system

Abstract

The system according to this embodiment aims to efficiently detect and take countermeasures against illegal part-time job postings. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a notification unit, and a patrol unit. The collection unit collects information from job posting pages. The analysis unit analyzes the information collected by the collection unit and determines whether or not it is an illegal job posting. The notification unit generates a warning message and notifies the site administrator if the analysis unit determines that it is an illegal job posting. The patrol unit monitors social media and detects illegal job postings.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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 conventional technology, there is a problem that it is difficult to efficiently detect the recruitment of dark bytes and take countermeasures.

[0005] The system according to the embodiment aims to efficiently detect the recruitment of dark bytes and take countermeasures.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, and a patrol unit. The collection unit collects information from job posting pages. The analysis unit analyzes the information collected by the collection unit and determines whether or not it is an illegal job posting. If the analysis unit determines that it is an illegal job posting, the notification unit generates a warning message and notifies the site administrator. The patrol unit monitors social media and detects illegal job postings. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently detect and take countermeasures against illegal job postings. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable 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 anti-illegal part-time job system according to an embodiment of the present invention is a system that uses AI to detect and take countermeasures against illegal part-time job postings. This system collects and analyzes information from job posting pages to determine whether or not it is an illegal part-time job posting, generates a warning message, and notifies the site administrator. It also monitors social media to detect illegal part-time job postings and sends a warning message in response to those posts. For example, the anti-illegal part-time job system constantly monitors job posting pages 24 hours a day, 365 days a year, reading the content of new postings as they appear and determining whether or not it is an illegal part-time job posting based on specific keywords or phrases (e.g., "high pay," "instant cash," "just do XX," etc.). If it is determined to be an illegal part-time job posting, the anti-illegal part-time job system automatically generates a warning message and notifies the site administrator. This enables a swift response. Furthermore, the anti-illegal part-time job system also patrols social media to identify illegal part-time job postings. For example, it can detect illegal part-time job postings on social media and send a warning message in response to those posts. This prevents illegal part-time job postings on social media as well. This allows the anti-illegal job system to quickly detect and address illegal job postings.

[0029] The anti-illegal part-time job system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, and a patrol unit. The collection unit collects information from job posting pages. The collection unit collects information from job posting pages using, for example, web scraping technology. The collection unit can also obtain information from job posting pages using an API. Furthermore, the collection unit can also collect information from bulletin boards and applications. For example, the collection unit periodically collects job posting information from specific websites. The collection unit can also obtain job posting information in real time using an API. The collection unit collects information from bulletin boards and applications and centrally manages the job posting information. The analysis unit analyzes the information collected by the collection unit and determines whether or not it is an illegal part-time job posting. The analysis unit analyzes the job posting information using, for example, text analysis technology. Furthermore, the analysis unit can also determine whether or not it is an illegal part-time job posting using a machine learning algorithm. Furthermore, the analysis unit can also determine whether or not it is an illegal part-time job posting based on specific keywords or phrases. For example, the analysis unit uses text analysis technology to detect keywords such as "high pay" or "instant cash" from the job posting information. The analysis unit uses machine learning algorithms to learn the characteristics of job postings and determine whether or not they are illegal or unlicensed job postings. The analysis unit classifies job postings based on specific keywords and phrases and determines whether or not they are illegal or unlicensed job postings. The notification unit generates a warning message and notifies the site administrator if the analysis unit determines that a job posting is illegal or unlicensed. The notification unit generates warning messages using templates, for example. The notification unit can also automatically generate warning messages using generation algorithms. Furthermore, the notification unit can notify the site administrator using email or text messages. For example, the notification unit can quickly generate warning messages using templates. The notification unit can automatically generate warning messages using generation algorithms and notify the site administrator. The notification unit sends warning messages to the site administrator using email or text messages. The patrol unit monitors social media and detects illegal job postings.The patrol unit can, for example, use keyword search technology to detect illegal job postings on social media. The patrol unit can also use image analysis technology to detect illegal job postings on social media. Furthermore, the patrol unit can send warning messages to detected illegal job postings. For example, the patrol unit uses keyword search technology to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit uses image analysis technology to detect illegal job postings on social media and sends warning messages to those posts. The patrol unit automatically generates and sends warning messages to detected illegal job postings. As a result, the illegal job prevention system according to this embodiment can quickly detect and take countermeasures against illegal job postings.

[0030] The data collection unit collects information from job posting pages. For example, the data collection unit uses web scraping technology to collect information from job posting pages. Web scraping is a technology that automatically extracts data from specific websites, and the data collection unit uses this to regularly collect job posting information. Specifically, the data collection unit analyzes the HTML structure of websites, identifies the sections containing job posting information, and extracts that information. The data collection unit can also obtain information from job posting pages using APIs. Using APIs allows the unit to directly obtain data provided by website operators, enabling the collection of more accurate and up-to-date information. Furthermore, the data collection unit can also collect information from bulletin boards and applications. Bulletin boards and applications are platforms where users can freely post information, and the data collection unit also collects job posting information from these platforms. For example, the data collection unit regularly monitors specific bulletin boards and applications, collecting information whenever new job postings are posted. The data collection unit centrally manages this information and makes it accessible to the analysis and notification units. This allows the data collection unit to collect a wide range of job posting information from diverse sources and update it in real time. Furthermore, the data collection unit can store the collected information in a database and, as needed, link with other systems and departments. For example, the collected information can be stored on a cloud server, making it accessible to the analysis and notification units. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0031] The analysis unit analyzes the information collected by the collection unit to determine whether or not it is a recruitment for illegal part-time work. For example, the analysis unit analyzes the job posting information using text analysis technology. Text analysis technology is a technology that analyzes text data using natural language processing technology to detect specific patterns and keywords. The analysis unit uses this to detect keywords such as "high pay" and "instant payment" from the job posting information and determines whether or not it is a recruitment for illegal part-time work. The analysis unit can also use machine learning algorithms to determine whether or not it is a recruitment for illegal part-time work. Machine learning algorithms are technologies that learn from large amounts of data and identify specific patterns and features. The analysis unit uses this to learn the features of the job posting information and determines whether or not it is a recruitment for illegal part-time work. Furthermore, the analysis unit can also determine whether or not it is a recruitment for illegal part-time work based on specific keywords or phrases. For example, the analysis unit uses text analysis technology to detect keywords such as "high pay" and "instant payment" from the job posting information. The analysis unit uses machine learning algorithms to learn the features of the job posting information and determines whether or not it is a recruitment for illegal part-time work. The analysis unit classifies job postings based on specific keywords and phrases to determine whether or not they are for illegal or unlicensed work. This allows the analysis unit to quickly and accurately analyze collected data and grasp the risks of illegal work in real time. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk in specific regions and time periods based on past illegal job posting data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0032] The notification unit generates a warning message and notifies the site administrator if the analysis unit determines that a job posting is for illegal or unethical work. The notification unit generates warning messages using templates, for example. Using templates allows for the rapid and consistent generation of messages. The notification unit can also automatically generate warning messages using a generation algorithm. This algorithm automatically generates appropriate warning messages based on collected data and analysis results, and the notification unit uses this to efficiently generate warning messages. Furthermore, the notification unit can notify the site administrator via email or text message. For example, the notification unit can quickly generate warning messages using templates. The notification unit can also automatically generate warning messages using a generation algorithm and notify the site administrator. The notification unit sends warning messages to the site administrator via email or text message. This allows the notification unit to quickly notify the site administrator of the risks of illegal work and enable them to take appropriate measures. Furthermore, the notification unit can continuously improve the accuracy and effectiveness of its notifications. For example, the notification unit can collect feedback from the site administrator and review the content of the warning messages and the notification methods. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, in addition to email, it can use voice calls, SMS, and push notifications to ensure that important information is delivered reliably. This allows the notification unit to quickly and reliably provide warning messages to site administrators, minimizing the risks associated with illegal part-time jobs.

[0033] The patrol unit monitors social media to detect illegal job postings. For example, the patrol unit uses keyword search technology to detect illegal job postings on social media. Keyword search technology is a technology that automatically detects posts containing specific keywords or phrases, and the patrol unit uses this to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit can also use image analysis technology to detect illegal job postings on social media. Image analysis technology is a technology that analyzes image data to identify specific patterns or features, and the patrol unit uses this to detect illegal job postings on social media. Furthermore, the patrol unit can also send warning messages to detected illegal job postings. For example, the patrol unit uses keyword search technology to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit uses image analysis technology to detect illegal job postings on social media and sends warning messages to those posts. The patrol unit automatically generates and sends warning messages to detected illegal job postings. This allows the patrol team to quickly detect and take action against illegal job postings on social media. Furthermore, the patrol team can analyze trends in illegal job postings on social media and predict future risks. For example, they can analyze the frequency of specific keywords and phrases to identify increasing trends in illegal job postings. The patrol team can also analyze user reactions and comments on social media to assess the impact of illegal job postings. This enables the patrol team to effectively monitor illegal job postings on social media and take swift and appropriate action.

[0034] The data collection unit monitors job posting pages 24 hours a day, 365 days a year, and can read the content of new postings whenever they appear. For example, the data collection unit can periodically check job posting pages 24 hours a day, 365 days a year, and read the content of new postings whenever they appear. Alternatively, the data collection unit can work in shifts to monitor job posting pages 24 hours a day, 365 days a year. Furthermore, the data collection unit can use monitoring tools to monitor job posting pages 24 hours a day, 365 days a year. For example, the data collection unit can periodically check job posting pages 24 hours a day, 365 days a year, and read the content of new postings whenever they appear. The data collection unit can work in shifts to monitor job posting pages 24 hours a day, 365 days a year, and read the content of new postings whenever they appear. The data collection unit can use monitoring tools to monitor job posting pages 24 hours a day, 365 days a year, and read the content of new postings whenever they appear. This ensures that the latest job posting information is always available through 24 / 7 monitoring. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that periodically checks job posting pages 24 hours a day, 365 days a year, and reads the content of new postings whenever they are published.

[0035] The analysis unit can determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. For example, the analysis unit can list specific keywords or phrases and analyze the job posting information based on them. The analysis unit can also use machine learning algorithms to determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. Furthermore, the analysis unit can use text analysis technology to determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. For example, the analysis unit can list keywords such as "high pay" or "instant payment" and analyze the job posting information based on them. The analysis unit can use machine learning algorithms to determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. The analysis unit can use text analysis technology to determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. In this way, job postings for illegal or unlicensed work can be accurately detected by making judgments based on specific keywords or phrases. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can analyze information using an AI model that determines whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases.

[0036] The notification unit can automatically generate a warning message and notify the site administrator if it determines that a job posting is for illegal or illegal work. The notification unit can generate warning messages using templates, for example. Alternatively, the notification unit can automatically generate warning messages using a generation algorithm. Furthermore, the notification unit can notify the site administrator via email or text message. For example, the notification unit can quickly generate warning messages using templates. The notification unit can automatically generate warning messages and notify the site administrator using a generation algorithm. The notification unit can send warning messages to the site administrator via email or text message. This enables a quick response by automatically generating and notifying warning messages. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can generate warning messages using an AI model that automatically generates warning messages and notifies the site administrator if it determines that a job posting is for illegal or illegal work.

[0037] The patrol unit can detect illegal job recruitment posts on social media and send warning messages in response. The patrol unit can detect illegal job recruitment posts on social media using, for example, keyword search technology. The patrol unit can also detect illegal job recruitment posts on social media using image analysis technology. Furthermore, the patrol unit can send warning messages in response to detected illegal job recruitment posts. For example, the patrol unit can use keyword search technology to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit can use image analysis technology to detect illegal job recruitment posts on social media and send warning messages in response. The patrol unit automatically generates and sends warning messages in response to detected illegal job recruitment posts. In this way, illegal job recruitment on social media can be prevented by detecting such posts and sending warning messages in response. Some or all of the above processes in the patrol unit may be performed using, for example, AI, or not using AI. For example, the patrol unit can detect illegal job postings on social media and generate warning messages using an AI model that responds to those posts with warning messages.

[0038] The data collection unit can optimize its collection methods by referring to past data collection when collecting job posting pages. For example, the data collection unit can identify the most effective collection time slots based on past data and concentrate collection during those times. The data collection unit can also analyze past data and prioritize the collection of pages that contain a high number of specific keywords. Furthermore, the data collection unit can enhance collection from specific sites or platforms by referring to past data collection. For example, the data collection unit can identify the most effective collection time slots based on past data and concentrate collection during those times. The data collection unit analyzes past data and prioritizes the collection of pages that contain a high number of specific keywords. The data collection unit enhances collection from specific sites or platforms by referring to past data collection. This allows for efficient collection of job posting pages by optimizing the collection method by referring to past data collection. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect information using an AI model that optimizes the collection method by referring to past data collection.

[0039] The data collection unit can filter job postings based on specific regions or industries when collecting them. For example, the data collection unit can filter job postings based on specific regions (e.g., urban or rural areas). It can also filter job postings based on specific industries (e.g., food service or retail). Furthermore, the data collection unit can filter job postings based on specific time periods (e.g., nighttime or weekends). For example, the data collection unit can filter job postings based on specific regions (e.g., urban or rural areas). It can filter job postings based on specific industries (e.g., food service or retail). It can filter job postings based on specific time periods (e.g., nighttime or weekends). This allows for the efficient collection of necessary job postings by filtering based on specific regions or industries. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect information using an AI model that filters based on specific regions or industries.

[0040] The data collection unit can analyze a user's social media activity when collecting job postings and prioritize the collection of relevant job postings. For example, the data collection unit can analyze a user's social media activity and prioritize the collection of job postings that are likely to interest them. The data collection unit can also prioritize the collection of relevant job postings based on the content of posts from accounts that the user follows. Furthermore, the data collection unit can also prioritize the collection of relevant job postings based on the user's social media search history. For example, the data collection unit can analyze a user's social media activity and prioritize the collection of job postings that are likely to interest them. The data collection unit can prioritize the collection of relevant job postings based on the content of posts from accounts that the user follows. The data collection unit can prioritize the collection of relevant job postings based on the user's social media search history. By analyzing a user's social media activity and prioritizing the collection of relevant job postings, the data collection unit can collect the most suitable job postings for the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that analyzes users' social media activity and prioritizes collecting relevant job posting pages.

[0041] The data collection unit can select which job postings to collect by considering the user's past application history. For example, the data collection unit can analyze the user's past application history and prioritize the collection of relevant job postings. The data collection unit can also prioritize the collection of relevant job postings based on the conditions of jobs the user has applied for in the past. Furthermore, the data collection unit can prioritize the collection of job postings in specific industries or regions based on the user's past application history. For example, the data collection unit can analyze the user's past application history and prioritize the collection of relevant job postings. The data collection unit can prioritize the collection of relevant job postings based on the conditions of jobs the user has applied for in the past. The data collection unit can prioritize the collection of job postings in specific industries or regions based on the user's past application history. By selecting which pages to collect by considering the user's past application history, the data collection unit can collect the most suitable job postings for the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that selects pages to collect data from, taking into account the user's past application history.

[0042] The analysis unit can optimize its analysis algorithm by referring to past analysis data during analysis. For example, the analysis unit can select the most effective analysis algorithm based on past analysis data. The analysis unit can also analyze past analysis data and optimize the analysis algorithm based on the frequency of occurrence of specific keywords or phrases. Furthermore, the analysis unit can optimize the analysis algorithm for specific sites or platforms by referring to past analysis data. For example, the analysis unit can select the most effective analysis algorithm based on past analysis data. The analysis unit can analyze past analysis data and optimize the analysis algorithm based on the frequency of occurrence of specific keywords or phrases. The analysis unit can optimize the analysis algorithm for specific sites or platforms by referring to past analysis data. This improves the accuracy of the analysis by optimizing the analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that optimizes the analysis algorithm by referring to past analysis data.

[0043] The analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of specific keywords or phrases. For example, the analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of a specific keyword (e.g., "high reward"). The analysis unit can also improve the accuracy of its analysis based on the frequency of occurrence of a specific phrase (e.g., "instant payment"). Furthermore, the analysis unit can also improve the accuracy of its analysis based on the frequency of occurrence of specific keyword or phrase combinations. For example, the analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of a specific keyword (e.g., "high reward"). The analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of a specific phrase (e.g., "instant payment"). The analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of specific keyword or phrase combinations. By improving the accuracy of the analysis based on the frequency of occurrence of specific keywords or phrases, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that improves the accuracy of analysis based on the frequency of occurrence of specific keywords or phrases.

[0044] The analysis unit can perform analysis while considering the geographical distribution of job posting pages. For example, the analysis unit can prioritize the analysis of pages in a specific region based on the geographical distribution of job posting pages. The analysis unit can also analyze the geographical distribution of job posting pages to understand the trends of illegal jobs in a specific region. Furthermore, the analysis unit can optimize the analysis algorithm while considering the geographical distribution of job posting pages. For example, the analysis unit can prioritize the analysis of pages in a specific region based on the geographical distribution of job posting pages. The analysis unit can analyze the geographical distribution of job posting pages to understand the trends of illegal jobs in a specific region. The analysis unit can optimize the analysis algorithm while considering the geographical distribution of job posting pages. As a result, by performing analysis while considering the geographical distribution of job posting pages, it is possible to understand the trends of illegal jobs in each region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that performs analysis while considering the geographical distribution of job posting pages.

[0045] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the job posting page during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the job posting page. Furthermore, the analysis unit can analyze the frequency of occurrence of specific keywords and phrases based on the relevant literature on the job posting page. In addition, the analysis unit can optimize its analysis algorithm by referring to relevant literature on the job posting page. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the job posting page. The analysis unit analyzes the frequency of occurrence of specific keywords and phrases based on the relevant literature on the job posting page. The analysis unit optimizes its analysis algorithm by referring to relevant literature on the job posting page. This allows for more accurate analysis results by improving the accuracy of the analysis by referring to relevant literature on the job posting page. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that improves the accuracy of its analysis by referring to relevant literature on the job posting page.

[0046] The notification unit can optimize notification methods by referring to past notification data when sending notifications. For example, the notification unit can select the most effective notification method based on past notification data. The notification unit can also analyze past notification data and optimize notification methods based on the frequency of occurrence of specific keywords or phrases. Furthermore, the notification unit can optimize notification methods for specific sites or platforms by referring to past notification data. For example, the notification unit can select the most effective notification method based on past notification data. The notification unit can analyze past notification data and optimize notification methods based on the frequency of occurrence of specific keywords or phrases. The notification unit can optimize notification methods for specific sites or platforms by referring to past notification data. This maximizes the effectiveness of notifications by optimizing notification methods by referring to past notification data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send information using an AI model that optimizes notification methods by referring to past notification data.

[0047] The notification unit can adjust the timing of notifications based on specific time periods or days of the week. For example, the notification unit can adjust the timing of notifications based on specific time periods (e.g., morning or evening). It can also adjust the timing of notifications based on specific days of the week (e.g., weekdays or weekends). Furthermore, the notification unit can adjust the timing of notifications based on specific events (e.g., payday or holidays). For example, the notification unit can adjust the timing of notifications based on specific time periods (e.g., morning or evening). The notification unit can adjust the timing of notifications based on specific days of the week (e.g., weekdays or weekends). The notification unit can adjust the timing of notifications based on specific events (e.g., payday or holidays). This maximizes the effectiveness of notifications by adjusting the timing of notifications based on specific time periods or days of the week. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use an AI model that adjusts the timing of notifications based on specific time periods or days of the week to send information.

[0048] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize sending push notifications. The notification unit can also prioritize sending email notifications if the user is using a tablet. Furthermore, the notification unit can prioritize sending browser notifications if the user is using a PC. For example, if the notification unit is using a smartphone, it will prioritize sending push notifications. If the user is using a tablet, it will prioritize sending email notifications. If the user is using a PC, it will prioritize sending browser notifications. This maximizes the effectiveness of notifications by selecting the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send information using an AI model that selects the optimal notification method by considering the user's device information.

[0049] The notification unit can customize notification content by referring to the user's past response history when sending a notification. For example, the notification unit can select the most effective notification content based on the user's past response history. The notification unit can also analyze the user's past response history and customize notification content to include specific keywords or phrases. Furthermore, the notification unit can refer to the user's past response history and customize notification content to be optimal for specific times of day or days of the week. For example, the notification unit can select the most effective notification content based on the user's past response history. The notification unit can analyze the user's past response history and customize notification content to include specific keywords or phrases. The notification unit can refer to the user's past response history and customize notification content to be optimal for specific times of day or days of the week. This maximizes the effectiveness of notifications by customizing notification content by referring to the user's past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send information using an AI model that customizes notification content by referring to the user's past response history.

[0050] The patrol unit can optimize its patrol algorithm by referring to past patrol data during patrols. For example, the patrol unit can identify the most effective patrol time slots based on past patrol data and concentrate patrols during those times. The patrol unit can also analyze past patrol data and optimize its patrol algorithm based on the frequency of occurrence of specific keywords or phrases. Furthermore, the patrol unit can optimize its patrol algorithm for specific sites or platforms by referring to past patrol data. For example, the patrol unit can identify the most effective patrol time slots based on past patrol data and concentrate patrols during those times. The patrol unit analyzes past patrol data and optimizes its patrol algorithm based on the frequency of occurrence of specific keywords or phrases. The patrol unit optimizes its patrol algorithm for specific sites or platforms by referring to past patrol data. As a result, optimizing the patrol algorithm by referring to past patrol data improves the accuracy of patrols. Some or all of the above-described processes in the patrol unit may be performed using AI, for example, or without AI. For example, the patrol unit can patrol for information using an AI model that optimizes the patrol algorithm by referring to past patrol data.

[0051] The patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of specific keywords or phrases. For example, the patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of a specific keyword (e.g., "high reward"). The patrol unit can also improve the accuracy of its patrols based on the frequency of occurrence of a specific phrase (e.g., "instant cash"). Furthermore, the patrol unit can also improve the accuracy of its patrols based on the frequency of occurrence of a specific combination of keywords or phrases. For example, the patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of a specific keyword (e.g., "high reward"). The patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of a specific phrase (e.g., "instant cash"). The patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of a specific combination of keywords or phrases. By improving the accuracy of patrols based on the frequency of occurrence of specific keywords or phrases, more accurate patrol results can be provided. Some or all of the above processing in the patrol unit may be performed using AI, for example, or without using AI. For example, the patrol unit can use an AI model that improves the accuracy of patrols based on the frequency of occurrence of specific keywords or phrases to patrol for information.

[0052] The patrol unit can conduct patrols while considering the geographical distribution of social media. For example, the patrol unit can prioritize patrolling posts from specific regions based on the geographical distribution of social media. The patrol unit can also analyze the geographical distribution of social media to understand trends in illegal part-time jobs in specific regions. Furthermore, the patrol unit can optimize its patrol algorithm by considering the geographical distribution of social media. For example, the patrol unit can prioritize patrolling posts from specific regions based on the geographical distribution of social media. The patrol unit can analyze the geographical distribution of social media to understand trends in illegal part-time jobs in specific regions. The patrol unit can optimize its patrol algorithm by considering the geographical distribution of social media. As a result, by conducting patrols while considering the geographical distribution of social media, it is possible to understand trends in illegal part-time jobs in each region. Some or all of the above processes in the patrol unit may be performed using AI, for example, or not. For example, the patrol unit can patrol for information using an AI model that performs patrols while considering the geographical distribution of social media.

[0053] The patrol unit can improve the accuracy of its patrols by referring to relevant SNS literature during patrols. For example, the patrol unit can improve the accuracy of its patrols by referring to relevant SNS literature. The patrol unit can also analyze the frequency of occurrence of specific keywords and phrases based on relevant SNS literature. Furthermore, the patrol unit can optimize its patrol algorithm by referring to relevant SNS literature. For example, the patrol unit can improve the accuracy of its patrols by referring to relevant SNS literature. The patrol unit analyzes the frequency of occurrence of specific keywords and phrases based on relevant SNS literature. The patrol unit optimizes its patrol algorithm by referring to relevant SNS literature. By improving the accuracy of patrols by referring to relevant SNS literature, it is possible to provide more accurate patrol results. Some or all of the above processing in the patrol unit may be performed using AI, for example, or not using AI. For example, the patrol unit can patrol information using an AI model that improves the accuracy of patrols by referring to relevant SNS literature.

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

[0055] The data collection unit can prioritize collecting relevant job postings based on the user's past search history. For example, the unit analyzes keywords the user has searched for in the past and prioritizes collecting relevant job postings. It can also prioritize collecting relevant job postings based on the content of job postings the user has viewed in the past. Furthermore, the unit can prioritize collecting job postings for specific industries or regions based on the user's past search history. This allows the system to select the most relevant job postings for each user by selecting them based on their past search history.

[0056] The data collection unit can analyze a user's social media activity when collecting job postings and prioritize the collection of relevant job postings. For example, the unit can analyze a user's social media activity and prioritize the collection of job postings that are likely to interest them. The unit can also prioritize the collection of relevant job postings based on the content of posts from accounts the user follows. Furthermore, the unit can prioritize the collection of relevant job postings based on the user's social media search history. This allows the system to collect the most relevant job postings for each user by analyzing their social media activity and prioritizing the collection of relevant job postings.

[0057] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the most effective analysis algorithm based on past analysis data. Furthermore, the analysis unit can analyze past analysis data and optimize the analysis algorithm based on the frequency of occurrence of specific keywords or phrases. In addition, the analysis unit can optimize the analysis algorithm for specific sites or platforms by referring to past analysis data. This improves the accuracy of the analysis by optimizing the analysis algorithm based on past analysis data.

[0058] The notification unit can adjust the timing of notifications based on specific times of day or days of the week. For example, it can adjust the timing of notifications based on specific times of day (e.g., morning or evening). It can also adjust the timing of notifications based on specific days of the week (e.g., weekdays or weekends). Furthermore, it can adjust the timing of notifications based on specific events (e.g., payday or holidays). This allows for maximizing the effectiveness of notifications by adjusting the timing based on specific times of day or days of the week.

[0059] The patrol team can conduct patrols while considering the geographical distribution of social media. For example, the patrol team can prioritize patrolling posts from specific regions based on the geographical distribution of social media. Furthermore, the patrol team can analyze the geographical distribution of social media to understand trends in illegal part-time jobs in specific regions. In addition, the patrol team can optimize its patrol algorithms by considering the geographical distribution of social media. This allows them to understand trends in illegal part-time jobs in each region by conducting patrols while considering the geographical distribution of social media.

[0060] The data collection unit can filter job postings based on specific regions or industries. For example, it can filter job postings based on specific regions (e.g., urban or rural areas). It can also filter job postings based on specific industries (e.g., food service or retail). Furthermore, it can filter job postings based on specific time periods (e.g., nighttime or weekends). This allows for the efficient collection of necessary job postings by filtering based on specific regions and industries.

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

[0062] Step 1: The collection unit collects information from job posting pages. The collection unit collects information from job posting pages using techniques such as web scraping. The collection unit can also obtain information from job posting pages using APIs. Furthermore, the collection unit can also collect information from bulletin boards and applications. For example, the collection unit periodically collects job posting information from specific websites. The collection unit can also obtain job posting information in real time using APIs. The collection unit collects information from bulletin boards and applications and centrally manages the job posting information. Step 2: The analysis unit analyzes the information collected by the collection unit to determine whether or not it is an advertisement for illegal part-time work. The analysis unit analyzes the job advertisement information using, for example, text analysis technology. The analysis unit can also use machine learning algorithms to determine whether or not it is an advertisement for illegal part-time work. Furthermore, the analysis unit can determine whether or not it is an advertisement for illegal part-time work based on specific keywords or phrases. For example, the analysis unit uses text analysis technology to detect keywords such as "high pay" or "instant cash" from the job advertisement information. The analysis unit uses machine learning algorithms to learn the characteristics of the job advertisement information and determine whether or not it is an advertisement for illegal part-time work. The analysis unit classifies the job advertisement information based on specific keywords or phrases and determines whether or not it is an advertisement for illegal part-time work. Step 3: The notification unit generates a warning message and notifies the site administrator if the analysis unit determines that the advertisement is for illegal part-time work. The notification unit generates the warning message using a template, for example. The notification unit can also automatically generate the warning message using a generation algorithm. Furthermore, the notification unit can notify the site administrator using email or text message. For example, the notification unit can quickly generate a warning message using a template. The notification unit can automatically generate a warning message using a generation algorithm and notify the site administrator. The notification unit can send the warning message to the site administrator using email or text message. Step 4: The patrol unit monitors social media and detects illegal job postings. The patrol unit can, for example, use keyword search technology to detect illegal job postings on social media. The patrol unit can also use image analysis technology to detect illegal job postings on social media. Furthermore, the patrol unit can send warning messages to detected illegal job postings. For example, the patrol unit uses keyword search technology to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit uses image analysis technology to detect illegal job postings on social media and sends warning messages to those posts. The patrol unit automatically generates and sends warning messages to detected illegal job postings.

[0063] (Example of form 2) The anti-illegal part-time job system according to an embodiment of the present invention is a system that uses AI to detect and take countermeasures against illegal part-time job postings. This system collects and analyzes information from job posting pages to determine whether or not it is an illegal part-time job posting, generates a warning message, and notifies the site administrator. It also monitors social media to detect illegal part-time job postings and sends a warning message in response to those posts. For example, the anti-illegal part-time job system constantly monitors job posting pages 24 hours a day, 365 days a year, reading the content of new postings as they appear and determining whether or not it is an illegal part-time job posting based on specific keywords or phrases (e.g., "high pay," "instant cash," "just do XX," etc.). If it is determined to be an illegal part-time job posting, the anti-illegal part-time job system automatically generates a warning message and notifies the site administrator. This enables a swift response. Furthermore, the anti-illegal part-time job system also patrols social media to identify illegal part-time job postings. For example, it can detect illegal part-time job postings on social media and send a warning message in response to those posts. This prevents illegal part-time job postings on social media as well. This allows the anti-illegal job system to quickly detect and address illegal job postings.

[0064] The anti-illegal part-time job system according to this embodiment comprises a collection unit, an analysis unit, a notification unit, and a patrol unit. The collection unit collects information from job posting pages. The collection unit collects information from job posting pages using, for example, web scraping technology. The collection unit can also obtain information from job posting pages using an API. Furthermore, the collection unit can also collect information from bulletin boards and applications. For example, the collection unit periodically collects job posting information from specific websites. The collection unit can also obtain job posting information in real time using an API. The collection unit collects information from bulletin boards and applications and centrally manages the job posting information. The analysis unit analyzes the information collected by the collection unit and determines whether or not it is an illegal part-time job posting. The analysis unit analyzes the job posting information using, for example, text analysis technology. Furthermore, the analysis unit can also determine whether or not it is an illegal part-time job posting using a machine learning algorithm. Furthermore, the analysis unit can also determine whether or not it is an illegal part-time job posting based on specific keywords or phrases. For example, the analysis unit uses text analysis technology to detect keywords such as "high pay" or "instant cash" from the job posting information. The analysis unit uses machine learning algorithms to learn the characteristics of job postings and determine whether or not they are illegal or unlicensed job postings. The analysis unit classifies job postings based on specific keywords and phrases and determines whether or not they are illegal or unlicensed job postings. The notification unit generates a warning message and notifies the site administrator if the analysis unit determines that a job posting is illegal or unlicensed. The notification unit generates warning messages using templates, for example. The notification unit can also automatically generate warning messages using generation algorithms. Furthermore, the notification unit can notify the site administrator using email or text messages. For example, the notification unit can quickly generate warning messages using templates. The notification unit can automatically generate warning messages using generation algorithms and notify the site administrator. The notification unit sends warning messages to the site administrator using email or text messages. The patrol unit monitors social media and detects illegal job postings.The patrol unit can, for example, use keyword search technology to detect illegal job postings on social media. The patrol unit can also use image analysis technology to detect illegal job postings on social media. Furthermore, the patrol unit can send warning messages to detected illegal job postings. For example, the patrol unit uses keyword search technology to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit uses image analysis technology to detect illegal job postings on social media and sends warning messages to those posts. The patrol unit automatically generates and sends warning messages to detected illegal job postings. As a result, the illegal job prevention system according to this embodiment can quickly detect and take countermeasures against illegal job postings.

[0065] The data collection unit collects information from job posting pages. For example, the data collection unit uses web scraping technology to collect information from job posting pages. Web scraping is a technology that automatically extracts data from specific websites, and the data collection unit uses this to regularly collect job posting information. Specifically, the data collection unit analyzes the HTML structure of websites, identifies the sections containing job posting information, and extracts that information. The data collection unit can also obtain information from job posting pages using APIs. Using APIs allows the unit to directly obtain data provided by website operators, enabling the collection of more accurate and up-to-date information. Furthermore, the data collection unit can also collect information from bulletin boards and applications. Bulletin boards and applications are platforms where users can freely post information, and the data collection unit also collects job posting information from these platforms. For example, the data collection unit regularly monitors specific bulletin boards and applications, collecting information whenever new job postings are posted. The data collection unit centrally manages this information and makes it accessible to the analysis and notification units. This allows the data collection unit to collect a wide range of job posting information from diverse sources and update it in real time. Furthermore, the data collection unit can store the collected information in a database and, as needed, link with other systems and departments. For example, the collected information can be stored on a cloud server, making it accessible to the analysis and notification units. The data collection unit can also adjust the frequency and accuracy of data collection, enabling flexible responses to specific situations and conditions. This allows the data collection unit to collect data efficiently and effectively, improving the overall system performance.

[0066] The analysis unit analyzes the information collected by the collection unit to determine whether or not it is a recruitment for illegal part-time work. For example, the analysis unit analyzes the job posting information using text analysis technology. Text analysis technology is a technology that analyzes text data using natural language processing technology to detect specific patterns and keywords. The analysis unit uses this to detect keywords such as "high pay" and "instant payment" from the job posting information and determines whether or not it is a recruitment for illegal part-time work. The analysis unit can also use machine learning algorithms to determine whether or not it is a recruitment for illegal part-time work. Machine learning algorithms are technologies that learn from large amounts of data and identify specific patterns and features. The analysis unit uses this to learn the features of the job posting information and determines whether or not it is a recruitment for illegal part-time work. Furthermore, the analysis unit can also determine whether or not it is a recruitment for illegal part-time work based on specific keywords or phrases. For example, the analysis unit uses text analysis technology to detect keywords such as "high pay" and "instant payment" from the job posting information. The analysis unit uses machine learning algorithms to learn the features of the job posting information and determines whether or not it is a recruitment for illegal part-time work. The analysis unit classifies job postings based on specific keywords and phrases to determine whether or not they are for illegal or unlicensed work. This allows the analysis unit to quickly and accurately analyze collected data and grasp the risks of illegal work in real time. Furthermore, the analysis unit can utilize historical data and statistical information to conduct long-term risk assessments and trend analyses. For example, it can predict fluctuations in risk in specific regions and time periods based on past illegal job posting data and formulate future countermeasures. In addition, the analysis unit can use anomaly detection algorithms to detect unusual patterns and abnormal data, and issue warnings early. As a result, the analysis unit can not only grasp the situation in real time but also handle long-term risk management and anomaly detection, improving the reliability and security of the entire system.

[0067] The notification unit generates a warning message and notifies the site administrator if the analysis unit determines that a job posting is for illegal or unethical work. The notification unit generates warning messages using templates, for example. Using templates allows for the rapid and consistent generation of messages. The notification unit can also automatically generate warning messages using a generation algorithm. This algorithm automatically generates appropriate warning messages based on collected data and analysis results, and the notification unit uses this to efficiently generate warning messages. Furthermore, the notification unit can notify the site administrator via email or text message. For example, the notification unit can quickly generate warning messages using templates. The notification unit can also automatically generate warning messages using a generation algorithm and notify the site administrator. The notification unit sends warning messages to the site administrator via email or text message. This allows the notification unit to quickly notify the site administrator of the risks of illegal work and enable them to take appropriate measures. Furthermore, the notification unit can continuously improve the accuracy and effectiveness of its notifications. For example, the notification unit can collect feedback from the site administrator and review the content of the warning messages and the notification methods. Furthermore, the notification unit can reliably transmit information using multiple communication methods. For example, in addition to email, it can use voice calls, SMS, and push notifications to ensure that important information is delivered reliably. This allows the notification unit to quickly and reliably provide warning messages to site administrators, minimizing the risks associated with illegal part-time jobs.

[0068] The patrol unit monitors social media to detect illegal job postings. For example, the patrol unit uses keyword search technology to detect illegal job postings on social media. Keyword search technology is a technology that automatically detects posts containing specific keywords or phrases, and the patrol unit uses this to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit can also use image analysis technology to detect illegal job postings on social media. Image analysis technology is a technology that analyzes image data to identify specific patterns or features, and the patrol unit uses this to detect illegal job postings on social media. Furthermore, the patrol unit can also send warning messages to detected illegal job postings. For example, the patrol unit uses keyword search technology to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit uses image analysis technology to detect illegal job postings on social media and sends warning messages to those posts. The patrol unit automatically generates and sends warning messages to detected illegal job postings. This allows the patrol team to quickly detect and take action against illegal job postings on social media. Furthermore, the patrol team can analyze trends in illegal job postings on social media and predict future risks. For example, they can analyze the frequency of specific keywords and phrases to identify increasing trends in illegal job postings. The patrol team can also analyze user reactions and comments on social media to assess the impact of illegal job postings. This enables the patrol team to effectively monitor illegal job postings on social media and take swift and appropriate action.

[0069] The data collection unit monitors job posting pages 24 hours a day, 365 days a year, and can read the content of new postings whenever they appear. For example, the data collection unit can periodically check job posting pages 24 hours a day, 365 days a year, and read the content of new postings whenever they appear. Alternatively, the data collection unit can work in shifts to monitor job posting pages 24 hours a day, 365 days a year. Furthermore, the data collection unit can use monitoring tools to monitor job posting pages 24 hours a day, 365 days a year. For example, the data collection unit can periodically check job posting pages 24 hours a day, 365 days a year, and read the content of new postings whenever they appear. The data collection unit can work in shifts to monitor job posting pages 24 hours a day, 365 days a year, and read the content of new postings whenever they appear. The data collection unit can use monitoring tools to monitor job posting pages 24 hours a day, 365 days a year, and read the content of new postings whenever they appear. This ensures that the latest job posting information is always available through 24 / 7 monitoring. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that periodically checks job posting pages 24 hours a day, 365 days a year, and reads the content of new postings whenever they are published.

[0070] The analysis unit can determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. For example, the analysis unit can list specific keywords or phrases and analyze the job posting information based on them. The analysis unit can also use machine learning algorithms to determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. Furthermore, the analysis unit can use text analysis technology to determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. For example, the analysis unit can list keywords such as "high pay" or "instant payment" and analyze the job posting information based on them. The analysis unit can use machine learning algorithms to determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. The analysis unit can use text analysis technology to determine whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases. In this way, job postings for illegal or unlicensed work can be accurately detected by making judgments based on specific keywords or phrases. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not. For example, the analysis unit can analyze information using an AI model that determines whether or not a job posting is for illegal or unlicensed work based on specific keywords or phrases.

[0071] The notification unit can automatically generate a warning message and notify the site administrator if it determines that a job posting is for illegal or illegal work. The notification unit can generate warning messages using templates, for example. Alternatively, the notification unit can automatically generate warning messages using a generation algorithm. Furthermore, the notification unit can notify the site administrator via email or text message. For example, the notification unit can quickly generate warning messages using templates. The notification unit can automatically generate warning messages and notify the site administrator using a generation algorithm. The notification unit can send warning messages to the site administrator via email or text message. This enables a quick response by automatically generating and notifying warning messages. Some or all of the above processes in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can generate warning messages using an AI model that automatically generates warning messages and notifies the site administrator if it determines that a job posting is for illegal or illegal work.

[0072] The patrol unit can detect illegal job recruitment posts on social media and send warning messages in response. The patrol unit can detect illegal job recruitment posts on social media using, for example, keyword search technology. The patrol unit can also detect illegal job recruitment posts on social media using image analysis technology. Furthermore, the patrol unit can send warning messages in response to detected illegal job recruitment posts. For example, the patrol unit can use keyword search technology to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit can use image analysis technology to detect illegal job recruitment posts on social media and send warning messages in response. The patrol unit automatically generates and sends warning messages in response to detected illegal job recruitment posts. In this way, illegal job recruitment on social media can be prevented by detecting such posts and sending warning messages in response. Some or all of the above processes in the patrol unit may be performed using, for example, AI, or not using AI. For example, the patrol unit can detect illegal job postings on social media and generate warning messages using an AI model that responds to those posts with warning messages.

[0073] The data collection unit can estimate the user's emotions and determine the priority of job postings to collect based on the estimated emotions. For example, the data collection unit can estimate the user's emotions using an emotion analysis algorithm. Alternatively, the data collection unit can estimate the user's emotions using text analysis techniques. Furthermore, the data collection unit can determine the priority of job postings to collect based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting reliable job postings to provide reassurance. If the user is excited, the data collection unit will prioritize collecting interesting job postings. If the user is tired, the data collection unit will prioritize collecting simple and short-duration job postings. This allows the data collection to prioritize job postings based on the user's emotions, thereby collecting the most suitable job postings for the user. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that estimates the user's emotions and determines the priority of job posting pages to collect based on the estimated user emotions.

[0074] The data collection unit can optimize its collection methods by referring to past data collection when collecting job posting pages. For example, the data collection unit can identify the most effective collection time slots based on past data and concentrate collection during those times. The data collection unit can also analyze past data and prioritize the collection of pages that contain a high number of specific keywords. Furthermore, the data collection unit can enhance collection from specific sites or platforms by referring to past data collection. For example, the data collection unit can identify the most effective collection time slots based on past data and concentrate collection during those times. The data collection unit analyzes past data and prioritizes the collection of pages that contain a high number of specific keywords. The data collection unit enhances collection from specific sites or platforms by referring to past data collection. This allows for efficient collection of job posting pages by optimizing the collection method by referring to past data collection. Some or all of the above processes in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect information using an AI model that optimizes the collection method by referring to past data collection.

[0075] The data collection unit can filter job postings based on specific regions or industries when collecting them. For example, the data collection unit can filter job postings based on specific regions (e.g., urban or rural areas). It can also filter job postings based on specific industries (e.g., food service or retail). Furthermore, the data collection unit can filter job postings based on specific time periods (e.g., nighttime or weekends). For example, the data collection unit can filter job postings based on specific regions (e.g., urban or rural areas). It can filter job postings based on specific industries (e.g., food service or retail). It can filter job postings based on specific time periods (e.g., nighttime or weekends). This allows for the efficient collection of necessary job postings by filtering based on specific regions or industries. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not. For example, the data collection unit can collect information using an AI model that filters based on specific regions or industries.

[0076] The data collection unit can estimate the user's emotions and adjust the content of the job postings it collects based on those emotions. For example, the unit can estimate the user's emotions using an emotion analysis algorithm. It can also estimate the user's emotions using text analysis techniques. Furthermore, the unit can adjust the content of the job postings it collects based on the estimated user emotions. For example, if the user is feeling anxious, the unit will prioritize collecting job postings with reassuring content. If the user is excited, the unit will prioritize collecting job postings with interesting content. If the user is tired, the unit will prioritize collecting simple and short-duration job postings. This allows the system to collect the most suitable job postings for the user by adjusting the content based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that estimates the user's emotions and adjusts the content of the job posting pages to be collected based on the estimated user emotions.

[0077] The data collection unit can analyze a user's social media activity when collecting job postings and prioritize the collection of relevant job postings. For example, the data collection unit can analyze a user's social media activity and prioritize the collection of job postings that are likely to interest them. The data collection unit can also prioritize the collection of relevant job postings based on the content of posts from accounts that the user follows. Furthermore, the data collection unit can also prioritize the collection of relevant job postings based on the user's social media search history. For example, the data collection unit can analyze a user's social media activity and prioritize the collection of job postings that are likely to interest them. The data collection unit can prioritize the collection of relevant job postings based on the content of posts from accounts that the user follows. The data collection unit can prioritize the collection of relevant job postings based on the user's social media search history. By analyzing a user's social media activity and prioritizing the collection of relevant job postings, the data collection unit can collect the most suitable job postings for the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that analyzes users' social media activity and prioritizes collecting relevant job posting pages.

[0078] The data collection unit can select which job postings to collect by considering the user's past application history. For example, the data collection unit can analyze the user's past application history and prioritize the collection of relevant job postings. The data collection unit can also prioritize the collection of relevant job postings based on the conditions of jobs the user has applied for in the past. Furthermore, the data collection unit can prioritize the collection of job postings in specific industries or regions based on the user's past application history. For example, the data collection unit can analyze the user's past application history and prioritize the collection of relevant job postings. The data collection unit can prioritize the collection of relevant job postings based on the conditions of jobs the user has applied for in the past. The data collection unit can prioritize the collection of job postings in specific industries or regions based on the user's past application history. By selecting which pages to collect by considering the user's past application history, the data collection unit can collect the most suitable job postings for the user. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can collect information using an AI model that selects pages to collect data from, taking into account the user's past application history.

[0079] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on the estimated emotions. For example, the analysis unit can estimate the user's emotions using an emotion analysis algorithm. It can also estimate the user's emotions using text analysis techniques. Furthermore, the analysis unit can adjust the analysis criteria based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will perform the analysis using criteria that provide a sense of security. If the user is excited, the analysis unit will perform the analysis using criteria that attract interest. If the user is tired, the analysis unit will perform the analysis using criteria that are simple and quick. By adjusting the analysis criteria based on the user's emotions, the system can provide the user with the most optimal analysis results. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can analyze information using an AI model that estimates the user's emotions and adjusts the analysis criteria based on the estimated user emotions.

[0080] The analysis unit can optimize its analysis algorithm by referring to past analysis data during analysis. For example, the analysis unit can select the most effective analysis algorithm based on past analysis data. The analysis unit can also analyze past analysis data and optimize the analysis algorithm based on the frequency of occurrence of specific keywords or phrases. Furthermore, the analysis unit can optimize the analysis algorithm for specific sites or platforms by referring to past analysis data. For example, the analysis unit can select the most effective analysis algorithm based on past analysis data. The analysis unit can analyze past analysis data and optimize the analysis algorithm based on the frequency of occurrence of specific keywords or phrases. The analysis unit can optimize the analysis algorithm for specific sites or platforms by referring to past analysis data. This improves the accuracy of the analysis by optimizing the analysis algorithm by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that optimizes the analysis algorithm by referring to past analysis data.

[0081] The analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of specific keywords or phrases. For example, the analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of a specific keyword (e.g., "high reward"). The analysis unit can also improve the accuracy of its analysis based on the frequency of occurrence of a specific phrase (e.g., "instant payment"). Furthermore, the analysis unit can also improve the accuracy of its analysis based on the frequency of occurrence of specific keyword or phrase combinations. For example, the analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of a specific keyword (e.g., "high reward"). The analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of a specific phrase (e.g., "instant payment"). The analysis unit can improve the accuracy of its analysis based on the frequency of occurrence of specific keyword or phrase combinations. By improving the accuracy of the analysis based on the frequency of occurrence of specific keywords or phrases, it is possible to provide more accurate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that improves the accuracy of analysis based on the frequency of occurrence of specific keywords or phrases.

[0082] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. The analysis unit can estimate the user's emotions, for example, using an emotion analysis algorithm. The analysis unit can also estimate the user's emotions using text analysis technology. Furthermore, the analysis unit can adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling anxious, the analysis unit provides a display method that provides a sense of security. If the user is excited, the analysis unit provides a display method that attracts attention. If the user is tired, the analysis unit provides a simple and highly visible display method. In this way, by adjusting the display method of the analysis results based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can analyze information using an AI model that estimates the user's emotions and adjusts the display method of the analysis results based on the estimated user emotions.

[0083] The analysis unit can perform analysis while considering the geographical distribution of job posting pages. For example, the analysis unit can prioritize the analysis of pages in a specific region based on the geographical distribution of job posting pages. The analysis unit can also analyze the geographical distribution of job posting pages to understand the trends of illegal jobs in a specific region. Furthermore, the analysis unit can optimize the analysis algorithm while considering the geographical distribution of job posting pages. For example, the analysis unit can prioritize the analysis of pages in a specific region based on the geographical distribution of job posting pages. The analysis unit can analyze the geographical distribution of job posting pages to understand the trends of illegal jobs in a specific region. The analysis unit can optimize the analysis algorithm while considering the geographical distribution of job posting pages. As a result, by performing analysis while considering the geographical distribution of job posting pages, it is possible to understand the trends of illegal jobs in each region. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that performs analysis while considering the geographical distribution of job posting pages.

[0084] The analysis unit can improve the accuracy of its analysis by referring to relevant literature on the job posting page during the analysis process. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the job posting page. Furthermore, the analysis unit can analyze the frequency of occurrence of specific keywords and phrases based on the relevant literature on the job posting page. In addition, the analysis unit can optimize its analysis algorithm by referring to relevant literature on the job posting page. For example, the analysis unit can improve the accuracy of its analysis by referring to relevant literature on the job posting page. The analysis unit analyzes the frequency of occurrence of specific keywords and phrases based on the relevant literature on the job posting page. The analysis unit optimizes its analysis algorithm by referring to relevant literature on the job posting page. This allows for more accurate analysis results by improving the accuracy of the analysis by referring to relevant literature on the job posting page. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can analyze information using an AI model that improves the accuracy of its analysis by referring to relevant literature on the job posting page.

[0085] The notification unit can estimate the user's emotions and adjust the content of the notification message based on the estimated emotions. The notification unit can estimate the user's emotions, for example, using an emotion analysis algorithm. It can also estimate the user's emotions using text analysis techniques. Furthermore, the notification unit can adjust the content of the notification message based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will generate a notification message that provides reassurance. If the user is excited, the notification unit will generate a notification message that is engaging. If the user is tired, the notification unit will generate a simple and short notification message. In this way, by adjusting the content of the notification message based on the user's emotions, the system can provide the user with the most appropriate notification message. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can use an AI model that estimates the user's emotions and adjusts the content of the notification message based on those emotions to deliver information.

[0086] The notification unit can optimize notification methods by referring to past notification data when sending notifications. For example, the notification unit can select the most effective notification method based on past notification data. The notification unit can also analyze past notification data and optimize notification methods based on the frequency of occurrence of specific keywords or phrases. Furthermore, the notification unit can optimize notification methods for specific sites or platforms by referring to past notification data. For example, the notification unit can select the most effective notification method based on past notification data. The notification unit can analyze past notification data and optimize notification methods based on the frequency of occurrence of specific keywords or phrases. The notification unit can optimize notification methods for specific sites or platforms by referring to past notification data. This maximizes the effectiveness of notifications by optimizing notification methods by referring to past notification data. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send information using an AI model that optimizes notification methods by referring to past notification data.

[0087] The notification unit can adjust the timing of notifications based on specific time periods or days of the week. For example, the notification unit can adjust the timing of notifications based on specific time periods (e.g., morning or evening). It can also adjust the timing of notifications based on specific days of the week (e.g., weekdays or weekends). Furthermore, the notification unit can adjust the timing of notifications based on specific events (e.g., payday or holidays). For example, the notification unit can adjust the timing of notifications based on specific time periods (e.g., morning or evening). The notification unit can adjust the timing of notifications based on specific days of the week (e.g., weekdays or weekends). The notification unit can adjust the timing of notifications based on specific events (e.g., payday or holidays). This maximizes the effectiveness of notifications by adjusting the timing of notifications based on specific time periods or days of the week. Some or all of the above processing in the notification unit may be performed using AI, for example, or not using AI. For example, the notification unit can use an AI model that adjusts the timing of notifications based on specific time periods or days of the week to send information.

[0088] The notification unit can estimate the user's emotions and prioritize notification messages based on the estimated emotions. For example, the notification unit may use an emotion analysis algorithm to estimate the user's emotions. Alternatively, it may use text analysis techniques to estimate the user's emotions. Furthermore, the notification unit can prioritize notification messages based on the estimated emotions. For example, if the user is feeling anxious, the notification unit will prioritize sending reassuring notification messages. If the user is excited, the notification unit will prioritize sending interesting notification messages. If the user is tired, the notification unit will prioritize sending simple, short notification messages. This allows the notification unit to provide the user with the most appropriate notification messages by prioritizing them based on their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the notification unit may be performed using AI, or not. For example, the notification unit can use an AI model that estimates the user's emotions and determines the priority of notification messages based on those emotions to deliver information.

[0089] The notification unit can select the optimal notification method by considering the user's device information when sending a notification. For example, if the user is using a smartphone, the notification unit will prioritize sending push notifications. The notification unit can also prioritize sending email notifications if the user is using a tablet. Furthermore, the notification unit can prioritize sending browser notifications if the user is using a PC. For example, if the notification unit is using a smartphone, it will prioritize sending push notifications. If the user is using a tablet, it will prioritize sending email notifications. If the user is using a PC, it will prioritize sending browser notifications. This maximizes the effectiveness of notifications by selecting the optimal notification method by considering the user's device information. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send information using an AI model that selects the optimal notification method by considering the user's device information.

[0090] The notification unit can customize notification content by referring to the user's past response history when sending a notification. For example, the notification unit can select the most effective notification content based on the user's past response history. The notification unit can also analyze the user's past response history and customize notification content to include specific keywords or phrases. Furthermore, the notification unit can refer to the user's past response history and customize notification content to be optimal for specific times of day or days of the week. For example, the notification unit can select the most effective notification content based on the user's past response history. The notification unit can analyze the user's past response history and customize notification content to include specific keywords or phrases. The notification unit can refer to the user's past response history and customize notification content to be optimal for specific times of day or days of the week. This maximizes the effectiveness of notifications by customizing notification content by referring to the user's past response history. Some or all of the above processing in the notification unit may be performed using AI, for example, or without AI. For example, the notification unit can send information using an AI model that customizes notification content by referring to the user's past response history.

[0091] The patrol unit can estimate the user's emotions and adjust its patrol range based on the estimated emotions. For example, the patrol unit can estimate the user's emotions using an emotion analysis algorithm. It can also estimate the user's emotions using text analysis techniques. Furthermore, the patrol unit can adjust its patrol range based on the estimated emotions. For example, if the user is feeling anxious, the patrol unit will patrol a wide area to provide reassurance. If the user is excited, the patrol unit will focus its patrol on specific areas that might interest them. If the user is tired, the patrol unit will patrol a short, easy area. This allows the patrol unit to provide the optimal patrol experience for the user by adjusting the patrol range based on their emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the patrol unit may be performed using AI, for example, or without AI. For example, the patrol unit can patrol for information using an AI model that estimates the user's emotions and adjusts the scope of patrol based on the estimated user emotions.

[0092] The patrol unit can optimize its patrol algorithm by referring to past patrol data during patrols. For example, the patrol unit can identify the most effective patrol time slots based on past patrol data and concentrate patrols during those times. The patrol unit can also analyze past patrol data and optimize its patrol algorithm based on the frequency of occurrence of specific keywords or phrases. Furthermore, the patrol unit can optimize its patrol algorithm for specific sites or platforms by referring to past patrol data. For example, the patrol unit can identify the most effective patrol time slots based on past patrol data and concentrate patrols during those times. The patrol unit analyzes past patrol data and optimizes its patrol algorithm based on the frequency of occurrence of specific keywords or phrases. The patrol unit optimizes its patrol algorithm for specific sites or platforms by referring to past patrol data. As a result, optimizing the patrol algorithm by referring to past patrol data improves the accuracy of patrols. Some or all of the above-described processes in the patrol unit may be performed using AI, for example, or without AI. For example, the patrol unit can patrol for information using an AI model that optimizes the patrol algorithm by referring to past patrol data.

[0093] The patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of specific keywords or phrases. For example, the patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of a specific keyword (e.g., "high reward"). The patrol unit can also improve the accuracy of its patrols based on the frequency of occurrence of a specific phrase (e.g., "instant cash"). Furthermore, the patrol unit can also improve the accuracy of its patrols based on the frequency of occurrence of a specific combination of keywords or phrases. For example, the patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of a specific keyword (e.g., "high reward"). The patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of a specific phrase (e.g., "instant cash"). The patrol unit can improve the accuracy of its patrols based on the frequency of occurrence of a specific combination of keywords or phrases. By improving the accuracy of patrols based on the frequency of occurrence of specific keywords or phrases, more accurate patrol results can be provided. Some or all of the above processing in the patrol unit may be performed using AI, for example, or without using AI. For example, the patrol unit can use an AI model that improves the accuracy of patrols based on the frequency of occurrence of specific keywords or phrases to patrol for information.

[0094] The patrol unit can estimate the user's emotions and adjust the display method of the patrol results based on the estimated user emotions. The patrol unit can estimate the user's emotions using, for example, an emotion analysis algorithm. The patrol unit can also estimate the user's emotions using text analysis technology. Furthermore, the patrol unit can adjust the display method of the patrol results based on the estimated user emotions. For example, if the patrol unit is feeling anxious, it can provide a display method that provides a sense of security. If the patrol unit is excited, it can provide a display method that attracts attention. If the patrol unit is tired, it can provide a simple and highly visible display method. In this way, by adjusting the display method of the patrol results based on the user's emotions, the optimal display method can be provided to the user. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the patrol unit may be performed using, for example, AI, or not using AI. For example, the patrol unit can patrol information using an AI model that estimates the user's emotions and adjusts how the patrol results are displayed based on the estimated user emotions.

[0095] The patrol unit can conduct patrols while considering the geographical distribution of social media. For example, the patrol unit can prioritize patrolling posts from specific regions based on the geographical distribution of social media. The patrol unit can also analyze the geographical distribution of social media to understand trends in illegal part-time jobs in specific regions. Furthermore, the patrol unit can optimize its patrol algorithm by considering the geographical distribution of social media. For example, the patrol unit can prioritize patrolling posts from specific regions based on the geographical distribution of social media. The patrol unit can analyze the geographical distribution of social media to understand trends in illegal part-time jobs in specific regions. The patrol unit can optimize its patrol algorithm by considering the geographical distribution of social media. As a result, by conducting patrols while considering the geographical distribution of social media, it is possible to understand trends in illegal part-time jobs in each region. Some or all of the above processes in the patrol unit may be performed using AI, for example, or not. For example, the patrol unit can patrol for information using an AI model that performs patrols while considering the geographical distribution of social media.

[0096] The patrol unit can improve the accuracy of its patrols by referring to relevant SNS literature during patrols. For example, the patrol unit can improve the accuracy of its patrols by referring to relevant SNS literature. The patrol unit can also analyze the frequency of occurrence of specific keywords and phrases based on relevant SNS literature. Furthermore, the patrol unit can optimize its patrol algorithm by referring to relevant SNS literature. For example, the patrol unit can improve the accuracy of its patrols by referring to relevant SNS literature. The patrol unit analyzes the frequency of occurrence of specific keywords and phrases based on relevant SNS literature. The patrol unit optimizes its patrol algorithm by referring to relevant SNS literature. By improving the accuracy of patrols by referring to relevant SNS literature, it is possible to provide more accurate patrol results. Some or all of the above processing in the patrol unit may be performed using AI, for example, or not using AI. For example, the patrol unit can patrol information using an AI model that improves the accuracy of patrols by referring to relevant SNS literature.

[0097] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0098] The data collection unit can prioritize collecting relevant job postings based on the user's past search history. For example, the unit analyzes keywords the user has searched for in the past and prioritizes collecting relevant job postings. It can also prioritize collecting relevant job postings based on the content of job postings the user has viewed in the past. Furthermore, the unit can prioritize collecting job postings for specific industries or regions based on the user's past search history. This allows the system to select the most relevant job postings for each user by selecting them based on their past search history.

[0099] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit provides a display method that provides a sense of security. If the user is excited, the analysis unit provides a display method that attracts their interest. If the user is tired, the analysis unit provides a simple and easy-to-read display method. In this way, by adjusting the display method of the analysis results based on the user's emotions, the system can provide the optimal display method for the user.

[0100] The notification unit can estimate the user's emotions and adjust the content of notification messages based on those emotions. For example, if the user is feeling anxious, the notification unit will generate a reassuring notification message. If the user is excited, the notification unit will generate an engaging notification message. If the user is tired, the notification unit will generate a simple and short notification message. By adjusting the content of notification messages based on the user's emotions, the system can provide the most appropriate notification messages for the user.

[0101] The patrol unit can estimate the user's emotions and adjust its patrol range based on those emotions. For example, if the patrol unit is feeling anxious, it will patrol a wide area to provide reassurance. If the patrol unit is excited, it will focus its patrol on specific areas that might interest the user. If the patrol unit is tired, it will patrol a short, easy area. By adjusting the patrol range based on the user's emotions, it can provide the optimal patrol experience for the user.

[0102] The data collection unit can analyze a user's social media activity when collecting job postings and prioritize the collection of relevant job postings. For example, the unit can analyze a user's social media activity and prioritize the collection of job postings that are likely to interest them. The unit can also prioritize the collection of relevant job postings based on the content of posts from accounts the user follows. Furthermore, the unit can prioritize the collection of relevant job postings based on the user's social media search history. This allows the system to collect the most relevant job postings for each user by analyzing their social media activity and prioritizing the collection of relevant job postings.

[0103] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can select the most effective analysis algorithm based on past analysis data. Furthermore, the analysis unit can analyze past analysis data and optimize the analysis algorithm based on the frequency of occurrence of specific keywords or phrases. In addition, the analysis unit can optimize the analysis algorithm for specific sites or platforms by referring to past analysis data. This improves the accuracy of the analysis by optimizing the analysis algorithm based on past analysis data.

[0104] The notification unit can adjust the timing of notifications based on specific times of day or days of the week. For example, it can adjust the timing of notifications based on specific times of day (e.g., morning or evening). It can also adjust the timing of notifications based on specific days of the week (e.g., weekdays or weekends). Furthermore, it can adjust the timing of notifications based on specific events (e.g., payday or holidays). This allows for maximizing the effectiveness of notifications by adjusting the timing based on specific times of day or days of the week.

[0105] The patrol team can conduct patrols while considering the geographical distribution of social media. For example, the patrol team can prioritize patrolling posts from specific regions based on the geographical distribution of social media. Furthermore, the patrol team can analyze the geographical distribution of social media to understand trends in illegal part-time jobs in specific regions. In addition, the patrol team can optimize its patrol algorithms by considering the geographical distribution of social media. This allows them to understand trends in illegal part-time jobs in each region by conducting patrols while considering the geographical distribution of social media.

[0106] The data collection unit can filter job postings based on specific regions or industries. For example, it can filter job postings based on specific regions (e.g., urban or rural areas). It can also filter job postings based on specific industries (e.g., food service or retail). Furthermore, it can filter job postings based on specific time periods (e.g., nighttime or weekends). This allows for the efficient collection of necessary job postings by filtering based on specific regions and industries.

[0107] The analysis unit can estimate the user's emotions and adjust the analysis criteria based on those emotions. For example, if the user is feeling anxious, the analysis unit will perform the analysis using criteria that provide a sense of security. If the user is excited, the analysis unit will perform the analysis using criteria that attract interest. If the user is tired, the analysis unit will perform the analysis using criteria that are simple and quick. By adjusting the analysis criteria based on the user's emotions, the system can provide the user with the most optimal analysis results.

[0108] The following briefly describes the processing flow for example form 2.

[0109] Step 1: The collection unit collects information from job posting pages. The collection unit collects information from job posting pages using techniques such as web scraping. The collection unit can also obtain information from job posting pages using APIs. Furthermore, the collection unit can also collect information from bulletin boards and applications. For example, the collection unit periodically collects job posting information from specific websites. The collection unit can also obtain job posting information in real time using APIs. The collection unit collects information from bulletin boards and applications and centrally manages the job posting information. Step 2: The analysis unit analyzes the information collected by the collection unit to determine whether or not it is an advertisement for illegal part-time work. The analysis unit analyzes the job advertisement information using, for example, text analysis technology. The analysis unit can also use machine learning algorithms to determine whether or not it is an advertisement for illegal part-time work. Furthermore, the analysis unit can determine whether or not it is an advertisement for illegal part-time work based on specific keywords or phrases. For example, the analysis unit uses text analysis technology to detect keywords such as "high pay" or "instant cash" from the job advertisement information. The analysis unit uses machine learning algorithms to learn the characteristics of the job advertisement information and determine whether or not it is an advertisement for illegal part-time work. The analysis unit classifies the job advertisement information based on specific keywords or phrases and determines whether or not it is an advertisement for illegal part-time work. Step 3: The notification unit generates a warning message and notifies the site administrator if the analysis unit determines that the advertisement is for illegal part-time work. The notification unit generates the warning message using a template, for example. The notification unit can also automatically generate the warning message using a generation algorithm. Furthermore, the notification unit can notify the site administrator using email or text message. For example, the notification unit can quickly generate a warning message using a template. The notification unit can automatically generate a warning message using a generation algorithm and notify the site administrator. The notification unit can send the warning message to the site administrator using email or text message. Step 4: The patrol unit monitors social media and detects illegal job postings. The patrol unit can, for example, use keyword search technology to detect illegal job postings on social media. The patrol unit can also use image analysis technology to detect illegal job postings on social media. Furthermore, the patrol unit can send warning messages to detected illegal job postings. For example, the patrol unit uses keyword search technology to detect posts on social media containing keywords such as "high pay" or "instant cash." The patrol unit uses image analysis technology to detect illegal job postings on social media and sends warning messages to those posts. The patrol unit automatically generates and sends warning messages to detected illegal job postings.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, and patrol unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart device 14 and collects information from job posting pages using web scraping technology or APIs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines whether or not a job posting is for illegal work using text analysis technology or machine learning algorithms. The notification unit is implemented by the control unit 46A of the smart device 14 and generates a warning message and notifies the site administrator. The patrol unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects illegal job postings on social media and sends a warning message back. 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.

[0114] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, and patrol unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from job posting pages using web scraping technology or APIs. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and determines whether or not a job posting is for illegal work using text analysis technology or machine learning algorithms. The notification unit is implemented, for example, by the control unit 46A of the smart glasses 214 and generates a warning message and notifies the site administrator. The patrol unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and detects illegal job postings on social media and sends a warning message back. 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.

[0130] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, and patrol unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information from job posting pages using web scraping technology and APIs. The analysis unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines whether or not a job posting is for illegal work using text analysis technology and machine learning algorithms. The notification unit is implemented by the control unit 46A of the headset terminal 314 and generates a warning message and notifies the site administrator. The patrol unit is implemented by the identification processing unit 290 of the data processing unit 12 and detects illegal job postings on social media and sends a warning message back. 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.

[0146] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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).

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.).

[0159] 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.

[0160] 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.

[0161] 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.

[0162] Each of the multiple elements described above, including the collection unit, analysis unit, notification unit, and patrol unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the collection unit is implemented by the control unit 46A of the robot 414 and collects information from job posting pages using web scraping technology or APIs. The analysis unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and determines whether or not a job posting is for illegal work using text analysis technology or machine learning algorithms. The notification unit is implemented by, for example, the control unit 46A of the robot 414 and generates a warning message and notifies the site administrator. The patrol unit is implemented by, for example, the identification processing unit 290 of the data processing unit 12 and detects illegal job postings on social media and sends a warning message back. 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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."

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] (Note 1) A collection department that collects information from job posting pages, An analysis unit analyzes the information collected by the aforementioned collection unit and determines whether or not it is a recruitment for illegal part-time work, The aforementioned analysis unit generates a warning message and notifies the site administrator if it determines that the advertisement is for an illegal part-time job. It includes a patrol unit that monitors social media and detects illegal job postings. A system characterized by the following features. (Note 2) The aforementioned collection unit is The system monitors job posting pages 24 hours a day, 365 days a year, and reads the content of new postings whenever they appear. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Based on specific keywords or phrases, we determine whether or not a job posting is for an illegal or unlicensed part-time job. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned notification unit, If the advertisement is determined to be for illegal or illegal work, a warning message will be automatically generated and the site administrator will be notified. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned patrol unit, It detects posts soliciting illegal part-time jobs on social media and sends a warning message in response to those posts. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned collection unit is It estimates user sentiment and prioritizes the job postings to collect based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned collection unit is When collecting data from job posting pages, we optimize the collection method by referring to past data collections. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is When collecting job postings, filter them based on specific regions or industries. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is We estimate user sentiment and adjust the content of the job postings we collect based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is When collecting job postings, the system analyzes users' social media activity and prioritizes collecting relevant job postings. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is When collecting job postings, the system selects which pages to collect based on the user's past application history. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, We estimate the user's emotions and adjust the analysis criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, improve the accuracy of the analysis based on the frequency of occurrence of specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, It estimates the user's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, During the analysis, the geographical distribution of job posting pages will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, we refer to relevant literature on the job posting page to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned notification unit, It estimates the user's emotions and adjusts the content of notification messages based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned notification unit, When sending notifications, the system optimizes the notification method by referring to past notification data. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned notification unit, When sending notifications, adjust the timing of notifications based on specific time slots or days of the week. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned notification unit, It estimates the user's emotions and prioritizes notification messages based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned notification unit, When sending notifications, the system selects the most suitable notification method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned notification unit, When sending notifications, the content of the notifications will be customized by referring to the user's past response history. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned patrol unit, It estimates the user's emotions and adjusts the patrol range based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned patrol unit, During patrols, the patrol algorithm is optimized by referring to past patrol data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned patrol unit, During patrols, improve patrol accuracy based on the frequency of occurrence of specific keywords and phrases. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned patrol unit, It estimates the user's emotions and adjusts how patrol results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned patrol unit, When conducting patrols, consider the geographical distribution of social media users. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned patrol unit, During patrols, we refer to relevant literature on social media to improve the accuracy of our patrols. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0182] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A collection department that collects information from job posting pages, An analysis unit analyzes the information collected by the aforementioned collection unit and determines whether or not it is a recruitment for illegal part-time work, The aforementioned analysis unit generates a warning message and notifies the site administrator if it determines that the advertisement is for an illegal part-time job. It includes a patrol unit that monitors social media and detects illegal job postings. A system characterized by the following features.

2. The aforementioned collection unit is The system monitors job posting pages 24 hours a day, 365 days a year, and reads the content of new postings whenever they appear. The system according to feature 1.

3. The aforementioned analysis unit, Based on specific keywords or phrases, we determine whether or not a job posting is for an illegal or unlicensed part-time job. The system according to feature 1.

4. The aforementioned notification unit, If the advertisement is determined to be for illegal or illegal work, a warning message will be automatically generated and the site administrator will be notified. The system according to feature 1.

5. The aforementioned patrol unit, It detects posts soliciting illegal part-time jobs on social media and sends a warning message in response to those posts. The system according to feature 1.

6. The aforementioned collection unit is It estimates user sentiment and prioritizes the job postings to collect based on the estimated user sentiment. The system according to feature 1.

7. The aforementioned collection unit is When collecting data from job posting pages, we optimize the collection method by referring to past data collections. The system according to feature 1.

8. The aforementioned collection unit is When collecting job postings, filter them based on specific regions or industries. The system according to feature 1.

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