system

Abstract

The system according to this embodiment aims to monitor and block or warn users of illegal part-time job recruitment sites and social media posts. [Solution] The system according to the embodiment comprises a monitoring unit, a blocking unit, a warning unit, an adjustment unit, and an analysis unit. The monitoring unit monitors illegal job recruitment sites and SNS posts. The blocking unit blocks sites and posts detected by the monitoring unit. The warning unit warns the user based on the information blocked by the blocking unit. The adjustment unit allows the user to adjust the intensity. The analysis unit analyzes the information using a generation AI.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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 monitor and block recruitment sites for dark bytes and posts on SNS, and sufficient warnings have not been given.

[0005] The system according to the embodiment aims to monitor recruitment sites for dark bytes and posts on SNS, and perform blocking and warning.

Means for Solving the Problems

[0006] <C The system according to this embodiment comprises a monitoring unit, a blocking unit, a warning unit, an adjustment unit, and an analysis unit. The monitoring unit monitors illegal job recruitment sites and social media posts. The blocking unit blocks sites and posts detected by the monitoring unit. The warning unit issues warnings to users based on the information blocked by the blocking unit. The adjustment unit allows users to adjust the intensity of the warnings. The analysis unit analyzes the information using a generative AI. [Effects of the Invention]

[0007] The system according to this embodiment can monitor illegal job recruitment sites and social media posts, and block and warn users. [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 receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The system according to an embodiment of the present invention is a solution for preventing victims of illegal part-time jobs. This system can prevent victims of illegal part-time jobs by monitoring, blocking, and issuing warnings about illegal part-time job recruitment sites and SNS posts. Illegal part-time jobs are recruitment activities for criminals who are tricked into submitting personal information by promises of "high pay," "no illegality," "short hours," "easy," and "instant cash" through acquaintances or SNS, and are then used as disposable perpetrators or supporters of criminal activities by anonymous, mobile criminal groups (Tokuryu). Recruitment channels for illegal part-time jobs include solicitation by acquaintances, SNS, internet bulletin boards, and job sites. There is no end to the number of people who fall for these illegal part-time jobs, especially minors and young people. Criminal groups lure people in by saying "you won't get caught," but the perpetrators become "disposable" or "scapegoats." For example, the system monitors illegal part-time job recruitment sites and SNS posts. For example, the system uses a monitoring unit to monitor illegal part-time job recruitment sites and SNS posts. The monitoring unit may include AI processing. Next, the system blocks sites and posts detected by the monitoring unit. For example, the system uses a blocking unit to block sites and posts detected by the monitoring unit. The blocking unit may include AI processing. Next, the system alerts the user based on the information blocked by the blocking unit. For example, the system uses an alert unit to alert the user based on the information blocked by the blocking unit. The alert unit may include AI processing. Next, the system includes an adjustment unit where parents can adjust the intensity. For example, the system uses the adjustment unit to allow parents to adjust the intensity. The adjustment unit may include AI processing. Next, the system includes an analysis unit that uses generative AI to analyze information. For example, the system uses the analysis unit to analyze information using generative AI. The analysis unit includes generative AI processing. This allows the system to prevent victims of illegal part-time jobs.

[0029] The system according to this embodiment comprises a monitoring unit, a blocking unit, a warning unit, an adjustment unit, and an analysis unit. The monitoring unit monitors illegal job recruitment sites and SNS posts. The monitoring unit may include, for example, AI processing. The monitoring unit can perform monitoring based on, for example, specific keywords or phrases. The monitoring unit can, for example, intensify monitoring during specific time periods. The monitoring unit can, for example, adjust the intensity of monitoring for specific user groups. The blocking unit blocks sites and posts detected by the monitoring unit. The blocking unit may include, for example, AI processing. The blocking unit can perform blocking based on, for example, specific keywords or phrases. The blocking unit can, for example, intensify blocking during specific time periods. The blocking unit can, for example, adjust the intensity of blocking for specific user groups. The warning unit warns users based on information blocked by the blocking unit. The warning unit may include, for example, AI processing. The warning unit can, for example, issue warnings based on specific keywords or phrases. The warning unit can, for example, intensify warnings during specific time periods. The alerting unit can, for example, adjust the intensity of alerts for specific user groups. The adjustment unit allows parents to adjust the intensity. The adjustment unit may include, for example, AI processing. The adjustment unit can perform adjustments based on, for example, specific keywords or phrases. The adjustment unit can, for example, strengthen adjustments during specific time periods. The adjustment unit can, for example, adjust the intensity of adjustments for specific user groups. The analysis unit analyzes information using generative AI. The analysis unit includes, for example, generative AI processing. The analysis unit can perform analysis based on, for example, specific keywords or phrases. The analysis unit can, for example, strengthen analysis during specific time periods. The analysis unit can, for example, adjust the intensity of analysis for specific user groups. As a result, the system according to this embodiment can prevent damage from illegal part-time jobs.

[0030] The monitoring unit monitors job posting sites and social media posts related to illegal part-time jobs. The monitoring unit can, for example, incorporate AI processing. Specifically, the monitoring unit uses natural language processing (NLP) technology to analyze text data and detect keywords and phrases related to illegal part-time jobs. For example, it can automatically extract posts containing keywords such as "high pay," "short-term work," and "immediate payment." Furthermore, the AI ​​understands the context of the posts and can determine whether they are actually job postings for illegal part-time jobs, not just based on keyword matches. The monitoring unit can intensify monitoring during specific time periods. For example, by strengthening monitoring during times when job postings for illegal part-time jobs are most active, such as at night or on weekends, it can more effectively detect fraudulent postings. It can also adjust the intensity of monitoring for specific user groups. For example, it can conduct stricter monitoring of users who have previously posted job postings for illegal part-time jobs or who repeatedly engage in suspicious behavior. This allows the monitoring unit to detect job postings for illegal part-time jobs early and prevent harm before it occurs.

[0031] The blocking unit blocks sites and posts detected by the monitoring unit. The blocking unit may include, for example, AI processing. Specifically, the blocking unit blocks malicious sites and posts in real time based on information provided by the monitoring unit. The AI ​​can block based on specific keywords or phrases. For example, it can automatically block posts containing keywords such as "high pay," "short-term work," and "same-day payment." The AI ​​can also understand the context of the post and determine whether it is actually a recruitment for illegal work, not just a simple keyword match. The blocking unit can strengthen blocking during specific time periods. For example, strengthening blocking during times when recruitment for illegal work is active, such as at night or on weekends, can more effectively prevent fraudulent recruitment. It can also adjust the strength of blocking for specific user groups. For example, it can block users who have previously recruited for illegal work or who repeatedly engage in suspicious behavior. This allows the blocking unit to quickly and effectively prevent recruitment for illegal work and minimize damage.

[0032] The alerting unit alerts users based on information blocked by the blocking unit. The alerting unit may include, for example, AI processing. Specifically, the alerting unit analyzes the content of blocked posts and sites and generates appropriate warning messages for users. The AI ​​can issue alerts based on specific keywords or phrases. For example, for posts containing keywords such as "high pay," "short-term work," and "same-day payment," it can send a warning message to users such as, "This post may be related to illegal work. Please be careful." The alerting unit can strengthen alerts during specific time periods. For example, by strengthening alerts during times when illegal work recruitment is active, such as at night or on weekends, it can more effectively warn users. It can also adjust the intensity of alerts for specific user groups. For example, it can issue stricter alerts to users who have been victims of illegal work in the past or who repeatedly engage in suspicious behavior. This allows the alerting unit to quickly provide appropriate warnings to users and prevent them from becoming victims.

[0033] The adjustment unit allows parents to adjust the intensity. The adjustment unit may include, for example, AI processing. Specifically, the adjustment unit provides an interface for parents to adjust the intensity of system monitoring, blocking, and warnings. Parents can make adjustments based on specific keywords or phrases. For example, they can set the intensity of monitoring, blocking, and warnings for keywords such as "high pay," "short-term work," and "same-day payment." The adjustment unit can strengthen adjustments during specific time periods. For example, strengthening adjustments during times when illegal job postings are most active, such as at night or on weekends, can more effectively prevent fraudulent postings. It can also adjust the intensity of adjustments for specific user groups. For example, stricter adjustments can be made for users who have been victims of illegal jobs in the past or who repeatedly engage in suspicious behavior. This allows the adjustment unit to enable parents to flexibly adjust the intensity of system monitoring, blocking, and warnings, protecting their children from becoming victims of illegal jobs.

[0034] The analysis department uses generative AI to analyze information. This includes, for example, processing by the generative AI. Specifically, the analysis department uses generative AI to analyze data provided by the monitoring, blocking, and warning departments to identify trends and patterns in illegal part-time jobs. Generative AI can rapidly process large amounts of data and discover new trends and methods related to illegal part-time job postings. For example, based on past data, generative AI can analyze the frequency, time of day, and region of specific keywords and phrases to predict when and where illegal part-time job postings will increase. The analysis department can perform analysis based on specific keywords and phrases. For example, it can analyze posts containing keywords such as "high pay," "short-term work," and "same-day payment" to identify new trends in illegal part-time job postings. The analysis department can intensify its analysis during specific time periods. For example, by intensifying analysis during times when illegal part-time job postings are active, such as at night or on weekends, it can more effectively detect fraudulent postings. Furthermore, it can adjust the intensity of analysis for specific user groups. For example, it can perform more rigorous analysis on users who have previously posted illegal part-time job postings or who repeatedly engage in suspicious behavior. This allows the analysis department to quickly and accurately identify trends and patterns in illegal part-time jobs, thereby improving the overall effectiveness of the system.

[0035] The optimization unit uses generative AI to update and optimize information daily. For example, the optimization unit can update information daily using generative AI. For example, the optimization unit can optimize information using generative AI. For example, the optimization unit can adjust the frequency of information updates using generative AI. For example, the optimization unit can adjust the optimization algorithm using generative AI. As a result, by using generative AI to update and optimize information daily, the accuracy of the system improves.

[0036] The Information Acquisition Department obtains information to be analyzed by collaborating with multiple institutions. For example, the Information Acquisition Department can obtain information in collaboration with universities. For example, the Information Acquisition Department can obtain information in collaboration with research institutions. For example, the Information Acquisition Department can obtain information in collaboration with government agencies. For example, the Information Acquisition Department can obtain information through industry-academia-government collaboration. This allows for improved accuracy of analysis by obtaining information through industry-academia-government collaboration.

[0037] The monitoring unit can optimize its monitoring algorithm by referring to past monitoring data during monitoring. For example, the monitoring unit can analyze past monitoring data to identify frequently occurring keywords and phrases and reflect them in the monitoring algorithm. For example, the monitoring unit can find from past monitoring data a tendency for the number of illegal job postings to increase during specific time periods and strengthen monitoring during those times. For example, the monitoring unit can adjust the intensity of monitoring for specific user groups based on past monitoring data. In this way, the accuracy of the monitoring algorithm is improved by referring to past monitoring data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without using AI.

[0038] The monitoring unit can improve the accuracy of its monitoring based on specific keywords or phrases. For example, the monitoring unit can list keywords or phrases related to illegal part-time jobs and perform monitoring based on them. For example, the monitoring unit can prioritize monitoring posts containing specific keywords or phrases to improve accuracy. For example, the monitoring unit can analyze the frequency of occurrence of keywords or phrases to improve monitoring accuracy. In this way, monitoring based on specific keywords or phrases improves the accuracy of monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0039] The monitoring unit can prioritize monitoring highly relevant posts by considering the user's geographical location information during monitoring. For example, the monitoring unit can prioritize monitoring posts advertising illegal part-time jobs in nearby areas based on the user's geographical location information. For example, the monitoring unit can analyze trends in illegal part-time jobs in specific areas by considering the user's geographical location information. For example, the monitoring unit can monitor highly relevant posts in real time based on the user's geographical location information. This allows for priority monitoring of highly relevant posts by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0040] The monitoring unit can analyze a user's social media activity and monitor relevant posts during monitoring. For example, the monitoring unit can analyze a user's social media activity and identify posts related to illegal part-time jobs. For example, the monitoring unit can analyze a user's followers and accounts they follow and monitor relevant posts. For example, the monitoring unit can analyze a user's past posting history and prioritize monitoring relevant posts. This allows for efficient monitoring of relevant posts by analyzing a user's social media activity. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI.

[0041] The blocking unit can optimize its blocking algorithm by referring to past blocking data when blocking. For example, the blocking unit can analyze past blocking data to identify frequently occurring keywords and phrases and reflect them in the blocking algorithm. For example, the blocking unit can find a tendency for the number of illegal job postings to increase during specific time periods from past blocking data and strengthen blocking during those times. For example, the blocking unit can adjust the strength of blocking for specific user groups based on past blocking data. This improves the accuracy of the blocking algorithm by referring to past blocking data. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without using AI.

[0042] The blocking unit can improve the accuracy of blocking based on specific keywords or phrases. For example, the blocking unit can list keywords or phrases related to illegal part-time jobs and block based on them. For example, the blocking unit can prioritize blocking posts that contain specific keywords or phrases to improve accuracy. For example, the blocking unit can analyze the frequency of occurrence of keywords or phrases to improve blocking accuracy. As a result, blocking based on specific keywords or phrases improves the accuracy of blocking. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without using AI.

[0043] The blocking unit can prioritize blocking highly relevant posts by considering the user's geographical location information when blocking. For example, the blocking unit prioritizes blocking illegal job postings in nearby areas based on the user's geographical location information. For example, the blocking unit analyzes trends in illegal jobs in specific areas by considering the user's geographical location information. For example, the blocking unit blocks highly relevant posts in real time based on the user's geographical location information. This allows for the priority blocking of highly relevant posts by considering the user's geographical location information. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without using AI.

[0044] The blocking unit can analyze a user's social media activity and block relevant posts when blocking content. For example, the blocking unit can analyze a user's social media activity and identify posts related to illegal part-time jobs. For example, the blocking unit can analyze a user's followers and accounts they follow and block relevant posts. For example, the blocking unit can analyze a user's past posting history and prioritize blocking relevant posts. This allows for efficient blocking of relevant posts by analyzing a user's social media activity. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI.

[0045] The alerting unit can optimize its alerting algorithm by referring to past alerting data when issuing an alert. For example, the alerting unit analyzes past alerting data to identify frequently occurring keywords and phrases and reflects them in the alerting algorithm. For example, the alerting unit can find a tendency for the number of illegal job postings to increase during specific time periods from past alerting data and strengthen alerts during those times. For example, the alerting unit can adjust the intensity of alerts for specific user groups based on past alerting data. This improves the accuracy of the alerting algorithm by referring to past alerting data. Some or all of the above processing in the alerting unit may be performed using AI, for example, or without using AI.

[0046] The alerting unit can improve the accuracy of alerts based on specific keywords or phrases when issuing alerts. For example, the alerting unit can list keywords or phrases related to illegal part-time jobs and issue alerts based on them. For example, the alerting unit can prioritize alerting posts containing specific keywords or phrases to improve accuracy. For example, the alerting unit can analyze the frequency of occurrence of keywords or phrases to improve the accuracy of alerts. As a result, the accuracy of alerts is improved by issuing alerts based on specific keywords or phrases. Some or all of the above processing in the alerting unit may be performed using AI, for example, or without using AI.

[0047] The alerting unit can prioritize highly relevant alerts by considering the user's geographical location information when issuing alerts. For example, the alerting unit can issue alerts for illegal job postings in nearby areas based on the user's geographical location information. For example, the alerting unit can analyze trends in illegal jobs in specific areas by considering the user's geographical location information. For example, the alerting unit can issue highly relevant alerts in real time based on the user's geographical location information. This allows for prioritization of highly relevant alerts by considering the user's geographical location information. Some or all of the above processing in the alerting unit may be performed using AI, for example, or without using AI.

[0048] The alerting unit can analyze the user's social media activity and issue relevant alerts when issuing alerts. For example, the alerting unit can analyze the user's social media activity and issue alerts for posts related to illegal part-time jobs. For example, the alerting unit can analyze the user's followers and accounts they follow and issue relevant alerts. For example, the alerting unit can analyze the user's past posting history and prioritize issuing relevant alerts. This allows for efficient distribution of relevant alerts by analyzing the user's social media activity. Some or all of the above processing in the alerting unit may be performed using AI, for example, or without using AI.

[0049] The adjustment unit can optimize the adjustment algorithm by referring to past adjustment data during the adjustment process. For example, the adjustment unit can analyze past adjustment data to identify frequently occurring keywords and phrases and reflect them in the adjustment algorithm. For example, the adjustment unit can find from past adjustment data a tendency for the number of illegal job postings to increase during specific time periods and strengthen the adjustments during those times. For example, the adjustment unit can adjust the intensity of the adjustments for specific user groups based on past adjustment data. This improves the accuracy of the adjustment algorithm by referring to past adjustment data. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without using AI.

[0050] The adjustment unit can improve the accuracy of adjustments based on specific keywords or phrases during the adjustment process. For example, the adjustment unit can list keywords or phrases related to illegal part-time jobs and perform adjustments based on them. For example, the adjustment unit can prioritize adjusting posts that contain specific keywords or phrases to improve accuracy. For example, the adjustment unit can analyze the frequency of occurrence of keywords or phrases to improve the accuracy of adjustments. As a result, the accuracy of adjustments is improved by adjusting based on specific keywords or phrases. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI.

[0051] The adjustment unit can prioritize highly relevant adjustments by considering the user's geographical location information during the adjustment process. For example, the adjustment unit can adjust job postings for illegal part-time work in nearby areas based on the user's geographical location information. For example, the adjustment unit can analyze trends in illegal part-time work in specific areas by considering the user's geographical location information. For example, the adjustment unit can perform highly relevant adjustments in real time based on the user's geographical location information. This allows for prioritizing highly relevant adjustments by considering the user's geographical location information. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI.

[0052] The adjustment unit can analyze the user's social media activity and make relevant adjustments during the adjustment process. For example, the adjustment unit can analyze the user's social media activity and make adjustments to posts related to illegal part-time jobs. For example, the adjustment unit can analyze the user's followers and accounts they follow and make relevant adjustments. For example, the adjustment unit can analyze the user's past posting history and prioritize making relevant adjustments. This allows for efficient adjustment of relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI.

[0053] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can analyze past analysis data to identify frequently occurring keywords and phrases and reflect them in the analysis algorithm. For example, the analysis unit can find a tendency for the number of illegal job postings to increase during specific time periods from past analysis data and intensify the analysis during those times. For example, the analysis unit can adjust the intensity of the analysis for specific user groups based on past analysis data. In this way, the accuracy of the analysis algorithm is improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI.

[0054] The analysis unit can improve the accuracy of its analysis based on specific keywords or phrases. For example, the analysis unit can list keywords or phrases related to illegal part-time jobs and perform analysis based on them. For example, the analysis unit can prioritize the analysis of posts containing specific keywords or phrases to improve accuracy. For example, the analysis unit can analyze the frequency of occurrence of keywords or phrases to improve the accuracy of its analysis. In this way, the accuracy of the analysis is improved by analyzing based on specific keywords or phrases. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0055] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location information during analysis. For example, the analysis unit can prioritize the analysis of illegal job postings in nearby areas based on the user's geographical location information. For example, the analysis unit can analyze trends in illegal jobs in specific areas by considering the user's geographical location information. For example, the analysis unit can analyze highly relevant data in real time based on the user's geographical location information. This allows for the priority analysis of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without using generative AI.

[0056] The analysis unit can analyze a user's social media activity and analyze related data during the analysis process. For example, the analysis unit can analyze a user's social media activity and identify posts related to illegal part-time jobs. For example, the analysis unit can analyze a user's followers and the accounts they follow and analyze related data. For example, the analysis unit can analyze a user's past posting history and prioritize the analysis of related data. This allows for efficient analysis of related data by analyzing a user's social media activity. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI.

[0057] The optimization unit can optimize the optimization algorithm by referring to past optimization data during the optimization process. For example, the optimization unit can analyze past optimization data to identify frequently occurring keywords and phrases and reflect them in the optimization algorithm. For example, the optimization unit can find a tendency for the number of illegal part-time job postings to increase during specific time periods from past optimization data and strengthen the optimization during those times. For example, the optimization unit can adjust the intensity of optimization for specific user groups based on past optimization data. This improves the accuracy of the optimization algorithm by referring to past optimization data. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI.

[0058] The optimization unit can improve the accuracy of optimization based on specific keywords or phrases during the optimization process. For example, the optimization unit can list keywords or phrases related to illegal part-time jobs and perform optimization based on them. For example, the optimization unit can prioritize the optimization of posts containing specific keywords or phrases to improve accuracy. For example, the optimization unit can analyze the frequency of occurrence of keywords and phrases to improve optimization accuracy. As a result, the accuracy of optimization is improved by optimizing based on specific keywords or phrases. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI.

[0059] The optimization unit can prioritize optimizing highly relevant data by considering the user's geographical location information during optimization. For example, the optimization unit prioritizes optimizing job postings for illegal part-time work in nearby areas based on the user's geographical location information. For example, the optimization unit analyzes trends in illegal part-time work in specific areas by considering the user's geographical location information. For example, the optimization unit optimizes highly relevant data in real time based on the user's geographical location information. This allows for the prioritization of highly relevant data by considering the user's geographical location information. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI.

[0060] The optimization unit can analyze the user's social media activity and optimize the relevant data during the optimization process. For example, the optimization unit can analyze the user's social media activity and identify posts related to illegal part-time jobs. For example, the optimization unit can analyze the user's followers and followed accounts and optimize the relevant data. For example, the optimization unit can analyze the user's past posting history and prioritize the optimization of relevant data. This allows for efficient optimization of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI.

[0061] The information acquisition unit can optimize its information acquisition algorithm by referring to past information acquisition data when acquiring information. For example, the information acquisition unit can analyze past information acquisition data to identify frequently occurring keywords and phrases and reflect them in the information acquisition algorithm. For example, the information acquisition unit can find a tendency for the number of illegal part-time job postings to increase during specific time periods from past information acquisition data and strengthen information acquisition during those times. For example, the information acquisition unit can adjust the intensity of information acquisition for specific user groups based on past information acquisition data. In this way, the accuracy of the information acquisition algorithm is improved by referring to past information acquisition data. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without using AI.

[0062] The information acquisition unit can improve the accuracy of information acquisition based on specific keywords or phrases. For example, the information acquisition unit can list keywords or phrases related to illegal part-time jobs and acquire information based on them. For example, the information acquisition unit can prioritize acquiring information from posts containing specific keywords or phrases to improve accuracy. For example, the information acquisition unit can analyze the frequency of occurrence of keywords or phrases to improve the accuracy of information acquisition. As a result, the accuracy of information acquisition is improved by acquiring information based on specific keywords or phrases. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without using AI.

[0063] The information acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location information when acquiring information. For example, the information acquisition unit prioritizes the acquisition of job postings for illegal part-time work in nearby areas based on the user's geographical location information. For example, the information acquisition unit analyzes trends in illegal part-time work in specific areas by considering the user's geographical location information. For example, the information acquisition unit acquires highly relevant information in real time based on the user's geographical location information. This identifies relevant posts by considering the user's geographical location information. For example, the information acquisition unit analyzes the user's followers and followed accounts to acquire relevant information. For example, the information acquisition unit analyzes the user's past posting history to prioritize the acquisition of relevant information. This allows for the efficient acquisition of relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the information acquisition unit may be performed using AI, for example, or without using AI.

[0064] The information acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring information. For example, the information acquisition unit can analyze the user's social media activity and identify posts related to illegal part-time jobs. For example, the information acquisition unit can analyze the user's followers and accounts that the user follows and obtain relevant information. For example, the information acquisition unit can analyze the user's past posting history and prioritize the acquisition of relevant information. In this way, relevant information can be efficiently acquired by analyzing the user's social media activity. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without using AI.

[0065] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0066] The monitoring unit can optimize its monitoring algorithm by referring to past monitoring data during monitoring. For example, it can analyze past monitoring data to identify frequently occurring keywords and phrases and reflect them in the monitoring algorithm. From past monitoring data, it can identify a tendency for the number of illegal job postings to increase during specific time periods and strengthen monitoring during those times. Based on past monitoring data, it can adjust the intensity of monitoring for specific user groups. In this way, the accuracy of the monitoring algorithm is improved by referring to past monitoring data.

[0067] The monitoring unit can improve the accuracy of its monitoring based on specific keywords and phrases. For example, it can list keywords and phrases related to illegal part-time jobs and perform monitoring based on that list. It can prioritize monitoring posts containing specific keywords and phrases to improve accuracy. It can also analyze the frequency of keyword and phrase occurrences to improve monitoring accuracy. In this way, monitoring based on specific keywords and phrases improves the accuracy of monitoring.

[0068] The monitoring unit can prioritize monitoring highly relevant posts by considering the user's geographical location during monitoring. For example, it can prioritize monitoring of illegal job postings in nearby areas based on the user's geographical location. It can also analyze trends in illegal jobs in specific areas by considering the user's geographical location. It can monitor highly relevant posts in real time based on the user's geographical location. In this way, by considering the user's geographical location, it can prioritize monitoring of highly relevant posts.

[0069] The blocking section can optimize the blocking algorithm by referring to past blocking data during the blocking process. For example, it can analyze past blocking data to identify frequently occurring keywords and phrases and reflect them in the blocking algorithm. By analyzing past blocking data, it can identify trends in the increase of illegal job postings during specific time periods and strengthen blocking during those times. Based on past blocking data, it can adjust the strength of blocking for specific user groups. In this way, the accuracy of the blocking algorithm is improved by referring to past blocking data.

[0070] The blocking function can improve the accuracy of blocking based on specific keywords or phrases. For example, it can list keywords and phrases related to illegal part-time jobs and block based on that list. It can prioritize blocking posts containing specific keywords or phrases to improve accuracy. It can also analyze the frequency of keyword and phrase occurrences to improve blocking accuracy. As a result, blocking based on specific keywords or phrases improves the accuracy of blocking.

[0071] The blocking function can prioritize blocking highly relevant posts by considering the user's geographical location. For example, it can prioritize blocking illegal job postings in nearby areas based on the user's geographical location. It can also analyze trends in illegal jobs in specific areas by considering the user's geographical location. It can block highly relevant posts in real time based on the user's geographical location. This allows for the priority blocking of highly relevant posts by considering the user's geographical location.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The monitoring unit monitors job posting sites and social media posts related to illegal part-time work. The monitoring unit can perform monitoring based on specific keywords and phrases, and can intensify monitoring during specific time periods. It can also adjust the intensity of monitoring for specific user groups. Step 2: The blocking unit blocks sites and posts detected by the monitoring unit. The blocking unit can block based on specific keywords or phrases and can strengthen blocking during specific time periods. It can also adjust the strength of blocking for specific user groups. Step 3: The alerting unit alerts the user based on the information blocked by the blocking unit. The alerting unit can alert based on specific keywords or phrases and can intensify alerts during specific time periods. It can also adjust the alert intensity for specific user groups. Step 4: The adjustment unit allows the user to adjust the intensity. The adjustment unit can make adjustments based on specific keywords or phrases and can strengthen the adjustments during specific time periods. It can also adjust the intensity of the adjustments for specific user groups. Step 5: The analysis unit uses generative AI to analyze the information. The analysis unit performs analysis based on specific keywords or phrases and can intensify the analysis during specific time periods. It can also adjust the intensity of the analysis for specific user groups.

[0074] (Example of form 2) The system according to an embodiment of the present invention is a solution for preventing victims of illegal part-time jobs. This system can prevent victims of illegal part-time jobs by monitoring, blocking, and issuing warnings about illegal part-time job recruitment sites and SNS posts. Illegal part-time jobs are recruitment activities for criminals who are tricked into submitting personal information by promises of "high pay," "no illegality," "short hours," "easy," and "instant cash" through acquaintances or SNS, and are then used as disposable perpetrators or supporters of criminal activities by anonymous, mobile criminal groups (Tokuryu). Recruitment channels for illegal part-time jobs include solicitation by acquaintances, SNS, internet bulletin boards, and job sites. There is no end to the number of people who fall for these illegal part-time jobs, especially minors and young people. Criminal groups lure people in by saying "you won't get caught," but the perpetrators become "disposable" or "scapegoats." For example, the system monitors illegal part-time job recruitment sites and SNS posts. For example, the system uses a monitoring unit to monitor illegal part-time job recruitment sites and SNS posts. The monitoring unit may include AI processing. Next, the system blocks sites and posts detected by the monitoring unit. For example, the system uses a blocking unit to block sites and posts detected by the monitoring unit. The blocking unit may include AI processing. Next, the system alerts the user based on the information blocked by the blocking unit. For example, the system uses an alert unit to alert the user based on the information blocked by the blocking unit. The alert unit may include AI processing. Next, the system includes an adjustment unit where parents can adjust the intensity. For example, the system uses the adjustment unit to allow parents to adjust the intensity. The adjustment unit may include AI processing. Next, the system includes an analysis unit that uses generative AI to analyze information. For example, the system uses the analysis unit to analyze information using generative AI. The analysis unit includes generative AI processing. This allows the system to prevent victims of illegal part-time jobs.

[0075] The system according to this embodiment comprises a monitoring unit, a blocking unit, a warning unit, an adjustment unit, and an analysis unit. The monitoring unit monitors illegal job recruitment sites and SNS posts. The monitoring unit may include, for example, AI processing. The monitoring unit can perform monitoring based on, for example, specific keywords or phrases. The monitoring unit can, for example, intensify monitoring during specific time periods. The monitoring unit can, for example, adjust the intensity of monitoring for specific user groups. The blocking unit blocks sites and posts detected by the monitoring unit. The blocking unit may include, for example, AI processing. The blocking unit can perform blocking based on, for example, specific keywords or phrases. The blocking unit can, for example, intensify blocking during specific time periods. The blocking unit can, for example, adjust the intensity of blocking for specific user groups. The warning unit warns users based on information blocked by the blocking unit. The warning unit may include, for example, AI processing. The warning unit can, for example, issue warnings based on specific keywords or phrases. The warning unit can, for example, intensify warnings during specific time periods. The alerting unit can, for example, adjust the intensity of alerts for specific user groups. The adjustment unit allows parents to adjust the intensity. The adjustment unit may include, for example, AI processing. The adjustment unit can perform adjustments based on, for example, specific keywords or phrases. The adjustment unit can, for example, strengthen adjustments during specific time periods. The adjustment unit can, for example, adjust the intensity of adjustments for specific user groups. The analysis unit analyzes information using generative AI. The analysis unit includes, for example, generative AI processing. The analysis unit can perform analysis based on, for example, specific keywords or phrases. The analysis unit can, for example, strengthen analysis during specific time periods. The analysis unit can, for example, adjust the intensity of analysis for specific user groups. As a result, the system according to this embodiment can prevent damage from illegal part-time jobs.

[0076] The monitoring unit monitors job posting sites and social media posts related to illegal part-time jobs. The monitoring unit can, for example, incorporate AI processing. Specifically, the monitoring unit uses natural language processing (NLP) technology to analyze text data and detect keywords and phrases related to illegal part-time jobs. For example, it can automatically extract posts containing keywords such as "high pay," "short-term work," and "immediate payment." Furthermore, the AI ​​understands the context of the posts and can determine whether they are actually job postings for illegal part-time jobs, not just based on keyword matches. The monitoring unit can intensify monitoring during specific time periods. For example, by strengthening monitoring during times when job postings for illegal part-time jobs are most active, such as at night or on weekends, it can more effectively detect fraudulent postings. It can also adjust the intensity of monitoring for specific user groups. For example, it can conduct stricter monitoring of users who have previously posted job postings for illegal part-time jobs or who repeatedly engage in suspicious behavior. This allows the monitoring unit to detect job postings for illegal part-time jobs early and prevent harm before it occurs.

[0077] The blocking unit blocks sites and posts detected by the monitoring unit. The blocking unit may include, for example, AI processing. Specifically, the blocking unit blocks malicious sites and posts in real time based on information provided by the monitoring unit. The AI ​​can block based on specific keywords or phrases. For example, it can automatically block posts containing keywords such as "high pay," "short-term work," and "same-day payment." The AI ​​can also understand the context of the post and determine whether it is actually a recruitment for illegal work, not just a simple keyword match. The blocking unit can strengthen blocking during specific time periods. For example, strengthening blocking during times when recruitment for illegal work is active, such as at night or on weekends, can more effectively prevent fraudulent recruitment. It can also adjust the strength of blocking for specific user groups. For example, it can block users who have previously recruited for illegal work or who repeatedly engage in suspicious behavior. This allows the blocking unit to quickly and effectively prevent recruitment for illegal work and minimize damage.

[0078] The alerting unit alerts users based on information blocked by the blocking unit. The alerting unit may include, for example, AI processing. Specifically, the alerting unit analyzes the content of blocked posts and sites and generates appropriate warning messages for users. The AI ​​can issue alerts based on specific keywords or phrases. For example, for posts containing keywords such as "high pay," "short-term work," and "same-day payment," it can send a warning message to users such as, "This post may be related to illegal work. Please be careful." The alerting unit can strengthen alerts during specific time periods. For example, by strengthening alerts during times when illegal work recruitment is active, such as at night or on weekends, it can more effectively warn users. It can also adjust the intensity of alerts for specific user groups. For example, it can issue stricter alerts to users who have been victims of illegal work in the past or who repeatedly engage in suspicious behavior. This allows the alerting unit to quickly provide appropriate warnings to users and prevent them from becoming victims.

[0079] The adjustment unit allows parents to adjust the intensity. The adjustment unit may include, for example, AI processing. Specifically, the adjustment unit provides an interface for parents to adjust the intensity of system monitoring, blocking, and warnings. Parents can make adjustments based on specific keywords or phrases. For example, they can set the intensity of monitoring, blocking, and warnings for keywords such as "high pay," "short-term work," and "same-day payment." The adjustment unit can strengthen adjustments during specific time periods. For example, strengthening adjustments during times when illegal job postings are most active, such as at night or on weekends, can more effectively prevent fraudulent postings. It can also adjust the intensity of adjustments for specific user groups. For example, stricter adjustments can be made for users who have been victims of illegal jobs in the past or who repeatedly engage in suspicious behavior. This allows the adjustment unit to enable parents to flexibly adjust the intensity of system monitoring, blocking, and warnings, protecting their children from becoming victims of illegal jobs.

[0080] The analysis department uses generative AI to analyze information. This includes, for example, processing by the generative AI. Specifically, the analysis department uses generative AI to analyze data provided by the monitoring, blocking, and warning departments to identify trends and patterns in illegal part-time jobs. Generative AI can rapidly process large amounts of data and discover new trends and methods related to illegal part-time job postings. For example, based on past data, generative AI can analyze the frequency, time of day, and region of specific keywords and phrases to predict when and where illegal part-time job postings will increase. The analysis department can perform analysis based on specific keywords and phrases. For example, it can analyze posts containing keywords such as "high pay," "short-term work," and "same-day payment" to identify new trends in illegal part-time job postings. The analysis department can intensify its analysis during specific time periods. For example, by intensifying analysis during times when illegal part-time job postings are active, such as at night or on weekends, it can more effectively detect fraudulent postings. Furthermore, it can adjust the intensity of analysis for specific user groups. For example, it can perform more rigorous analysis on users who have previously posted illegal part-time job postings or who repeatedly engage in suspicious behavior. This allows the analysis department to quickly and accurately identify trends and patterns in illegal part-time jobs, thereby improving the overall effectiveness of the system.

[0081] The optimization unit uses generative AI to update and optimize information daily. For example, the optimization unit can update information daily using generative AI. For example, the optimization unit can optimize information using generative AI. For example, the optimization unit can adjust the frequency of information updates using generative AI. For example, the optimization unit can adjust the optimization algorithm using generative AI. As a result, by using generative AI to update and optimize information daily, the accuracy of the system improves.

[0082] The Information Acquisition Department obtains information to be analyzed by collaborating with multiple institutions. For example, the Information Acquisition Department can obtain information in collaboration with universities. For example, the Information Acquisition Department can obtain information in collaboration with research institutions. For example, the Information Acquisition Department can obtain information in collaboration with government agencies. For example, the Information Acquisition Department can obtain information through industry-academia-government collaboration. This allows for improved accuracy of analysis by obtaining information through industry-academia-government collaboration.

[0083] The monitoring unit can estimate the user's emotions and adjust the monitoring intensity based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit will increase the monitoring intensity and perform more detailed monitoring. For example, if the user is relaxed, the monitoring unit will decrease the monitoring intensity and perform normal monitoring. For example, if the user is excited, the monitoring unit will adjust the monitoring intensity to a moderate level and perform appropriate monitoring. This allows for more appropriate monitoring by adjusting the monitoring intensity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0084] The monitoring unit can optimize its monitoring algorithm by referring to past monitoring data during monitoring. For example, the monitoring unit can analyze past monitoring data to identify frequently occurring keywords and phrases and reflect them in the monitoring algorithm. For example, the monitoring unit can find from past monitoring data a tendency for the number of illegal job postings to increase during specific time periods and strengthen monitoring during those times. For example, the monitoring unit can adjust the intensity of monitoring for specific user groups based on past monitoring data. In this way, the accuracy of the monitoring algorithm is improved by referring to past monitoring data. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without using AI.

[0085] The monitoring unit can improve the accuracy of its monitoring based on specific keywords or phrases. For example, the monitoring unit can list keywords or phrases related to illegal part-time jobs and perform monitoring based on them. For example, the monitoring unit can prioritize monitoring posts containing specific keywords or phrases to improve accuracy. For example, the monitoring unit can analyze the frequency of occurrence of keywords or phrases to improve monitoring accuracy. In this way, monitoring based on specific keywords or phrases improves the accuracy of monitoring. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0086] The monitoring unit can estimate the user's emotions and adjust how the monitoring results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the monitoring unit may display detailed monitoring results to provide reassurance. For example, if the user is relaxed, the monitoring unit may display concise monitoring results to avoid providing excessive information. For example, if the user is excited, the monitoring unit may display visually easy-to-understand monitoring results to aid in understanding the information. This allows for more appropriate information to be provided by adjusting how the monitoring results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0087] The monitoring unit can prioritize monitoring highly relevant posts by considering the user's geographical location information during monitoring. For example, the monitoring unit can prioritize monitoring posts advertising illegal part-time jobs in nearby areas based on the user's geographical location information. For example, the monitoring unit can analyze trends in illegal part-time jobs in specific areas by considering the user's geographical location information. For example, the monitoring unit can monitor highly relevant posts in real time based on the user's geographical location information. This allows for priority monitoring of highly relevant posts by considering the user's geographical location information. Some or all of the above processing in the monitoring unit may be performed using AI, for example, or without using AI.

[0088] The monitoring unit can analyze a user's social media activity and monitor relevant posts during monitoring. For example, the monitoring unit can analyze a user's social media activity and identify posts related to illegal part-time jobs. For example, the monitoring unit can analyze a user's followers and accounts they follow and monitor relevant posts. For example, the monitoring unit can analyze a user's past posting history and prioritize monitoring relevant posts. This allows for efficient monitoring of relevant posts by analyzing a user's social media activity. Some or all of the above processes in the monitoring unit may be performed using AI, for example, or without AI.

[0089] The blocking function can estimate the user's emotions and adjust the blocking intensity based on the estimated emotions. For example, if the user is feeling anxious, the blocking function will increase the blocking intensity and block more posts. For example, if the user is relaxed, the blocking function will decrease the blocking intensity and perform normal blocking. For example, if the user is excited, the blocking function will adjust the blocking intensity to a moderate level and perform appropriate blocking. This allows for more appropriate blocking by adjusting the blocking intensity according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0090] The blocking unit can optimize its blocking algorithm by referring to past blocking data when blocking. For example, the blocking unit can analyze past blocking data to identify frequently occurring keywords and phrases and reflect them in the blocking algorithm. For example, the blocking unit can find a tendency for the number of illegal job postings to increase during specific time periods from past blocking data and strengthen blocking during those times. For example, the blocking unit can adjust the strength of blocking for specific user groups based on past blocking data. This improves the accuracy of the blocking algorithm by referring to past blocking data. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without using AI.

[0091] The blocking unit can improve the accuracy of blocking based on specific keywords or phrases. For example, the blocking unit can list keywords or phrases related to illegal part-time jobs and block based on them. For example, the blocking unit can prioritize blocking posts that contain specific keywords or phrases to improve accuracy. For example, the blocking unit can analyze the frequency of occurrence of keywords or phrases to improve blocking accuracy. As a result, blocking based on specific keywords or phrases improves the accuracy of blocking. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without using AI.

[0092] The blocking function can estimate the user's emotions and adjust how the blocking results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the blocking function may display detailed blocking results to provide reassurance. If the user is relaxed, for example, the blocking function may display concise blocking results to avoid providing excessive information. If the user is excited, for example, the blocking function may display visually easy-to-understand blocking results to aid in understanding the information. This allows for more appropriate information to be provided by adjusting how the blocking results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may include, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0093] The blocking unit can prioritize blocking highly relevant posts by considering the user's geographical location information when blocking. For example, the blocking unit prioritizes blocking illegal job postings in nearby areas based on the user's geographical location information. For example, the blocking unit analyzes trends in illegal jobs in specific areas by considering the user's geographical location information. For example, the blocking unit blocks highly relevant posts in real time based on the user's geographical location information. This allows for the priority blocking of highly relevant posts by considering the user's geographical location information. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without using AI.

[0094] The blocking unit can analyze a user's social media activity and block relevant posts when blocking content. For example, the blocking unit can analyze a user's social media activity and identify posts related to illegal part-time jobs. For example, the blocking unit can analyze a user's followers and accounts they follow and block relevant posts. For example, the blocking unit can analyze a user's past posting history and prioritize blocking relevant posts. This allows for efficient blocking of relevant posts by analyzing a user's social media activity. Some or all of the above processing in the blocking unit may be performed using AI, for example, or without AI.

[0095] The alerting unit can estimate the user's emotions and adjust the alerting method based on the estimated emotions. For example, if the user is feeling anxious, the alerting unit will provide a detailed alert to reassure them. For example, if the user is relaxed, the alerting unit will provide a concise alert, avoiding excessive information. For example, if the user is excited, the alerting unit will provide a visually easy-to-understand alert to help them understand the information. This allows for more appropriate alerting by adjusting the alerting method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0096] The alerting unit can optimize its alerting algorithm by referring to past alerting data when issuing an alert. For example, the alerting unit analyzes past alerting data to identify frequently occurring keywords and phrases and reflects them in the alerting algorithm. For example, the alerting unit can find a tendency for the number of illegal job postings to increase during specific time periods from past alerting data and strengthen alerts during those times. For example, the alerting unit can adjust the intensity of alerts for specific user groups based on past alerting data. This improves the accuracy of the alerting algorithm by referring to past alerting data. Some or all of the above processing in the alerting unit may be performed using AI, for example, or without using AI.

[0097] The alerting unit can improve the accuracy of alerts based on specific keywords or phrases when issuing alerts. For example, the alerting unit can list keywords or phrases related to illegal part-time jobs and issue alerts based on them. For example, the alerting unit can prioritize alerting posts containing specific keywords or phrases to improve accuracy. For example, the alerting unit can analyze the frequency of occurrence of keywords or phrases to improve the accuracy of alerts. As a result, the accuracy of alerts is improved by issuing alerts based on specific keywords or phrases. Some or all of the above processing in the alerting unit may be performed using AI, for example, or without using AI.

[0098] The alert unit can estimate the user's emotions and adjust the way alerts are displayed based on the estimated emotions. For example, if the user is feeling anxious, the alert unit will display a detailed alert to provide reassurance. For example, if the user is relaxed, the alert unit will display a concise alert to avoid providing excessive information. For example, if the user is excited, the alert unit will display a visually easy-to-understand alert to help the user understand the information. This allows for more appropriate information to be provided by adjusting the way alerts are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0099] The alerting unit can prioritize highly relevant alerts by considering the user's geographical location information when issuing alerts. For example, the alerting unit can issue alerts for illegal job postings in nearby areas based on the user's geographical location information. For example, the alerting unit can analyze trends in illegal jobs in specific areas by considering the user's geographical location information. For example, the alerting unit can issue highly relevant alerts in real time based on the user's geographical location information. This allows for prioritization of highly relevant alerts by considering the user's geographical location information. Some or all of the above processing in the alerting unit may be performed using AI, for example, or without using AI.

[0100] The alerting unit can analyze the user's social media activity and issue relevant alerts when issuing alerts. For example, the alerting unit can analyze the user's social media activity and issue alerts for posts related to illegal part-time jobs. For example, the alerting unit can analyze the user's followers and accounts they follow and issue relevant alerts. For example, the alerting unit can analyze the user's past posting history and prioritize issuing relevant alerts. This allows for efficient distribution of relevant alerts by analyzing the user's social media activity. Some or all of the above processing in the alerting unit may be performed using AI, for example, or without using AI.

[0101] The adjustment unit can estimate the user's emotions and modify the adjustment method based on the estimated emotions. For example, if the user is feeling anxious, the adjustment unit can provide detailed adjustment methods to reassure them. For example, if the user is relaxed, the adjustment unit can provide concise adjustment methods to avoid providing excessive information. For example, if the user is excited, the adjustment unit can provide visually easy-to-understand adjustment methods to aid in understanding the information. This allows for more appropriate adjustments by changing the adjustment method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0102] The adjustment unit can optimize the adjustment algorithm by referring to past adjustment data during the adjustment process. For example, the adjustment unit can analyze past adjustment data to identify frequently occurring keywords and phrases and reflect them in the adjustment algorithm. For example, the adjustment unit can find from past adjustment data a tendency for the number of illegal job postings to increase during specific time periods and strengthen the adjustments during those times. For example, the adjustment unit can adjust the intensity of the adjustments for specific user groups based on past adjustment data. This improves the accuracy of the adjustment algorithm by referring to past adjustment data. Some or all of the above processes in the adjustment unit may be performed using AI, for example, or without using AI.

[0103] The adjustment unit can improve the accuracy of adjustments based on specific keywords or phrases during the adjustment process. For example, the adjustment unit can list keywords or phrases related to illegal part-time jobs and perform adjustments based on them. For example, the adjustment unit can prioritize adjusting posts that contain specific keywords or phrases to improve accuracy. For example, the adjustment unit can analyze the frequency of occurrence of keywords or phrases to improve the accuracy of adjustments. As a result, the accuracy of adjustments is improved by adjusting based on specific keywords or phrases. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI.

[0104] The adjustment unit can estimate the user's emotions and change how the adjustment results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the adjustment unit will display detailed adjustment results to provide reassurance. For example, if the user is relaxed, the adjustment unit will display concise adjustment results to avoid providing excessive information. For example, if the user is excited, the adjustment unit will display visually easy-to-understand adjustment results to aid in understanding the information. This allows for more appropriate information to be provided by changing how the adjustment results are displayed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0105] The adjustment unit can prioritize highly relevant adjustments by considering the user's geographical location information during the adjustment process. For example, the adjustment unit can adjust job postings for illegal part-time work in nearby areas based on the user's geographical location information. For example, the adjustment unit can analyze trends in illegal part-time work in specific areas by considering the user's geographical location information. For example, the adjustment unit can perform highly relevant adjustments in real time based on the user's geographical location information. This allows for prioritizing highly relevant adjustments by considering the user's geographical location information. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without using AI.

[0106] The adjustment unit can analyze the user's social media activity and make relevant adjustments during the adjustment process. For example, the adjustment unit can analyze the user's social media activity and make adjustments to posts related to illegal part-time jobs. For example, the adjustment unit can analyze the user's followers and accounts they follow and make relevant adjustments. For example, the adjustment unit can analyze the user's past posting history and prioritize making relevant adjustments. This allows for efficient adjustment of relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the adjustment unit may be performed using AI, for example, or without AI.

[0107] The analysis unit can estimate the user's emotions and adjust the analysis method based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will perform a detailed analysis to provide reassurance. If the user is relaxed, the analysis unit will perform a concise analysis and avoid providing excessive information. If the user is excited, the analysis unit will perform a visually easy-to-understand analysis to aid in understanding the information. By adjusting the analysis method according to the user's emotions, a more appropriate analysis becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0108] The analysis unit can optimize its analysis algorithm by referring to past analysis data during the analysis process. For example, the analysis unit can analyze past analysis data to identify frequently occurring keywords and phrases and reflect them in the analysis algorithm. For example, the analysis unit can find a tendency for the number of illegal job postings to increase during specific time periods from past analysis data and intensify the analysis during those times. For example, the analysis unit can adjust the intensity of the analysis for specific user groups based on past analysis data. In this way, the accuracy of the analysis algorithm is improved by referring to past analysis data. Some or all of the above processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI.

[0109] The analysis unit can improve the accuracy of its analysis based on specific keywords or phrases. For example, the analysis unit can list keywords or phrases related to illegal part-time jobs and perform analysis based on them. For example, the analysis unit can prioritize the analysis of posts containing specific keywords or phrases to improve accuracy. For example, the analysis unit can analyze the frequency of occurrence of keywords or phrases to improve the accuracy of its analysis. In this way, the accuracy of the analysis is improved by analyzing based on specific keywords or phrases. Some or all of the above processes in the analysis unit may be performed using, for example, a generative AI, or without using a generative AI.

[0110] The analysis unit can estimate the user's emotions and adjust how the analysis results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the analysis unit will display detailed analysis results to provide reassurance. If the user is relaxed, the analysis unit will display concise analysis results, avoiding excessive information. If the user is excited, the analysis unit will display visually easy-to-understand analysis results to aid in understanding the information. By adjusting how the analysis results are displayed according to the user's emotions, more appropriate information can be provided. 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.

[0111] The analysis unit can prioritize the analysis of highly relevant data by considering the user's geographical location information during analysis. For example, the analysis unit can prioritize the analysis of illegal job postings in nearby areas based on the user's geographical location information. For example, the analysis unit can analyze trends in illegal jobs in specific areas by considering the user's geographical location information. For example, the analysis unit can analyze highly relevant data in real time based on the user's geographical location information. This allows for the priority analysis of highly relevant data by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using, for example, generative AI, or without using generative AI.

[0112] The analysis unit can analyze a user's social media activity and analyze related data during the analysis process. For example, the analysis unit can analyze a user's social media activity and identify posts related to illegal part-time jobs. For example, the analysis unit can analyze a user's followers and the accounts they follow and analyze related data. For example, the analysis unit can analyze a user's past posting history and prioritize the analysis of related data. This allows for efficient analysis of related data by analyzing a user's social media activity. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without using generative AI.

[0113] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is feeling anxious, the optimization unit will perform detailed optimization to provide a sense of security. For example, if the user is relaxed, the optimization unit will perform concise optimization and avoid providing excessive information. For example, if the user is excited, the optimization unit will perform visually easy-to-understand optimization to help the user understand the information. This allows for more appropriate optimization by adjusting the optimization method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI.

[0114] The optimization unit can optimize the optimization algorithm by referring to past optimization data during the optimization process. For example, the optimization unit can analyze past optimization data to identify frequently occurring keywords and phrases and reflect them in the optimization algorithm. For example, the optimization unit can find a tendency for the number of illegal part-time job postings to increase during specific time periods from past optimization data and strengthen the optimization during those times. For example, the optimization unit can adjust the intensity of optimization for specific user groups based on past optimization data. This improves the accuracy of the optimization algorithm by referring to past optimization data. Some or all of the above processing in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI.

[0115] The optimization unit can improve the accuracy of optimization based on specific keywords or phrases during the optimization process. For example, the optimization unit can list keywords or phrases related to illegal part-time jobs and perform optimization based on them. For example, the optimization unit can prioritize the optimization of posts containing specific keywords or phrases to improve accuracy. For example, the optimization unit can analyze the frequency of occurrence of keywords and phrases to improve optimization accuracy. As a result, the accuracy of optimization is improved by optimizing based on specific keywords or phrases. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI.

[0116] The optimization unit can estimate the user's emotions and adjust how the optimization results are displayed based on the estimated emotions. For example, if the user is feeling anxious, the optimization unit will display detailed optimization results to provide reassurance. For example, if the user is relaxed, the optimization unit will display concise optimization results to avoid providing excessive information. For example, if the user is excited, the optimization unit will display visually easy-to-understand optimization results to aid in understanding the information. By adjusting how the optimization results are displayed according to the user's emotions, it becomes possible to provide more appropriate information. 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.

[0117] The optimization unit can prioritize optimizing highly relevant data by considering the user's geographical location information during optimization. For example, the optimization unit prioritizes optimizing job postings for illegal part-time work in nearby areas based on the user's geographical location information. For example, the optimization unit analyzes trends in illegal part-time work in specific areas by considering the user's geographical location information. For example, the optimization unit optimizes highly relevant data in real time based on the user's geographical location information. This allows for the prioritization of highly relevant data by considering the user's geographical location information. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI.

[0118] The optimization unit can analyze the user's social media activity and optimize the relevant data during the optimization process. For example, the optimization unit can analyze the user's social media activity and identify posts related to illegal part-time jobs. For example, the optimization unit can analyze the user's followers and followed accounts and optimize the relevant data. For example, the optimization unit can analyze the user's past posting history and prioritize the optimization of relevant data. This allows for efficient optimization of relevant data by analyzing the user's social media activity. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without using a generative AI.

[0119] The information acquisition unit can estimate the user's emotions and adjust the method of information acquisition based on the estimated emotions. For example, if the user is feeling anxious, the information acquisition unit will acquire detailed information to provide reassurance. For example, if the user is relaxed, the information acquisition unit will acquire concise information and avoid providing excessive information. For example, if the user is excited, the information acquisition unit will acquire information in a visually easy-to-understand manner to aid in understanding the information. In this way, by adjusting the method of information acquisition according to the user's emotions, more appropriate information acquisition becomes possible. 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.

[0120] The information acquisition unit can optimize its information acquisition algorithm by referring to past information acquisition data when acquiring information. For example, the information acquisition unit can analyze past information acquisition data to identify frequently occurring keywords and phrases and reflect them in the information acquisition algorithm. For example, the information acquisition unit can find a tendency for the number of illegal part-time job postings to increase during specific time periods from past information acquisition data and strengthen information acquisition during those times. For example, the information acquisition unit can adjust the intensity of information acquisition for specific user groups based on past information acquisition data. In this way, the accuracy of the information acquisition algorithm is improved by referring to past information acquisition data. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without using AI.

[0121] The information acquisition unit can improve the accuracy of information acquisition based on specific keywords or phrases. For example, the information acquisition unit can list keywords or phrases related to illegal part-time jobs and acquire information based on them. For example, the information acquisition unit can prioritize acquiring information from posts containing specific keywords or phrases to improve accuracy. For example, the information acquisition unit can analyze the frequency of occurrence of keywords or phrases to improve the accuracy of information acquisition. As a result, the accuracy of information acquisition is improved by acquiring information based on specific keywords or phrases. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without using AI.

[0122] The information acquisition unit can estimate the user's emotions and adjust the display method of the information acquisition results based on the estimated user emotions. For example, if the user is feeling anxious, the information acquisition unit may display detailed information acquisition results to provide a sense of security. For example, if the user is relaxed, the information acquisition unit may display concise information acquisition results to avoid providing excessive information. For example, if the user is excited, the information acquisition unit may display visually easy-to-understand information acquisition results to aid in understanding the information. In this way, by adjusting the display method of information acquisition results according to the user's emotions, it becomes possible to provide more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. It may be performed using generative AI or without using AI.

[0123] The information acquisition unit can prioritize the acquisition of highly relevant information by considering the user's geographical location information when acquiring information. For example, the information acquisition unit prioritizes the acquisition of job postings for illegal part-time work in nearby areas based on the user's geographical location information. For example, the information acquisition unit analyzes trends in illegal part-time work in specific areas by considering the user's geographical location information. For example, the information acquisition unit acquires highly relevant information in real time based on the user's geographical location information. This identifies relevant posts by considering the user's geographical location information. For example, the information acquisition unit analyzes the user's followers and followed accounts to acquire relevant information. For example, the information acquisition unit analyzes the user's past posting history to prioritize the acquisition of relevant information. This allows for the efficient acquisition of relevant information by analyzing the user's social media activity. Some or all of the above-described processes in the information acquisition unit may be performed using AI, for example, or without using AI.

[0124] The information acquisition unit can analyze the user's social media activity and obtain relevant information when acquiring information. For example, the information acquisition unit can analyze the user's social media activity and identify posts related to illegal part-time jobs. For example, the information acquisition unit can analyze the user's followers and accounts that the user follows and obtain relevant information. For example, the information acquisition unit can analyze the user's past posting history and prioritize the acquisition of relevant information. In this way, relevant information can be efficiently acquired by analyzing the user's social media activity. Some or all of the above processing in the information acquisition unit may be performed using AI, for example, or without using AI.

[0125] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0126] The monitoring unit can estimate the user's emotions and adjust the monitoring intensity based on the estimated emotions. For example, if the user is feeling anxious, the monitoring intensity can be increased for more detailed monitoring. If the user is relaxed, the monitoring intensity can be lowered for normal monitoring. If the user is excited, the monitoring intensity can be adjusted to a moderate level for appropriate monitoring. This allows for more appropriate monitoring by adjusting the monitoring intensity according to the user's emotions.

[0127] The monitoring unit can optimize its monitoring algorithm by referring to past monitoring data during monitoring. For example, it can analyze past monitoring data to identify frequently occurring keywords and phrases and reflect them in the monitoring algorithm. From past monitoring data, it can identify a tendency for the number of illegal job postings to increase during specific time periods and strengthen monitoring during those times. Based on past monitoring data, it can adjust the intensity of monitoring for specific user groups. In this way, the accuracy of the monitoring algorithm is improved by referring to past monitoring data.

[0128] The monitoring unit can improve the accuracy of its monitoring based on specific keywords and phrases. For example, it can list keywords and phrases related to illegal part-time jobs and perform monitoring based on that list. It can prioritize monitoring posts containing specific keywords and phrases to improve accuracy. It can also analyze the frequency of keyword and phrase occurrences to improve monitoring accuracy. In this way, monitoring based on specific keywords and phrases improves the accuracy of monitoring.

[0129] The monitoring unit can estimate the user's emotions and adjust how the monitoring results are displayed based on those emotions. For example, if the user is feeling anxious, detailed monitoring results can be displayed to provide reassurance. If the user is relaxed, concise monitoring results can be displayed to avoid providing excessive information. If the user is agitated, visually easy-to-understand monitoring results can be displayed to aid in understanding the information. In this way, by adjusting how monitoring results are displayed according to the user's emotions, more appropriate information can be provided.

[0130] The monitoring unit can prioritize monitoring highly relevant posts by considering the user's geographical location during monitoring. For example, it can prioritize monitoring of illegal job postings in nearby areas based on the user's geographical location. It can also analyze trends in illegal jobs in specific areas by considering the user's geographical location. It can monitor highly relevant posts in real time based on the user's geographical location. In this way, by considering the user's geographical location, it can prioritize monitoring of highly relevant posts.

[0131] The blocking function can estimate the user's emotions and adjust the blocking intensity based on that estimation. For example, if the user is feeling anxious, the blocking intensity can be increased to block more posts. If the user is relaxed, the blocking intensity can be lowered for normal blocking. If the user is excited, the blocking intensity can be adjusted to a moderate level for appropriate blocking. This allows for more appropriate blocking by adjusting the blocking intensity according to the user's emotions.

[0132] The blocking section can optimize the blocking algorithm by referring to past blocking data during the blocking process. For example, it can analyze past blocking data to identify frequently occurring keywords and phrases and reflect them in the blocking algorithm. By analyzing past blocking data, it can identify trends in the increase of illegal job postings during specific time periods and strengthen blocking during those times. Based on past blocking data, it can adjust the strength of blocking for specific user groups. In this way, the accuracy of the blocking algorithm is improved by referring to past blocking data.

[0133] The blocking function can improve the accuracy of blocking based on specific keywords or phrases. For example, it can list keywords and phrases related to illegal part-time jobs and block based on that list. It can prioritize blocking posts containing specific keywords or phrases to improve accuracy. It can also analyze the frequency of keyword and phrase occurrences to improve blocking accuracy. As a result, blocking based on specific keywords or phrases improves the accuracy of blocking.

[0134] The blocking function can estimate the user's emotions and adjust how the blocking results are displayed based on those emotions. For example, if the user is feeling anxious, a detailed blocking result can be displayed to provide reassurance. If the user is relaxed, a concise blocking result can be displayed to avoid providing excessive information. If the user is agitated, a visually easy-to-understand blocking result can be displayed to aid in understanding the information. In this way, by adjusting how the blocking results are displayed according to the user's emotions, more appropriate information can be provided.

[0135] The blocking function can prioritize blocking highly relevant posts by considering the user's geographical location. For example, it can prioritize blocking illegal job postings in nearby areas based on the user's geographical location. It can also analyze trends in illegal jobs in specific areas by considering the user's geographical location. It can block highly relevant posts in real time based on the user's geographical location. This allows for the priority blocking of highly relevant posts by considering the user's geographical location.

[0136] The following briefly describes the processing flow for example form 2.

[0137] Step 1: The monitoring unit monitors job posting sites and social media posts related to illegal part-time work. The monitoring unit can perform monitoring based on specific keywords and phrases, and can intensify monitoring during specific time periods. It can also adjust the intensity of monitoring for specific user groups. Step 2: The blocking unit blocks sites and posts detected by the monitoring unit. The blocking unit can block based on specific keywords or phrases and can strengthen blocking during specific time periods. It can also adjust the strength of blocking for specific user groups. Step 3: The alerting unit alerts the user based on the information blocked by the blocking unit. The alerting unit can alert based on specific keywords or phrases and can intensify alerts during specific time periods. It can also adjust the alert intensity for specific user groups. Step 4: The adjustment unit allows the user to adjust the intensity. The adjustment unit can make adjustments based on specific keywords or phrases and can strengthen the adjustments during specific time periods. It can also adjust the intensity of the adjustments for specific user groups. Step 5: The analysis unit uses generative AI to analyze the information. The analysis unit performs analysis based on specific keywords or phrases and can intensify the analysis during specific time periods. It can also adjust the intensity of the analysis for specific user groups.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the monitoring unit, blocking unit, warning unit, adjustment unit, analysis unit, optimization unit, and information acquisition unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart device 14 and monitors illegal job recruitment sites and SNS posts. The blocking unit is implemented by the specific processing unit 290 of the data processing unit 12 and blocks sites and posts detected by the monitoring unit. The warning unit is implemented by the control unit 46A of the smart device 14 and warns the user based on the information blocked by the blocking unit. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows parents to adjust the intensity. The analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes information using generative AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates and optimizes information daily using generative AI. The information acquisition unit is implemented, for example, by the control unit 46A of the smart device 14, and acquires information in cooperation with multiple organizations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0142] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0143] 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.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The 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.

[0146] 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.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 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.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the 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.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 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.

[0157] Each of the multiple elements described above, including the monitoring unit, blocking unit, warning unit, adjustment unit, analysis unit, optimization unit, and information acquisition unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the smart glasses 214 and monitors illegal job recruitment sites and SNS posts. The blocking unit is implemented by the specific processing unit 290 of the data processing unit 12 and blocks sites and posts detected by the monitoring unit. The warning unit is implemented by the control unit 46A of the smart glasses 214 and warns the user based on the information blocked by the blocking unit. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows parents to adjust the intensity. The analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes information using generative AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates and optimizes information daily using generative AI. The information acquisition unit is implemented, for example, by the control unit 46A of the smart glasses 214, and acquires information in cooperation with multiple organizations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0158] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0159] 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.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The 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.

[0162] 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.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (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).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the monitoring unit, blocking unit, warning unit, adjustment unit, analysis unit, optimization unit, and information acquisition unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the headset terminal 314 and monitors illegal job recruitment sites and SNS posts. The blocking unit is implemented by the specific processing unit 290 of the data processing unit 12 and blocks sites and posts detected by the monitoring unit. The warning unit is implemented by the control unit 46A of the headset terminal 314 and warns the user based on the information blocked by the blocking unit. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows parents to adjust the intensity. The analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes information using generative AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates and optimizes information daily using generative AI. The information acquisition unit is implemented, for example, by the control unit 46A of the headset-type terminal 314, and acquires information in cooperation with multiple organizations. The correspondence between each unit and the device or control unit is not limited to the example described above, and various modifications are possible.

[0174] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.).

[0187] 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.

[0188] 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.

[0189] 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.

[0190] Each of the multiple elements described above, including the monitoring unit, blocking unit, warning unit, adjustment unit, analysis unit, optimization unit, and information acquisition unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the monitoring unit is implemented by the control unit 46A of the robot 414 and monitors illegal job recruitment sites and SNS posts. The blocking unit is implemented by the specific processing unit 290 of the data processing unit 12 and blocks sites and posts detected by the monitoring unit. The warning unit is implemented by the control unit 46A of the robot 414 and warns the user based on the information blocked by the blocking unit. The adjustment unit is implemented by the specific processing unit 290 of the data processing unit 12 and allows parents to adjust the intensity. The analysis unit is implemented by the control unit 46A of the robot 414 and analyzes information using generative AI. The optimization unit is implemented by the specific processing unit 290 of the data processing unit 12 and updates and optimizes information daily using generative AI. The information acquisition unit is implemented, for example, by the control unit 46A of the robot 414, and acquires information in cooperation with multiple systems. The correspondence between each unit and the devices and control units is not limited to the example described above, and various modifications are possible.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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."

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] (Note 1) A monitoring unit that monitors job posting sites and social media posts related to illegal part-time work, A blocking unit that blocks sites and posts detected by the monitoring unit, A warning unit that alerts the user based on the information blocked by the aforementioned blocking unit, An adjustment unit that allows the user to adjust the strength, It includes an analysis unit that uses generation AI to analyze information. A system characterized by the following features. (Note 2) It features an optimization unit that utilizes generation AI to update and optimize information on a daily basis. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an information acquisition unit that obtains the source information for analysis in cooperation with multiple organizations. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned monitoring unit, It estimates the user's emotions and adjusts the monitoring intensity based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned monitoring unit, During monitoring, the monitoring algorithm is optimized by referring to past monitoring data. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned monitoring unit, During monitoring, improve the accuracy of monitoring based on specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned monitoring unit, During monitoring, posts with high relevance are prioritized based on the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned monitoring unit, During monitoring, the system analyzes users' social media activity and monitors relevant posts. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned block section is It estimates the user's emotions and adjusts the strength of the block based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned block section is When blocking, the blocking algorithm is optimized by referring to past blocking data. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned block section is When blocking, improve the accuracy of blocking based on specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned block section is The system estimates the user's emotions and adjusts how the blocking result is displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned block section is When blocking, the system prioritizes blocking posts that are highly relevant to the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned block section is When blocking a user, the system analyzes their social media activity and blocks related posts. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned warning unit is, The system estimates the user's emotions and adjusts the method of alerting users based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned warning unit is, When issuing a warning, the warning algorithm is optimized by referring to past warning data. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned warning unit is, When issuing warnings, improve the accuracy of the warnings based on specific keywords and phrases. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned warning unit is, The system estimates the user's emotions and adjusts how warnings are displayed based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned warning unit is, When issuing warnings, the system prioritizes highly relevant warnings by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned warning unit is, When issuing a warning, the system analyzes the user's social media activity and provides relevant warnings. The system described in Appendix 1, characterized by the features described herein. (Note 22) The adjustment unit is, We estimate the user's emotions and change the adjustment method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The adjustment unit is, During adjustment, the adjustment algorithm is optimized by referring to past adjustment data. The system described in Appendix 1, characterized by the features described herein. (Note 24) The adjustment unit is, During the adjustment process, improve the accuracy of the adjustments based on specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 25) The adjustment unit is, The system estimates the user's emotions and changes how the adjustment results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The adjustment unit is, During adjustments, the system prioritizes adjustments that are highly relevant, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 27) The adjustment unit is, During the adjustment process, we analyze users' social media activity and make relevant adjustments. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned analysis unit is It estimates the user's emotions and adjusts the analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned analysis unit is 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 30) The aforementioned analysis unit is During analysis, improve the accuracy of the analysis based on specific keywords or phrases. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned analysis unit is It estimates the user's emotions and adjusts how the analysis results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned analysis unit is During analysis, the system prioritizes analyzing highly relevant data, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned analysis unit is During the analysis, we analyze users' social media activity and analyze relevant data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The optimization unit, During optimization, the optimization algorithm is optimized by referring to past optimization data. The system described in Appendix 2, characterized by the features described herein. (Note 36) The optimization unit, During optimization, improve the accuracy of optimization based on specific keywords or phrases. The system described in Appendix 2, characterized by the features described herein. (Note 37) The optimization unit, It estimates the user's emotions and adjusts how the optimization results are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 38) The optimization unit, During optimization, the system prioritizes optimizing highly relevant data, taking into account the user's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 39) The optimization unit, During optimization, we analyze users' social media activity and optimize relevant data. The system described in Appendix 2, characterized by the features described herein. (Note 40) The aforementioned information acquisition unit, It estimates the user's emotions and adjusts the information acquisition method based on the estimated user emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned information acquisition unit, When acquiring information, the information acquisition algorithm is optimized by referring to past information acquisition data. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned information acquisition unit, When acquiring information, improve the accuracy of information acquisition based on specific keywords or phrases. The system described in Appendix 3, characterized by the features described herein. (Note 43) The aforementioned information acquisition unit, The system estimates the user's emotions and adjusts how the information retrieved is displayed based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 44) The aforementioned information acquisition unit, When retrieving information, the system prioritizes retrieving highly relevant information by considering the user's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 45) The aforementioned information acquisition unit, When acquiring information, the system analyzes the user's social media activity and retrieves relevant information. The system described in Appendix 3, characterized by the features described herein. [Explanation of Symbols]

[0210] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A monitoring unit that monitors job posting sites and social media posts related to illegal part-time work, A blocking unit that blocks sites and posts detected by the monitoring unit, A warning unit that alerts the user based on the information blocked by the aforementioned blocking unit, An adjustment unit that allows the user to adjust the strength, It includes an analysis unit that analyzes information using generational AI. A system characterized by the following features.

2. It features an optimization unit that utilizes generation AI to update and optimize information on a daily basis. The system according to feature 1.

3. It includes an information acquisition unit that obtains the source information for analysis in cooperation with multiple organizations. The system according to feature 1.

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

5. The aforementioned monitoring unit, During monitoring, the monitoring algorithm is optimized by referring to past monitoring data. The system according to feature 1.

6. The aforementioned monitoring unit, During monitoring, improve the accuracy of monitoring based on specific keywords or phrases. The system according to feature 1.

7. The aforementioned monitoring unit, It estimates the user's emotions and adjusts how monitoring results are displayed based on those estimated emotions. The system according to feature 1.

8. The aforementioned monitoring unit, During monitoring, posts with high relevance are prioritized based on the user's geographical location. The system according to feature 1.

9. The aforementioned monitoring unit, During monitoring, the system analyzes users' social media activity and monitors relevant posts. The system according to feature 1.

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