system

Abstract

The system according to this embodiment aims to generate and provide job postings that include elements that appeal to the emotions of job seekers. [Solution] The system according to the embodiment comprises a generation unit, a provision unit, and an extraction unit. The generation unit generates job postings. The provision unit provides the job postings generated by the generation unit. The extraction unit extracts elements that appeal to the emotions of job seekers based on the job postings provided by the provision 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 a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the job offer information does not sufficiently contain elements that appeal to the emotions of job seekers, and it is difficult for job seekers to find jobs that match their suitability and interests.

[0005] The system according to the embodiment aims to generate and provide job offer information including elements that appeal to the emotions of job seekers.

Means for Solving the Problems

[0006] The system according to the embodiment includes a generation unit, a provision unit, and an extraction unit. The generation unit generates job offer information. The provision unit provides the job offer information generated by the generation unit. The extraction unit extracts elements that appeal to the emotions of job seekers based on the job offer information provided by the provision unit. [Effects of the Invention]

[0007] The system according to this embodiment can generate and provide job postings that include elements that appeal to the emotions of job seekers. [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).

[0019] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. 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 generation system according to an embodiment of the present invention is a system that uses AI to automatically generate job postings that appeal to the emotions of job seekers. This job posting generation system generates information that includes elements that appeal to the emotions of job seekers, in addition to information such as employment conditions and salary, in order to make it easier for job seekers to find a job that suits their aptitude and interests. For example, the job posting generation system can generate images that show the atmosphere of the workplace and the enjoyment of working there, as well as text that job seekers can easily relate to. As a result, job seekers can feel the atmosphere of the workplace and the enjoyment of working there just by looking at the job posting. Next, the generated job postings are provided to make it easier for job seekers to find a job that suits their aptitude and interests. Specifically, when a job seeker searches for job postings, the AI ​​prioritizes displaying job postings that include elements that appeal to the emotions of the job seeker. As a result, it becomes easier for job seekers to find a job that suits their aptitude and interests. For example, if a job seeker searches for "fun workplace," the AI ​​prioritizes displaying job postings that show the atmosphere of the workplace and the enjoyment of working there. Furthermore, the AI ​​utilizes one of the largest job posting databases in the industry when generating job postings. This allows companies to differentiate themselves from competitors and provide attractive job opportunities to a wider range of job seekers. For example, AI can analyze one of the industry's largest job databases and extract elements that are likely to interest job seekers, generating unique job postings that are not available from other companies. This system makes it easier for job seekers to find jobs that match their aptitudes and interests, and allows companies to receive applications from more job seekers. For instance, if a job seeker searches for a "fun workplace," they are more likely to see the job posting generated by the AI, become interested in that workplace, and apply. Furthermore, companies can reduce the effort required to generate job postings. For example, by having the AI ​​automatically generate job postings, companies can reduce the time and effort spent creating job postings. As a result, the job posting generation system automatically generates job postings that appeal to job seekers' emotions, making it easier for them to find jobs that match their aptitudes and interests.

[0029] The job posting generation system according to this embodiment comprises a generation unit, a provision unit, and an extraction unit. The generation unit generates job postings. The generation unit generates information that includes not only information such as employment conditions and salary, but also elements that appeal to the emotions of job seekers. For example, the generation unit can generate images that show the atmosphere of the workplace and the enjoyment of working, and text that job seekers can easily relate to. For example, the generation unit uses AI to analyze images and text included in the job postings and extracts elements that job seekers are likely to be interested in. The provision unit provides the job postings generated by the generation unit. For example, when a job seeker searches for job postings, the provision unit prioritizes displaying job postings that include elements that appeal to the emotions of the job seeker. For example, if a job seeker searches for "fun workplace," the provision unit uses AI to prioritize displaying job postings that show the atmosphere of the workplace and the enjoyment of working. The extraction unit extracts elements that appeal to the emotions of job seekers based on the job postings provided by the provision unit. For example, the extraction unit analyzes images and text included in the job postings and extracts elements that job seekers are likely to be interested in. The extraction unit, for example, uses AI to analyze images and text included in job postings and extracts elements that job seekers are likely to empathize with. As a result, the job posting generation system according to this embodiment can automatically generate job postings that appeal to the emotions of job seekers, making it easier for them to find jobs that match their aptitudes and interests.

[0030] The generation unit generates job postings. This unit generates information that includes not only employment conditions and salary, but also elements that appeal to job seekers' emotions. Specifically, the generation unit automatically generates job postings using AI. The AI ​​analyzes past job posting databases and learns elements that job seekers are likely to find interesting. For example, it can generate images that show the workplace atmosphere and the enjoyment of working there, as well as text that job seekers can easily relate to. The AI ​​uses natural language processing technology to generate the text of the job postings and image recognition technology to select appropriate images. Furthermore, the generation unit can also generate individually customized job postings based on job seekers' profile information and past application history. For example, by prioritizing the inclusion of information related to the types of jobs the job seeker has applied for in the past or the industries they are interested in, it provides more personalized job postings. This allows the generation unit to efficiently generate job postings that meet the needs and interests of job seekers, making it easier for them to find a suitable job.

[0031] The provisioning unit provides job postings generated by the generation unit. For example, when a job seeker searches for job postings, the provisioning unit prioritizes displaying job postings that contain elements that appeal to the job seeker's emotions. Specifically, the provisioning unit uses AI to analyze the job seeker's search and browsing history to identify job postings that are likely to interest them. For example, if a job seeker searches for "fun workplace," the provisioning unit will prioritize displaying job postings that show the workplace atmosphere and the enjoyment of working there. The AI ​​analyzes the job seeker's search keywords and browsing patterns and displays highly relevant job postings in real time. In addition, the provisioning unit can collect feedback from job seekers and continuously improve the accuracy of the displayed job postings. For example, if a job seeker provides feedback such as "interested" or "want to apply" for a particular job posting, the provisioning unit will use that information to display similar job postings to other job seekers. In this way, the provisioning unit can effectively provide job postings that appeal to the emotions of job seekers, making it easier for them to find a job that suits them.

[0032] The extraction unit extracts elements that appeal to job seekers' emotions based on the job information provided by the supply unit. Specifically, the extraction unit uses AI to analyze images and text included in the job information and extract elements that are likely to interest job seekers. The AI ​​uses image recognition technology to analyze images included in the job information and identify elements that indicate the workplace atmosphere and the enjoyment of working there. It also uses natural language processing technology to analyze the text of the job information and extract phrases and expressions that job seekers are likely to empathize with. Furthermore, the extraction unit can learn which elements of the job information are attractive to job seekers based on their feedback. For example, if a job seeker rates a particular image or text as "interesting," the extraction unit can use that information to include similar elements in other job information. In this way, the extraction unit can effectively extract elements that appeal to job seekers' emotions and, in cooperation with the generation and supply units, provide more attractive job information.

[0033] The generation unit can generate information that includes elements that appeal to the emotions of job seekers, in addition to information such as employment conditions and salary. For example, the generation unit can generate images that show the workplace atmosphere and the enjoyment of working, and text that job seekers can easily relate to. For example, the generation unit uses AI to analyze images and text included in job postings and extract elements that job seekers are likely to be interested in. For example, the generation unit uses AI to analyze images and text included in job postings and extract elements that job seekers are likely to relate to. By generating job postings that include elements that appeal to the emotions of job seekers, it becomes easier for job seekers to become interested.

[0034] The extraction unit can analyze images and text contained in job postings and extract elements that are likely to interest job seekers. For example, the extraction unit can analyze images and text contained in job postings and extract elements that are likely to interest job seekers. For example, the extraction unit can use AI to analyze images and text contained in job postings and extract elements that job seekers can easily relate to. By extracting elements that are likely to interest job seekers, the attractiveness of job postings is enhanced.

[0035] The system can prioritize displaying job postings that contain elements that appeal to the job seeker's emotions when they search for job information. For example, if a job seeker searches for "fun workplace," the system can prioritize displaying job postings that show the workplace atmosphere and the enjoyment of working there. By prioritizing the display of job postings that appeal to the job seeker's emotions, the system makes it easier for job seekers to find jobs that match their aptitudes and interests.

[0036] The generation unit can analyze the industry's largest job information database and extract elements that are likely to interest job seekers. For example, the generation unit can analyze the industry's largest job information database and extract elements that are likely to interest job seekers. For example, the generation unit can use AI to analyze the industry's largest job information database and extract elements that are likely to interest job seekers. For example, the generation unit can use AI to analyze the industry's largest job information database and extract elements that are likely to interest job seekers. This allows for differentiation from competitors by leveraging the industry's largest job information database.

[0037] The generation unit can analyze a job seeker's past application history and generate the most suitable job postings. For example, the generation unit generates relevant job postings based on the job type and industry the job seeker has applied to in the past. For example, the generation unit analyzes the characteristics of companies the job seeker has applied to in the past and generates job postings for similar companies. For example, the generation unit generates the most suitable job postings based on the success rate of positions the job seeker has applied for in the past. In this way, by generating the most suitable job postings based on the job seeker's past application history, it is possible to provide job postings that meet the job seeker's needs.

[0038] The generation unit can customize job postings based on job seekers' current skill sets and career goals. For example, the generation unit can analyze a job seeker's skill set and generate job postings that utilize those skills. For example, the generation unit can consider a job seeker's career goals and generate job postings that help them achieve those goals. For example, the generation unit can identify a job seeker's skill gaps and generate job postings that offer opportunities for skill development. This increases job seeker satisfaction by providing job postings that match their skill sets and career goals.

[0039] The generation unit can generate region-specific job postings by considering the job seeker's geographical location information. For example, the generation unit can generate job postings with short commute times based on the job seeker's current location. For example, the generation unit can generate job postings that are suited to the characteristics of a region based on the job seeker's geographical location information. For example, the generation unit can generate job postings that reflect the trends of the regional job market based on the job seeker's geographical location information. In this way, by providing job postings based on the job seeker's geographical location information, it is possible to provide job postings that are suited to commute times and regional characteristics.

[0040] The generation unit can analyze job seekers' social media activity and generate relevant job postings. For example, the generation unit generates relevant job postings based on job seekers' interests and preferences on social media. For example, the generation unit analyzes job seekers' social media activity history and generates suitable job postings. For example, the generation unit considers job seekers' social media networks and generates job postings from companies within those networks. By providing job postings based on job seekers' social media activity, it is possible to provide job postings that match job seekers' interests and preferences.

[0041] The service provider can provide job seekers with the most suitable job information by referring to their past search history. For example, the service provider can provide relevant job information based on the job types and industries that job seekers have searched for in the past. For example, the service provider can analyze job seekers' past search history and provide job information that is likely to interest them. For example, the service provider can prioritize providing job information from specific companies based on job seekers' past search history. In this way, by providing job information based on job seekers' past search history, the service provider can provide job information that matches the needs of job seekers.

[0042] The service provider can customize the display order of job postings based on the job seeker's current areas of interest. For example, the service provider can prioritize displaying relevant job postings based on the job seeker's current areas of interest. For example, the service provider can customize the display order of job postings based on the job seeker's areas of interest. For example, the service provider can analyze the job seeker's areas of interest and display the most relevant job postings at the top. This makes it easier to attract the job seeker's interest by providing a display order of job postings that is tailored to the job seeker's areas of interest.

[0043] The service provider can select the optimal display method considering the job seeker's device information. For example, if the job seeker is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the job seeker is using a tablet, the service provider will provide a display method optimized for a larger screen. For example, if the job seeker is using a desktop, the service provider will provide a display method that includes detailed information. By providing a display method based on the job seeker's device information, the service provider can provide job information that is easy for job seekers to use.

[0044] The service provider can analyze a job seeker's past application results and provide relevant job information. For example, the service provider can provide relevant job information based on the results of job applications the job seeker has previously submitted. For example, the service provider can analyze a job seeker's past application results and provide job information with a high success rate. For example, the service provider can prioritize providing job information from specific companies based on a job seeker's past application results. In this way, by providing job information based on a job seeker's past application results, it is possible to provide job information that matches the job seeker's needs.

[0045] The extraction unit can analyze images and text from job postings and extract elements that job seekers are likely to relate to. For example, the extraction unit can analyze images from job postings and extract elements that indicate the workplace atmosphere. For example, the extraction unit can analyze text from job postings and extract words and phrases that job seekers are likely to relate to. For example, the extraction unit can combine images and text from job postings to extract elements that job seekers are likely to be interested in. In this way, the attractiveness of job postings is enhanced by extracting elements that job seekers are likely to relate to.

[0046] The extraction unit can extract the most relevant elements by referring to the job seeker's past application history. For example, the extraction unit extracts relevant elements based on the job seeker's past application history. For example, the extraction unit analyzes the job seeker's past application history and extracts elements that are likely to be of interest. For example, the extraction unit prioritizes extracting elements related to specific companies from the job seeker's past application history. In this way, by extracting elements based on the job seeker's past application history, it is possible to provide elements that match the job seeker's needs.

[0047] The extraction unit can extract relevant elements considering the geographical distribution of job postings. For example, the extraction unit can extract elements that are appropriate to the characteristics of a region based on the geographical distribution of job postings. For example, the extraction unit can analyze the geographical distribution of job postings and extract elements that are likely to interest job seekers. For example, the extraction unit can extract elements that reflect trends in the local job market, taking into account the geographical distribution of job postings. In this way, by providing elements based on the geographical distribution of job postings, it is possible to provide job postings that are appropriate to the characteristics of a region.

[0048] The extraction unit can improve the accuracy of its extraction by referring to relevant literature on job postings. For example, the extraction unit can refer to relevant literature on job postings and extract elements that job seekers are likely to relate to. The extraction unit can, for example, analyze relevant literature on job postings to improve the accuracy of its extraction. For example, the extraction unit can extract elements that job seekers are likely to be interested in based on relevant literature on job postings. This allows for the extraction of elements that job seekers are likely to relate to by referring to relevant literature on job postings.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The job posting generation system can also include a feedback collection unit. This unit collects reactions and feedback from job seekers after they view job postings. For example, it can record click-through rates and time spent viewing job postings, and analyze which elements were appealing to them. It can also provide a function for job seekers to leave comments and ratings on job postings, allowing for improvements to the content based on this feedback. This enables the job posting generation system to provide job postings that reflect actual job seekers' reactions, thereby increasing job seeker satisfaction.

[0051] The job posting generation system can also include a skills matching unit. This unit compares the job seeker's skill set with the skill requirements listed in the job posting to achieve the best possible match. For example, if a job seeker's skills match the skill requirements in a job posting, that job posting will be displayed preferentially. The system can also provide training information to supplement any skills lacking in the job seeker's skills. This makes it easier for job seekers to find job postings that match their skills, and enables companies to hire appropriate personnel based on the job seeker's skills.

[0052] The job posting generation system can also include a career advice section. This section provides appropriate job postings and career advice based on the job seeker's career path and goals. For example, if a job seeker has a specific career goal, it can provide job postings that outline the steps and necessary skills to reach that goal. Furthermore, if a job seeker is considering a career change, it can provide career paths in new fields and related job postings. This makes it easier for job seekers to find job postings that match their career goals and clarify their career direction.

[0053] The job posting generation system can also include a company evaluation section. This section provides job seekers with evaluation information about companies they are considering applying to. For example, it collects and provides information on employee satisfaction, turnover rates, and corporate culture. It also provides a function that allows job seekers to leave evaluations and comments about companies, and share this information with other job seekers. This makes it easier for job seekers to understand the reality of companies and make more informed decisions.

[0054] The job posting generation system can also be equipped with interactive job posting generation functions. These interactive functions allow job seekers to customize job postings based on their interests. For example, if a job seeker is interested in a particular industry or job type, the system will prioritize displaying information related to that industry or job type. Furthermore, by specifying specific conditions (salary, location, working hours, etc.), the system can generate job postings that match those conditions. This makes it easy for job seekers to find job postings that meet their needs.

[0055] The following briefly describes the processing flow for example form 1.

[0056] Step 1: The generation unit generates job postings. The generation unit generates information that includes not only employment conditions and salary, but also elements that appeal to the emotions of job seekers. For example, it can generate images that show the workplace atmosphere and the enjoyment of working there, and text that job seekers can easily relate to. The generation unit uses AI to analyze the images and text included in the job postings and extract elements that are likely to interest job seekers. Step 2: The providing unit provides the job information generated by the generating unit. When a job seeker searches for job information, the providing unit prioritizes displaying job information that includes elements that appeal to the job seeker's emotions. For example, if a job seeker searches for "fun workplace" using AI, the providing unit will prioritize displaying job information that shows the workplace atmosphere and the enjoyment of working there. Step 3: The extraction unit extracts elements that appeal to job seekers' emotions based on the job information provided by the supply unit. The extraction unit analyzes the images and text contained in the job information and extracts elements that job seekers are likely to be interested in. For example, AI is used to analyze the images and text contained in the job information and extract elements that job seekers are likely to empathize with.

[0057] (Example of form 2) The job posting generation system according to an embodiment of the present invention is a system that uses AI to automatically generate job postings that appeal to the emotions of job seekers. This job posting generation system generates information that includes elements that appeal to the emotions of job seekers, in addition to information such as employment conditions and salary, in order to make it easier for job seekers to find a job that suits their aptitude and interests. For example, the job posting generation system can generate images that show the atmosphere of the workplace and the enjoyment of working there, as well as text that job seekers can easily relate to. As a result, job seekers can feel the atmosphere of the workplace and the enjoyment of working there just by looking at the job posting. Next, the generated job postings are provided to make it easier for job seekers to find a job that suits their aptitude and interests. Specifically, when a job seeker searches for job postings, the AI ​​prioritizes displaying job postings that include elements that appeal to the emotions of the job seeker. As a result, it becomes easier for job seekers to find a job that suits their aptitude and interests. For example, if a job seeker searches for "fun workplace," the AI ​​prioritizes displaying job postings that show the atmosphere of the workplace and the enjoyment of working there. Furthermore, the AI ​​utilizes one of the largest job posting databases in the industry when generating job postings. This allows companies to differentiate themselves from competitors and provide attractive job opportunities to a wider range of job seekers. For example, AI can analyze one of the industry's largest job databases and extract elements that are likely to interest job seekers, generating unique job postings that are not available from other companies. This system makes it easier for job seekers to find jobs that match their aptitudes and interests, and allows companies to receive applications from more job seekers. For instance, if a job seeker searches for a "fun workplace," they are more likely to see the job posting generated by the AI, become interested in that workplace, and apply. Furthermore, companies can reduce the effort required to generate job postings. For example, by having the AI ​​automatically generate job postings, companies can reduce the time and effort spent creating job postings. As a result, the job posting generation system automatically generates job postings that appeal to job seekers' emotions, making it easier for them to find jobs that match their aptitudes and interests.

[0058] The job posting generation system according to this embodiment comprises a generation unit, a provision unit, and an extraction unit. The generation unit generates job postings. The generation unit generates information that includes not only information such as employment conditions and salary, but also elements that appeal to the emotions of job seekers. For example, the generation unit can generate images that show the atmosphere of the workplace and the enjoyment of working, and text that job seekers can easily relate to. For example, the generation unit uses AI to analyze images and text included in the job postings and extracts elements that job seekers are likely to be interested in. The provision unit provides the job postings generated by the generation unit. For example, when a job seeker searches for job postings, the provision unit prioritizes displaying job postings that include elements that appeal to the emotions of the job seeker. For example, if a job seeker searches for "fun workplace," the provision unit uses AI to prioritize displaying job postings that show the atmosphere of the workplace and the enjoyment of working. The extraction unit extracts elements that appeal to the emotions of job seekers based on the job postings provided by the provision unit. For example, the extraction unit analyzes images and text included in the job postings and extracts elements that job seekers are likely to be interested in. The extraction unit, for example, uses AI to analyze images and text included in job postings and extracts elements that job seekers are likely to empathize with. As a result, the job posting generation system according to this embodiment can automatically generate job postings that appeal to the emotions of job seekers, making it easier for them to find jobs that match their aptitudes and interests.

[0059] The generation unit generates job postings. This unit generates information that includes not only employment conditions and salary, but also elements that appeal to job seekers' emotions. Specifically, the generation unit automatically generates job postings using AI. The AI ​​analyzes past job posting databases and learns elements that job seekers are likely to find interesting. For example, it can generate images that show the workplace atmosphere and the enjoyment of working there, as well as text that job seekers can easily relate to. The AI ​​uses natural language processing technology to generate the text of the job postings and image recognition technology to select appropriate images. Furthermore, the generation unit can also generate individually customized job postings based on job seekers' profile information and past application history. For example, by prioritizing the inclusion of information related to the types of jobs the job seeker has applied for in the past or the industries they are interested in, it provides more personalized job postings. This allows the generation unit to efficiently generate job postings that meet the needs and interests of job seekers, making it easier for them to find a suitable job.

[0060] The provisioning unit provides job postings generated by the generation unit. For example, when a job seeker searches for job postings, the provisioning unit prioritizes displaying job postings that contain elements that appeal to the job seeker's emotions. Specifically, the provisioning unit uses AI to analyze the job seeker's search and browsing history to identify job postings that are likely to interest them. For example, if a job seeker searches for "fun workplace," the provisioning unit will prioritize displaying job postings that show the workplace atmosphere and the enjoyment of working there. The AI ​​analyzes the job seeker's search keywords and browsing patterns and displays highly relevant job postings in real time. In addition, the provisioning unit can collect feedback from job seekers and continuously improve the accuracy of the displayed job postings. For example, if a job seeker provides feedback such as "interested" or "want to apply" for a particular job posting, the provisioning unit will use that information to display similar job postings to other job seekers. In this way, the provisioning unit can effectively provide job postings that appeal to the emotions of job seekers, making it easier for them to find a job that suits them.

[0061] The extraction unit extracts elements that appeal to job seekers' emotions based on the job information provided by the supply unit. Specifically, the extraction unit uses AI to analyze images and text included in the job information and extract elements that are likely to interest job seekers. The AI ​​uses image recognition technology to analyze images included in the job information and identify elements that indicate the workplace atmosphere and the enjoyment of working there. It also uses natural language processing technology to analyze the text of the job information and extract phrases and expressions that job seekers are likely to empathize with. Furthermore, the extraction unit can learn which elements of the job information are attractive to job seekers based on their feedback. For example, if a job seeker rates a particular image or text as "interesting," the extraction unit can use that information to include similar elements in other job information. In this way, the extraction unit can effectively extract elements that appeal to job seekers' emotions and, in cooperation with the generation and supply units, provide more attractive job information.

[0062] The generation unit can generate information that includes elements that appeal to the emotions of job seekers, in addition to information such as employment conditions and salary. For example, the generation unit can generate images that show the workplace atmosphere and the enjoyment of working, and text that job seekers can easily relate to. For example, the generation unit uses AI to analyze images and text included in job postings and extract elements that job seekers are likely to be interested in. For example, the generation unit uses AI to analyze images and text included in job postings and extract elements that job seekers are likely to relate to. By generating job postings that include elements that appeal to the emotions of job seekers, it becomes easier for job seekers to become interested.

[0063] The extraction unit can analyze images and text contained in job postings and extract elements that are likely to interest job seekers. For example, the extraction unit can analyze images and text contained in job postings and extract elements that are likely to interest job seekers. For example, the extraction unit can use AI to analyze images and text contained in job postings and extract elements that job seekers can easily relate to. By extracting elements that are likely to interest job seekers, the attractiveness of job postings is enhanced.

[0064] The system can prioritize displaying job postings that contain elements that appeal to the job seeker's emotions when they search for job information. For example, if a job seeker searches for "fun workplace," the system can prioritize displaying job postings that show the workplace atmosphere and the enjoyment of working there. By prioritizing the display of job postings that appeal to the job seeker's emotions, the system makes it easier for job seekers to find jobs that match their aptitudes and interests.

[0065] The generation unit can analyze the industry's largest job information database and extract elements that are likely to interest job seekers. For example, the generation unit can analyze the industry's largest job information database and extract elements that are likely to interest job seekers. For example, the generation unit can use AI to analyze the industry's largest job information database and extract elements that are likely to interest job seekers. For example, the generation unit can use AI to analyze the industry's largest job information database and extract elements that are likely to interest job seekers. This allows for differentiation from competitors by leveraging the industry's largest job information database.

[0066] The generation unit can estimate the emotions of job seekers and adjust the way job postings are presented based on those estimated emotions. For example, if a job seeker is feeling stressed, the generation unit will generate job postings that emphasize a relaxing work environment. For example, if a job seeker is feeling excited, the generation unit will generate job postings that emphasize challenging work and growth opportunities. For example, if a job seeker is feeling anxious, the generation unit will generate job postings that emphasize stable employment conditions and support systems. This makes it easier to attract job seekers' interest by generating job postings that are tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI is not limited to, but may include, text generation AI (e.g., LLM) or multimodal generation AI.

[0067] The generation unit can analyze a job seeker's past application history and generate the most suitable job postings. For example, the generation unit generates relevant job postings based on the job type and industry the job seeker has applied to in the past. For example, the generation unit analyzes the characteristics of companies the job seeker has applied to in the past and generates job postings for similar companies. For example, the generation unit generates the most suitable job postings based on the success rate of positions the job seeker has applied for in the past. In this way, by generating the most suitable job postings based on the job seeker's past application history, it is possible to provide job postings that meet the job seeker's needs.

[0068] The generation unit can customize job postings based on job seekers' current skill sets and career goals. For example, the generation unit can analyze a job seeker's skill set and generate job postings that utilize those skills. For example, the generation unit can consider a job seeker's career goals and generate job postings that help them achieve those goals. For example, the generation unit can identify a job seeker's skill gaps and generate job postings that offer opportunities for skill development. This increases job seeker satisfaction by providing job postings that match their skill sets and career goals.

[0069] The generation unit can estimate the emotions of job seekers and prioritize job postings based on those emotions. For example, if a job seeker is relaxed, the generation unit will prioritize displaying job postings that emphasize a relaxing work environment. For example, if a job seeker is excited, the generation unit will prioritize displaying job postings that emphasize challenging work. For example, if a job seeker is feeling anxious, the generation unit will prioritize displaying job postings that emphasize stable employment conditions. In this way, by prioritizing job postings according to the emotions of job seekers, it is possible to prioritize displaying job postings that are likely to interest job seekers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples.

[0070] The generation unit can generate region-specific job postings by considering the job seeker's geographical location information. For example, the generation unit can generate job postings with short commute times based on the job seeker's current location. For example, the generation unit can generate job postings that are suited to the characteristics of a region based on the job seeker's geographical location information. For example, the generation unit can generate job postings that reflect the trends of the regional job market based on the job seeker's geographical location information. In this way, by providing job postings based on the job seeker's geographical location information, it is possible to provide job postings that are suited to commute times and regional characteristics.

[0071] The generation unit can analyze job seekers' social media activity and generate relevant job postings. For example, the generation unit generates relevant job postings based on job seekers' interests and preferences on social media. For example, the generation unit analyzes job seekers' social media activity history and generates suitable job postings. For example, the generation unit considers job seekers' social media networks and generates job postings from companies within those networks. By providing job postings based on job seekers' social media activity, it is possible to provide job postings that match job seekers' interests and preferences.

[0072] The service provider can estimate the emotions of job seekers and adjust how job postings are displayed based on those estimated emotions. For example, if a job seeker is relaxed, the service provider will display job postings that emphasize a relaxing work environment. For example, if a job seeker is excited, the service provider will display job postings that emphasize challenging work. For example, if a job seeker is feeling anxious, the service provider will display job postings that emphasize stable employment conditions. This makes it easier to attract job seekers' interest by providing a way to display job postings that is tailored to their 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.

[0073] The service provider can provide job seekers with the most suitable job information by referring to their past search history. For example, the service provider can provide relevant job information based on the job types and industries that job seekers have searched for in the past. For example, the service provider can analyze job seekers' past search history and provide job information that is likely to interest them. For example, the service provider can prioritize providing job information from specific companies based on job seekers' past search history. In this way, by providing job information based on job seekers' past search history, the service provider can provide job information that matches the needs of job seekers.

[0074] The service provider can customize the display order of job postings based on the job seeker's current areas of interest. For example, the service provider can prioritize displaying relevant job postings based on the job seeker's current areas of interest. For example, the service provider can customize the display order of job postings based on the job seeker's areas of interest. For example, the service provider can analyze the job seeker's areas of interest and display the most relevant job postings at the top. This makes it easier to attract the job seeker's interest by providing a display order of job postings that is tailored to the job seeker's areas of interest.

[0075] The service provider can estimate the emotions of job seekers and adjust the frequency of job postings based on the estimated emotions. For example, if a job seeker is relaxed, the service provider will frequently display job postings that emphasize a relaxing work environment. For example, if a job seeker is excited, the service provider will frequently display job postings that emphasize challenging work. For example, if a job seeker is feeling anxious, the service provider will frequently display job postings that emphasize stable employment conditions. This makes it easier to attract job seekers' interest by providing job postings with a frequency that matches their 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.

[0076] The service provider can select the optimal display method considering the job seeker's device information. For example, if the job seeker is using a smartphone, the service provider will provide a display method that matches the screen size. For example, if the job seeker is using a tablet, the service provider will provide a display method optimized for a larger screen. For example, if the job seeker is using a desktop, the service provider will provide a display method that includes detailed information. By providing a display method based on the job seeker's device information, the service provider can provide job information that is easy for job seekers to use.

[0077] The service provider can analyze a job seeker's past application results and provide relevant job information. For example, the service provider can provide relevant job information based on the results of job applications the job seeker has previously submitted. For example, the service provider can analyze a job seeker's past application results and provide job information with a high success rate. For example, the service provider can prioritize providing job information from specific companies based on a job seeker's past application results. In this way, by providing job information based on a job seeker's past application results, it is possible to provide job information that matches the job seeker's needs.

[0078] The extraction unit can estimate the job seeker's emotions and determine the priority of elements to extract based on the estimated emotions. For example, if the job seeker is relaxed, the extraction unit will prioritize elements that emphasize a relaxing work environment. For example, if the job seeker is excited, the extraction unit will prioritize elements that emphasize challenging work. For example, if the job seeker is anxious, the extraction unit will prioritize elements that emphasize stable employment conditions. In this way, by determining the priority of elements according to the job seeker's emotions, elements that are likely to attract the job seeker's interest can be prioritized. 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.

[0079] The extraction unit can analyze images and text from job postings and extract elements that job seekers are likely to relate to. For example, the extraction unit can analyze images from job postings and extract elements that indicate the workplace atmosphere. For example, the extraction unit can analyze text from job postings and extract words and phrases that job seekers are likely to relate to. For example, the extraction unit can combine images and text from job postings to extract elements that job seekers are likely to be interested in. In this way, the attractiveness of job postings is enhanced by extracting elements that job seekers are likely to relate to.

[0080] The extraction unit can extract the most relevant elements by referring to the job seeker's past application history. For example, the extraction unit extracts relevant elements based on the job seeker's past application history. For example, the extraction unit analyzes the job seeker's past application history and extracts elements that are likely to be of interest. For example, the extraction unit prioritizes extracting elements related to specific companies from the job seeker's past application history. In this way, by extracting elements based on the job seeker's past application history, it is possible to provide elements that match the job seeker's needs.

[0081] The extraction unit can estimate the job seeker's emotions and adjust the display method of the extracted elements based on the estimated emotions. For example, if the job seeker is relaxed, the extraction unit will display elements that emphasize a relaxing work environment. For example, if the job seeker is excited, the extraction unit will display elements that emphasize challenging work. For example, if the job seeker is anxious, the extraction unit will display elements that emphasize stable employment conditions. This makes it easier to attract the job seeker's interest by providing an element display method that corresponds to the job seeker'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.

[0082] The extraction unit can extract relevant elements considering the geographical distribution of job postings. For example, the extraction unit can extract elements that are appropriate to the characteristics of a region based on the geographical distribution of job postings. For example, the extraction unit can analyze the geographical distribution of job postings and extract elements that are likely to interest job seekers. For example, the extraction unit can extract elements that reflect trends in the local job market, taking into account the geographical distribution of job postings. In this way, by providing elements based on the geographical distribution of job postings, it is possible to provide job postings that are appropriate to the characteristics of a region.

[0083] The extraction unit can improve the accuracy of its extraction by referring to relevant literature on job postings. For example, the extraction unit can refer to relevant literature on job postings and extract elements that job seekers are likely to relate to. The extraction unit can, for example, analyze relevant literature on job postings to improve the accuracy of its extraction. For example, the extraction unit can extract elements that job seekers are likely to be interested in based on relevant literature on job postings. This allows for the extraction of elements that job seekers are likely to relate to by referring to relevant literature on job postings.

[0084] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0085] The job posting generation system can also include a feedback collection unit. This unit collects reactions and feedback from job seekers after they view job postings. For example, it can record click-through rates and time spent viewing job postings, and analyze which elements were appealing to them. It can also provide a function for job seekers to leave comments and ratings on job postings, allowing for improvements to the content based on this feedback. This enables the job posting generation system to provide job postings that reflect actual job seekers' reactions, thereby increasing job seeker satisfaction.

[0086] The job posting generation system can also include a skills matching unit. This unit compares the job seeker's skill set with the skill requirements listed in the job posting to achieve the best possible match. For example, if a job seeker's skills match the skill requirements in a job posting, that job posting will be displayed preferentially. The system can also provide training information to supplement any skills lacking in the job seeker's skills. This makes it easier for job seekers to find job postings that match their skills, and enables companies to hire appropriate personnel based on the job seeker's skills.

[0087] The job posting generation system can also include a career advice section. This section provides appropriate job postings and career advice based on the job seeker's career path and goals. For example, if a job seeker has a specific career goal, it can provide job postings that outline the steps and necessary skills to reach that goal. Furthermore, if a job seeker is considering a career change, it can provide career paths in new fields and related job postings. This makes it easier for job seekers to find job postings that match their career goals and clarify their career direction.

[0088] The job posting generation system can also include a company evaluation section. This section provides job seekers with evaluation information about companies they are considering applying to. For example, it collects and provides information on employee satisfaction, turnover rates, and corporate culture. It also provides a function that allows job seekers to leave evaluations and comments about companies, and share this information with other job seekers. This makes it easier for job seekers to understand the reality of companies and make more informed decisions.

[0089] The job posting generation system can also be equipped with interactive job posting generation functions. These interactive functions allow job seekers to customize job postings based on their interests. For example, if a job seeker is interested in a particular industry or job type, the system will prioritize displaying information related to that industry or job type. Furthermore, by specifying specific conditions (salary, location, working hours, etc.), the system can generate job postings that match those conditions. This makes it easy for job seekers to find job postings that meet their needs.

[0090] The job posting generation system can estimate the emotions of job seekers and adjust the way job postings are presented based on those estimated emotions. For example, if a job seeker is feeling stressed, the system will generate job postings that emphasize a relaxing work environment. If a job seeker is excited, the system will generate job postings that emphasize challenging work and growth opportunities. If a job seeker is feeling anxious, the system will generate job postings that emphasize stable employment conditions and support systems. By generating job postings that are tailored to the emotions of job seekers, the system makes it easier to attract their interest.

[0091] The job posting generation system can estimate the emotions of job seekers and prioritize job postings based on those emotions. For example, if a job seeker is relaxed, it will prioritize job postings that emphasize a relaxing work environment. If a job seeker is excited, it will prioritize job postings that emphasize challenging work. If a job seeker is feeling anxious, it will prioritize job postings that emphasize stable employment conditions. By prioritizing job postings according to the emotions of job seekers, the system can prioritize displaying job postings that are likely to interest them.

[0092] The job posting generation system can estimate the emotions of job seekers and adjust how job postings are displayed based on those emotions. For example, if a job seeker is relaxed, it will display job postings that emphasize a relaxing work environment. If a job seeker is excited, it will display job postings that emphasize challenging work. If a job seeker is feeling anxious, it will display job postings that emphasize stable employment conditions. By providing job postings that are tailored to the emotions of job seekers, the system makes it easier to attract their interest.

[0093] The job posting generation system can estimate the emotions of job seekers and adjust the frequency of job postings displayed based on those emotions. For example, if a job seeker is relaxed, job postings emphasizing a relaxing work environment will be displayed frequently. If a job seeker is excited, job postings emphasizing challenging work will be displayed frequently. If a job seeker is feeling anxious, job postings emphasizing stable employment conditions will be displayed frequently. By providing job postings with a frequency that matches the emotions of job seekers, the system makes it easier to attract their interest.

[0094] The job posting generation system can estimate the emotions of job seekers and determine the priority of elements to extract based on those estimated emotions. For example, if a job seeker is relaxed, elements emphasizing a relaxing work environment will be prioritized. If a job seeker is excited, elements emphasizing challenging work will be prioritized. If a job seeker is anxious, elements emphasizing stable employment conditions will be prioritized. In this way, by determining the priority of elements according to the job seeker's emotions, elements that are likely to attract job seekers' interest can be prioritized.

[0095] The following briefly describes the processing flow for example form 2.

[0096] Step 1: The generation unit generates job postings. The generation unit generates information that includes not only employment conditions and salary, but also elements that appeal to the emotions of job seekers. For example, it can generate images that show the workplace atmosphere and the enjoyment of working there, and text that job seekers can easily relate to. The generation unit uses AI to analyze the images and text included in the job postings and extract elements that are likely to interest job seekers. Step 2: The providing unit provides the job information generated by the generating unit. When a job seeker searches for job information, the providing unit prioritizes displaying job information that includes elements that appeal to the job seeker's emotions. For example, if a job seeker searches for "fun workplace" using AI, the providing unit will prioritize displaying job information that shows the workplace atmosphere and the enjoyment of working there. Step 3: The extraction unit extracts elements that appeal to job seekers' emotions based on the job information provided by the supply unit. The extraction unit analyzes the images and text contained in the job information and extracts elements that job seekers are likely to be interested in. For example, AI is used to analyze the images and text contained in the job information and extract elements that job seekers are likely to empathize with.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] Each of the multiple elements, including the generation unit, provision unit, and extraction unit described above, is implemented in at least one of the smart device 14 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 and generates job information. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides the generated job information to job seekers. The extraction unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the elements contained in the job information and extracts elements that appeal to the emotions of job seekers. 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.

[0101] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] Each of the multiple elements, including the generation unit, provision unit, and extraction unit described above, is implemented in at least one of the smart glasses 214 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 and generates job information. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides the generated job information to job seekers. The extraction unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the elements contained in the job information and extracts elements that appeal to the emotions of job seekers. 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.

[0117] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.).

[0129] 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.

[0130] 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.

[0131] 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.

[0132] Each of the multiple elements, including the generation unit, provision unit, and extraction unit described above, is implemented in at least one of the headset terminal 314 and the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 and generates job information. The provision unit is implemented by the specific processing unit 290 of the data processing device 12 and provides the generated job information to job seekers. The extraction unit is implemented by the specific processing unit 290 of the data processing device 12 and analyzes the elements contained in the job information and extracts elements that appeal to the emotions of job seekers. 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.

[0133] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.).

[0146] 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.

[0147] 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.

[0148] 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.

[0149] Each of the multiple elements, including the generation unit, provision unit, and extraction unit described above, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 and generates job information. The provision unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and provides the generated job information to job seekers. The extraction unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the elements contained in the job information and extracts elements that appeal to the emotions of job seekers. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] (Note 1) A generation unit that generates job postings, A provisioning unit that provides the job information generated by the generation unit, The system includes an extraction unit that extracts elements that appeal to the emotions of job seekers based on the job information provided by the aforementioned provision unit. A system characterized by the following features. (Note 2) The generating unit is It generates information that includes not only employment conditions and salary, but also elements that appeal to the emotions of job seekers. The system described in Appendix 1, characterized by the features described herein. (Note 3) The extraction unit is The system analyzes images and text included in job postings to extract elements that are likely to attract the interest of job seekers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, When job seekers search for job postings, the system prioritizes displaying job postings that include elements that appeal to the job seeker's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We analyze the industry's largest job information database to extract elements that are likely to attract job seekers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The generating unit is The system estimates the emotions of job seekers and adjusts the way job postings are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The generating unit is Analyze job seekers' past application history to generate optimal job postings. The system described in Appendix 1, characterized by the features described herein. (Note 8) The generating unit is Customize job postings based on the job seeker's current skill set and career goals. The system described in Appendix 1, characterized by the features described herein. (Note 9) The generating unit is The system estimates the emotions of job seekers and prioritizes job postings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The generating unit is By considering the geographical location of job seekers, we generate region-specific job postings. The system described in Appendix 1, characterized by the features described herein. (Note 11) The generating unit is Analyze job seekers' social media activity and generate relevant job postings. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned supply unit is, The system estimates the emotions of job seekers and adjusts how job postings are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned supply unit is, We provide the most suitable job information by referring to the job seeker's past search history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned supply unit is, Customize the display order of job postings based on the job seeker's current areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned supply unit is, The system estimates the emotions of job seekers and adjusts the frequency of displaying job postings based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned supply unit is, The optimal display method is selected considering the job seeker's device information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned supply unit is, We analyze job seekers' past application results and provide relevant job information. The system described in Appendix 1, characterized by the features described herein. (Note 18) The extraction unit is The system estimates the emotions of job seekers and determines the priority of elements to extract based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The extraction unit is The system analyzes images and text in job postings to extract elements that job seekers are likely to relate to. The system described in Appendix 1, characterized by the features described herein. (Note 20) The extraction unit is By referring to the applicant's past application history, we extract the most relevant elements. The system described in Appendix 1, characterized by the features described herein. (Note 21) The extraction unit is We estimate the emotions of job seekers and adjust how elements extracted based on those estimated emotions are displayed. The system described in Appendix 1, characterized by the features described herein. (Note 22) The extraction unit is Extract relevant elements, taking into account the geographical distribution of job postings. The system described in Appendix 1, characterized by the features described herein. (Note 23) The extraction unit is Referencing relevant literature on job postings improves the accuracy of extraction. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A generation unit that generates job postings, A provisioning unit that provides the job information generated by the generation unit, The system includes an extraction unit that extracts elements that appeal to the emotions of job seekers based on the job information provided by the aforementioned provision unit. A system characterized by the following features.

2. The generating unit is It generates information that includes not only employment conditions and salary, but also elements that appeal to the emotions of job seekers. The system according to feature 1.

3. The extraction unit is The system analyzes images and text included in job postings to extract elements that are likely to attract the interest of job seekers. The system according to feature 1.

4. The aforementioned supply unit is, When job seekers search for job postings, the system prioritizes displaying job postings that include elements that appeal to the job seeker's emotions. The system according to feature 1.

5. The generating unit is We analyze the industry's largest job information database to extract elements that are likely to attract job seekers. The system according to feature 1.

6. The generating unit is The system estimates the emotions of job seekers and adjusts the way job postings are presented based on those estimated emotions. The system according to feature 1.

7. The generating unit is Analyze job seekers' past application history to generate optimal job postings. The system according to feature 1.

8. The generating unit is Customize job postings based on the job seeker's current skill set and career goals. The system according to feature 1.

Images