegal content, thereby improving the accuracy of removal. The analysis unit, for example, incorporates the opinions of experts from different industries or fields into the identified highly illegal content. For example, the analysis unit incorporates the opinions of legal experts into the highly illegal content identified by the generation AI and deletes it. The analysis unit can also incorporate the opinions of experts from different industries or fields. For example, the analysis unit incorporates the opinions of medical experts and deletes the content. The analysis unit can also incorporate the opinions of experts from different industries or fields. For example, the analysis unit incorporates the opinions of financial experts and deletes the content. In this way, by incorporating the opinions of experts from different industries or fields, the accuracy of removal can be improved.

[0094] The analysis unit can automate the removal process for the identified highly illegal content, enabling a rapid response. The analysis unit, for example, automates the removal process for identified highly illegal content. For example, the analysis unit can automate the removal process for highly illegal content identified by the generation AI, enabling a rapid response. The analysis unit can also build a system that automates the removal procedure. For example, the analysis unit builds a system that automates the removal procedure. By automating the removal process, a rapid response can be enabled.

[0095] The analysis unit uses the emotion estimation function on the identified highly illegal content to monitor the user's emotional reaction after deletion, thereby maintaining a healthy Internet environment. The analysis unit, for example, uses the emotion estimation function on the identified highly illegal content. For example, the analysis unit uses the emotion estimation function on the identified highly illegal content to monitor the user's emotional reaction after deletion of the highly illegal content identified by the generation AI. The analysis unit can also measure an emotion score and take appropriate measures. For example, the analysis unit measures an emotion score after deletion and takes appropriate measures. In this way, by using the emotion estimation function, the user's emotional reaction after deletion can be monitored, thereby maintaining a healthy Internet environment.

[0096] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0097] The Internet police system may further include a behavior analysis unit that analyzes a user's behavioral history. The behavior analysis unit may, for example, analyze a user's past posting history and browsing history to detect signs of illegal activity. The behavior analysis unit may also assess the risk of illegal activity based on specific behavioral patterns. For example, the behavior analysis unit may identify users who frequently view illegal content and issue a warning. The behavior analysis unit may also add users who are at high risk of illegal activity to a watch list based on the user's behavioral history. In this way, by analyzing a user's behavioral history, signs of illegal activity can be detected early and effective countermeasures can be taken.

[0098] The Internet police system may further include an emotion filtering unit that estimates a user's emotion and filters out highly illegal information based on the estimated emotion. The emotion filtering unit, for example, performs emotion analysis on the collected information and prioritizes analysis of posts with strong negative emotions. The emotion filtering unit may also filter out posts with strong positive emotions. For example, the emotion filtering unit identifies highly illegal information based on an emotion score. The emotion filtering unit may also filter information based on the intensity or type of emotion. In this way, by performing emotion analysis, information can be filtered based on the intensity or type of emotion, and highly illegal information can be efficiently identified.

[0099] The Internet police system may further include a multilingual analysis unit that takes into account the illegality standards of different languages ​​and cultural spheres. The multilingual analysis unit, for example, analyzes collected information taking into account the illegality standards of different languages ​​and cultural spheres. The multilingual analysis unit may also determine illegality based on the laws and regulations of each country. For example, the multilingual analysis unit may determine illegality based on the laws of each country. The multilingual analysis unit may also determine illegality by taking cultural background into account. In this way, by taking into account the illegality standards of different languages ​​and cultural spheres, illegality can be determined from a global perspective.

[0100] The Internet police system may further include a visualization unit that converts the collected information into a visual note or a mind map. The visualization unit, for example, converts the collected information into a visual note and visually displays it. The visualization unit may also indicate important points with diagrams or icons. For example, the visualization unit may indicate important information with a diagram. The visualization unit may also convert the information into a mind map to make it easier to understand visually. By converting the information into a visual note or a mind map, it is possible to make it easier to understand visually.

[0101] The Internet Police system may further include a data integration unit that integrates with different data sets. For example, the data integration unit may integrate the collected information with patent data to analyze technological trends and competitive situations. The data integration unit may also integrate with market data. For example, the data integration unit may integrate with market data to gain new insights. The data integration unit may also integrate with different data sets. This allows new insights to be gained by integrating with different data sets.

[0102] The Internet police system may further include an emotion-prioritizing analysis unit that estimates a user's emotions and prioritizes the analysis of highly illegal information based on the estimated emotions. The emotion-prioritizing analysis unit, for example, performs emotion analysis on the collected information and prioritizes the analysis of posts with strong negative emotions. The emotion-prioritizing analysis unit may also exclude posts with strong positive emotions. For example, the emotion-prioritizing analysis unit identifies highly illegal information based on an emotion score. The emotion-prioritizing analysis unit may also filter information based on the intensity and type of emotion. In this way, by performing emotion analysis, information can be filtered based on the intensity and type of emotion, and highly illegal information can be efficiently identified.

[0103] The Internet police system may further include a priority setting unit that uses an emotion estimation function to set priorities for the identified highly illegal information to be dealt with promptly by the police. The priority setting unit, for example, uses the emotion estimation function to set priorities for the identified highly illegal information. The priority setting unit may also preferentially notify the police of information with a high emotion score. In this way, by using the emotion estimation function, priorities for the police to be dealt with promptly can be set.

[0104] The Internet police system may further include an investigation efficiency improvement unit that compares the identified highly illegal information with past investigation data to improve the efficiency of the investigation. The investigation efficiency improvement unit, for example, compares the identified highly illegal information with past investigation data to improve the efficiency of the investigation. The investigation efficiency improvement unit may also identify information that matches past investigation results. This allows the efficiency of the investigation to be improved by comparing it with past investigation data.

[0105] The Internet police system may further include an impact assessment unit that uses an emotion estimation function to collect users' emotional reactions to the identified highly illegal information and evaluates the impact of the illegal information. The impact assessment unit, for example, uses the emotion estimation function to collect users' emotional reactions to the identified highly illegal information. The impact assessment unit may also evaluate the impact of the illegal information based on the emotional reactions. In this way, by using the emotion estimation function, it is possible to collect users' emotional reactions and evaluate the impact of the illegal information.

[0106] The Internet police system may further include an information sharing unit that strengthens information sharing between different platforms in response to identified highly illegal information and takes a unified response. For example, the information sharing unit strengthens information sharing between different platforms in response to identified highly illegal information and takes a unified response. The information sharing unit may also share information across multiple social media platforms. This strengthens information sharing between different platforms, enabling a unified response.

[0107] The processing flow of the second embodiment will be briefly explained below.

[0108] Step 1: The information gathering unit collects information from the internet. For example, it can use a web crawler to gather information from websites, or it can use an API to gather information from social media posts. It can also gather information based on specific keywords or hashtags, such as keywords related to the sale of illegal drugs. Step 2: The analysis unit analyzes the collected information and determines whether it is illegal. For example, the generative AI can analyze the collected information and identify highly illegal content and posts. The generative AI can also use pre-finished models to determine illegality, detecting posts containing illegal drug sales or defamatory comments. Step 3: The notification unit notifies the police, internet service providers, and social media operators of any highly illegal information identified by the analysis unit. For example, the generation AI can automatically send highly illegal information identified to the police, and then to the internet service providers and social media operators. This allows for swift investigation, deletion, and account suspension.

[0109] 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.

[0110] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. 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 a data format such as voice data and text data. 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] 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.

[0112] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0113] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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).

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0122] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0123] 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.

[0124] 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.

[0125] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] 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.

[0127] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] 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 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0143] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0153] In the robot 414, the processor 46 performs the identification process. 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. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0154] 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.

[0155] 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.

[0156] 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 containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. 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 a data format such as voice data and text 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 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 can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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. [Explanation of symbols]

[0176] 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. an information collection unit that collects information on the Internet; an analysis unit that analyzes the information collected by the information collection unit and determines whether it is illegal; a notification unit that notifies the police, a provider, or an SNS operator of the highly illegal information identified by the analysis unit. A system characterized by:

2. The information collecting unit Collect information based on specific keywords or hashtags 2. The system of claim 1.

3. The analysis unit Detect posts containing illegal drug sales or derogatory comments 2. The system of claim 1.

4. The information collecting unit Multimodal information, including image or audio data, is simultaneously collected in addition to the information collected above, and illegality is detected from visual or audio information as well.

2. The system of claim 1.

5. The analysis unit The collected information will be reviewed by experts in different industries or fields to improve the accuracy of illegality detection.

2. The system of claim 1.

6. The notification unit Set priorities for the police to respond quickly to identified highly illegal content 2. The system of claim 1.

7. The notification unit Set priorities for the provider or social networking service operator to promptly respond to identified highly illegal content 2. The system of claim 1.

8. The analysis unit Maintain a healthy internet environment by monitoring users' emotional reactions after removing identified highly illegal content.

2. The system of claim 1.

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