system

Abstract

The system according to this embodiment aims to efficiently detect posts and advertisements for illegal part-time jobs on social media and job search websites, and to escalate them to the relevant authorities. [Solution] The system according to the embodiment comprises a collection unit, a detection unit, an escalation unit, an approach unit, and a transmission unit. The collection unit collects information from social networking services (SNS) and job search websites. The detection unit analyzes the information collected by the collection unit and detects posts and advertisements related to illegal part-time jobs. The escalation unit escalates the information detected by the detection unit to the relevant organizations. The approach unit makes individual approaches and provides education to potential victims based on the information escalated by the escalation unit. The transmission unit transmits the information educated by the approach unit to an integrated data platform.

Description

Technical Field

[0006] ,

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, the detection of postings and advertisements of illegal part-time jobs on SNS and job hunting sites and the escalation to relevant institutions have not been sufficiently carried out, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently detect postings and advertisements of illegal part-time jobs on SNS and job hunting sites and escalate them to relevant institutions.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, a detection unit, an escalation unit, an approach unit, and a transmission unit. The collection unit collects information from social networking services (SNS) and job search websites. The detection unit analyzes the information collected by the collection unit and detects posts and advertisements related to illegal part-time jobs. The escalation unit escalates the information detected by the detection unit to the relevant organizations. The approach unit makes individual approaches and provides education to potential victims based on the information escalated by the escalation unit. The transmission unit transmits the information educated by the approach unit to an integrated data platform. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently detect posts and advertisements for illegal part-time jobs on social media and job search websites, and escalate them to the relevant authorities. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The comprehensive platform according to an embodiment of the present invention integrates an AI-powered SNS monitoring system, a job site analysis system, a local safety network, and a proprietary safety reporting system. This platform aims to detect and prevent illegal jobs on both SNS and job sites, simultaneously ensuring the safety of the local community. For example, the comprehensive platform detects posts and advertisements related to illegal jobs on SNS and job sites through its integrated SNS / job site monitoring system. This system uses natural language processing and image recognition AI to monitor in real-time 24 hours a day, and immediately escalates detected information to relevant authorities (police, local government, schools, etc.). Furthermore, the comprehensive platform provides individual approaches and awareness-raising to potential victims via AI chatbots and develops and implements AI algorithms to identify fake job postings on job sites. Next, the comprehensive platform provides an environment where users can safely report concerns through its safety reporting system. This system uses AI to analyze reports and evaluate their urgency and reliability. High-risk information is automatically notified to relevant authorities, and blockchain technology is used to ensure the anonymity of reporters and the reliability of the information. Furthermore, the comprehensive platform, through a community safety network, predicts the risk of illegal work through local data analysis and plans effective awareness campaigns in cooperation with local governments. It also automatically generates plans for creating legal short-term jobs using AI and builds and operates a community-participatory monitoring network. Sentiment analysis is also performed to understand feelings and attitudes towards illegal work by analyzing voices on social media and within local communities, enabling early detection of negative trends and automatic proposal of countermeasures. Finally, the comprehensive platform integrates and utilizes data obtained from various functions, such as social media, job sites, and local data, through an integrated data platform. This platform continuously improves accuracy and discovers new trends through machine learning, and protects personal information through data anonymization and encryption. In this way, the comprehensive platform, which integrates an AI-powered social media monitoring system, a job site analysis system, a community safety network, and a safety reporting system, can eradicate illegal work and protect community safety.This allows the comprehensive platform to eradicate illegal jobs and protect local safety.

[0029] The comprehensive platform according to the embodiment comprises a collection unit, a detection unit, an escalation unit, an approach unit, and a transmission unit. The collection unit collects information from social networking services (SNS) and job search websites. For example, the collection unit can collect posts and advertisements from SNS and job search websites. The collection unit can use AI to analyze the collected information and detect posts and advertisements related to illegal part-time jobs. The detection unit analyzes the information collected by the collection unit and detects posts and advertisements related to illegal part-time jobs. The detection unit can perform 24-hour real-time monitoring, for example, using natural language processing and image recognition AI. The detection unit can use AI to escalate the detected information to relevant organizations. The escalation unit escalates the information detected by the detection unit to relevant organizations. For example, the escalation unit can immediately escalate the detected information to relevant organizations. The escalation unit can use AI to make individual approaches and provide education to potential victims based on the escalated information. The approach unit makes individual approaches and provides education to potential victims based on the information escalated by the escalation unit. The Approach Unit can, for example, conduct individual approaches and awareness campaigns using AI chatbots. The Approach Unit can use AI to transmit the awareness-raising information to an integrated data platform. The Transmission Unit transmits the awareness-raising information from the Approach Unit to the integrated data platform. The Transmission Unit can, for example, develop and implement AI algorithms to identify fake job postings on job sites. The Transmission Unit can use AI to analyze reported content and evaluate its urgency and reliability. The Transmission Unit can use AI to automatically notify relevant organizations of high-risk information. The Transmission Unit can use AI to ensure the anonymity of reporters and the reliability of information using blockchain technology. The Transmission Unit can use AI to predict the risk of illegal part-time work through regional data analysis. The Transmission Unit can use AI to plan effective awareness campaigns in cooperation with local governments. The Transmission Unit can use AI to automatically generate plans for creating legitimate short-term jobs. The Transmission Unit can use AI to build and operate community-participatory monitoring networks.The transmission unit can use AI to analyze voices from social media and local communities, and perform sentiment analysis to understand feelings and attitudes regarding illegal part-time jobs. The transmission unit can use AI to detect negative trends early and automatically propose countermeasures. The transmission unit can use AI to integrate and utilize data obtained from various functions such as social media, job sites, and local data. The transmission unit can use AI to continuously improve accuracy through machine learning and discover new trends. The transmission unit can use AI to protect personal information through data anonymization and encryption. As a result, the comprehensive platform according to this embodiment can eradicate illegal part-time jobs and protect local safety.

[0030] The data collection unit collects information from social media and job search websites. For example, it can collect posts and advertisements from social media and job search websites. Specifically, the unit uses web scraping technology to automatically collect social media posts and job search website advertisements. This allows the unit to efficiently collect vast amounts of data and update it in real time. The unit also uses AI to analyze the collected information and detect posts and advertisements related to illegal or unethical jobs. The AI ​​uses natural language processing technology to analyze the text of posts and advertisements, detecting specific keywords and phrases. For example, it prioritizes extracting posts and advertisements containing keywords such as "high income," "short-term work," and "immediate payment." Furthermore, it uses image recognition technology to analyze images in posts and advertisements, detecting elements that suggest illegal activity. This allows the unit to detect information related to illegal or unethical jobs with high accuracy and send it to the next processing step. In addition, the unit centrally manages the collected data and can collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the detection and escalation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The detection unit analyzes information collected by the collection unit to detect posts and advertisements related to illegal part-time jobs. The detection unit can perform 24 / 7 real-time monitoring using, for example, natural language processing and image recognition AI. Specifically, it uses natural language processing technology to analyze collected text data and detect keywords and phrases suggesting illegal activity. For example, it prioritizes extracting posts and advertisements containing keywords such as "high income," "short-term work," and "same-day payment." It also uses image recognition technology to analyze images contained in posts and advertisements and detect elements suggesting illegal activity. For example, it can identify specific symbols, logos, or the actions of individuals. The detection unit can use AI to escalate detected information to relevant authorities. The AI ​​assesses the urgency and reliability of the detected information and immediately notifies the relevant authorities. This allows the detection unit to quickly and accurately analyze collected data and grasp the risks related to illegal part-time jobs in real time. Furthermore, the detection unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past data related to illegal part-time jobs, the system can predict risk fluctuations in specific regions and time periods and formulate future countermeasures. Furthermore, the detection unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the detection unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0032] The Escalation Department escalates information detected by the Detection Department to the relevant agencies. For example, the Escalation Department can immediately escalate detected information to the relevant agencies. Specifically, the Escalation Department uses AI to evaluate the urgency and reliability of detected information and notifies the appropriate agencies. For example, it can immediately provide information to the police, labor inspection agencies, and local crime prevention organizations. The Escalation Department can use AI to make individual approaches and raise awareness among potential victims based on the escalated information. Specifically, it uses an AI chatbot to individually approach potential victims and raise awareness about the risks and countermeasures of illegal work. For example, the chatbot can provide victims with specific advice and support information and, if necessary, encourage them to contact the relevant agencies. This allows the Escalation Department to quickly and appropriately escalate detected information to the relevant agencies and provide support to potential victims. Furthermore, the Escalation Department can transmit escalated information to an integrated data platform and collaborate with other departments and systems. This allows the Escalation Department to escalate information efficiently and effectively and improve the overall performance of the system.

[0033] The Approach Department conducts individual approaches and awareness campaigns for potential victims based on information escalated by the Escalation Department. The Approach Department can, for example, use an AI chatbot for individual approaches and awareness campaigns. Specifically, the AI ​​chatbot can individually approach potential victims and educate them about the risks and countermeasures of illegal work. For example, the chatbot can provide victims with specific advice and support information and, if necessary, encourage them to contact relevant organizations. The Approach Department can use AI to transmit the educated information to an integrated data platform. Specifically, the AI ​​analyzes the educated information and transmits it to the integrated data platform. This allows the Approach Department to efficiently conduct individual approaches and awareness campaigns for potential victims. Furthermore, the Approach Department can evaluate the effectiveness of awareness campaigns and continuously improve them. For example, it can analyze the results of awareness campaigns and identify effective approaches and messages. The Approach Department can also plan more effective awareness campaigns by considering regional characteristics and historical data. This allows the Approach Department to effectively support and educate potential victims and reduce the risks of illegal work.

[0034] The transmission unit transmits information enlightened by the approach unit to an integrated data platform. For example, the transmission unit can develop and implement an AI algorithm to identify fake job postings on job sites. Specifically, the AI ​​algorithm analyzes job site posts and extracts features to identify fake job postings. For example, it can identify fake job postings with high accuracy based on specific keywords or phrases, posting frequency, and patterns. The transmission unit can use AI to analyze reports and evaluate their urgency and reliability. Specifically, the AI ​​analyzes the text and images of reports and calculates a score to evaluate urgency and reliability. This allows the transmission unit to automatically notify relevant organizations of high-risk information. The transmission unit can use AI and blockchain technology to ensure reporter anonymity and information reliability. Specifically, blockchain technology is used to prevent tampering with reports and ensure reporter anonymity. This allows the transmission unit to enhance the reliability of reports and protect reporter privacy. The transmission unit can use AI to predict the risk of illegal part-time work through regional data analysis. Specifically, it analyzes regional crime and economic data to identify areas with a high risk of illegal part-time work. This allows the transmission unit to plan effective awareness campaigns in collaboration with local governments. The transmission unit can use AI to automatically generate plans for creating legal short-term jobs. Specifically, it analyzes local employment and economic data to predict the demand for legal short-term jobs and proposes appropriate employment plans. The transmission unit can use AI to build and operate community-participatory monitoring networks. Specifically, it organizes local residents and volunteers to build a network to monitor the risks of illegal jobs. The transmission unit can use AI to perform sentiment analysis to understand feelings and attitudes towards illegal jobs by analyzing voices on social media and in local communities. Specifically, it analyzes social media posts and comments on local bulletin boards to understand feelings and attitudes towards illegal jobs. This allows the transmission unit to detect negative trends early and automatically propose countermeasures. The transmission unit can use AI to integrate and utilize data obtained from various functions such as social media, job sites, and local data.Specifically, the system centrally manages information obtained from each data source and correlates it to perform more accurate analysis. The transmission unit can use AI to continuously improve accuracy and discover new trends through machine learning. Specifically, it trains machine learning models based on collected data to continuously improve accuracy. The transmission unit can use AI to protect personal information through data anonymization and encryption. Specifically, it prevents the leakage of personal information by anonymizing and encrypting the collected data. This allows the transmission unit to ensure the reliability and security of the data and improve the overall system performance.

[0035] The detection unit can perform 24-hour real-time monitoring using natural language processing and image recognition AI. For example, the detection unit can use natural language processing technology to analyze posts and advertisements on social media and job sites to detect keywords and phrases related to illegal part-time jobs. The detection unit can use image recognition AI to analyze images contained in posts and advertisements to detect images related to illegal part-time jobs. By combining natural language processing and image recognition AI, the detection unit can perform more accurate monitoring. As a result, by using natural language processing and image recognition AI, it is possible to monitor posts and advertisements related to illegal part-time jobs in 24 hours a day in real time.

[0036] The escalation unit can immediately escalate detected information to the relevant agencies. For example, it can escalate detected information to the relevant agencies within seconds. The escalation unit can use AI to assess the urgency of an escalation and select the optimal escalation method. The escalation unit can record the history of escalations and use this information for future escalations. This enables a rapid response by immediately escalating detected information to the relevant agencies.

[0037] The Approach Department can use an AI chatbot to provide individualized approaches and awareness-raising to potential victims. For example, the Approach Department can use the AI ​​chatbot to send individual messages to potential victims and raise awareness about the dangers of illegal part-time jobs. The Approach Department can use the AI ​​chatbot to respond to potential victims' questions in real time and provide appropriate advice. The Approach Department can use the AI ​​chatbot to analyze the emotions of potential victims and respond accordingly. This enables effective responses to potential victims through individualized approaches and awareness-raising using an AI chatbot.

[0038] The transmitting unit can develop and implement an AI algorithm to identify fake job postings on job search websites. For example, the transmitting unit can use the AI ​​algorithm to analyze job information on job search websites and extract the characteristics of fake job postings. The transmitting unit can use the AI ​​algorithm to evaluate the reliability of job information and identify fake job postings. The transmitting unit can use the AI ​​algorithm to learn patterns of fake job postings and detect new fake job postings. As a result, by developing and implementing an AI algorithm to identify fake job postings on job search websites, the accuracy of fake job posting detection is improved.

[0039] The transmission unit can use AI to analyze the report content and evaluate its urgency and reliability. For example, the transmission unit can use AI to analyze the text of the report content and score its urgency. The transmission unit can use AI to evaluate the reliability of the report content and calculate a reliability score. The transmission unit can use AI to determine the priority of the report content based on the urgency and reliability scores. This allows for appropriate action to be taken by analyzing the report content with AI and evaluating its urgency and reliability.

[0040] The transmission unit can automatically notify relevant organizations of high-risk information. For example, it can use AI to detect high-risk information in real time and automatically notify relevant organizations. It can also use AI to assess the urgency of high-risk information and select the optimal notification method. Furthermore, it can use AI to record notification history and utilize it for future notifications. This enables a rapid response by automatically notifying relevant organizations of high-risk information.

[0041] The transmitting unit can ensure the anonymity of reporters and the reliability of information using blockchain technology. For example, the transmitting unit can use a public blockchain to record the report content on a distributed ledger and prevent tampering. The transmitting unit can use a private blockchain to provide necessary information to relevant organizations while ensuring the anonymity of reporters. The transmitting unit can use blockchain technology to verify the reliability of the report content and calculate a reliability score. In this way, the anonymity of reporters and the reliability of information can be ensured by using blockchain technology.

[0042] The transmitting unit can predict the risk of illegal part-time work through regional data analysis. For example, it can use a Geographic Information System (GIS) to visualize the risk of illegal part-time work in each region. It can use statistical analysis to analyze regional data and identify areas with a high risk of illegal part-time work. It can use AI to predict the risk of illegal part-time work based on regional data and provide the prediction results to relevant organizations. This enables effective countermeasures by predicting the risk of illegal part-time work through regional data analysis.

[0043] The transmission department can plan effective awareness campaigns in cooperation with local governments. For example, the transmission department can use posters and leaflets to raise awareness among local residents about the dangers of illegal part-time jobs. The transmission department can hold events to raise awareness among local residents about the dangers of illegal part-time jobs. The transmission department can utilize social media to raise awareness among local residents about the dangers of illegal part-time jobs. By planning effective awareness campaigns in cooperation with local governments, it is possible to raise safety awareness in local communities.

[0044] The transmission unit can automatically generate legal short-term employment creation plans using AI. For example, the transmission unit can use AI to analyze local job information and identify legal short-term employment opportunities. The transmission unit can use AI to match the supply and demand for short-term employment and generate an optimal employment plan. The transmission unit can use AI to evaluate the effectiveness of short-term employment and improve the plan. In this way, by automatically generating legal short-term employment creation plans using AI, it is possible to provide legal employment opportunities.

[0045] The transmitting unit can build and operate a community-participatory monitoring network. For example, the transmitting unit can organize community groups so that local residents can cooperate in monitoring the risks of illegal part-time jobs. The transmitting unit can provide an online platform so that local residents can share information and cooperate in monitoring the risks of illegal part-time jobs. The transmitting unit can use AI to analyze reports from residents and evaluate the effectiveness of the monitoring network. In this way, community safety can be enhanced by building and operating a community-participatory monitoring network.

[0046] The transmission unit can analyze voices from social media and local communities to perform sentiment analysis and understand feelings and attitudes towards illegal part-time jobs. For example, the transmission unit can use sentiment analysis algorithms to analyze posts and comments on social media to understand feelings towards illegal part-time jobs. The transmission unit can use text mining to analyze voices from local communities to understand attitudes towards illegal part-time jobs. The transmission unit can use AI to take appropriate measures based on the results of the sentiment analysis. In this way, by analyzing voices from social media and local communities and understanding feelings and attitudes towards illegal part-time jobs, appropriate measures can be taken.

[0047] The transmission unit can detect negative trends early and automatically suggest countermeasures. For example, the transmission unit can use AI to analyze posts and comments on social media and local communities to detect negative trends. The transmission unit can use AI to identify the causes of negative trends and automatically suggest countermeasures. The transmission unit can use AI to evaluate the effectiveness of countermeasures and make improvements. In this way, by detecting negative trends early and automatically suggesting countermeasures, the risks of illegal part-time work can be reduced.

[0048] The transmission unit can integrate and utilize data obtained from various functions, such as social media, job search sites, and local data. For example, the transmission unit can centrally manage data obtained from each function using database integration technology. The transmission unit can extract useful information from the integrated data using data mining technology and utilize it for detecting and preventing illegal part-time jobs. The transmission unit can analyze the integrated data using AI and discover new trends. As a result, integrating and utilizing data obtained from each function improves the accuracy of detecting and preventing illegal part-time jobs.

[0049] The transmission unit can continuously improve accuracy and discover new trends through machine learning. For example, the transmission unit can perform periodic model updates to improve the accuracy of the machine learning model. The transmission unit can build a feedback loop and improve the model based on actual data. The transmission unit can use AI to discover new trends and predict the risks of illegal part-time jobs. As a result, the system's effectiveness is continuously improved through continuous accuracy improvements and the discovery of new trends using machine learning.

[0050] The transmission unit can protect personal information through data anonymization and encryption. For example, the transmission unit can use anonymization algorithms to anonymize personal information and protect privacy. The transmission unit can use encryption protocols to encrypt data and prevent unauthorized access. The transmission unit can use AI to evaluate and improve the effectiveness of anonymization and encryption. This allows for the protection of user privacy by achieving data anonymization and encryption.

[0051] The data collection unit can analyze past posting history on social media and job sites to select the optimal data collection method. For example, it can use AI to analyze past posting history and identify time periods when specific keywords frequently appear. It can also use AI to analyze past posting history, find patterns in the posting habits of specific users, and prioritize the collection of those users' posts. Furthermore, it can use AI to enhance information collection during specific events or campaign periods based on past posting history. This allows for efficient information collection by selecting the optimal method through analysis of past posting history.

[0052] The data collection unit can filter information based on specific keywords or phrases. For example, it can prioritize collecting posts containing specific keywords (e.g., high income, short hours). It can filter and collect advertisements containing specific phrases (e.g., earn money quickly, easy work). Based on combinations of keywords and phrases, the data collection unit can extract posts that are likely to be illegal or illicit jobs. This allows for the efficient collection of posts that are likely to be illegal or illicit jobs by filtering based on specific keywords and phrases.

[0053] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of nearby job postings based on the user's current location. The data collection unit can collect region-specific illegal part-time job information based on the user's geographical location. The data collection unit can collect highly relevant information by considering the user's travel history. As a result, region-specific information can be efficiently collected by prioritizing the collection of highly relevant information by considering the user's geographical location.

[0054] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can prioritize collecting posts from accounts that the user follows. The data collection unit can collect posts from groups and communities that the user participates in. The data collection unit can analyze a user's past posts and collect relevant information. This allows for the efficient collection of relevant information by analyzing a user's social media activity.

[0055] The detection unit can improve detection accuracy by considering the interrelationships between posts and advertisements during detection. For example, the detection unit can cross-reference the content of posts and advertisements to detect highly relevant information. The detection unit can analyze the temporal relationship between posts and advertisements to detect information posted around the same time. The detection unit can extract common keywords from posts and advertisements to detect highly relevant information. In this way, the detection accuracy can be improved by considering the interrelationships between posts and advertisements.

[0056] The detection unit can perform detection while considering the poster's attribute information. For example, the detection unit can detect information related to specific attributes by considering the poster's age and gender. The detection unit can analyze the poster's past posting history and detect highly relevant information. The detection unit can detect region-specific information by considering the poster's geographical location information. As a result, by considering the poster's attribute information, it is possible to detect more relevant information.

[0057] The detection unit can perform detection while considering the geographical distribution of posts and advertisements. For example, the detection unit can prioritize the detection of information that is frequently posted in a specific area. The detection unit can associate and detect posts and advertisements that are geographically close. The detection unit can detect highly relevant information based on region-specific keywords. As a result, by considering the geographical distribution of posts and advertisements, region-specific information can be detected efficiently.

[0058] The detection unit can improve detection accuracy by referring to related literature during detection. For example, the detection unit can improve detection accuracy based on keywords extracted from related literature. The detection unit can cross-reference the contents of related literature to detect highly relevant information. The detection unit can improve detection accuracy based on citation information from related literature. Thus, by referring to related literature, detection accuracy can be improved.

[0059] The escalation unit can determine the priority of escalations based on the urgency of the information during the escalation process. For example, the escalation unit can use AI to evaluate the urgency of the information and prioritize escalating highly urgent information. The escalation unit can then escalate moderately urgent information next. The escalation unit can escalate low-urgency information last. This allows for a rapid response by determining the priority of escalations based on the urgency of the information.

[0060] The escalation department can select the optimal escalation method by referring to the response history of the relevant organizations during an escalation. For example, the escalation department can select the optimal escalation method based on past response history. The escalation department can analyze the response history of the relevant organizations and escalate to an organization capable of a rapid response. The escalation department can refer to the response history of the relevant organizations and select an appropriate escalation procedure. This allows for the selection of the optimal escalation method and a rapid response by referring to the response history of the relevant organizations.

[0061] The escalation department can prioritize escalations based on when the information was submitted. For example, the escalation department can prioritize escalating the most recent information. It can then escalate older information next. Finally, it can escalate very old information last. This allows for a quicker response by prioritizing escalations based on when the information was submitted.

[0062] The escalation department can select the optimal escalation method when escalating, taking into account the geographical location information of the relevant organizations. For example, the escalation department can escalate to the nearest organization based on the geographical location information of the relevant organizations. The escalation department can escalate to an organization capable of a rapid response, taking into account the geographical location information of the relevant organizations. The escalation department can select an appropriate escalation procedure by referring to the geographical location information of the relevant organizations. This allows for the selection of the optimal escalation method and a rapid response by considering the geographical location information of the relevant organizations.

[0063] The approach unit can select an individualized approach method based on the victim's attribute information during the approach. For example, the approach unit can select an appropriate approach method considering the victim's age and gender. The approach unit can select the optimal approach method by analyzing the victim's past behavioral history. The approach unit can select a region-specific approach method considering the victim's geographical location information. This allows for a more effective approach by selecting an individualized approach method based on the victim's attribute information.

[0064] The approach unit can select the optimal approach method by referring to past approach history during the approach process. For example, the approach unit can select the optimal approach method based on past approach history. The approach unit can analyze past approach history and select an effective approach method. The approach unit can refer to past approach history and select an appropriate approach procedure. This allows for the selection of the optimal approach method and an effective approach by referring to past approach history.

[0065] The approach unit can select the optimal approach method by considering the victim's geographical location information during the approach. For example, the approach unit can introduce nearby support organizations based on the victim's current location. The approach unit can provide region-specific support information based on the victim's geographical location information. The approach unit can provide highly relevant support information by considering the victim's travel history. As a result, by considering the victim's geographical location information, the optimal approach method can be selected, enabling effective support.

[0066] The approach team can analyze the victim's social media activity and adjust their approach accordingly. For example, they can select an appropriate approach based on information about accounts the victim follows. They can also adjust their approach based on information about groups and communities the victim participates in. Furthermore, they can analyze the victim's past posts and select a relevant approach. This allows for the selection of the optimal approach and effective support by analyzing the victim's social media activity.

[0067] The transmitting unit can determine the transmission priority based on the urgency of the information at the time of transmission. For example, the transmitting unit can use AI to evaluate the urgency of the information and prioritize the transmission of high-urgency information. The transmitting unit can then transmit medium-urgency information next. The transmitting unit can transmit low-urgency information last. This allows for a rapid response by determining the transmission priority based on the urgency of the information.

[0068] The transmitting unit can evaluate the reliability of the information and select the transmission method at the time of transmission. For example, the transmitting unit can use AI to evaluate the reliability of the information and prioritize the transmission of highly reliable information. The transmitting unit can then transmit information of medium reliability. The transmitting unit can transmit information of low reliability last. In this way, by evaluating the reliability of the information, it is possible to provide more reliable information.

[0069] The transmitting unit can determine the transmission priority based on when the information was submitted. For example, the transmitting unit can prioritize the transmission of the most recent information. It can then transmit older information next. It can also transmit very old information last. This allows for a quicker response by prioritizing transmission based on when the information was submitted.

[0070] The transmitting unit can select the optimal transmission method when transmitting information, taking into account its geographical location. For example, the transmitting unit can transmit information to the most relevant region based on its geographical location. The transmitting unit can also transmit information to a region where a rapid response is possible, taking its geographical location into consideration. Furthermore, the transmitting unit can select an appropriate transmission procedure by referring to the geographical location of the information. This allows for the selection of the optimal transmission method and enables a rapid response by considering the geographical location of the information.

[0071] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0072] The data collection unit can analyze past posting history on social media and job sites to select the optimal data collection method. For example, it can use AI to analyze past posting history and identify time periods when specific keywords frequently appear. It can also use AI to analyze past posting history, find patterns in the posting habits of specific users, and prioritize the collection of those users' posts. Furthermore, it can use AI to enhance information collection during specific events or campaign periods based on past posting history. This allows for efficient information collection by selecting the optimal method through analysis of past posting history.

[0073] The data collection unit can filter information based on specific keywords or phrases. For example, it can prioritize collecting posts containing specific keywords (e.g., high income, short hours). It can filter and collect advertisements containing specific phrases (e.g., earn money quickly, easy work). Based on combinations of keywords and phrases, the data collection unit can extract posts that are likely to be illegal or illicit jobs. This allows for the efficient collection of posts that are likely to be illegal or illicit jobs by filtering based on specific keywords and phrases.

[0074] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of nearby job postings based on the user's current location. The data collection unit can collect region-specific illegal part-time job information based on the user's geographical location. The data collection unit can collect highly relevant information by considering the user's travel history. As a result, region-specific information can be efficiently collected by prioritizing the collection of highly relevant information by considering the user's geographical location.

[0075] The detection unit can improve detection accuracy by considering the interrelationships between posts and advertisements during detection. For example, the detection unit can cross-reference the content of posts and advertisements to detect highly relevant information. The detection unit can analyze the temporal relationship between posts and advertisements to detect information posted around the same time. The detection unit can extract common keywords from posts and advertisements to detect highly relevant information. In this way, the detection accuracy can be improved by considering the interrelationships between posts and advertisements.

[0076] The detection unit can perform detection while considering the poster's attribute information. For example, the detection unit can detect information related to specific attributes by considering the poster's age and gender. The detection unit can analyze the poster's past posting history and detect highly relevant information. The detection unit can detect region-specific information by considering the poster's geographical location information. As a result, by considering the poster's attribute information, it is possible to detect more relevant information.

[0077] The detection unit can perform detection while considering the geographical distribution of posts and advertisements. For example, the detection unit can prioritize the detection of information that is frequently posted in a specific area. The detection unit can associate and detect posts and advertisements that are geographically close. The detection unit can detect highly relevant information based on region-specific keywords. As a result, by considering the geographical distribution of posts and advertisements, region-specific information can be detected efficiently.

[0078] The following briefly describes the processing flow for example form 1.

[0079] Step 1: The collection unit collects information from social media and job sites. For example, it can collect posts and advertisements from social media and job sites. Step 2: The detection unit analyzes the information collected by the collection unit to detect posts and advertisements related to illegal part-time jobs. For example, it can perform 24-hour real-time monitoring using natural language processing and image recognition AI. Step 3: The escalation unit escalates the information detected by the detection unit to the relevant organizations. For example, the detected information can be escalated to the relevant organizations immediately. Step 4: The Approach Department conducts individual approaches and awareness campaigns for potential victims based on the information escalated by the Escalation Department. For example, individual approaches and awareness campaigns can be conducted using an AI chatbot. Step 5: The transmission unit sends the information revealed by the approach unit to the integrated data platform. For example, an AI algorithm can be developed and implemented to identify fake job postings on job search websites.

[0080] (Example of form 2) The comprehensive platform according to an embodiment of the present invention integrates an AI-powered SNS monitoring system, a job site analysis system, a local safety network, and a proprietary safety reporting system. This platform aims to detect and prevent illegal jobs on both SNS and job sites, simultaneously ensuring the safety of the local community. For example, the comprehensive platform detects posts and advertisements related to illegal jobs on SNS and job sites through its integrated SNS / job site monitoring system. This system uses natural language processing and image recognition AI to monitor in real-time 24 hours a day, and immediately escalates detected information to relevant authorities (police, local government, schools, etc.). Furthermore, the comprehensive platform provides individual approaches and awareness-raising to potential victims via AI chatbots and develops and implements AI algorithms to identify fake job postings on job sites. Next, the comprehensive platform provides an environment where users can safely report concerns through its safety reporting system. This system uses AI to analyze reports and evaluate their urgency and reliability. High-risk information is automatically notified to relevant authorities, and blockchain technology is used to ensure the anonymity of reporters and the reliability of the information. Furthermore, the comprehensive platform, through a community safety network, predicts the risk of illegal work through local data analysis and plans effective awareness campaigns in cooperation with local governments. It also automatically generates plans for creating legal short-term jobs using AI and builds and operates a community-participatory monitoring network. Sentiment analysis is also performed to understand feelings and attitudes towards illegal work by analyzing voices on social media and within local communities, enabling early detection of negative trends and automatic proposal of countermeasures. Finally, the comprehensive platform integrates and utilizes data obtained from various functions, such as social media, job sites, and local data, through an integrated data platform. This platform continuously improves accuracy and discovers new trends through machine learning, and protects personal information through data anonymization and encryption. In this way, the comprehensive platform, which integrates an AI-powered social media monitoring system, a job site analysis system, a community safety network, and a safety reporting system, can eradicate illegal work and protect community safety.This allows the comprehensive platform to eradicate illegal jobs and protect local safety.

[0081] The comprehensive platform according to the embodiment comprises a collection unit, a detection unit, an escalation unit, an approach unit, and a transmission unit. The collection unit collects information from social networking services (SNS) and job search websites. For example, the collection unit can collect posts and advertisements from SNS and job search websites. The collection unit can use AI to analyze the collected information and detect posts and advertisements related to illegal part-time jobs. The detection unit analyzes the information collected by the collection unit and detects posts and advertisements related to illegal part-time jobs. The detection unit can perform 24-hour real-time monitoring, for example, using natural language processing and image recognition AI. The detection unit can use AI to escalate the detected information to relevant organizations. The escalation unit escalates the information detected by the detection unit to relevant organizations. For example, the escalation unit can immediately escalate the detected information to relevant organizations. The escalation unit can use AI to make individual approaches and provide education to potential victims based on the escalated information. The approach unit makes individual approaches and provides education to potential victims based on the information escalated by the escalation unit. The Approach Unit can, for example, conduct individual approaches and awareness campaigns using AI chatbots. The Approach Unit can use AI to transmit the awareness-raising information to an integrated data platform. The Transmission Unit transmits the awareness-raising information from the Approach Unit to the integrated data platform. The Transmission Unit can, for example, develop and implement AI algorithms to identify fake job postings on job sites. The Transmission Unit can use AI to analyze reported content and evaluate its urgency and reliability. The Transmission Unit can use AI to automatically notify relevant organizations of high-risk information. The Transmission Unit can use AI to ensure the anonymity of reporters and the reliability of information using blockchain technology. The Transmission Unit can use AI to predict the risk of illegal part-time work through regional data analysis. The Transmission Unit can use AI to plan effective awareness campaigns in cooperation with local governments. The Transmission Unit can use AI to automatically generate plans for creating legitimate short-term jobs. The Transmission Unit can use AI to build and operate community-participatory monitoring networks.The transmission unit can use AI to analyze voices from social media and local communities, and perform sentiment analysis to understand feelings and attitudes regarding illegal part-time jobs. The transmission unit can use AI to detect negative trends early and automatically propose countermeasures. The transmission unit can use AI to integrate and utilize data obtained from various functions such as social media, job sites, and local data. The transmission unit can use AI to continuously improve accuracy through machine learning and discover new trends. The transmission unit can use AI to protect personal information through data anonymization and encryption. As a result, the comprehensive platform according to this embodiment can eradicate illegal part-time jobs and protect local safety.

[0082] The data collection unit collects information from social media and job search websites. For example, it can collect posts and advertisements from social media and job search websites. Specifically, the unit uses web scraping technology to automatically collect social media posts and job search website advertisements. This allows the unit to efficiently collect vast amounts of data and update it in real time. The unit also uses AI to analyze the collected information and detect posts and advertisements related to illegal or unethical jobs. The AI ​​uses natural language processing technology to analyze the text of posts and advertisements, detecting specific keywords and phrases. For example, it prioritizes extracting posts and advertisements containing keywords such as "high income," "short-term work," and "immediate payment." Furthermore, it uses image recognition technology to analyze images in posts and advertisements, detecting elements that suggest illegal activity. This allows the unit to detect information related to illegal or unethical jobs with high accuracy and send it to the next processing step. In addition, the unit centrally manages the collected data and can collaborate with other systems and departments as needed. For example, the collected data can be stored on a cloud server and made accessible to the detection and escalation units. Furthermore, by adjusting the frequency and accuracy of data collection, flexible responses to specific situations and conditions become possible. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0083] The detection unit analyzes information collected by the collection unit to detect posts and advertisements related to illegal part-time jobs. The detection unit can perform 24 / 7 real-time monitoring using, for example, natural language processing and image recognition AI. Specifically, it uses natural language processing technology to analyze collected text data and detect keywords and phrases suggesting illegal activity. For example, it prioritizes extracting posts and advertisements containing keywords such as "high income," "short-term work," and "same-day payment." It also uses image recognition technology to analyze images contained in posts and advertisements and detect elements suggesting illegal activity. For example, it can identify specific symbols, logos, or the actions of individuals. The detection unit can use AI to escalate detected information to relevant authorities. The AI ​​assesses the urgency and reliability of the detected information and immediately notifies the relevant authorities. This allows the detection unit to quickly and accurately analyze collected data and grasp the risks related to illegal part-time jobs in real time. Furthermore, the detection unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, based on past data related to illegal part-time jobs, the system can predict risk fluctuations in specific regions and time periods and formulate future countermeasures. Furthermore, the detection unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, issuing early warnings. This allows the detection unit to not only grasp the situation in real time but also to handle long-term risk management and anomaly detection, improving the overall reliability and security of the system.

[0084] The Escalation Department escalates information detected by the Detection Department to the relevant agencies. For example, the Escalation Department can immediately escalate detected information to the relevant agencies. Specifically, the Escalation Department uses AI to evaluate the urgency and reliability of detected information and notifies the appropriate agencies. For example, it can immediately provide information to the police, labor inspection agencies, and local crime prevention organizations. The Escalation Department can use AI to make individual approaches and raise awareness among potential victims based on the escalated information. Specifically, it uses an AI chatbot to individually approach potential victims and raise awareness about the risks and countermeasures of illegal work. For example, the chatbot can provide victims with specific advice and support information and, if necessary, encourage them to contact the relevant agencies. This allows the Escalation Department to quickly and appropriately escalate detected information to the relevant agencies and provide support to potential victims. Furthermore, the Escalation Department can transmit escalated information to an integrated data platform and collaborate with other departments and systems. This allows the Escalation Department to escalate information efficiently and effectively and improve the overall performance of the system.

[0085] The Approach Department conducts individual approaches and awareness campaigns for potential victims based on information escalated by the Escalation Department. The Approach Department can, for example, use an AI chatbot for individual approaches and awareness campaigns. Specifically, the AI ​​chatbot can individually approach potential victims and educate them about the risks and countermeasures of illegal work. For example, the chatbot can provide victims with specific advice and support information and, if necessary, encourage them to contact relevant organizations. The Approach Department can use AI to transmit the educated information to an integrated data platform. Specifically, the AI ​​analyzes the educated information and transmits it to the integrated data platform. This allows the Approach Department to efficiently conduct individual approaches and awareness campaigns for potential victims. Furthermore, the Approach Department can evaluate the effectiveness of awareness campaigns and continuously improve them. For example, it can analyze the results of awareness campaigns and identify effective approaches and messages. The Approach Department can also plan more effective awareness campaigns by considering regional characteristics and historical data. This allows the Approach Department to effectively support and educate potential victims and reduce the risks of illegal work.

[0086] The transmission unit transmits information enlightened by the approach unit to an integrated data platform. For example, the transmission unit can develop and implement an AI algorithm to identify fake job postings on job sites. Specifically, the AI ​​algorithm analyzes job site posts and extracts features to identify fake job postings. For example, it can identify fake job postings with high accuracy based on specific keywords or phrases, posting frequency, and patterns. The transmission unit can use AI to analyze reports and evaluate their urgency and reliability. Specifically, the AI ​​analyzes the text and images of reports and calculates a score to evaluate urgency and reliability. This allows the transmission unit to automatically notify relevant organizations of high-risk information. The transmission unit can use AI and blockchain technology to ensure reporter anonymity and information reliability. Specifically, blockchain technology is used to prevent tampering with reports and ensure reporter anonymity. This allows the transmission unit to enhance the reliability of reports and protect reporter privacy. The transmission unit can use AI to predict the risk of illegal part-time work through regional data analysis. Specifically, it analyzes regional crime and economic data to identify areas with a high risk of illegal part-time work. This allows the transmission unit to plan effective awareness campaigns in collaboration with local governments. The transmission unit can use AI to automatically generate plans for creating legal short-term jobs. Specifically, it analyzes local employment and economic data to predict the demand for legal short-term jobs and proposes appropriate employment plans. The transmission unit can use AI to build and operate community-participatory monitoring networks. Specifically, it organizes local residents and volunteers to build a network to monitor the risks of illegal jobs. The transmission unit can use AI to perform sentiment analysis to understand feelings and attitudes towards illegal jobs by analyzing voices on social media and in local communities. Specifically, it analyzes social media posts and comments on local bulletin boards to understand feelings and attitudes towards illegal jobs. This allows the transmission unit to detect negative trends early and automatically propose countermeasures. The transmission unit can use AI to integrate and utilize data obtained from various functions such as social media, job sites, and local data.Specifically, the system centrally manages information obtained from each data source and correlates it to perform more accurate analysis. The transmission unit can use AI to continuously improve accuracy and discover new trends through machine learning. Specifically, it trains machine learning models based on collected data to continuously improve accuracy. The transmission unit can use AI to protect personal information through data anonymization and encryption. Specifically, it prevents the leakage of personal information by anonymizing and encrypting the collected data. This allows the transmission unit to ensure the reliability and security of the data and improve the overall system performance.

[0087] The detection unit can perform 24-hour real-time monitoring using natural language processing and image recognition AI. For example, the detection unit can use natural language processing technology to analyze posts and advertisements on social media and job sites to detect keywords and phrases related to illegal part-time jobs. The detection unit can use image recognition AI to analyze images contained in posts and advertisements to detect images related to illegal part-time jobs. By combining natural language processing and image recognition AI, the detection unit can perform more accurate monitoring. As a result, by using natural language processing and image recognition AI, it is possible to monitor posts and advertisements related to illegal part-time jobs in 24 hours a day in real time.

[0088] The escalation unit can immediately escalate detected information to the relevant agencies. For example, it can escalate detected information to the relevant agencies within seconds. The escalation unit can use AI to assess the urgency of an escalation and select the optimal escalation method. The escalation unit can record the history of escalations and use this information for future escalations. This enables a rapid response by immediately escalating detected information to the relevant agencies.

[0089] The Approach Department can use an AI chatbot to provide individualized approaches and awareness-raising to potential victims. For example, the Approach Department can use the AI ​​chatbot to send individual messages to potential victims and raise awareness about the dangers of illegal part-time jobs. The Approach Department can use the AI ​​chatbot to respond to potential victims' questions in real time and provide appropriate advice. The Approach Department can use the AI ​​chatbot to analyze the emotions of potential victims and respond accordingly. This enables effective responses to potential victims through individualized approaches and awareness-raising using an AI chatbot.

[0090] The transmitting unit can develop and implement an AI algorithm to identify fake job postings on job search websites. For example, the transmitting unit can use the AI ​​algorithm to analyze job information on job search websites and extract the characteristics of fake job postings. The transmitting unit can use the AI ​​algorithm to evaluate the reliability of job information and identify fake job postings. The transmitting unit can use the AI ​​algorithm to learn patterns of fake job postings and detect new fake job postings. As a result, by developing and implementing an AI algorithm to identify fake job postings on job search websites, the accuracy of fake job posting detection is improved.

[0091] The transmission unit can use AI to analyze the report content and evaluate its urgency and reliability. For example, the transmission unit can use AI to analyze the text of the report content and score its urgency. The transmission unit can use AI to evaluate the reliability of the report content and calculate a reliability score. The transmission unit can use AI to determine the priority of the report content based on the urgency and reliability scores. This allows for appropriate action to be taken by analyzing the report content with AI and evaluating its urgency and reliability.

[0092] The transmission unit can automatically notify relevant organizations of high-risk information. For example, it can use AI to detect high-risk information in real time and automatically notify relevant organizations. It can also use AI to assess the urgency of high-risk information and select the optimal notification method. Furthermore, it can use AI to record notification history and utilize it for future notifications. This enables a rapid response by automatically notifying relevant organizations of high-risk information.

[0093] The transmitting unit can ensure the anonymity of reporters and the reliability of information using blockchain technology. For example, the transmitting unit can use a public blockchain to record the report content on a distributed ledger and prevent tampering. The transmitting unit can use a private blockchain to provide necessary information to relevant organizations while ensuring the anonymity of reporters. The transmitting unit can use blockchain technology to verify the reliability of the report content and calculate a reliability score. In this way, the anonymity of reporters and the reliability of information can be ensured by using blockchain technology.

[0094] The transmitting unit can predict the risk of illegal part-time work through regional data analysis. For example, it can use a Geographic Information System (GIS) to visualize the risk of illegal part-time work in each region. It can use statistical analysis to analyze regional data and identify areas with a high risk of illegal part-time work. It can use AI to predict the risk of illegal part-time work based on regional data and provide the prediction results to relevant organizations. This enables effective countermeasures by predicting the risk of illegal part-time work through regional data analysis.

[0095] The transmission department can plan effective awareness campaigns in cooperation with local governments. For example, the transmission department can use posters and leaflets to raise awareness among local residents about the dangers of illegal part-time jobs. The transmission department can hold events to raise awareness among local residents about the dangers of illegal part-time jobs. The transmission department can utilize social media to raise awareness among local residents about the dangers of illegal part-time jobs. By planning effective awareness campaigns in cooperation with local governments, it is possible to raise safety awareness in local communities.

[0096] The transmission unit can automatically generate legal short-term employment creation plans using AI. For example, the transmission unit can use AI to analyze local job information and identify legal short-term employment opportunities. The transmission unit can use AI to match the supply and demand for short-term employment and generate an optimal employment plan. The transmission unit can use AI to evaluate the effectiveness of short-term employment and improve the plan. In this way, by automatically generating legal short-term employment creation plans using AI, it is possible to provide legal employment opportunities.

[0097] The transmitting unit can build and operate a community-participatory monitoring network. For example, the transmitting unit can organize community groups so that local residents can cooperate in monitoring the risks of illegal part-time jobs. The transmitting unit can provide an online platform so that local residents can share information and cooperate in monitoring the risks of illegal part-time jobs. The transmitting unit can use AI to analyze reports from residents and evaluate the effectiveness of the monitoring network. In this way, community safety can be enhanced by building and operating a community-participatory monitoring network.

[0098] The transmission unit can analyze voices from social media and local communities to perform sentiment analysis and understand feelings and attitudes towards illegal part-time jobs. For example, the transmission unit can use sentiment analysis algorithms to analyze posts and comments on social media to understand feelings towards illegal part-time jobs. The transmission unit can use text mining to analyze voices from local communities to understand attitudes towards illegal part-time jobs. The transmission unit can use AI to take appropriate measures based on the results of the sentiment analysis. In this way, by analyzing voices from social media and local communities and understanding feelings and attitudes towards illegal part-time jobs, appropriate measures can be taken.

[0099] The transmission unit can detect negative trends early and automatically suggest countermeasures. For example, the transmission unit can use AI to analyze posts and comments on social media and local communities to detect negative trends. The transmission unit can use AI to identify the causes of negative trends and automatically suggest countermeasures. The transmission unit can use AI to evaluate the effectiveness of countermeasures and make improvements. In this way, by detecting negative trends early and automatically suggesting countermeasures, the risks of illegal part-time work can be reduced.

[0100] The transmission unit can integrate and utilize data obtained from various functions, such as social media, job search sites, and local data. For example, the transmission unit can centrally manage data obtained from each function using database integration technology. The transmission unit can extract useful information from the integrated data using data mining technology and utilize it for detecting and preventing illegal part-time jobs. The transmission unit can analyze the integrated data using AI and discover new trends. As a result, integrating and utilizing data obtained from each function improves the accuracy of detecting and preventing illegal part-time jobs.

[0101] The transmission unit can continuously improve accuracy and discover new trends through machine learning. For example, the transmission unit can perform periodic model updates to improve the accuracy of the machine learning model. The transmission unit can build a feedback loop and improve the model based on actual data. The transmission unit can use AI to discover new trends and predict the risks of illegal part-time jobs. As a result, the system's effectiveness is continuously improved through continuous accuracy improvements and the discovery of new trends using machine learning.

[0102] The transmission unit can protect personal information through data anonymization and encryption. For example, the transmission unit can use anonymization algorithms to anonymize personal information and protect privacy. The transmission unit can use encryption protocols to encrypt data and prevent unauthorized access. The transmission unit can use AI to evaluate and improve the effectiveness of anonymization and encryption. This allows for the protection of user privacy by achieving data anonymization and encryption.

[0103] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, the data collection unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The data collection unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The data collection unit can use an emotion engine to adjust the timing of information collection based on the user's emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection to alleviate the user's burden. If the user is relaxed, the data collection unit can increase the frequency of information collection to collect more detailed data. If the user is in a hurry, the data collection unit can prioritize the collection of important information and process it quickly. In this way, by adjusting the timing of information collection based on the user's emotions, the user's burden is reduced and efficient information collection becomes possible.

[0104] The data collection unit can analyze past posting history on social media and job sites to select the optimal data collection method. For example, it can use AI to analyze past posting history and identify time periods when specific keywords frequently appear. It can also use AI to analyze past posting history, find patterns in the posting habits of specific users, and prioritize the collection of those users' posts. Furthermore, it can use AI to enhance information collection during specific events or campaign periods based on past posting history. This allows for efficient information collection by selecting the optimal method through analysis of past posting history.

[0105] The data collection unit can filter information based on specific keywords or phrases. For example, it can prioritize collecting posts containing specific keywords (e.g., high income, short hours). It can filter and collect advertisements containing specific phrases (e.g., earn money quickly, easy work). Based on combinations of keywords and phrases, the data collection unit can extract posts that are likely to be illegal or illicit jobs. This allows for the efficient collection of posts that are likely to be illegal or illicit jobs by filtering based on specific keywords and phrases.

[0106] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The data collection unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The data collection unit can use an emotion engine to determine the priority of information to collect based on the user's emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting information that provides a sense of security. If the user is excited, the data collection unit can prioritize collecting information that is of interest. If the user is tired, the data collection unit can prioritize collecting concise and important information. In this way, by determining the priority of information to collect based on the user's emotions, it is possible to prioritize collecting information that is important to the user.

[0107] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of nearby job postings based on the user's current location. The data collection unit can collect region-specific illegal part-time job information based on the user's geographical location. The data collection unit can collect highly relevant information by considering the user's travel history. As a result, region-specific information can be efficiently collected by prioritizing the collection of highly relevant information by considering the user's geographical location.

[0108] The data collection unit can analyze a user's social media activity and collect relevant information during data collection. For example, the data collection unit can prioritize collecting posts from accounts that the user follows. The data collection unit can collect posts from groups and communities that the user participates in. The data collection unit can analyze a user's past posts and collect relevant information. This allows for the efficient collection of relevant information by analyzing a user's social media activity.

[0109] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated emotions. For example, the detection unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The detection unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The detection unit can use an emotion engine to adjust the detection criteria based on the user's emotions. For example, if the user is feeling anxious, the detection unit can perform detection with stricter criteria. If the user is relaxed, the detection unit can perform detection with normal criteria. If the user is excited, the detection unit can dynamically adjust the detection criteria in response to changes in emotions. This allows for more appropriate detection by adjusting the detection criteria based on the user's emotions.

[0110] The detection unit can improve detection accuracy by considering the interrelationships between posts and advertisements during detection. For example, the detection unit can cross-reference the content of posts and advertisements to detect highly relevant information. The detection unit can analyze the temporal relationship between posts and advertisements to detect information posted around the same time. The detection unit can extract common keywords from posts and advertisements to detect highly relevant information. In this way, the detection accuracy can be improved by considering the interrelationships between posts and advertisements.

[0111] The detection unit can perform detection while considering the poster's attribute information. For example, the detection unit can detect information related to specific attributes by considering the poster's age and gender. The detection unit can analyze the poster's past posting history and detect highly relevant information. The detection unit can detect region-specific information by considering the poster's geographical location information. As a result, by considering the poster's attribute information, it is possible to detect more relevant information.

[0112] The detection unit can estimate the user's emotions and adjust the display order of detection results based on the estimated emotions. For example, the detection unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The detection unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The detection unit can use an emotion engine to adjust the display order of detection results based on the user's emotions. For example, if the user is feeling anxious, the detection unit can prioritize displaying important detection results. If the user is relaxed, the detection unit can display detection results in the normal order. If the user is excited, the detection unit can dynamically adjust the display order according to changes in emotions. In this way, by adjusting the display order of detection results based on the user's emotions, information that is important to the user can be prioritized.

[0113] The detection unit can perform detection while considering the geographical distribution of posts and advertisements. For example, the detection unit can prioritize the detection of information that is frequently posted in a specific area. The detection unit can associate and detect posts and advertisements that are geographically close. The detection unit can detect highly relevant information based on region-specific keywords. As a result, by considering the geographical distribution of posts and advertisements, region-specific information can be detected efficiently.

[0114] The detection unit can improve detection accuracy by referring to related literature during detection. For example, the detection unit can improve detection accuracy based on keywords extracted from related literature. The detection unit can cross-reference the contents of related literature to detect highly relevant information. The detection unit can improve detection accuracy based on citation information from related literature. Thus, by referring to related literature, detection accuracy can be improved.

[0115] The escalation unit can estimate the user's emotions and adjust the escalation method based on the estimated emotions. For example, the escalation unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The escalation unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The escalation unit can use an emotion engine to adjust the escalation method based on the user's emotions. For example, if the user is feeling anxious, the escalation unit can quickly escalate the situation to provide reassurance. If the user is relaxed, the escalation unit can apply the normal escalation method. If the user is excited, the escalation unit can dynamically adjust the escalation method in response to changes in emotions. This allows for more appropriate escalation by adjusting the escalation method based on the user's emotions.

[0116] The escalation unit can determine the priority of escalations based on the urgency of the information during the escalation process. For example, the escalation unit can use AI to evaluate the urgency of the information and prioritize escalating highly urgent information. The escalation unit can then escalate moderately urgent information next. The escalation unit can escalate low-urgency information last. This allows for a rapid response by determining the priority of escalations based on the urgency of the information.

[0117] The escalation department can select the optimal escalation method by referring to the response history of the relevant organizations during an escalation. For example, the escalation department can select the optimal escalation method based on past response history. The escalation department can analyze the response history of the relevant organizations and escalate to an organization capable of a rapid response. The escalation department can refer to the response history of the relevant organizations and select an appropriate escalation procedure. This allows for the selection of the optimal escalation method and a rapid response by referring to the response history of the relevant organizations.

[0118] The escalation unit can estimate the user's emotions and adjust the frequency of escalation based on those emotions. For example, the escalation unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The escalation unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The escalation unit can use an emotion engine to adjust the frequency of escalation based on the user's emotions. For example, if the user is feeling anxious, the escalation unit can increase the frequency of escalation to provide a sense of security. If the user is relaxed, the escalation unit can escalate at a normal frequency. If the user is excited, the escalation unit can dynamically adjust the frequency of escalation in response to changes in emotion. This allows for more appropriate escalation by adjusting the frequency of escalation based on the user's emotions.

[0119] The escalation department can prioritize escalations based on when the information was submitted. For example, the escalation department can prioritize escalating the most recent information. It can then escalate older information next. Finally, it can escalate very old information last. This allows for a quicker response by prioritizing escalations based on when the information was submitted.

[0120] The escalation department can select the optimal escalation method when escalating, taking into account the geographical location information of the relevant organizations. For example, the escalation department can escalate to the nearest organization based on the geographical location information of the relevant organizations. The escalation department can escalate to an organization capable of a rapid response, taking into account the geographical location information of the relevant organizations. The escalation department can select an appropriate escalation procedure by referring to the geographical location information of the relevant organizations. This allows for the selection of the optimal escalation method and a rapid response by considering the geographical location information of the relevant organizations.

[0121] The approach unit can estimate the user's emotions and adjust the approach's presentation based on those estimated emotions. For example, the approach unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The approach unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The approach unit can use an emotion engine to adjust the approach's presentation based on the user's emotions. For example, if the user is feeling anxious, the approach unit can use a reassuring presentation. If the user is relaxed, the approach unit can use a normal presentation. If the user is excited, the approach unit can dynamically adjust its presentation according to the change in emotion. This allows for a more appropriate approach by adjusting the approach's presentation based on the user's emotions.

[0122] The approach unit can select an individualized approach method based on the victim's attribute information during the approach. For example, the approach unit can select an appropriate approach method considering the victim's age and gender. The approach unit can select the optimal approach method by analyzing the victim's past behavioral history. The approach unit can select a region-specific approach method considering the victim's geographical location information. This allows for a more effective approach by selecting an individualized approach method based on the victim's attribute information.

[0123] The approach unit can select the optimal approach method by referring to past approach history during the approach process. For example, the approach unit can select the optimal approach method based on past approach history. The approach unit can analyze past approach history and select an effective approach method. The approach unit can refer to past approach history and select an appropriate approach procedure. This allows for the selection of the optimal approach method and an effective approach by referring to past approach history.

[0124] The approach unit can estimate the user's emotions and determine the priority of approaches based on the estimated emotions. For example, the approach unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The approach unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The approach unit can use an emotion engine to determine the priority of approaches based on the user's emotions. For example, if the user is feeling anxious, the approach unit can prioritize approaches that provide a sense of security. If the user is relaxed, the approach unit can prioritize normal approaches. If the user is excited, the approach unit can dynamically adjust the priority of approaches according to the change in emotions. This makes it possible to make more appropriate approaches by determining the priority of approaches based on the user's emotions.

[0125] The approach unit can select the optimal approach method by considering the victim's geographical location information during the approach. For example, the approach unit can introduce nearby support organizations based on the victim's current location. The approach unit can provide region-specific support information based on the victim's geographical location information. The approach unit can provide highly relevant support information by considering the victim's travel history. As a result, by considering the victim's geographical location information, the optimal approach method can be selected, enabling effective support.

[0126] The approach team can analyze the victim's social media activity and adjust their approach accordingly. For example, they can select an appropriate approach based on information about accounts the victim follows. They can also adjust their approach based on information about groups and communities the victim participates in. Furthermore, they can analyze the victim's past posts and select a relevant approach. This allows for the selection of the optimal approach and effective support by analyzing the victim's social media activity.

[0127] The transmitting unit can estimate the user's emotions and determine the priority of information to transmit based on the estimated emotions. For example, the transmitting unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The transmitting unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The transmitting unit can use an emotion engine to determine the priority of information to transmit based on the user's emotions. For example, if the user is feeling anxious, the transmitting unit can prioritize transmitting information that provides a sense of security. If the user is relaxed, the transmitting unit can prioritize transmitting normal information. If the user is excited, the transmitting unit can dynamically adjust the priority of information transmitted in accordance with the change in emotions. This allows for the provision of more appropriate information by determining the priority of information transmitted based on the user's emotions.

[0128] The transmitting unit can determine the transmission priority based on the urgency of the information at the time of transmission. For example, the transmitting unit can use AI to evaluate the urgency of the information and prioritize the transmission of high-urgency information. The transmitting unit can then transmit medium-urgency information next. The transmitting unit can transmit low-urgency information last. This allows for a rapid response by determining the transmission priority based on the urgency of the information.

[0129] The transmitting unit can evaluate the reliability of the information and select the transmission method at the time of transmission. For example, the transmitting unit can use AI to evaluate the reliability of the information and prioritize the transmission of highly reliable information. The transmitting unit can then transmit information of medium reliability. The transmitting unit can transmit information of low reliability last. In this way, by evaluating the reliability of the information, it is possible to provide more reliable information.

[0130] The transmitting unit can estimate the user's emotions and adjust the transmission frequency based on the estimated emotions. For example, the transmitting unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The transmitting unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The transmitting unit can use an emotion engine to adjust the transmission frequency based on the user's emotions. For example, if the user is feeling anxious, the transmitting unit can increase the transmission frequency to provide reassurance. If the user is relaxed, the transmitting unit can transmit at a normal frequency. If the user is excited, the transmitting unit can dynamically adjust the transmission frequency in response to changes in emotions. This allows for more appropriate information to be provided by adjusting the transmission frequency based on the user's emotions.

[0131] The transmitting unit can determine the transmission priority based on when the information was submitted. For example, the transmitting unit can prioritize the transmission of the most recent information. It can then transmit older information next. It can also transmit very old information last. This allows for a quicker response by prioritizing transmission based on when the information was submitted.

[0132] The transmitting unit can select the optimal transmission method when transmitting information, taking into account its geographical location. For example, the transmitting unit can transmit information to the most relevant region based on its geographical location. The transmitting unit can also transmit information to a region where a rapid response is possible, taking its geographical location into consideration. Furthermore, the transmitting unit can select an appropriate transmission procedure by referring to the geographical location of the information. This allows for the selection of the optimal transmission method and enables a rapid response by considering the geographical location of the information.

[0133] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0134] The data collection unit can estimate the user's emotions and adjust the timing of information collection based on those emotions. For example, the data collection unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The data collection unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The data collection unit can use an emotion engine to adjust the timing of information collection based on the user's emotions. For example, if the user is stressed, the data collection unit can reduce the frequency of information collection to alleviate the user's burden. If the user is relaxed, the data collection unit can increase the frequency of information collection to collect more detailed data. If the user is in a hurry, the data collection unit can prioritize the collection of important information and process it quickly. In this way, by adjusting the timing of information collection based on the user's emotions, the user's burden is reduced and efficient information collection becomes possible.

[0135] The data collection unit can analyze past posting history on social media and job sites to select the optimal data collection method. For example, it can use AI to analyze past posting history and identify time periods when specific keywords frequently appear. It can also use AI to analyze past posting history, find patterns in the posting habits of specific users, and prioritize the collection of those users' posts. Furthermore, it can use AI to enhance information collection during specific events or campaign periods based on past posting history. This allows for efficient information collection by selecting the optimal method through analysis of past posting history.

[0136] The data collection unit can filter information based on specific keywords or phrases. For example, it can prioritize collecting posts containing specific keywords (e.g., high income, short hours). It can filter and collect advertisements containing specific phrases (e.g., earn money quickly, easy work). Based on combinations of keywords and phrases, the data collection unit can extract posts that are likely to be illegal or illicit jobs. This allows for the efficient collection of posts that are likely to be illegal or illicit jobs by filtering based on specific keywords and phrases.

[0137] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, the data collection unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The data collection unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The data collection unit can use an emotion engine to determine the priority of information to collect based on the user's emotions. For example, if the user is feeling anxious, the data collection unit can prioritize collecting information that provides a sense of security. If the user is excited, the data collection unit can prioritize collecting information that is of interest. If the user is tired, the data collection unit can prioritize collecting concise and important information. In this way, by determining the priority of information to collect based on the user's emotions, it is possible to prioritize collecting information that is important to the user.

[0138] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location. For example, the data collection unit can prioritize the collection of nearby job postings based on the user's current location. The data collection unit can collect region-specific illegal part-time job information based on the user's geographical location. The data collection unit can collect highly relevant information by considering the user's travel history. As a result, region-specific information can be efficiently collected by prioritizing the collection of highly relevant information by considering the user's geographical location.

[0139] The detection unit can estimate the user's emotions and adjust the detection criteria based on the estimated emotions. For example, the detection unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The detection unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The detection unit can use an emotion engine to adjust the detection criteria based on the user's emotions. For example, if the user is feeling anxious, the detection unit can perform detection with stricter criteria. If the user is relaxed, the detection unit can perform detection with normal criteria. If the user is excited, the detection unit can dynamically adjust the detection criteria in response to changes in emotions. This allows for more appropriate detection by adjusting the detection criteria based on the user's emotions.

[0140] The detection unit can improve detection accuracy by considering the interrelationships between posts and advertisements during detection. For example, the detection unit can cross-reference the content of posts and advertisements to detect highly relevant information. The detection unit can analyze the temporal relationship between posts and advertisements to detect information posted around the same time. The detection unit can extract common keywords from posts and advertisements to detect highly relevant information. In this way, the detection accuracy can be improved by considering the interrelationships between posts and advertisements.

[0141] The detection unit can perform detection while considering the poster's attribute information. For example, the detection unit can detect information related to specific attributes by considering the poster's age and gender. The detection unit can analyze the poster's past posting history and detect highly relevant information. The detection unit can detect region-specific information by considering the poster's geographical location information. As a result, by considering the poster's attribute information, it is possible to detect more relevant information.

[0142] The detection unit can estimate the user's emotions and adjust the display order of the detection results based on the estimated emotions. For example, the detection unit can use an emotion engine to analyze the user's facial expressions and voice to estimate emotions. The detection unit can use an emotion engine to analyze the user's behavioral data to estimate emotions. The detection unit can use an emotion engine to adjust the display order of the detection results based on the user's emotions. For example, if the user is feeling anxious, the detection unit can prioritize displaying important detection results. If the user is relaxed, the detection unit can display the detection results in the normal order. If the user is excited, the detection unit can dynamically adjust the display order according to the change in emotions. In this way, by adjusting the display order of detection results based on the user's emotions, information that is important to the user can be prioritized.

[0143] The detection unit can perform detection while considering the geographical distribution of posts and advertisements. For example, the detection unit can prioritize the detection of information that is frequently posted in a specific area. The detection unit can associate and detect posts and advertisements that are geographically close. The detection unit can detect highly relevant information based on region-specific keywords. As a result, by considering the geographical distribution of posts and advertisements, region-specific information can be detected efficiently.

[0144] The following briefly describes the processing flow for example form 2.

[0145] Step 1: The collection unit collects information from social media and job sites. For example, it can collect posts and advertisements from social media and job sites. Step 2: The detection unit analyzes the information collected by the collection unit to detect posts and advertisements related to illegal part-time jobs. For example, it can perform 24-hour real-time monitoring using natural language processing and image recognition AI. Step 3: The escalation unit escalates the information detected by the detection unit to the relevant organizations. For example, the detected information can be escalated to the relevant organizations immediately. Step 4: The Approach Department conducts individual approaches and awareness campaigns for potential victims based on the information escalated by the Escalation Department. For example, individual approaches and awareness campaigns can be conducted using an AI chatbot. Step 5: The transmission unit sends the information revealed by the approach unit to the integrated data platform. For example, an AI algorithm can be developed and implemented to identify fake job postings on job search websites.

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0147] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0148] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the collection unit, detection unit, escalation unit, approach unit, and transmission unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 38B of the smart device 14, estimates the user's emotions using the control unit 46A, and adjusts the timing of information collection. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the detection criteria based on the user's emotions using an emotion engine. The escalation unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the method and frequency of escalation based on the user's emotions. The approach unit is implemented by the control unit 46A of the smart device 14 and adjusts the expression method and priority of the approach based on the user's emotions. The transmission unit is implemented by the specific processing unit 290 of the data processing unit 12 and adjusts the priority and frequency of information to be transmitted based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0151] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the collection unit, detection unit, escalation unit, approach unit, and transmission unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the smart glasses 214, estimates the user's emotions using the control unit 46A, and adjusts the timing of information collection. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12, and adjusts the detection criteria based on the user's emotions using an emotion engine. The escalation unit is implemented by the specific processing unit 290 of the data processing unit 12, and adjusts the method and frequency of escalation based on the user's emotions. The approach unit is implemented by the control unit 46A of the smart glasses 214, and adjusts the expression method and priority of the approach based on the user's emotions. The transmission unit is implemented by the specific processing unit 290 of the data processing unit 12, and adjusts the priority and frequency of information to be transmitted based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0166] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0167] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0174] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0175] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0176] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0177] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0178] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0179] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0180] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0181] Each of the multiple elements described above, including the collection unit, detection unit, escalation unit, approach unit, and transmission unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the headset terminal 314, estimates the user's emotions using the control unit 46A, and adjusts the timing of information collection. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12, and adjusts the detection criteria based on the user's emotions using an emotion engine. The escalation unit is implemented by the specific processing unit 290 of the data processing unit 12, and adjusts the method and frequency of escalation based on the user's emotions. The approach unit is implemented by the control unit 46A of the headset terminal 314, and adjusts the expression method and priority of the approach based on the user's emotions. The transmission unit is implemented by the specific processing unit 290 of the data processing unit 12, and adjusts the priority and frequency of information to be transmitted based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0182] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0183] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0184] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0185] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0186] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0187] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0188] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0189] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0190] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0191] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0192] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0193] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0194] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0195] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0196] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0197] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0198] Each of the multiple elements described above, including the collection unit, detection unit, escalation unit, approach unit, and transmission unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects information using the camera 42 and microphone 238 of the robot 414, estimates the user's emotions using the control unit 46A, and adjusts the timing of information collection. The detection unit is implemented by the specific processing unit 290 of the data processing unit 12, and adjusts the detection criteria based on the user's emotions using an emotion engine. The escalation unit is implemented by the specific processing unit 290 of the data processing unit 12, and adjusts the method and frequency of escalation based on the user's emotions. The approach unit is implemented by the control unit 46A of the robot 414, and adjusts the expression method and priority of the approach based on the user's emotions. The transmission unit is implemented by the specific processing unit 290 of the data processing unit 12, and adjusts the priority and frequency of information to be transmitted based on the user's emotions. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0199] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0200] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0201] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0202] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0203] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0204] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0205] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0206] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0207] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0208] 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.

[0209] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0210] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0211] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0212] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0213] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0214] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0215] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0216] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0217] (Note 1) The collection department collects information from social media and job sites, The collection unit analyzes the information collected and detects posts and advertisements related to illegal part-time jobs, An escalation unit that escalates the information detected by the aforementioned detection unit to the relevant organizations, Based on the information escalated by the aforementioned escalation unit, an approach unit conducts individual approaches and awareness-raising activities for potential victims. The system includes a transmission unit that transmits the information revealed by the approach unit to an integrated data platform. A system characterized by the following features. (Note 2) The detection unit, We use natural language processing and image recognition AI to provide 24 / 7 real-time monitoring. The system described in Appendix 1, characterized by the features described herein. (Note 3) The escalation unit is, Detected information will be immediately escalated to the relevant authorities. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned approach section is We will use AI chatbots to provide personalized approaches and awareness campaigns to potential victims. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned transmitting unit Develop and implement an AI algorithm to identify fake job postings on job search websites. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned transmitting unit The AI ​​analyzes the report content and evaluates its urgency and reliability. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned transmitting unit High-risk information is automatically notified to relevant organizations. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned transmitting unit Blockchain technology is used to ensure the anonymity of reporters and the reliability of information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned transmitting unit Predicting the risks of illegal part-time jobs through regional data analysis The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned transmitting unit Develop an effective awareness campaign in collaboration with local governments. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned transmitting unit Automated generation of legal short-term employment creation plans using AI. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned transmitting unit To build and operate a community-based neighborhood watch network. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned transmitting unit We conduct sentiment analysis to understand feelings and attitudes towards illegal part-time jobs by analyzing voices on social media and within local communities. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned transmitting unit It detects negative trends early and automatically suggests countermeasures. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned transmitting unit Integrate and utilize data obtained from various functions, such as social media, job search sites, and local data. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned transmitting unit We will continuously improve accuracy and discover new trends using machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned transmitting unit Achieve personal information protection through data anonymization and encryption. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned collection unit is It estimates the user's emotions and adjusts the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned collection unit is We analyze past posting history on social media and job sites to select the most suitable data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned collection unit is When gathering information, filter it based on specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned collection unit is When collecting information, the system prioritizes collecting highly relevant information by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned collection unit is When gathering information, we analyze users' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 24) The detection unit, It estimates the user's emotions and adjusts the detection criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The detection unit, When detecting an issue, the accuracy of the detection is improved by considering the interrelationships between posts and advertisements. The system described in Appendix 1, characterized by the features described herein. (Note 26) The detection unit, When detection occurs, the poster's attribute information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 27) The detection unit, It estimates the user's emotions and adjusts the display order of the detection results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The detection unit, During detection, the geographical distribution of posts and advertisements is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 29) The detection unit, During detection, we refer to relevant literature to improve detection accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 30) The escalation unit is, It estimates the user's emotions and adjusts the escalation method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The escalation unit is, During escalation, prioritize escalations based on the urgency of the information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The escalation unit is, During escalation, the most appropriate escalation method will be selected by referring to the response history of the relevant organizations. The system described in Appendix 1, characterized by the features described herein. (Note 33) The escalation unit is, It estimates the user's emotions and adjusts the frequency of escalation based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The escalation unit is, During escalation, the priority of the escalation is determined based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 35) The escalation unit is, During escalation, the most appropriate escalation method will be selected, taking into account the geographical location of the relevant organizations. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned approach section is It estimates the user's emotions and adjusts the way the approach is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned approach section is When making an approach, select an individualized approach method based on the victim's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The aforementioned approach section is When making an approach, refer to past approach history to select the optimal approach method. The system described in Appendix 1, characterized by the features described herein. (Note 39) The aforementioned approach section is It estimates the user's emotions and determines the priority of approaches based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned approach section is When approaching the victim, the optimal approach method will be selected considering the victim's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 41) The approach unit analyzes the victim's social media activities during an approach and adjusts the approach method. The system according to Appendix 1, characterized by the above. (Appendix 42) The transmission unit estimates the user's emotion and determines the priority of the information to be transmitted based on the estimated user emotion. The system according to Appendix 1, characterized by the above. (Appendix 43) The transmission unit determines the transmission priority based on the urgency of the information during transmission. The system according to Appendix 1, characterized by the above. (Appendix 44) The transmission unit evaluates the reliability of the information during transmission and selects the transmission method. The system according to Appendix 1, characterized by the above. (Appendix 45) The transmission unit estimates the user's emotion and adjusts the transmission frequency based on the estimated user emotion. The system according to Appendix 1, characterized by the above. (Appendix 46) The transmission unit determines the transmission priority based on the submission time of the information during transmission. The system according to Appendix 1, characterized by the above. (Appendix 47) The transmission unit selects the optimal transmission method considering the geographical location information of the information during transmission. The system according to Appendix 1, characterized by the above.

Explanation of Signs

[0218] 10, 210, 310, 410 Data processing system 12 Data processing device 14 Smart device 214 Smart glasses 314 Headset-type terminal 414 Robots

Claims

1. The collection department collects information from social media and job sites, The collection unit analyzes the information collected and detects posts and advertisements related to illegal part-time jobs, An escalation unit that escalates the information detected by the aforementioned detection unit to the relevant organizations, Based on the information escalated by the aforementioned escalation unit, an approach unit conducts individual approaches and awareness-raising activities for potential victims. The system includes a transmission unit that transmits the information revealed by the approach unit to an integrated data platform. A system characterized by the following features.

2. The detection unit is We use natural language processing and image recognition AI to provide 24 / 7 real-time monitoring. The system according to feature 1.

3. The escalation unit is, Detected information will be immediately escalated to the relevant authorities. The system according to feature 1.

4. The aforementioned approach section is We will use AI chatbots to provide personalized approaches and awareness campaigns to potential victims. The system according to feature 1.

5. The aforementioned transmitting unit Develop and implement an AI algorithm to identify fake job postings on job search websites. The system according to feature 1.

6. The aforementioned transmitting unit The AI ​​analyzes the report content and evaluates its urgency and reliability. The system according to feature 1.

7. The aforementioned transmitting unit High-risk information is automatically notified to relevant organizations. The system according to feature 1.

8. The aforementioned transmitting unit Blockchain technology is used to ensure the anonymity of reporters and the reliability of information. The system according to feature 1.

Images