system

Abstract

The system according to this embodiment aims to effectively identify and eliminate illegal job postings on the internet. [Solution] The system according to the embodiment comprises a collection unit, a determination unit, and a report creation unit. The collection unit collects information from the internet. The determination unit determines whether or not the job postings are illegal part-time jobs based on the information collected by the collection unit. The report creation unit creates a report based on the results determined by the determination unit.

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, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance that responds 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, it is difficult to completely eliminate the job information of dark bytes on the Internet, and there is room for improvement.

[0005] The system according to the embodiment aims to effectively determine and eliminate the job information of dark bytes on the Internet.

Means for Solving the Problems

[0006] The system according to the embodiment includes a collection unit, a determination unit, and a report creation unit. The collection unit collects information on the Internet. The determination unit determines whether the job information corresponds to dark bytes based on the information collected by the collection unit. The report creation unit creates a report based on the result determined by the determination unit. [Effects of the Invention]

[0007] The system according to this embodiment can effectively identify and eliminate job postings for illegal part-time work on the internet. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the signed communication interface (I / F) is an interface that includes a communication processor and an antenna. The communication interface manages communication between multiple computers. Examples of communication standards applicable to the communication interface 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). <00叭098> The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. Also, the reception device 38, the output device 40, and the camera 42 are 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 job posting determination system according to an embodiment of the present invention is a system that uses AI to determine whether or not a job posting falls under the category of illegal part-time work. This job posting determination system connects job postings from job sites and social media to the AI ​​via APIs, etc. Next, the AI ​​determines whether the connected job postings fall under the category of illegal part-time work. If the determination result is that the job postings fall under the category of illegal part-time work, the job postings are not published; otherwise, they are published as is. This system not only prevents the publication of illegal part-time work, but also collects information from the internet and trains the AI ​​on the latest characteristics of illegal part-time work, so that it can always make determinations based on an understanding of the latest trends. It is also possible to have the AI ​​analyze information on items that were rejected in the illegal part-time work determination and the collected information to create a report on trends. This mechanism can prevent illegal part-time work from being published on the internet in the first place, contributing to the improvement of public safety in Japan. The target market is companies that operate job sites and social media, and this system can solve the problem of preventing the publication of illegal part-time work that these companies face. For example, the job posting determination system connects job postings from job sites and social media to the AI ​​via APIs, etc. Next, the AI ​​determines whether or not the connected job postings fall under the category of illegal part-time work. If the system determines that a job posting is an illegal or illicit job, it will not be published; otherwise, it will be published as is. This system not only prevents the publication of illegal jobs, but also collects information from the internet and trains its AI on the latest characteristics of illegal jobs, ensuring that it always understands the latest trends and makes informed decisions. It is also possible to have the AI ​​analyze information on jobs that have been flagged as illegal or illicit and to generate trend reports. This mechanism prevents illegal jobs from being published on the internet in the first place, contributing to improved public safety in Japan. The target market is companies that operate job search websites and social media platforms, and this system can solve the problem these companies face of preventing the publication of illegal jobs. In this way, the job posting screening system can prevent the publication of illegal jobs and prevent the spread of illegal job information on the internet.

[0029] The job information determination system according to the embodiment comprises a collection unit, a determination unit, and a report creation unit. The collection unit collects information from the internet. For example, the collection unit collects job information from job sites and social networking services (SNS). The collection unit can collect information from specific websites, SNS, bulletin boards, etc. The collection unit can automatically collect job information using APIs. For example, the collection unit can search for and collect job information using specific keywords. The collection unit can store the collected information in a database. The determination unit determines whether or not a job is an illegal job based on the information collected by the collection unit. For example, the determination unit uses a machine learning algorithm to determine whether or not a job is an illegal job. The determination unit makes a determination based on criteria such as the detection of specific keywords, the presence or absence of illegality, the lack of transparency in compensation, and the danger of the work content. The determination unit can analyze the collected information and extract the characteristics of illegal jobs. For example, the determination unit identifies job information that is highly likely to be an illegal job based on the collected information. The determination unit can store the determination results in a database. The report creation unit creates a report based on the results determined by the determination unit. The report creation unit creates reports using, for example, text format, graphs, and charts. Based on the judgment results and collected information, the report creation unit can analyze trends in illegal part-time jobs. The report creation unit can save the created reports to a database. The report creation unit can output the created reports. As a result, the job information judgment system according to this embodiment can prevent the posting of illegal part-time jobs and prevent the spread of illegal part-time job information on the internet.

[0030] The data collection unit collects information from the internet. For example, it collects job postings from job search websites and social media. Specifically, it can collect information from specific websites, social media, bulletin boards, etc. The data collection unit can automatically collect job postings using APIs. For example, it can search for and collect job postings using specific keywords. The data collection unit can store the collected information in a database. The data collection unit can analyze the content of web pages and extract job postings using web scraping technology. Web scraping is a technology that analyzes HTML structure and extracts information based on specific tags and class names. The data collection unit can periodically update information on the internet and collect new job postings. The data collection unit can preprocess the collected information and remove unnecessary information before saving it to the database. For example, the data collection unit filters out duplicate and irrelevant information to improve data quality. The data collection unit stores the collected information as structured data to facilitate subsequent processing. This allows the data collection unit to efficiently and accurately collect job postings from the internet and store them in a database.

[0031] The judgment unit determines whether a job posting is an illegal job based on the information collected by the collection unit. The judgment unit uses, for example, a machine learning algorithm to determine whether it is an illegal job. Specifically, the judgment unit makes a determination based on criteria such as the detection of specific keywords, the presence or absence of illegality, the lack of transparency in compensation, and the danger of the work content. The judgment unit can analyze the collected information and extract the characteristics of illegal jobs. For example, the judgment unit identifies job postings that are highly likely to be illegal jobs based on the collected information. The judgment unit can analyze the text of job postings using natural language processing technology and detect expressions that are illegal or dangerous. For example, the judgment unit extracts specific keywords or phrases from the text of job postings and determines whether they correspond to the characteristics of illegal jobs. The judgment unit can train a machine learning model and learn the characteristics of illegal jobs based on past data. This allows the judgment unit to determine whether new job postings are illegal jobs with high accuracy. The judgment unit can save the determination results in a database and use them for subsequent processing. This allows the judgment unit to efficiently and accurately determine whether a job is an illegal job based on the collected information.

[0032] The report creation unit creates reports based on the results determined by the judgment unit. The report creation unit creates reports using, for example, text format, graphs, and charts. Specifically, the report creation unit can analyze trends in illegal part-time jobs based on the judgment results and collected information. The report creation unit can save the created reports to a database. The report creation unit can output the created reports. The report creation unit can generate graphs and charts to display the data provided by the judgment unit in an easy-to-understand visual format. For example, by graphing the trend in the number of illegal part-time job occurrences or the frequency of appearance of specific keywords, trends in illegal part-time jobs can be grasped at a glance. The report creation unit can customize the content of reports and create reports tailored to specific users and purposes. For example, for companies, it can provide reports analyzing the risks of illegal part-time jobs in specific industries or regions, and for individuals, it can provide reports evaluating the safety of specific job postings. The report creation unit can regularly update the content of reports to reflect the latest information. This makes the report creation unit a useful tool for providing reliable information to users and reducing the risks of illegal part-time jobs.

[0033] The collection unit can collect job information from job sites and social media. The collection unit can collect information from specific job sites and social media, for example. The collection unit can collect job information using specific platforms or keyword searches. When collecting job information, the collection unit can automatically acquire information using APIs. The collection unit can store the collected information in a database. This allows the collection unit to efficiently acquire information from job sites and social media. Some or all of the above processing in the collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the collection unit can input job information from job sites and social media into a generating AI, and the generating AI can collect the information.

[0034] The judgment unit can determine whether a job is illegal or not based on the collected information. The judgment unit can determine whether a job is illegal or not using, for example, a machine learning algorithm. The judgment unit makes a determination based on criteria such as the detection of specific keywords, the presence or absence of illegality, the lack of transparency in compensation, and the danger of the work content. The judgment unit can analyze the collected information and extract the characteristics of illegal jobs. The judgment unit can identify job postings that are highly likely to be illegal or not based on the collected information. The judgment unit can save the determination results in a database. In this way, the judgment unit can prevent the posting of illegal or not by determining whether a job is illegal or not based on the collected information. Some or all of the above processing in the judgment unit may be performed using, for example, AI, or not using AI. For example, the judgment unit can input the collected information into AI, and the AI ​​can determine whether a job is illegal or not.

[0035] The report creation unit can create reports based on the judgment results and collected information. The report creation unit can create reports using, for example, text format, graphs, and charts. The report creation unit can analyze trends in illegal part-time jobs based on the judgment results and collected information. The report creation unit can save the created reports to a database. The report creation unit can output the created reports. In this way, the report creation unit can grasp trends in illegal part-time jobs by creating reports based on the judgment results and collected information. Some or all of the above processes in the report creation unit may be performed using, for example, AI, or not using AI. For example, the report creation unit can input the judgment results and collected information into AI, and the AI ​​can create a report.

[0036] The data collection unit may include a learning unit that trains an AI with the collected information. For example, by training the AI ​​with the collected information, the data collection unit can constantly learn the characteristics of the latest illegal jobs and improve its judgment accuracy. The data collection unit needs to clarify the specific methods and learning content for training the AI. For example, it needs to set the algorithm to be used and the type of training data. The data collection unit can use the learning unit to train the AI ​​with the collected information. This allows the data collection unit to constantly learn the characteristics of the latest illegal jobs and improve its judgment accuracy by training the AI ​​with the collected information. Some or all of the above-described processes in the data collection unit may be performed using an AI, or they may be performed without an AI. For example, the data collection unit can input the collected information into an AI, which can then learn.

[0037] The determination unit may include a providing unit that provides the determination result. The determination unit can, for example, provide the determination result to the user so that the user can confirm the result. The determination unit needs to clarify the method and format of providing the determination result. For example, it can set the notification method and display format. The determination unit can provide the determination result to the user using the providing unit. In this way, the determination unit can provide the determination result so that the user can confirm the result. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the determination result to the AI, and the AI ​​can provide it.

[0038] The report creation unit may include an output unit that outputs the report. For example, the report creation unit can output the created report to the user, allowing the user to review it. The report creation unit needs to clearly define the method and format of report output. For example, it may set PDF format, printing, or email transmission. The report creation unit can output the created report to the user using the output unit. This allows the user to review the report by outputting it. Some or all of the above-described processes in the report creation unit may be performed using AI, for example, or without AI. For example, the report creation unit can input the created report into AI, which can then output it.

[0039] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can analyze trends in the information the user has collected in the past and prioritize the collection of similar information. The data collection unit can evaluate the accuracy of the information the user has collected in the past and prioritize the collection of highly accurate information. The data collection unit can analyze the categories of the information the user has collected in the past and prioritize the collection of information in related categories. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI, and the AI ​​can select the optimal data collection method.

[0040] The data collection unit can filter data based on the user's current areas of interest during collection. For example, the data collection unit can prioritize collecting information in areas the user is currently interested in. The data collection unit can filter information based on keywords the user is currently interested in. The data collection unit can prioritize collecting information in regions the user is currently interested in. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into the AI, which can then filter the information.

[0041] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during collection. For example, the data collection unit can prioritize the collection of nearby job postings based on the user's current location. The data collection unit can collect relevant regional information by referring to the user's past travel history. The data collection unit can prioritize the collection of relevant information based on the region set by the user. In this way, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can collect the information.

[0042] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can prioritize collecting job postings that the user has shown interest in on social media. The data collection unit can also collect information that the user's social media followers have shown interest in. The data collection unit can analyze the content of the user's social media posts and collect relevant information. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can collect the information.

[0043] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of the collected information during the judgment process. For example, the judgment unit analyzes the relationships between the collected information and makes a judgment based on the interrelated information. The judgment unit can identify factors that increase the likelihood of illegal part-time work by considering the interrelationships of the collected information. The judgment unit can improve the accuracy of its judgment based on the interrelationships of the collected information. In this way, the judgment unit can improve the accuracy of its judgment by considering the interrelationships of the collected information. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the interrelationships of the collected information into the AI, and the AI ​​can improve the accuracy of its judgment.

[0044] The judgment unit can make a judgment by considering the attribute information of the information submitter. For example, the judgment unit can analyze the past posting history of the information submitter and evaluate its reliability. Based on the attribute information of the information submitter, the judgment unit can determine the possibility of illegal part-time work. The judgment unit can improve the accuracy of its judgment by considering the attribute information of the information submitter. In this way, the judgment unit can improve the accuracy of its judgment by considering the attribute information of the information submitter. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the attribute information of the information submitter into AI, and the AI ​​can make the judgment.

[0045] The determination unit can make a determination by considering the geographical distribution of information. For example, the determination unit can analyze the geographical distribution of information and determine the possibility of illegal part-time jobs in a particular area. Based on the geographical distribution of information, the determination unit can identify trends in illegal part-time jobs. The determination unit can improve the accuracy of its determination by considering the geographical distribution of information. As a result, the determination unit can determine the possibility of illegal part-time jobs in a particular area by considering the geographical distribution of information. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the geographical distribution of information into AI, and the AI ​​can make the determination.

[0046] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on the information during the judgment process. For example, the judgment unit can refer to relevant literature on the information to identify the characteristics of illegal part-time jobs. The judgment unit can improve the accuracy of its judgment based on the relevant literature on the information. The judgment unit can determine the possibility of an illegal part-time job by referring to relevant literature on the information. In this way, the judgment unit can improve the accuracy of its judgment by referring to relevant literature on the information. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input relevant literature on the information into AI, and the AI ​​can perform the judgment.

[0047] The report generation unit can optimize the current report by referring to past report data when creating a report. For example, the report generation unit can analyze past report data and reflect it in the current report. The report generation unit can improve the accuracy of the report based on past report data. The report generation unit can optimize the current report by referring to past report data. In this way, the report generation unit can improve the accuracy of the current report by referring to past report data. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without using AI. For example, the report generation unit can input past report data into AI, and the AI ​​can optimize the current report.

[0048] The report generation unit can apply different report generation methods to each category of information when creating a report. For example, the report generation unit can select an appropriate report generation method for each category of information. The report generation unit can create reports in different formats for each category of information. The report generation unit can create reports by applying different analytical methods for each category of information. In this way, the report generation unit can improve the accuracy of the report by applying an appropriate report generation method for each category of information. Some or all of the above processes in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can input the categories of information into the AI, and the AI ​​can apply different report generation methods.

[0049] The report creation unit can analyze changes in reports based on the timing of information submission when creating reports. For example, the report creation unit can analyze the timing of information submission and identify changes in reports. The report creation unit can analyze trends in reports based on the timing of information submission. The report creation unit can analyze changes in reports taking the timing of information submission into consideration. This allows the report creation unit to grasp trends in reports by analyzing changes in reports based on the timing of information submission. Some or all of the above processes in the report creation unit may be performed using AI, for example, or not using AI. For example, the report creation unit can input the timing of information submission into AI, and the AI ​​can analyze changes in reports.

[0050] The report creation unit can analyze the report by referring to relevant market data when creating the report. For example, the report creation unit can improve the accuracy of the report by referring to relevant market data. The report creation unit can optimize the content of the report based on relevant market data. The report creation unit can analyze the report by referring to relevant market data. In this way, the report creation unit can improve the accuracy of the report by referring to relevant market data. Some or all of the above processes in the report creation unit may be performed using AI, for example, or not using AI. For example, the report creation unit can input relevant market data into AI, and the AI ​​can analyze the report.

[0051] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit can analyze past learning data and optimize the learning algorithm. The learning unit can improve the accuracy of learning based on past learning data. The learning unit can optimize the learning algorithm by referring to past learning data. In this way, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can optimize the learning algorithm.

[0052] The learning unit can weight the training data based on when the information was submitted during training. For example, the learning unit can assign a higher weight to the most recent information, taking into account when it was submitted. The learning unit can assign a lower weight to older information, also based on when it was submitted. The learning unit can analyze when the information was submitted and assign appropriate weights. This allows the learning unit to prioritize the most recent information by weighting the training data based on when it was submitted. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the information submission dates into the AI, and the AI ​​can weight the training data.

[0053] The service provider can select the optimal service delivery method by referring to the user's past operation history at the time of delivery. For example, the service provider can analyze the user's past operation history and select the optimal service delivery method. The service provider can optimize the service delivery method based on the user's past operation history. The service provider can select the optimal service delivery method by referring to the user's past operation history. In this way, the service provider can select the optimal service delivery method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past operation history into AI, and the AI ​​can select the optimal service delivery method.

[0054] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can select a delivery method optimized for a large screen. If the user is using a smartwatch, the delivery unit can select a concise and highly visible delivery method. In this way, the delivery unit can select the optimal delivery method by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into AI, and the AI ​​can select the optimal delivery method.

[0055] The output unit can select the optimal output method by referring to the user's past operation history when outputting. For example, the output unit can analyze the user's past operation history and select the optimal output method. The output unit can optimize the output method based on the user's past operation history. The output unit can select the optimal output method by referring to the user's past operation history. In this way, the output unit can select the optimal output method by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, for example, or without using AI. For example, the output unit can input the user's past operation history into AI, and the AI ​​can select the optimal output method.

[0056] The output unit can select the optimal output method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can select an output method that matches the screen size. If the user is using a tablet, the output unit can select an output method optimized for a large screen. If the user is using a smartwatch, the output unit can select a concise and highly visible output method. In this way, the output unit can select the optimal output method by taking into account the user's device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device information into AI, and the AI ​​can select the optimal output method.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The data collection unit analyzes the user's past search history and prioritizes collecting job postings that are likely to interest the user. For example, if a user has previously shown interest in a particular industry or job type, it will prioritize collecting job postings related to that industry or job type. Furthermore, if a user has frequently searched for job postings in a particular region in the past, it will prioritize collecting job postings in that region. Also, if a user has frequently viewed job postings from a particular company in the past, it will prioritize collecting new job postings from that company. In this way, the data collection unit can efficiently collect job postings that are highly relevant to the user by taking into account the user's past search history.

[0059] The judgment unit can consider the reliability of the source and provider of the collected information in order to evaluate its reliability. For example, information collected from reliable job sites or official company websites will be judged as highly reliable. On the other hand, information collected from anonymous bulletin boards or unreliable sites will be judged as unreliable. Furthermore, the judgment unit can analyze the information provider's past posting history and ratings to evaluate reliability. In this way, the judgment unit can make more accurate judgments by evaluating the reliability of the collected information.

[0060] The report creation department can customize the content of reports according to the user's interests. For example, if a user is interested in a particular industry or job type, the report will focus on including information related to that industry or job type. Similarly, if a user is interested in job postings in a specific region, the report can include detailed information on job postings in that region. Furthermore, if a user is interested in a particular company, the report can include job postings and related news from that company. This allows the report creation department to provide customized reports tailored to the user's interests.

[0061] The data collection unit can update the collected information in real time. For example, when a new job posting is published, it can quickly collect it and add it to the database. Furthermore, if there are changes to existing job postings, those changes can be reflected immediately. In addition, the data collection unit can set a schedule for regular information updates, ensuring that the latest information is always available. This allows the data collection unit to consistently provide the most up-to-date job postings.

[0062] The judgment unit can analyze the collected information in detail and identify factors that increase the likelihood of it being an illegal job. For example, if the job posting contains illegal keywords or phrases, it will focus its analysis on that information. It can also analyze in detail if the compensation is unusually high or the job description is unclear. Furthermore, if the provider of the job posting has previously provided illegal job postings, it will focus its analysis on that information. As a result, the judgment unit can identify factors that increase the likelihood of it being an illegal job and make a more accurate judgment.

[0063] The following briefly describes the processing flow for example form 1.

[0064] Step 1: The collection unit collects information from the internet. For example, it can collect job postings from job search websites and social media, and can also collect information from specific websites, social media, and bulletin boards. The collection unit can automatically collect job postings using APIs and can search for and collect job postings using specific keywords. The collected information is stored in a database. Step 2: The judgment unit determines whether the job posting is an illegal job based on the information collected by the collection unit. For example, it may use a machine learning algorithm to determine whether it is an illegal job, and make a judgment based on criteria such as the detection of specific keywords, the presence or absence of illegality, the lack of transparency in compensation, and the danger of the work content. The judgment results are stored in a database. Step 3: The report generation unit creates a report based on the results determined by the judgment unit. For example, it creates a report using text format, graphs, and charts, and analyzes the trends of illegal part-time jobs based on the judgment results and collected information. The created report is saved in the database and can be output.

[0065] (Example of form 2) The job posting determination system according to an embodiment of the present invention is a system that uses AI to determine whether or not a job posting falls under the category of illegal part-time work. This job posting determination system connects job postings from job sites and social media to the AI ​​via APIs, etc. Next, the AI ​​determines whether the connected job postings fall under the category of illegal part-time work. If the determination result is that the job postings fall under the category of illegal part-time work, the job postings are not published; otherwise, they are published as is. This system not only prevents the publication of illegal part-time work, but also collects information from the internet and trains the AI ​​on the latest characteristics of illegal part-time work, so that it can always make determinations based on an understanding of the latest trends. It is also possible to have the AI ​​analyze information on items that were rejected in the illegal part-time work determination and the collected information to create a report on trends. This mechanism can prevent illegal part-time work from being published on the internet in the first place, contributing to the improvement of public safety in Japan. The target market is companies that operate job sites and social media, and this system can solve the problem of preventing the publication of illegal part-time work that these companies face. For example, the job posting determination system connects job postings from job sites and social media to the AI ​​via APIs, etc. Next, the AI ​​determines whether or not the connected job postings fall under the category of illegal part-time work. If the system determines that a job posting is an illegal or illicit job, it will not be published; otherwise, it will be published as is. This system not only prevents the publication of illegal jobs, but also collects information from the internet and trains its AI on the latest characteristics of illegal jobs, ensuring that it always understands the latest trends and makes informed decisions. It is also possible to have the AI ​​analyze information on jobs that have been flagged as illegal or illicit and to generate trend reports. This mechanism prevents illegal jobs from being published on the internet in the first place, contributing to improved public safety in Japan. The target market is companies that operate job search websites and social media platforms, and this system can solve the problem these companies face of preventing the publication of illegal jobs. In this way, the job posting screening system can prevent the publication of illegal jobs and prevent the spread of illegal job information on the internet.

[0066] The job information determination system according to the embodiment comprises a collection unit, a determination unit, and a report creation unit. The collection unit collects information from the internet. For example, the collection unit collects job information from job sites and social networking services (SNS). The collection unit can collect information from specific websites, SNS, bulletin boards, etc. The collection unit can automatically collect job information using APIs. For example, the collection unit can search for and collect job information using specific keywords. The collection unit can store the collected information in a database. The determination unit determines whether or not a job is an illegal job based on the information collected by the collection unit. For example, the determination unit uses a machine learning algorithm to determine whether or not a job is an illegal job. The determination unit makes a determination based on criteria such as the detection of specific keywords, the presence or absence of illegality, the lack of transparency in compensation, and the danger of the work content. The determination unit can analyze the collected information and extract the characteristics of illegal jobs. For example, the determination unit identifies job information that is highly likely to be an illegal job based on the collected information. The determination unit can store the determination results in a database. The report creation unit creates a report based on the results determined by the determination unit. The report creation unit creates reports using, for example, text format, graphs, and charts. Based on the judgment results and collected information, the report creation unit can analyze trends in illegal part-time jobs. The report creation unit can save the created reports to a database. The report creation unit can output the created reports. As a result, the job information judgment system according to this embodiment can prevent the posting of illegal part-time jobs and prevent the spread of illegal part-time job information on the internet.

[0067] The data collection unit collects information from the internet. For example, it collects job postings from job search websites and social media. Specifically, it can collect information from specific websites, social media, bulletin boards, etc. The data collection unit can automatically collect job postings using APIs. For example, it can search for and collect job postings using specific keywords. The data collection unit can store the collected information in a database. The data collection unit can analyze the content of web pages and extract job postings using web scraping technology. Web scraping is a technology that analyzes HTML structure and extracts information based on specific tags and class names. The data collection unit can periodically update information on the internet and collect new job postings. The data collection unit can preprocess the collected information and remove unnecessary information before saving it to the database. For example, the data collection unit filters out duplicate and irrelevant information to improve data quality. The data collection unit stores the collected information as structured data to facilitate subsequent processing. This allows the data collection unit to efficiently and accurately collect job postings from the internet and store them in a database.

[0068] The judgment unit determines whether a job posting is an illegal job based on the information collected by the collection unit. The judgment unit uses, for example, a machine learning algorithm to determine whether it is an illegal job. Specifically, the judgment unit makes a determination based on criteria such as the detection of specific keywords, the presence or absence of illegality, the lack of transparency in compensation, and the danger of the work content. The judgment unit can analyze the collected information and extract the characteristics of illegal jobs. For example, the judgment unit identifies job postings that are highly likely to be illegal jobs based on the collected information. The judgment unit can analyze the text of job postings using natural language processing technology and detect expressions that are illegal or dangerous. For example, the judgment unit extracts specific keywords or phrases from the text of job postings and determines whether they correspond to the characteristics of illegal jobs. The judgment unit can train a machine learning model and learn the characteristics of illegal jobs based on past data. This allows the judgment unit to determine whether new job postings are illegal jobs with high accuracy. The judgment unit can save the determination results in a database and use them for subsequent processing. This allows the judgment unit to efficiently and accurately determine whether a job is an illegal job based on the collected information.

[0069] The report creation unit creates reports based on the results determined by the judgment unit. The report creation unit creates reports using, for example, text format, graphs, and charts. Specifically, the report creation unit can analyze trends in illegal part-time jobs based on the judgment results and collected information. The report creation unit can save the created reports to a database. The report creation unit can output the created reports. The report creation unit can generate graphs and charts to display the data provided by the judgment unit in an easy-to-understand visual format. For example, by graphing the trend in the number of illegal part-time job occurrences or the frequency of appearance of specific keywords, trends in illegal part-time jobs can be grasped at a glance. The report creation unit can customize the content of reports and create reports tailored to specific users and purposes. For example, for companies, it can provide reports analyzing the risks of illegal part-time jobs in specific industries or regions, and for individuals, it can provide reports evaluating the safety of specific job postings. The report creation unit can regularly update the content of reports to reflect the latest information. This makes the report creation unit a useful tool for providing reliable information to users and reducing the risks of illegal part-time jobs.

[0070] The collection unit can collect job information from job sites and social media. The collection unit can collect information from specific job sites and social media, for example. The collection unit can collect job information using specific platforms or keyword searches. When collecting job information, the collection unit can automatically acquire information using APIs. The collection unit can store the collected information in a database. This allows the collection unit to efficiently acquire information from job sites and social media. Some or all of the above processing in the collection unit may be performed using, for example, a generating AI, or without a generating AI. For example, the collection unit can input job information from job sites and social media into a generating AI, and the generating AI can collect the information.

[0071] The judgment unit can determine whether a job is illegal or not based on the collected information. The judgment unit can determine whether a job is illegal or not using, for example, a machine learning algorithm. The judgment unit makes a determination based on criteria such as the detection of specific keywords, the presence or absence of illegality, the lack of transparency in compensation, and the danger of the work content. The judgment unit can analyze the collected information and extract the characteristics of illegal jobs. The judgment unit can identify job postings that are highly likely to be illegal or not based on the collected information. The judgment unit can save the determination results in a database. In this way, the judgment unit can prevent the posting of illegal or not by determining whether a job is illegal or not based on the collected information. Some or all of the above processing in the judgment unit may be performed using, for example, AI, or not using AI. For example, the judgment unit can input the collected information into AI, and the AI ​​can determine whether a job is illegal or not.

[0072] The report creation unit can create reports based on the judgment results and collected information. The report creation unit can create reports using, for example, text format, graphs, and charts. The report creation unit can analyze trends in illegal part-time jobs based on the judgment results and collected information. The report creation unit can save the created reports to a database. The report creation unit can output the created reports. In this way, the report creation unit can grasp trends in illegal part-time jobs by creating reports based on the judgment results and collected information. Some or all of the above processes in the report creation unit may be performed using, for example, AI, or not using AI. For example, the report creation unit can input the judgment results and collected information into AI, and the AI ​​can create a report.

[0073] The data collection unit may include a learning unit that trains an AI with the collected information. For example, by training the AI ​​with the collected information, the data collection unit can constantly learn the characteristics of the latest illegal jobs and improve its judgment accuracy. The data collection unit needs to clarify the specific methods and learning content for training the AI. For example, it needs to set the algorithm to be used and the type of training data. The data collection unit can use the learning unit to train the AI ​​with the collected information. This allows the data collection unit to constantly learn the characteristics of the latest illegal jobs and improve its judgment accuracy by training the AI ​​with the collected information. Some or all of the above-described processes in the data collection unit may be performed using an AI, or they may be performed without an AI. For example, the data collection unit can input the collected information into an AI, which can then learn.

[0074] The determination unit may include a providing unit that provides the determination result. The determination unit can, for example, provide the determination result to the user so that the user can confirm the result. The determination unit needs to clarify the method and format of providing the determination result. For example, it can set the notification method and display format. The determination unit can provide the determination result to the user using the providing unit. In this way, the determination unit can provide the determination result so that the user can confirm the result. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the determination result to the AI, and the AI ​​can provide it.

[0075] The report creation unit may include an output unit that outputs the report. For example, the report creation unit can output the created report to the user, allowing the user to review it. The report creation unit needs to clearly define the method and format of report output. For example, it may set PDF format, printing, or email transmission. The report creation unit can output the created report to the user using the output unit. This allows the user to review the report by outputting it. Some or all of the above-described processes in the report creation unit may be performed using AI, for example, or without AI. For example, the report creation unit can input the created report into AI, which can then output it.

[0076] The data collection unit can estimate the user's emotions and adjust the priority of the information it collects based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit will prioritize collecting information that is likely to be related to illegal part-time jobs. If the user is showing interest, the data collection unit can prioritize collecting relevant job postings. If the user is in a hurry, the data collection unit can prioritize collecting information that can be collected quickly. In this way, the data collection unit can collect more appropriate information by adjusting the priority of information based on 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. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then adjust the priority of the information.

[0077] The data collection unit can analyze the user's past data collection history and select the optimal data collection method. For example, the data collection unit can analyze trends in the information the user has collected in the past and prioritize the collection of similar information. The data collection unit can evaluate the accuracy of the information the user has collected in the past and prioritize the collection of highly accurate information. The data collection unit can analyze the categories of the information the user has collected in the past and prioritize the collection of information in related categories. In this way, the data collection unit can select the optimal data collection method by analyzing the user's past data collection history. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's past data collection history into AI, and the AI ​​can select the optimal data collection method.

[0078] The data collection unit can filter data based on the user's current areas of interest during collection. For example, the data collection unit can prioritize collecting information in areas the user is currently interested in. The data collection unit can filter information based on keywords the user is currently interested in. The data collection unit can prioritize collecting information in regions the user is currently interested in. In this way, the data collection unit can collect highly relevant information by filtering information based on the user's current areas of interest. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's current areas of interest into the AI, which can then filter the information.

[0079] The data collection unit can estimate the user's emotions and determine the timing of information collection based on the estimated emotions. For example, if the user is feeling anxious, the data collection unit can quickly collect information. If the user is relaxed, the data collection unit can collect detailed information. If the user is in a hurry, the data collection unit can prioritize collecting important information. This allows the data collection unit to collect information at the appropriate time by determining the timing of information collection based on 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. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit can input user emotion data into a generative AI, which can then determine the timing of information collection.

[0080] The data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information during collection. For example, the data collection unit can prioritize the collection of nearby job postings based on the user's current location. The data collection unit can collect relevant regional information by referring to the user's past travel history. The data collection unit can prioritize the collection of relevant information based on the region set by the user. In this way, the data collection unit can prioritize the collection of highly relevant information by considering the user's geographical location information. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's geographical location information into AI, and the AI ​​can collect the information.

[0081] The data collection unit can analyze the user's social media activity and collect relevant information during the collection process. For example, the data collection unit can prioritize collecting job postings that the user has shown interest in on social media. The data collection unit can also collect information that the user's social media followers have shown interest in. The data collection unit can analyze the content of the user's social media posts and collect relevant information. In this way, the data collection unit can collect relevant information by analyzing the user's social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input the user's social media activity into AI, and the AI ​​can collect the information.

[0082] The judgment unit can estimate the user's emotions and adjust the judgment criteria based on the estimated emotions. For example, if the user is feeling anxious, the judgment unit can make a judgment using strict criteria. If the user is relaxed, the judgment unit can make a judgment using flexible criteria. If the user is in a hurry, the judgment unit can make a judgment quickly. In this way, the judgment unit can make a more appropriate judgment by adjusting the judgment criteria based on 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. Some or all of the above processing in the judgment unit may be performed using AI, for example, or not using AI. For example, the judgment unit can input user emotion data into the generative AI, and the generative AI can adjust the judgment criteria.

[0083] The judgment unit can improve the accuracy of its judgment by considering the interrelationships of the collected information during the judgment process. For example, the judgment unit analyzes the relationships between the collected information and makes a judgment based on the interrelated information. The judgment unit can identify factors that increase the likelihood of illegal part-time work by considering the interrelationships of the collected information. The judgment unit can improve the accuracy of its judgment based on the interrelationships of the collected information. In this way, the judgment unit can improve the accuracy of its judgment by considering the interrelationships of the collected information. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the interrelationships of the collected information into the AI, and the AI ​​can improve the accuracy of its judgment.

[0084] The judgment unit can make a judgment by considering the attribute information of the information submitter. For example, the judgment unit can analyze the past posting history of the information submitter and evaluate its reliability. Based on the attribute information of the information submitter, the judgment unit can determine the possibility of illegal part-time work. The judgment unit can improve the accuracy of its judgment by considering the attribute information of the information submitter. In this way, the judgment unit can improve the accuracy of its judgment by considering the attribute information of the information submitter. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input the attribute information of the information submitter into AI, and the AI ​​can make the judgment.

[0085] The judgment unit can estimate the user's emotions and adjust the display method of the judgment result based on the estimated user emotions. For example, if the user is feeling anxious, the judgment unit can provide a display method that includes a detailed explanation. If the user is relaxed, the judgment unit can provide a concise display method. If the user is in a hurry, the judgment unit can display the judgment result quickly. In this way, the judgment unit can provide a display that is easy for the user to understand by adjusting the display method of the judgment result based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without AI. For example, the judgment unit can input the user's emotion data into the generative AI, and the generative AI can adjust the display method of the judgment result.

[0086] The determination unit can make a determination by considering the geographical distribution of information. For example, the determination unit can analyze the geographical distribution of information and determine the possibility of illegal part-time jobs in a particular area. Based on the geographical distribution of information, the determination unit can identify trends in illegal part-time jobs. The determination unit can improve the accuracy of its determination by considering the geographical distribution of information. As a result, the determination unit can determine the possibility of illegal part-time jobs in a particular area by considering the geographical distribution of information. Some or all of the above processing in the determination unit may be performed using AI, for example, or without using AI. For example, the determination unit can input the geographical distribution of information into AI, and the AI ​​can make the determination.

[0087] The judgment unit can improve the accuracy of its judgment by referring to relevant literature on the information during the judgment process. For example, the judgment unit can refer to relevant literature on the information to identify the characteristics of illegal part-time jobs. The judgment unit can improve the accuracy of its judgment based on the relevant literature on the information. The judgment unit can determine the possibility of an illegal part-time job by referring to relevant literature on the information. In this way, the judgment unit can improve the accuracy of its judgment by referring to relevant literature on the information. Some or all of the above processing in the judgment unit may be performed using AI, for example, or without using AI. For example, the judgment unit can input relevant literature on the information into AI, and the AI ​​can perform the judgment.

[0088] The reporting unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is feeling anxious, the reporting unit can provide a report with a detailed explanation. If the user is relaxed, the reporting unit can provide a concise report. If the user is in a hurry, the reporting unit can provide a report quickly. This allows the reporting unit to provide a user-friendly presentation by adjusting how the report is displayed based on 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 be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reporting unit may be performed using AI or not using AI. For example, the reporting unit can input user emotion data into a generative AI, which can then adjust how the report is displayed.

[0089] The report generation unit can optimize the current report by referring to past report data when creating a report. For example, the report generation unit can analyze past report data and reflect it in the current report. The report generation unit can improve the accuracy of the report based on past report data. The report generation unit can optimize the current report by referring to past report data. In this way, the report generation unit can improve the accuracy of the current report by referring to past report data. Some or all of the above processes in the report generation unit may be performed using AI, for example, or without using AI. For example, the report generation unit can input past report data into AI, and the AI ​​can optimize the current report.

[0090] The report generation unit can apply different report generation methods to each category of information when creating a report. For example, the report generation unit can select an appropriate report generation method for each category of information. The report generation unit can create reports in different formats for each category of information. The report generation unit can create reports by applying different analytical methods for each category of information. In this way, the report generation unit can improve the accuracy of the report by applying an appropriate report generation method for each category of information. Some or all of the above processes in the report generation unit may be performed using AI, for example, or not using AI. For example, the report generation unit can input the categories of information into the AI, and the AI ​​can apply different report generation methods.

[0091] The report generation unit can estimate the user's emotions and adjust the importance of the report based on the estimated emotions. For example, if the user is feeling anxious, the report generation unit can prioritize including highly important information in the report. If the user is relaxed, the report generation unit can include detailed information in the report. If the user is in a hurry, the report generation unit can provide a concise report. In this way, the report generation unit can prioritize providing important information by adjusting the importance of the report based on 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. Some or all of the above processing in the report generation unit may be performed using AI or not using AI. For example, the report generation unit can input user emotion data into a generative AI, which can then adjust the importance of the report.

[0092] The report creation unit can analyze changes in reports based on the timing of information submission when creating reports. For example, the report creation unit can analyze the timing of information submission and identify changes in reports. The report creation unit can analyze trends in reports based on the timing of information submission. The report creation unit can analyze changes in reports taking the timing of information submission into consideration. This allows the report creation unit to grasp trends in reports by analyzing changes in reports based on the timing of information submission. Some or all of the above processes in the report creation unit may be performed using AI, for example, or not using AI. For example, the report creation unit can input the timing of information submission into AI, and the AI ​​can analyze changes in reports.

[0093] The report creation unit can analyze the report by referring to relevant market data when creating the report. For example, the report creation unit can improve the accuracy of the report by referring to relevant market data. The report creation unit can optimize the content of the report based on relevant market data. The report creation unit can analyze the report by referring to relevant market data. In this way, the report creation unit can improve the accuracy of the report by referring to relevant market data. Some or all of the above processes in the report creation unit may be performed using AI, for example, or not using AI. For example, the report creation unit can input relevant market data into AI, and the AI ​​can analyze the report.

[0094] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is feeling anxious, the learning unit will prioritize selecting information that is likely to be related to illegal part-time jobs as training data. If the user shows interest, the learning unit can select relevant job postings as training data. If the user is in a hurry, the learning unit can prioritize selecting information that can be learned quickly. In this way, the learning unit can perform more appropriate learning by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the user's emotion data into a generative AI, and the generative AI can select training data.

[0095] The learning unit can optimize the learning algorithm by referring to past learning data during learning. For example, the learning unit can analyze past learning data and optimize the learning algorithm. The learning unit can improve the accuracy of learning based on past learning data. The learning unit can optimize the learning algorithm by referring to past learning data. In this way, the learning unit can improve the accuracy of the learning algorithm by referring to past learning data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without using AI. For example, the learning unit can input past learning data into AI, and the AI ​​can optimize the learning algorithm.

[0096] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is feeling anxious, the learning unit can learn frequently and provide the latest information. If the user is relaxed, the learning unit can learn at a moderate frequency. If the user is in a hurry, the learning unit can learn quickly and provide the necessary information. In this way, the learning unit can learn at an appropriate frequency by adjusting the learning frequency based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI, for example, or not using AI. For example, the learning unit can input user emotion data into the generative AI, and the generative AI can adjust the learning frequency.

[0097] The learning unit can weight the training data based on when the information was submitted during training. For example, the learning unit can assign a higher weight to the most recent information, taking into account when it was submitted. The learning unit can assign a lower weight to older information, also based on when it was submitted. The learning unit can analyze when the information was submitted and assign appropriate weights. This allows the learning unit to prioritize the most recent information by weighting the training data based on when it was submitted. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit can input the information submission dates into the AI, and the AI ​​can weight the training data.

[0098] The service provider can estimate the user's emotions and adjust how the information is displayed based on the estimated emotions. For example, if the user is feeling anxious, the service provider can provide a display method that includes detailed explanations. If the user is relaxed, the service provider can provide a concise display method. If the user is in a hurry, the service provider can provide information quickly. This allows the service provider to provide information in a way that is easy for the user to understand by adjusting how the information is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into the generative AI, and the generative AI can adjust how the information is displayed.

[0099] The service provider can select the optimal service delivery method by referring to the user's past operation history at the time of delivery. For example, the service provider can analyze the user's past operation history and select the optimal service delivery method. The service provider can optimize the service delivery method based on the user's past operation history. The service provider can select the optimal service delivery method by referring to the user's past operation history. In this way, the service provider can select the optimal service delivery method by referring to the user's past operation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without using AI. For example, the service provider can input the user's past operation history into AI, and the AI ​​can select the optimal service delivery method.

[0100] The service provider can estimate the user's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the user is feeling anxious, the service provider can prioritize providing high-priority information. If the user is relaxed, the service provider can provide detailed information. If the user is in a hurry, the service provider can prioritize providing information that can be delivered quickly. In this way, the service provider can prioritize providing important information by prioritizing information based on 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. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI, and the generative AI can determine the priority of information.

[0101] The delivery unit can select the optimal delivery method by considering the user's device information at the time of delivery. For example, if the user is using a smartphone, the delivery unit can select a delivery method that matches the screen size. If the user is using a tablet, the delivery unit can select a delivery method optimized for a large screen. If the user is using a smartwatch, the delivery unit can select a concise and highly visible delivery method. In this way, the delivery unit can select the optimal delivery method by considering the user's device information. Some or all of the above processing in the delivery unit may be performed using AI, for example, or without AI. For example, the delivery unit can input the user's device information into AI, and the AI ​​can select the optimal delivery method.

[0102] The output unit can estimate the user's emotions and adjust how the report is displayed based on the estimated emotions. For example, if the user is feeling anxious, the output unit can provide a report with a detailed explanation. If the user is relaxed, the output unit can provide a concise report. If the user is in a hurry, the output unit can provide a report quickly. This allows the output unit to provide a user-friendly presentation by adjusting how the report is displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the output unit may be performed using AI or not using AI. For example, the output unit can input user emotion data into a generative AI, which can then adjust how the report is displayed.

[0103] The output unit can select the optimal output method by referring to the user's past operation history when outputting. For example, the output unit can analyze the user's past operation history and select the optimal output method. The output unit can optimize the output method based on the user's past operation history. The output unit can select the optimal output method by referring to the user's past operation history. In this way, the output unit can select the optimal output method by referring to the user's past operation history. Some or all of the above processing in the output unit may be performed using AI, for example, or without using AI. For example, the output unit can input the user's past operation history into AI, and the AI ​​can select the optimal output method.

[0104] The output unit can estimate the user's emotions and determine the priority of the reports to be output based on the estimated user emotions. For example, if the user is feeling anxious, the output unit can prioritize including highly important information in the report. If the user is relaxed, the output unit can include detailed information in the report. If the user is in a hurry, the output unit can provide a concise report. In this way, the output unit can prioritize providing important information by determining the priority of the report based on 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. Some or all of the above processing in the output unit may be performed using AI or not using AI. For example, the output unit can input user emotion data into a generative AI, and the generative AI can determine the priority of the reports.

[0105] The output unit can select the optimal output method when outputting, taking into account the user's device information. For example, if the user is using a smartphone, the output unit can select an output method that matches the screen size. If the user is using a tablet, the output unit can select an output method optimized for a large screen. If the user is using a smartwatch, the output unit can select a concise and highly visible output method. In this way, the output unit can select the optimal output method by taking into account the user's device information. Some or all of the above processing in the output unit may be performed using AI, for example, or without AI. For example, the output unit can input the user's device information into AI, and the AI ​​can select the optimal output method.

[0106] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0107] The data collection unit analyzes the user's past search history and prioritizes collecting job postings that are likely to interest the user. For example, if a user has previously shown interest in a particular industry or job type, it will prioritize collecting job postings related to that industry or job type. Furthermore, if a user has frequently searched for job postings in a particular region in the past, it will prioritize collecting job postings in that region. Also, if a user has frequently viewed job postings from a particular company in the past, it will prioritize collecting new job postings from that company. In this way, the data collection unit can efficiently collect job postings that are highly relevant to the user by taking into account the user's past search history.

[0108] The judgment unit can consider the reliability of the source and provider of the collected information in order to evaluate its reliability. For example, information collected from reliable job sites or official company websites will be judged as highly reliable. On the other hand, information collected from anonymous bulletin boards or unreliable sites will be judged as unreliable. Furthermore, the judgment unit can analyze the information provider's past posting history and ratings to evaluate reliability. In this way, the judgment unit can make more accurate judgments by evaluating the reliability of the collected information.

[0109] The report creation department can customize the content of reports according to the user's interests. For example, if a user is interested in a particular industry or job type, the report will focus on including information related to that industry or job type. Similarly, if a user is interested in job postings in a specific region, the report can include detailed information on job postings in that region. Furthermore, if a user is interested in a particular company, the report can include job postings and related news from that company. This allows the report creation department to provide customized reports tailored to the user's interests.

[0110] The data collection unit can update the collected information in real time. For example, when a new job posting is published, it can quickly collect it and add it to the database. Furthermore, if there are changes to existing job postings, those changes can be reflected immediately. In addition, the data collection unit can set a schedule for regular information updates, ensuring that the latest information is always available. This allows the data collection unit to consistently provide the most up-to-date job postings.

[0111] The judgment unit can analyze the collected information in detail and identify factors that increase the likelihood of it being an illegal job. For example, if the job posting contains illegal keywords or phrases, it will focus its analysis on that information. It can also analyze in detail if the compensation is unusually high or the job description is unclear. Furthermore, if the provider of the job posting has previously provided illegal job postings, it will focus its analysis on that information. As a result, the judgment unit can identify factors that increase the likelihood of it being an illegal job and make a more accurate judgment.

[0112] The data collection unit can estimate the user's emotions and evaluate the reliability of the information it collects based on those emotions. For example, if the user is feeling anxious, it will prioritize collecting reliable information. If the user is showing interest, the data collection unit can prioritize collecting relevant information. If the user is in a hurry, the data collection unit can prioritize collecting information that can be retrieved quickly. By evaluating the reliability of information based on the user's emotions, the data collection unit can collect more appropriate information.

[0113] The judgment unit can estimate the user's emotions and adjust the notification method of the judgment result based on the estimated user emotions. For example, if the user is feeling anxious, it can provide a notification method that includes a detailed explanation. If the user is relaxed, the judgment unit can provide a concise notification method. If the user is in a hurry, the judgment unit can provide the judgment result quickly. In this way, by adjusting the notification method of the judgment result based on the user's emotions, the judgment unit can provide notifications that are easy for the user to understand.

[0114] The reporting department can estimate the user's emotions and adjust the report content based on those emotions. For example, if the user is feeling anxious, it can provide a report with detailed explanations. If the user is relaxed, it can provide a concise report. If the user is in a hurry, it can provide a report quickly. In this way, the reporting department can create reports that are easy for users to understand by adjusting the content based on their emotions.

[0115] The data collection unit can estimate the user's emotions and adjust the categories of information it collects based on those estimated emotions. For example, if the user is feeling anxious, it will prioritize collecting information that is likely to be related to illegal part-time jobs. If the user has shown interest, the data collection unit can prioritize collecting relevant job postings. If the user is in a hurry, the data collection unit can prioritize collecting information that can be retrieved quickly. In this way, the data collection unit can collect more relevant information by adjusting the categories of information based on the user's emotions.

[0116] The judgment unit can estimate the user's emotions and adjust the importance of the judgment results based on the estimated emotions. For example, if the user is feeling anxious, it will prioritize judging information of high importance. If the user is relaxed, the judgment unit can judge detailed information. If the user is in a hurry, the judgment unit can provide judgment results quickly. In this way, the judgment unit can prioritize providing important information by adjusting the importance of the judgment results based on the user's emotions.

[0117] The following briefly describes the processing flow for example form 2.

[0118] Step 1: The collection unit collects information from the internet. For example, it can collect job postings from job search websites and social media, and can also collect information from specific websites, social media, and bulletin boards. The collection unit can automatically collect job postings using APIs and can search for and collect job postings using specific keywords. The collected information is stored in a database. Step 2: The judgment unit determines whether the job posting is an illegal job based on the information collected by the collection unit. For example, it may use a machine learning algorithm to determine whether it is an illegal job, and make a judgment based on criteria such as the detection of specific keywords, the presence or absence of illegality, the lack of transparency in compensation, and the danger of the work content. The judgment results are stored in a database. Step 3: The report generation unit creates a report based on the results determined by the judgment unit. For example, it creates a report using text format, graphs, and charts, and analyzes the trends of illegal part-time jobs based on the judgment results and collected information. The created report is saved in the database and can be output.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] Each of the multiple elements described above, including the data collection unit, determination unit, and report generation unit, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart device 14 and collects information from the internet. The determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines whether the job postings are illegal part-time jobs based on the collected information. The report generation unit is implemented by the identification processing unit 290 of the data processing device 12 and creates a report based on the determination result. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0123] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] Each of the multiple elements described above, including the data collection unit, determination unit, and report generation unit, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the data collection unit is implemented by the control unit 46A of the smart glasses 214 and collects information from the internet. The determination unit is implemented by the identification processing unit 290 of the data processing device 12 and determines whether the job postings are illegal part-time jobs based on the collected information. The report generation unit is implemented by the identification processing unit 290 of the data processing device 12 and creates a report based on the determination result. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0139] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] Each of the multiple elements described above, including the data collection unit, determination unit, and report generation unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the headset terminal 314 and collects information from the internet. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines whether the job postings are illegal part-time jobs based on the collected information. The report generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates a report based on the determination result. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0155] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.).

[0168] 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.

[0169] 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.

[0170] 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.

[0171] Each of the multiple elements described above, including the data collection unit, determination unit, and report generation unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the data collection unit is implemented by the control unit 46A of the robot 414 and collects information from the internet. The determination unit is implemented by the identification processing unit 290 of the data processing unit 12 and determines whether the job postings are illegal part-time jobs based on the collected information. The report generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and creates a report based on the determination result. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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."

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] (Note 1) The collection department collects information from the internet, Based on the information collected by the aforementioned collection unit, a determination unit determines whether or not the job postings constitute illegal part-time work. The system includes a report generation unit that generates a report based on the results determined by the determination unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Collect job information from job websites and social media. The system described in Appendix 1, characterized by the features described herein. (Note 3) The determination unit, Based on the collected information, we will determine whether or not it is an illegal part-time job. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned report creation unit, A report is created based on the assessment results and collected information. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned collection unit is It includes a learning unit that trains the AI ​​using the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 6) The determination unit, It includes a unit that provides the judgment result. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned report creation unit, It includes an output unit for generating reports. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is It estimates the user's emotions and adjusts the priority of the information collected based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze the user's past data collection history and select the optimal data collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is During data collection, filtering is performed based on the user's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and determines the timing of information collection based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is During data collection, the system prioritizes collecting highly relevant information, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is During data collection, the user's social media activity is analyzed to gather relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The determination unit, The system estimates the user's emotions and adjusts the criteria for judgment based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The determination unit, When making a decision, the accuracy of the decision is improved by considering the interrelationships of the collected information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The determination unit, When making a decision, the attribute information of the information submitter is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 17) The determination unit, The system estimates the user's emotions and adjusts how the judgment results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The determination unit, When making a decision, the geographical distribution of the information is taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 19) The determination unit, When making a judgment, we refer to relevant literature to improve the accuracy of the judgment. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned report creation unit, It estimates user sentiment and adjusts how reports are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned report creation unit, When creating a report, refer to past report data to optimize the current report. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned report creation unit, When creating reports, apply different report creation methods to each category of information. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned report creation unit, We estimate user sentiment and adjust the importance of the report based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned report creation unit, When creating a report, analyze how the report changes based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned report creation unit, When creating a report, we analyze the report by referring to relevant market data. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned learning unit, During training, the learning algorithm is optimized by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned learning unit, During training, the training data is weighted based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned supply unit is, It estimates the user's emotions and adjusts how information is displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned supply unit is, When providing the service, the optimal delivery method is selected by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned supply unit is, It estimates the user's emotions and prioritizes the information provided based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned supply unit is, When providing the service, the optimal delivery method will be selected, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The output unit is, We estimate user sentiment and adjust how reports are displayed based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 35) The output unit is, During output, the system selects the optimal output method by referring to the user's past operation history. The system described in Appendix 1, characterized by the features described herein. (Note 36) The output unit is, It estimates user sentiment and determines the priority of reports to be generated based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 37) The output unit is, During output, the system selects the optimal output method, taking into account the user's device information. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0191] 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. The collection department collects information from the internet, Based on the information collected by the aforementioned collection unit, a determination unit determines whether or not the job postings constitute illegal part-time work. The system includes a report generation unit that generates a report based on the results determined by the determination unit. A system characterized by the following features.

2. The aforementioned collection unit is Collect job information from job websites and social media. The system according to feature 1.

3. The determination unit, Based on the collected information, we will determine whether or not it is an illegal part-time job. The system according to feature 1.

4. The aforementioned report creation unit, A report is created based on the assessment results and collected information. The system according to feature 1.

5. The aforementioned collection unit is It includes a learning unit that trains the AI ​​with the collected information. The system according to feature 1.

6. The determination unit, It includes a unit that provides the judgment result. The system according to feature 1.

7. The aforementioned report creation unit, It includes an output unit for generating reports. The system according to feature 1.

8. The aforementioned collection unit is It estimates the user's emotions and adjusts the priority of the information collected based on the estimated user emotions. The system according to feature 1.

9. The aforementioned collection unit is Analyze the user's past data collection history and select the optimal data collection method. The system according to feature 1.

10. The aforementioned collection unit is During data collection, filtering is performed based on the user's current areas of interest. The system according to feature 1.

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