system

Abstract

The system according to this embodiment aims to provide job seekers with the most suitable job information based on their skills and desired conditions. [Solution] The system according to this embodiment comprises a reception unit, a hearing unit, a recommendation unit, a customization unit, and an optimization unit. The reception unit receives the resume and work history of job seekers. The hearing unit communicates with job seekers via chat based on the information received by the reception unit to understand details such as skills, experience, and desired conditions. The recommendation unit analyzes the information gathered by the hearing unit and recommends the most suitable job postings. The customization unit customizes the resume and work history based on the job postings recommended by the recommendation unit. The optimization unit optimizes the job postings customized by the customization unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, it is difficult to find job information optimal for the skills and desired conditions of job seekers, and there is room for improvement in matching accuracy.

[0005] The system according to the embodiment aims to provide optimal job information based on the skills and desired conditions of job seekers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a hearing unit, a recommendation unit, a customization unit, and an optimization unit. The reception unit receives the resume and work history of job seekers. The hearing unit interacts with job seekers via chat based on the information received by the reception unit to understand details such as skills, experience, and desired conditions. The recommendation unit analyzes the information gathered by the hearing unit and recommends the most suitable job postings. The customization unit customizes the resume and work history based on the job postings recommended by the recommendation unit. The optimization unit optimizes the job postings customized by the customization unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide job seekers with the most suitable job information based on their skills and desired conditions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The job matching assistant system according to an embodiment of the present invention is a next-generation job matching assistant system that uses generative AI to improve the accuracy of matching job seekers with companies. In this job matching assistant system, job seekers submit their resumes and work histories, and the generative AI interacts with the job seekers via chat to understand details such as skills, experience, and desired conditions. Next, the generative AI analyzes the job seekers' information and recommends the most suitable job postings. The generative AI also customizes the resumes and work histories for each recruiting company. At the same time, the recruiting company's representative also interacts with the AI ​​to optimize the job postings, thereby significantly improving the accuracy of matching job seekers with companies. For example, when a job seeker submits their resume and work histories, they input their skills, experience, and desired conditions. For example, they might input conditions such as "I have marketing experience and I want to work remotely." This information is input into the generative AI. Next, the generative AI analyzes the input information to understand the job seeker's skills, experience, and desired conditions in detail. The generative AI interacts with the job seekers via chat to collect even more detailed information. For example, if a job seeker enters conditions such as "I have marketing experience and I want to work remotely," the generating AI will recommend job postings that match those conditions. The generating AI analyzes the job seeker's information and recommends the most suitable job postings. For example, if a job seeker enters conditions such as "I have marketing experience and I want to work remotely," the generating AI will recommend job postings that match those conditions. The generating AI also customizes resumes and work histories for each recruiting company. This allows the AI ​​to maximize the appeal of the job seeker. On the other hand, recruiters at recruiting companies are presented with job postings that have been customized for job seekers by the generating AI. The AI ​​analyzes the company's conditions and benefits and provides optimized job postings. For example, if a company enters conditions such as "We are looking for a marketing professional who can work remotely," the generating AI will suggest job seekers that match those conditions. This system allows job seekers to easily find job postings that suit them, and companies can efficiently recruit the talent they need. This creates significant benefits for both job seekers and companies.This allows the job matching assistant system to efficiently collect, analyze, customize, and optimize job seeker information, thereby improving the accuracy of matching job seekers with companies.

[0029] The job matching assistant system according to this embodiment comprises a reception unit, an interview unit, a recommendation unit, a customization unit, and an optimization unit. The reception unit receives resumes and work histories from job seekers. These resumes and work histories include, but are not limited to, format, items to be included, and necessary information. The reception unit can, for example, receive resumes and work histories submitted online by job seekers. The reception unit can also receive resumes and work histories submitted by mail. Furthermore, the reception unit can also receive resumes and work histories brought in person by job seekers. For example, the reception unit receives job seeker information through an online form. Resumes and work histories submitted by mail are scanned and saved as digital data. Resumes and work histories brought in person are manually converted into digital data by the reception staff. The interview unit uses a generation AI to chat with job seekers based on the information received by the reception unit and understand details such as skills, experience, and desired conditions. The interviewing department, for example, uses a generating AI to chat with job seekers and gather detailed information such as their skills, experience, and desired conditions. The interviewing department can also use the generating AI to analyze the job seekers' responses and collect detailed information. Furthermore, the interviewing department can use the generating AI to refer to the job seekers' past resumes and work histories to gather detailed information. For example, the generating AI might ask the job seeker, "Please tell me about your skills and experience," and analyze their responses. Based on the job seeker's responses, the generating AI gathers detailed information such as their skills, experience, and desired conditions. The generating AI also uses the job seeker's past resumes and work histories to gather information about their skills and experience. The recommendation department uses the generating AI to analyze the information gathered by the interviewing department and recommend the most suitable job postings. For example, the recommendation department uses the generating AI to analyze the job seeker's information and recommend the most suitable job postings. Furthermore, the recommendation section can use a generating AI to recommend job postings based on the job seeker's skills, experience, and desired conditions. The recommendation section can also use the generating AI to refer to the job seeker's past application history and recommend the most suitable job postings. For example, the generating AI recommends job postings based on the job seeker's skills, experience, and desired conditions.The generating AI refers to the job seeker's past application history and recommends suitable job postings. The generating AI recommends the most suitable job postings based on the job seeker's skills, experience, and desired conditions. The customization unit uses the generating AI to customize resumes and CVs based on the job postings recommended by the recommendation unit. For example, the customization unit uses the generating AI to customize the job seeker's resume and CV for each recruiting company. The customization unit can also use the generating AI to customize resumes and CVs based on the job seeker's skills, experience, and desired conditions. Furthermore, the customization unit can use the generating AI to customize resumes and CVs based on the requirements of recruiting companies. For example, the generating AI customizes resumes and CVs based on the job seeker's skills, experience, and desired conditions. The generating AI customizes resumes and CVs based on the requirements of recruiting companies. The generating AI customizes resumes and CVs based on the job seeker's skills, experience, and desired conditions. The optimization unit uses AI to optimize the job postings customized by the customization unit. The optimization unit, for example, uses AI to analyze a company's conditions and benefits and provides optimized job postings. The optimization unit can also optimize job postings based on a company's requirements. Furthermore, the optimization unit can refer to a company's past job postings and provide optimized job postings. For example, the AI ​​analyzes a company's conditions and benefits and provides optimized job postings. The AI ​​optimizes job postings based on a company's requirements. The AI ​​refers to a company's past job postings and provides optimized job postings. As a result, the job matching assistant system according to this embodiment can improve the accuracy of matching job seekers with companies by efficiently collecting, analyzing, customizing, and optimizing job seeker information.

[0030] The reception desk accepts resumes and CVs from job applicants. These documents include, but are not limited to, formatting, required fields, and necessary information. The reception desk accepts resumes and CVs submitted online, by mail, and in person. For example, it accepts applicant information through online forms. Resumes and CVs submitted by mail are scanned and saved as digital data. Resumes and CVs submitted in person are manually converted into digital data by reception staff. The reception desk is designed to be flexible regarding the format of resumes and CVs submitted by job applicants. For example, it supports common file formats such as PDF, Word, and Excel, allowing job applicants to submit documents according to their convenience. Furthermore, the reception desk has a function to automatically analyze the content of submitted documents and extract necessary information. For example, information such as name, address, contact information, educational background, work history, and skills is automatically extracted and registered in the database. This automated analysis function allows the reception department to process large volumes of documents quickly and accurately. The reception department also has a function to provide real-time feedback if there are any deficiencies in the documents submitted by job seekers. For example, if necessary information is missing or the format is inappropriate, a notification is sent to the job seeker prompting them to make corrections. This allows job seekers to quickly correct and resubmit their documents. Furthermore, the reception department has implemented security measures to protect the privacy of job seekers. For example, submitted documents are encrypted and stored on a secure server. Access rights are also strictly controlled, so that only authorized personnel can access the documents. This minimizes the risk of unauthorized access to job seekers' personal information.

[0031] The interviewing department uses generative AI to chat with job seekers based on information received by the reception department, gathering details such as skills, experience, and desired conditions. For example, the interviewing department uses generative AI to chat with job seekers and gather details such as skills, experience, and desired conditions. The interviewing department can also use generative AI to analyze job seekers' responses and gather detailed information. Furthermore, the interviewing department can use generative AI to refer to job seekers' past resumes and work histories to gather detailed information. For example, the generative AI might ask job seekers questions such as, "Please tell me about your skills and experience," and analyze their responses. Based on these responses, the generative AI gathers details such as skills, experience, and desired conditions. The generative AI also uses natural language processing technology to gather information about job seekers' skills and experience. The generative AI utilizes advanced natural language processing techniques to facilitate smooth conversations with job seekers. For example, when a job seeker explains specific skills or experience in detail, the generating AI analyzes the content in real time and asks related follow-up questions to extract deeper information. Furthermore, based on the job seeker's answers, the generating AI evaluates the level of skills and experience, clarifying the job seeker's strengths and weaknesses. For example, if a job seeker answers, "I have experience in project management," the generating AI will ask follow-up questions such as, "Specifically, what kind of projects did you work on?" to gather more detailed information. The generating AI also understands the job seeker's desired conditions based on their answers. For example, if a job seeker answers, "I would like to work remotely," the generating AI will ask questions such as, "Do you have experience working remotely?" to gather more detailed information about the job seeker's desired conditions. In addition, the generating AI refers to the job seeker's past resumes and work histories to understand their skills and experience. For example, it evaluates the job seeker's experience and abilities based on the work history and skills listed in their past resumes. This allows the interviewing department to accurately understand detailed information about the job seeker's skills, experience, and desired conditions, and provide it to the recommendation department in the next step.

[0032] The recommendation department uses generative AI to analyze information gathered by the interviewing department and recommend the most suitable job postings. For example, the recommendation department's generative AI analyzes job seeker information and recommends the most suitable job postings. The recommendation department can also recommend job postings based on the job seeker's skills, experience, and desired conditions. Furthermore, the recommendation department can refer to the job seeker's past application history to recommend the most suitable job postings. For example, the generative AI recommends job postings based on the job seeker's skills, experience, and desired conditions. The generative AI refers to the job seeker's past application history and recommends job postings suitable for the job seeker. The generative AI recommends the most suitable job postings based on the job seeker's skills, experience, and desired conditions. The generative AI uses machine learning algorithms to analyze job seeker information in detail. For example, it considers the job seeker's skill set, work experience, desired work location, and salary conditions to select the most suitable job postings. Furthermore, the generating AI analyzes past application history and job seeker behavior patterns to prioritize recommending job postings that are likely to interest the job seeker. For example, if a job seeker has previously applied to a specific industry or job type, the generating AI will use that information to recommend job postings in similar industries or job types. The generating AI also takes into account the requirements and conditions of the hiring company. For example, if a hiring company values ​​specific skills or experience, the generating AI will prioritize recommending job seekers who meet those requirements. This improves the accuracy of matching job seekers and hiring companies, leading to optimal results for both parties. In addition, the generating AI has a function to update recommendation results in real time. For example, if new job postings are added or job seeker information is updated, the generating AI immediately recalculates the recommendation results and provides the most suitable job postings based on the latest information. This ensures that job seekers always receive the latest job postings and can apply quickly.

[0033] The customization department uses a generation AI to customize resumes and CVs based on job postings recommended by the recommendation department. For example, the customization department uses the generation AI to customize a job seeker's resume and CV for each recruiting company. The customization department can also use the generation AI to customize resumes and CVs based on the job seeker's skills, experience, and desired conditions. Furthermore, the customization department can use the generation AI to customize resumes and CVs based on the requirements of recruiting companies. For example, the generation AI customizes resumes and CVs based on the job seeker's skills, experience, and desired conditions. The generation AI customizes resumes and CVs based on the requirements of recruiting companies. The generation AI customizes resumes and CVs based on the job seeker's skills, experience, and desired conditions. The generation AI analyzes the job seeker's information in detail and proposes the optimal format and content for each recruiting company. For example, it adjusts the resume layout and content to emphasize skills and experience valued by a particular company. The generation AI can also maximize the job seeker's appeal by using language that matches the culture and values ​​of the recruiting company. Furthermore, the generating AI learns effective customization methods based on job seekers' past application history and success stories. For example, it analyzes the resumes of job seekers who were previously hired by a specific company and incorporates their success factors to optimize the resumes of other job seekers. The generating AI also has the ability to reflect the requirements and conditions of recruiting companies in real time. For example, if a recruiting company adds new skills or experience requirements, the generating AI immediately incorporates that information and updates the job seeker's resume and CV. This ensures that job seekers can always submit customized documents based on the latest information. In addition, the generating AI continuously improves the customization based on job seeker feedback. For example, when a job seeker rates their satisfaction with a particular customization, the generating AI learns from that rating and incorporates it into future customizations. This allows the customization department to provide optimal resumes and CVs that meet the needs of job seekers and improve the accuracy of matching them with recruiting companies.

[0034] The Optimization Unit uses AI to optimize job postings customized by the Customization Unit. For example, the Optimization Unit uses AI to analyze a company's conditions and benefits and provide optimized job postings. The Optimization Unit can also optimize job postings based on a company's requirements. Furthermore, the Optimization Unit can refer to a company's past job postings and provide optimized job postings. For example, the AI ​​analyzes a company's conditions and benefits and provides optimized job postings. The AI ​​optimizes job postings based on a company's requirements. The AI ​​refers to a company's past job postings and provides optimized job postings. The Optimization Unit uses AI to further refine the job postings customized by the Customization Unit and provide them to job seekers in the most optimal form. For example, the AI ​​analyzes a company's conditions and benefits in detail and highlights the most attractive points for job seekers. The AI ​​also optimizes job postings based on a company's requirements and arranges them in a format that makes them easy for job seekers to apply. In addition, the AI ​​refers to a company's past job postings and hiring results to provide optimal job postings. For example, it learns patterns from successful past job postings and incorporates those elements to increase job seekers' motivation to apply. Furthermore, the AI ​​updates job postings in real time, providing job seekers with the latest information. For example, it instantly reflects newly offered benefits and salary conditions from companies, providing attractive job postings to job seekers. In addition, the optimization unit continuously improves job postings based on job seeker feedback. For example, if a job seeker shows interest in a particular job posting, it optimizes other job postings based on that information. This allows the optimization unit to provide job seekers with the most attractive and easy-to-apply-for job postings, improving the accuracy of matching with companies.

[0035] The interviewing department can use a generative AI to chat with job seekers and understand details such as their skills, experience, and desired conditions. For example, the generative AI chats with job seekers to understand details such as their skills, experience, and desired conditions. The generative AI asks job seekers questions such as, "Please tell me about your skills and experience," and analyzes the job seekers' answers. Based on the job seekers' answers, the generative AI understands details such as their skills, experience, and desired conditions. The generative AI also refers to the job seekers' past resumes and work histories to understand their skills and experience. In this way, detailed information about job seekers can be efficiently obtained by using the generative AI. Some or all of the above processes in the interviewing department are performed using the generative AI. For example, the interviewing department inputs the job seeker's information into the generative AI, and the generative AI understands the job seeker's skills, experience, and desired conditions.

[0036] The recommendation unit can analyze job seeker information using a generating AI and recommend the most suitable job postings. For example, the recommendation unit uses a generating AI to analyze job seeker information and recommend the most suitable job postings. The generating AI recommends job postings based on the job seeker's skills, experience, and desired conditions. The generating AI also refers to the job seeker's past application history and recommends job postings that are suitable for the job seeker. The generating AI recommends the most suitable job postings based on the job seeker's skills, experience, and desired conditions. As a result, by using a generating AI, the most suitable job postings can be efficiently recommended to job seekers. Some or all of the above processes in the recommendation unit are performed using a generating AI. For example, the recommendation unit inputs job seeker information into the generating AI, and the generating AI recommends the most suitable job postings.

[0037] The customization section can customize resumes and work histories for each recruiting company using a generation AI. For example, the generation AI in the customization section customizes job seekers' resumes and work histories for each recruiting company. The generation AI customizes resumes and work histories based on the job seeker's skills, experience, and desired conditions. The generation AI customizes resumes and work histories based on the requirements of the recruiting company. The generation AI customizes resumes and work histories based on the job seeker's skills, experience, and desired conditions. This allows for efficient customization of job seekers' resumes and work histories using the generation AI. Some or all of the above processes in the customization section are performed using the generation AI. For example, the customization section inputs the job seeker's information into the generation AI, which then customizes the resume and work histories.

[0038] The optimization unit can analyze a company's conditions and benefits using AI and provide optimized job postings. For example, the AI ​​analyzes a company's conditions and benefits and provides optimized job postings. The AI ​​optimizes job postings based on the company's requirements. The AI ​​refers to a company's past job postings and provides optimized job postings. The AI ​​analyzes a company's conditions and benefits and provides optimized job postings. Thus, by using AI, it is possible to analyze a company's conditions and benefits and provide optimized job postings. Some or all of the above processes in the optimization unit are performed using AI. For example, the optimization unit inputs company information into the AI, and the AI ​​provides optimized job postings.

[0039] The reception department can analyze a job seeker's past resume and work history submission history and select the most suitable submission method. For example, the reception department can recommend online submission to a job seeker who has previously preferred online submission. It can also recommend postal submission to a job seeker who has previously preferred postal submission. Based on past submission history, the reception department can also encourage job seekers who tend to submit at specific times to submit during those times. In this way, by analyzing past submission history, the reception department can provide job seekers with the most suitable submission method. Some or all of the above processes in the reception department are performed using AI. For example, the reception department inputs the job seeker's submission history data into the AI, and the AI ​​selects the most suitable submission method.

[0040] The reception desk can filter job seekers' current employment status and areas of interest when they submit their resumes and work histories. For example, the reception desk can prioritize displaying relevant job postings based on their current employment status. It can also prioritize displaying relevant job postings based on their areas of interest. The reception desk can even display the most relevant job postings based on both their current employment status and areas of interest. This allows the reception desk to provide highly relevant job postings by filtering based on the job seeker's current employment status and areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs data on the job seeker's employment status and areas of interest into the AI, which then performs the filtering.

[0041] The reception desk can prioritize retrieving highly relevant information by considering the job seeker's geographical location when they submit their resume and work history. For example, the reception desk can prioritize displaying nearby job postings based on the job seeker's current location. The reception desk can also prioritize displaying relevant job postings based on the job seeker's desired work location. The reception desk can even display the most suitable job postings based on both the job seeker's geographical location and desired work location. This allows the reception desk to provide highly relevant job postings by considering the job seeker's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the job seeker's geographical location into the AI, and the AI ​​retrieves highly relevant information.

[0042] The reception department can analyze a job seeker's social media activity and retrieve relevant information when they submit their resume and work history. For example, the reception department can identify areas of interest from the job seeker's social media activity and display relevant job postings. The reception department can also identify skills and experience from the job seeker's social media activity and display relevant job postings. The reception department can also identify desired conditions from the job seeker's social media activity and display relevant job postings. In this way, by analyzing the job seeker's social media activity, it is possible to provide highly relevant job postings. Some or all of the above processing in the reception department is performed using AI. For example, the reception department inputs the job seeker's social media data into the AI, and the AI ​​retrieves relevant information.

[0043] The interviewing unit can adjust the level of detail in the conversation based on the importance of the job seeker during the interview. For example, the interviewing unit can conduct a detailed conversation to prioritize the collection of important information. The interviewing unit can also collect less important information concisely. The interviewing unit can also dynamically adjust the level of detail in the conversation according to the importance of the information. This allows for efficient information collection by adjusting the level of detail in the conversation according to the importance of the job seeker. Some or all of the above processing in the interviewing unit is performed using a generative AI. For example, the interviewing unit inputs the importance data of the job seeker into the generative AI, and the generative AI adjusts the level of detail in the conversation.

[0044] The interviewing unit can apply different dialogue algorithms to job seekers depending on their occupational category during the interview. For example, the interviewing unit can conduct detailed conversations about technical skills with job seekers in the IT industry. It can also conduct detailed conversations about professional experience with job seekers in the medical industry. It can also conduct detailed conversations about educational experience and qualifications with job seekers in the education industry. By applying a dialogue algorithm tailored to the job seeker's occupational category, more appropriate information can be collected. Some or all of the above processing in the interviewing unit is performed using generative AI. For example, the interviewing unit inputs the job seeker's occupational category data into the generative AI, and the generative AI applies a dialogue algorithm.

[0045] The interviewing department can prioritize conversations based on the timing of job applicants' submissions. For example, the interviewing department will prioritize interviews with resumes and work histories submitted early. It can also prioritize interviews with resumes and work histories that are nearing their deadlines. The interviewing department can also dynamically adjust the priority of conversations based on the submission timing. This enables efficient information gathering by prioritizing conversations based on the timing of job applicants' submissions. Some or all of the above processing in the interviewing department is performed using a generative AI. For example, the interviewing department inputs job applicant submission timing data into the generative AI, and the generative AI determines the priority of conversations.

[0046] The interviewing unit can adjust the order of conversations based on the relevance of job seekers during interviews. For example, the interviewing unit will prioritize interviews with job seekers whose skills and experience are relevant to the job posting. It can also prioritize interviews with job seekers whose desired conditions are relevant to the job posting. The interviewing unit can also dynamically adjust the order of conversations based on the relevance of job seekers. This allows for efficient information gathering by adjusting the order of conversations based on the relevance of job seekers. Some or all of the above processing in the interviewing unit is performed using a generative AI. For example, the interviewing unit inputs the relevance data of job seekers into the generative AI, and the generative AI adjusts the order of conversations.

[0047] The recommendation unit can improve the accuracy of its recommendations by considering the relationships between job seekers. For example, the recommendation unit can make recommendations by considering the evaluations of job seekers' past colleagues and supervisors. The recommendation unit can also make recommendations by considering the evaluations of job seekers' past project members. The recommendation unit can also make recommendations by considering the evaluations of job seekers' past team members. This improves the accuracy of recommendations by considering the relationships between job seekers. Some or all of the above processing in the recommendation unit is performed using a generative AI. For example, the recommendation unit inputs the relationship data of job seekers into the generative AI, and the generative AI improves the accuracy of the recommendations.

[0048] The recommendation unit can make recommendations while considering the job seeker's attribute information. For example, the recommendation unit can make recommendations considering the job seeker's age and gender. The recommendation unit can also make recommendations considering the job seeker's educational background and work experience. The recommendation unit can also make recommendations considering the job seeker's skills and qualifications. In this way, by considering the job seeker's attribute information, more appropriate job information can be provided. Some or all of the above processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit inputs the job seeker's attribute information into the generation AI, and the generation AI makes recommendations.

[0049] The recommendation unit can make recommendations while considering the geographical distribution of job seekers. For example, the recommendation unit can prioritize recommending nearby job postings based on the job seeker's current location. The recommendation unit can also prioritize recommending relevant job postings based on the job seeker's desired work location. The recommendation unit can even recommend the most suitable job postings based on both the job seeker's geographical distribution and desired work location. This allows for the provision of more appropriate job postings by considering the job seeker's geographical distribution. Some or all of the above processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit inputs the job seeker's geographical distribution data into the generation AI, and the generation AI makes recommendations.

[0050] The recommendation system can improve the accuracy of its recommendations by referring to the job seeker's relevant literature. For example, the recommendation system can make recommendations by referring to the job seeker's past papers and articles. It can also make recommendations by referring to the job seeker's past project reports. It can also make recommendations by referring to the job seeker's past presentation materials. This improves the accuracy of recommendations by referring to the job seeker's relevant literature. Some or all of the above processing in the recommendation system is performed using a generative AI. For example, the recommendation system inputs the job seeker's relevant literature data into the generative AI, which then improves the accuracy of its recommendations.

[0051] The customization department can analyze a job seeker's past resumes and work histories to select the optimal customization method during the customization process. For example, the customization department can select a customization method that emphasizes the job seeker's strengths from their past resumes and work histories. The customization department can also select a customization method that compensates for the job seeker's weaknesses from their past resumes and work histories. The customization department can also select a customization method that matches the job seeker's desired conditions from their past resumes and work histories. In this way, by analyzing a job seeker's past resumes and work histories, the optimal customization method can be provided. Some or all of the above processes in the customization department are performed using a generation AI. For example, the customization department inputs the job seeker's past resume and work histories data into the generation AI, and the generation AI selects the optimal customization method.

[0052] The customization unit can customize the means of customization based on the job seeker's current occupational status during the customization process. For example, the customization unit can provide a customization method that emphasizes relevant skills and experience based on the current occupational status. The customization unit can also provide a customization method that matches the job seeker's desired conditions based on the current occupational status. The customization unit can also provide a customization method that maximizes the job seeker's strengths based on the current occupational status. This allows for more appropriate customization by providing customization means based on the job seeker's current occupational status. Some or all of the above processing in the customization unit is performed using a generative AI. For example, the customization unit inputs the job seeker's current occupational status data into the generative AI, and the generative AI customizes the means of customization.

[0053] The customization unit can select the optimal customization method by considering the job seeker's geographical location information during the customization process. For example, the customization unit can prioritize customizing relevant job postings based on the job seeker's current location. The customization unit can also prioritize customizing relevant job postings based on the job seeker's desired work location. The customization unit can also select the optimal customization method based on both the job seeker's geographical location information and their desired work location. This allows for the provision of a more appropriate customization method by considering the job seeker's geographical location information. Some or all of the above processing in the customization unit is performed using a generation AI. For example, the customization unit inputs the job seeker's geographical location information data into the generation AI, and the generation AI selects the optimal customization method.

[0054] The customization unit can analyze the job seeker's social media activity during the customization process and propose customization methods. For example, the customization unit can identify areas of interest from the job seeker's social media activity and customize relevant information. The customization unit can also identify skills and experience from the job seeker's social media activity and customize relevant information. The customization unit can also identify desired conditions from the job seeker's social media activity and customize relevant information. This allows for the provision of more appropriate customization methods by analyzing the job seeker's social media activity. Some or all of the above processing in the customization unit is performed using generative AI. For example, the customization unit inputs the job seeker's social media data into the generative AI, which then proposes customization methods.

[0055] The optimization unit can analyze a company's past job postings to select the optimal optimization method during the optimization process. For example, the optimization unit can identify the skills and experience a company is looking for from past job postings and provide the optimal optimization method. The optimization unit can also identify the conditions a company is looking for from past job postings and provide the optimal optimization method. The optimization unit can also identify the ideal candidate profile a company is looking for from past job postings and provide the optimal optimization method. In this way, by analyzing a company's past job postings, the optimal optimization method can be provided. Some or all of the above processes in the optimization unit are performed using AI. For example, the optimization unit inputs the company's past job posting data into the AI, and the AI ​​selects the optimal optimization method.

[0056] The optimization unit can customize the optimization methods based on the company's current recruitment situation during the optimization process. For example, the optimization unit can provide an optimization method that emphasizes relevant skills and experience based on the current recruitment situation. The optimization unit can also provide an optimization method that matches the company's requirements based on the current recruitment situation. The optimization unit can also provide an optimization method that maximizes the ideal candidate profile the company is looking for based on the current recruitment situation. This enables more appropriate optimization by providing optimization methods based on the company's current recruitment situation. Some or all of the above processes in the optimization unit are performed using AI. For example, the optimization unit inputs the company's current recruitment situation data into the AI, and the AI ​​customizes the optimization methods.

[0057] The optimization unit can select the optimal optimization method by considering the geographical location information of companies during the optimization process. For example, the optimization unit can prioritize optimizing relevant job postings based on the company's location. The optimization unit can also prioritize optimizing relevant job postings based on the company's preferred work location. The optimization unit can also select the optimal optimization method based on both the company's geographical location information and the preferred work location. This allows for the provision of a more appropriate optimization method by considering the company's geographical location information. Some or all of the above processing in the optimization unit is performed using AI. For example, the optimization unit inputs the company's geographical location information data into the AI, and the AI ​​selects the optimal optimization method.

[0058] The optimization unit can analyze a company's social media activities and propose optimization methods during the optimization process. For example, the optimization unit can identify the desired candidate profile from a company's social media activities and optimize the relevant information. The optimization unit can also identify the desired skills and experience from a company's social media activities and optimize the relevant information. The optimization unit can also identify the desired conditions from a company's social media activities and optimize the relevant information. In this way, by analyzing a company's social media activities, it can provide more appropriate optimization methods. Some or all of the above processing in the optimization unit is performed using AI. For example, the optimization unit inputs the company's social media data into the AI, and the AI ​​proposes optimization methods.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The job matching assistant system can also include a "feedback department." This department collects feedback from both job seekers and companies to improve the system's accuracy. For example, it collects feedback from job seekers about their interview experiences after applying, and from companies about their evaluations of the job seekers' interview results. The feedback department analyzes this feedback and provides it to the recommendation and customization departments, thereby improving the accuracy of future matching. Furthermore, the feedback department can survey job seekers' satisfaction with the job postings they've applied for, contributing to overall system improvements. This allows for more satisfying matches for both job seekers and companies.

[0061] The job matching assistant system can also include a "training department." This department provides job seekers with training for interview preparation and skill development. For example, it could allow job seekers to participate in mock interviews before their actual interviews. The training department can also use generative AI to create training programs based on job seekers' skills and experience. Furthermore, the training department can offer online courses to help job seekers acquire new skills. This allows job seekers to improve their skills and apply for a wider range of job postings.

[0062] The job matching assistant system can also include a "Networking Department." This department plans and manages networking events between job seekers and companies. For example, it could host online career fairs or networking events focused on specific industries. The Networking Department provides job seekers with opportunities to interact directly with company representatives, enabling them to gain a deeper understanding. Furthermore, the Networking Department can facilitate interaction among job seekers, facilitating information sharing and mutual support. This allows job seekers to advance their careers more effectively.

[0063] The job matching assistant system can also include a "career advice department." This department provides career path advice to job seekers. For example, it can offer specific advice on what skills job seekers should acquire and which industries they should enter. The career advice department can also use generative AI to create career plans based on job seekers' skills, experience, and desired conditions. Furthermore, the career advice department can clearly outline the steps job seekers need to take to achieve their career goals. This allows job seekers to plan their careers more effectively.

[0064] The job matching assistant system can also include a "Data Security Department." This department provides functions to securely protect the data of job seekers and companies. For example, it can encrypt and store job seekers' personal information and resume / work history data. The Data Security Department can also formulate the system's overall security policy and conduct regular security checks. Furthermore, the Data Security Department can strengthen access controls to prevent unauthorized access to job seeker and company data. This ensures that job seeker and company data is securely protected, allowing users to use the system with peace of mind.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives the applicant's resume and work history. The applicant's resume and work history include a format, required fields, and necessary information. The reception desk accepts resumes and work histories submitted online, as well as those sent by mail or delivered in person. Those submitted by mail are scanned and saved as digital data, while those delivered in person are manually converted into digital data. Step 2: The interviewing department uses a generative AI to chat with job seekers based on the information received by the reception department, and gathers detailed information such as skills, experience, and desired conditions. The generative AI collects detailed information by asking questions to job seekers and analyzing their answers. It can also gather information by referring to past resumes and work histories. Step 3: The recommendation department uses a generation AI to analyze the information gathered by the interview department and recommend the most suitable job postings. The generation AI recommends job postings based on the job seeker's skills, experience, and desired conditions, and also refers to their past application history. Step 4: The customization section uses a generation AI to customize resumes and work histories based on job postings recommended by the recommendation section. The generation AI customizes resumes and work histories based on the job seeker's skills and experience, desired conditions, and the requirements of the hiring company. Step 5: The optimization unit uses AI to optimize the job postings customized by the customization unit. The AI ​​analyzes the company's conditions, benefits, requirements, and past job postings to provide optimized job postings.

[0067] (Example of form 2) The job matching assistant system according to an embodiment of the present invention is a next-generation job matching assistant system that uses generative AI to improve the accuracy of matching job seekers with companies. In this job matching assistant system, job seekers submit their resumes and work histories, and the generative AI interacts with the job seekers via chat to understand details such as skills, experience, and desired conditions. Next, the generative AI analyzes the job seekers' information and recommends the most suitable job postings. The generative AI also customizes the resumes and work histories for each recruiting company. At the same time, the recruiting company's representative also interacts with the AI ​​to optimize the job postings, thereby significantly improving the accuracy of matching job seekers with companies. For example, when a job seeker submits their resume and work histories, they input their skills, experience, and desired conditions. For example, they might input conditions such as "I have marketing experience and I want to work remotely." This information is input into the generative AI. Next, the generative AI analyzes the input information to understand the job seeker's skills, experience, and desired conditions in detail. The generative AI interacts with the job seekers via chat to collect even more detailed information. For example, if a job seeker enters conditions such as "I have marketing experience and I want to work remotely," the generating AI will recommend job postings that match those conditions. The generating AI analyzes the job seeker's information and recommends the most suitable job postings. For example, if a job seeker enters conditions such as "I have marketing experience and I want to work remotely," the generating AI will recommend job postings that match those conditions. The generating AI also customizes resumes and work histories for each recruiting company. This allows the AI ​​to maximize the appeal of the job seeker. On the other hand, recruiters at recruiting companies are presented with job postings that have been customized for job seekers by the generating AI. The AI ​​analyzes the company's conditions and benefits and provides optimized job postings. For example, if a company enters conditions such as "We are looking for a marketing professional who can work remotely," the generating AI will suggest job seekers that match those conditions. This system allows job seekers to easily find job postings that suit them, and companies can efficiently recruit the talent they need. This creates significant benefits for both job seekers and companies.This allows the job matching assistant system to efficiently collect, analyze, customize, and optimize job seeker information, thereby improving the accuracy of matching job seekers with companies.

[0068] The job matching assistant system according to this embodiment comprises a reception unit, an interview unit, a recommendation unit, a customization unit, and an optimization unit. The reception unit receives resumes and work histories from job seekers. These resumes and work histories include, but are not limited to, format, items to be included, and necessary information. The reception unit can, for example, receive resumes and work histories submitted online by job seekers. The reception unit can also receive resumes and work histories submitted by mail. Furthermore, the reception unit can also receive resumes and work histories brought in person by job seekers. For example, the reception unit receives job seeker information through an online form. Resumes and work histories submitted by mail are scanned and saved as digital data. Resumes and work histories brought in person are manually converted into digital data by the reception staff. The interview unit uses a generation AI to chat with job seekers based on the information received by the reception unit and understand details such as skills, experience, and desired conditions. The interviewing department, for example, uses a generating AI to chat with job seekers and gather detailed information such as their skills, experience, and desired conditions. The interviewing department can also use the generating AI to analyze the job seekers' responses and collect detailed information. Furthermore, the interviewing department can use the generating AI to refer to the job seekers' past resumes and work histories to gather detailed information. For example, the generating AI might ask the job seeker, "Please tell me about your skills and experience," and analyze their responses. Based on the job seeker's responses, the generating AI gathers detailed information such as their skills, experience, and desired conditions. The generating AI also uses the job seeker's past resumes and work histories to gather information about their skills and experience. The recommendation department uses the generating AI to analyze the information gathered by the interviewing department and recommend the most suitable job postings. For example, the recommendation department uses the generating AI to analyze the job seeker's information and recommend the most suitable job postings. Furthermore, the recommendation section can use a generating AI to recommend job postings based on the job seeker's skills, experience, and desired conditions. The recommendation section can also use the generating AI to refer to the job seeker's past application history and recommend the most suitable job postings. For example, the generating AI recommends job postings based on the job seeker's skills, experience, and desired conditions.The generating AI refers to the job seeker's past application history and recommends suitable job postings. The generating AI recommends the most suitable job postings based on the job seeker's skills, experience, and desired conditions. The customization unit uses the generating AI to customize resumes and CVs based on the job postings recommended by the recommendation unit. For example, the customization unit uses the generating AI to customize the job seeker's resume and CV for each recruiting company. The customization unit can also use the generating AI to customize resumes and CVs based on the job seeker's skills, experience, and desired conditions. Furthermore, the customization unit can use the generating AI to customize resumes and CVs based on the requirements of recruiting companies. For example, the generating AI customizes resumes and CVs based on the job seeker's skills, experience, and desired conditions. The generating AI customizes resumes and CVs based on the requirements of recruiting companies. The generating AI customizes resumes and CVs based on the job seeker's skills, experience, and desired conditions. The optimization unit uses AI to optimize the job postings customized by the customization unit. The optimization unit, for example, uses AI to analyze a company's conditions and benefits and provides optimized job postings. The optimization unit can also optimize job postings based on a company's requirements. Furthermore, the optimization unit can refer to a company's past job postings and provide optimized job postings. For example, the AI ​​analyzes a company's conditions and benefits and provides optimized job postings. The AI ​​optimizes job postings based on a company's requirements. The AI ​​refers to a company's past job postings and provides optimized job postings. As a result, the job matching assistant system according to this embodiment can improve the accuracy of matching job seekers with companies by efficiently collecting, analyzing, customizing, and optimizing job seeker information.

[0069] The reception desk accepts resumes and CVs from job applicants. These documents include, but are not limited to, formatting, required fields, and necessary information. The reception desk accepts resumes and CVs submitted online, by mail, and in person. For example, it accepts applicant information through online forms. Resumes and CVs submitted by mail are scanned and saved as digital data. Resumes and CVs submitted in person are manually converted into digital data by reception staff. The reception desk is designed to be flexible regarding the format of resumes and CVs submitted by job applicants. For example, it supports common file formats such as PDF, Word, and Excel, allowing job applicants to submit documents according to their convenience. Furthermore, the reception desk has a function to automatically analyze the content of submitted documents and extract necessary information. For example, information such as name, address, contact information, educational background, work history, and skills is automatically extracted and registered in the database. This automated analysis function allows the reception department to process large volumes of documents quickly and accurately. The reception department also has a function to provide real-time feedback if there are any deficiencies in the documents submitted by job seekers. For example, if necessary information is missing or the format is inappropriate, a notification is sent to the job seeker prompting them to make corrections. This allows job seekers to quickly correct and resubmit their documents. Furthermore, the reception department has implemented security measures to protect the privacy of job seekers. For example, submitted documents are encrypted and stored on a secure server. Access rights are also strictly controlled, so that only authorized personnel can access the documents. This minimizes the risk of unauthorized access to job seekers' personal information.

[0070] The interviewing department uses generative AI to chat with job seekers based on information received by the reception department, gathering details such as skills, experience, and desired conditions. For example, the interviewing department uses generative AI to chat with job seekers and gather details such as skills, experience, and desired conditions. The interviewing department can also use generative AI to analyze job seekers' responses and gather detailed information. Furthermore, the interviewing department can use generative AI to refer to job seekers' past resumes and work histories to gather detailed information. For example, the generative AI might ask job seekers questions such as, "Please tell me about your skills and experience," and analyze their responses. Based on these responses, the generative AI gathers details such as skills, experience, and desired conditions. The generative AI also uses natural language processing technology to gather information about job seekers' skills and experience. The generative AI utilizes advanced natural language processing techniques to facilitate smooth conversations with job seekers. For example, when a job seeker explains specific skills or experience in detail, the generating AI analyzes the content in real time and asks related follow-up questions to extract deeper information. Furthermore, based on the job seeker's answers, the generating AI evaluates the level of skills and experience, clarifying the job seeker's strengths and weaknesses. For example, if a job seeker answers, "I have experience in project management," the generating AI will ask follow-up questions such as, "Specifically, what kind of projects did you work on?" to gather more detailed information. The generating AI also understands the job seeker's desired conditions based on their answers. For example, if a job seeker answers, "I would like to work remotely," the generating AI will ask questions such as, "Do you have experience working remotely?" to gather more detailed information about the job seeker's desired conditions. In addition, the generating AI refers to the job seeker's past resumes and work histories to understand their skills and experience. For example, it evaluates the job seeker's experience and abilities based on the work history and skills listed in their past resumes. This allows the interviewing department to accurately understand detailed information about the job seeker's skills, experience, and desired conditions, and provide it to the recommendation department in the next step.

[0071] The recommendation department uses generative AI to analyze information gathered by the interviewing department and recommend the most suitable job postings. For example, the recommendation department's generative AI analyzes job seeker information and recommends the most suitable job postings. The recommendation department can also recommend job postings based on the job seeker's skills, experience, and desired conditions. Furthermore, the recommendation department can refer to the job seeker's past application history to recommend the most suitable job postings. For example, the generative AI recommends job postings based on the job seeker's skills, experience, and desired conditions. The generative AI refers to the job seeker's past application history and recommends job postings suitable for the job seeker. The generative AI recommends the most suitable job postings based on the job seeker's skills, experience, and desired conditions. The generative AI uses machine learning algorithms to analyze job seeker information in detail. For example, it considers the job seeker's skill set, work experience, desired work location, and salary conditions to select the most suitable job postings. Furthermore, the generating AI analyzes past application history and job seeker behavior patterns to prioritize recommending job postings that are likely to interest the job seeker. For example, if a job seeker has previously applied to a specific industry or job type, the generating AI will use that information to recommend job postings in similar industries or job types. The generating AI also takes into account the requirements and conditions of the hiring company. For example, if a hiring company values ​​specific skills or experience, the generating AI will prioritize recommending job seekers who meet those requirements. This improves the accuracy of matching job seekers and hiring companies, leading to optimal results for both parties. In addition, the generating AI has a function to update recommendation results in real time. For example, if new job postings are added or job seeker information is updated, the generating AI immediately recalculates the recommendation results and provides the most suitable job postings based on the latest information. This ensures that job seekers always receive the latest job postings and can apply quickly.

[0072] The customization department uses a generation AI to customize resumes and CVs based on job postings recommended by the recommendation department. For example, the customization department uses the generation AI to customize a job seeker's resume and CV for each recruiting company. The customization department can also use the generation AI to customize resumes and CVs based on the job seeker's skills, experience, and desired conditions. Furthermore, the customization department can use the generation AI to customize resumes and CVs based on the requirements of recruiting companies. For example, the generation AI customizes resumes and CVs based on the job seeker's skills, experience, and desired conditions. The generation AI customizes resumes and CVs based on the requirements of recruiting companies. The generation AI customizes resumes and CVs based on the job seeker's skills, experience, and desired conditions. The generation AI analyzes the job seeker's information in detail and proposes the optimal format and content for each recruiting company. For example, it adjusts the resume layout and content to emphasize skills and experience valued by a particular company. The generation AI can also maximize the job seeker's appeal by using language that matches the culture and values ​​of the recruiting company. Furthermore, the generating AI learns effective customization methods based on job seekers' past application history and success stories. For example, it analyzes the resumes of job seekers who were previously hired by a specific company and incorporates their success factors to optimize the resumes of other job seekers. The generating AI also has the ability to reflect the requirements and conditions of recruiting companies in real time. For example, if a recruiting company adds new skills or experience requirements, the generating AI immediately incorporates that information and updates the job seeker's resume and CV. This ensures that job seekers can always submit customized documents based on the latest information. In addition, the generating AI continuously improves the customization based on job seeker feedback. For example, when a job seeker rates their satisfaction with a particular customization, the generating AI learns from that rating and incorporates it into future customizations. This allows the customization department to provide optimal resumes and CVs that meet the needs of job seekers and improve the accuracy of matching them with recruiting companies.

[0073] The Optimization Unit uses AI to optimize job postings customized by the Customization Unit. For example, the Optimization Unit uses AI to analyze a company's conditions and benefits and provide optimized job postings. The Optimization Unit can also optimize job postings based on a company's requirements. Furthermore, the Optimization Unit can refer to a company's past job postings and provide optimized job postings. For example, the AI ​​analyzes a company's conditions and benefits and provides optimized job postings. The AI ​​optimizes job postings based on a company's requirements. The AI ​​refers to a company's past job postings and provides optimized job postings. The Optimization Unit uses AI to further refine the job postings customized by the Customization Unit and provide them to job seekers in the most optimal form. For example, the AI ​​analyzes a company's conditions and benefits in detail and highlights the most attractive points for job seekers. The AI ​​also optimizes job postings based on a company's requirements and arranges them in a format that makes them easy for job seekers to apply. In addition, the AI ​​refers to a company's past job postings and hiring results to provide optimal job postings. For example, it learns patterns from successful past job postings and incorporates those elements to increase job seekers' motivation to apply. Furthermore, the AI ​​updates job postings in real time, providing job seekers with the latest information. For example, it instantly reflects newly offered benefits and salary conditions from companies, providing attractive job postings to job seekers. In addition, the optimization unit continuously improves job postings based on job seeker feedback. For example, if a job seeker shows interest in a particular job posting, it optimizes other job postings based on that information. This allows the optimization unit to provide job seekers with the most attractive and easy-to-apply-for job postings, improving the accuracy of matching with companies.

[0074] The interviewing department can use a generative AI to chat with job seekers and understand details such as their skills, experience, and desired conditions. For example, the generative AI chats with job seekers to understand details such as their skills, experience, and desired conditions. The generative AI asks job seekers questions such as, "Please tell me about your skills and experience," and analyzes the job seekers' answers. Based on the job seekers' answers, the generative AI understands details such as their skills, experience, and desired conditions. The generative AI also refers to the job seekers' past resumes and work histories to understand their skills and experience. In this way, detailed information about job seekers can be efficiently obtained by using the generative AI. Some or all of the above processes in the interviewing department are performed using the generative AI. For example, the interviewing department inputs the job seeker's information into the generative AI, and the generative AI understands the job seeker's skills, experience, and desired conditions.

[0075] The recommendation unit can analyze job seeker information using a generating AI and recommend the most suitable job postings. For example, the recommendation unit uses a generating AI to analyze job seeker information and recommend the most suitable job postings. The generating AI recommends job postings based on the job seeker's skills, experience, and desired conditions. The generating AI also refers to the job seeker's past application history and recommends job postings that are suitable for the job seeker. The generating AI recommends the most suitable job postings based on the job seeker's skills, experience, and desired conditions. As a result, by using a generating AI, the most suitable job postings can be efficiently recommended to job seekers. Some or all of the above processes in the recommendation unit are performed using a generating AI. For example, the recommendation unit inputs job seeker information into the generating AI, and the generating AI recommends the most suitable job postings.

[0076] The customization section can customize resumes and work histories for each recruiting company using a generation AI. For example, the generation AI in the customization section customizes job seekers' resumes and work histories for each recruiting company. The generation AI customizes resumes and work histories based on the job seeker's skills, experience, and desired conditions. The generation AI customizes resumes and work histories based on the requirements of the recruiting company. The generation AI customizes resumes and work histories based on the job seeker's skills, experience, and desired conditions. This allows for efficient customization of job seekers' resumes and work histories using the generation AI. Some or all of the above processes in the customization section are performed using the generation AI. For example, the customization section inputs the job seeker's information into the generation AI, which then customizes the resume and work histories.

[0077] The optimization unit can analyze a company's conditions and benefits using AI and provide optimized job postings. For example, the AI ​​analyzes a company's conditions and benefits and provides optimized job postings. The AI ​​optimizes job postings based on the company's requirements. The AI ​​refers to a company's past job postings and provides optimized job postings. The AI ​​analyzes a company's conditions and benefits and provides optimized job postings. Thus, by using AI, it is possible to analyze a company's conditions and benefits and provide optimized job postings. Some or all of the above processes in the optimization unit are performed using AI. For example, the optimization unit inputs company information into the AI, and the AI ​​provides optimized job postings.

[0078] The reception desk can estimate the emotions of job seekers and adjust the timing of resume and CV submission based on the estimated emotions. For example, if a job seeker is feeling stressed, the reception desk can encourage them to submit their resume and CV during a time when they can relax. If a job seeker is relaxed, the reception desk can also encourage them to submit their resume and CV immediately. If a job seeker is in a hurry, the reception desk can also encourage them to submit their resume and CV quickly. This allows for the submission of resumes and CVs at a more appropriate time by adjusting the submission timing according to the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the job seeker's emotion data into the AI, and the AI ​​adjusts the submission timing.

[0079] The reception department can analyze a job seeker's past resume and work history submission history and select the most suitable submission method. For example, the reception department can recommend online submission to a job seeker who has previously preferred online submission. It can also recommend postal submission to a job seeker who has previously preferred postal submission. Based on past submission history, the reception department can also encourage job seekers who tend to submit at specific times to submit during those times. In this way, by analyzing past submission history, the reception department can provide job seekers with the most suitable submission method. Some or all of the above processes in the reception department are performed using AI. For example, the reception department inputs the job seeker's submission history data into the AI, and the AI ​​selects the most suitable submission method.

[0080] The reception desk can filter job seekers' current employment status and areas of interest when they submit their resumes and work histories. For example, the reception desk can prioritize displaying relevant job postings based on their current employment status. It can also prioritize displaying relevant job postings based on their areas of interest. The reception desk can even display the most relevant job postings based on both their current employment status and areas of interest. This allows the reception desk to provide highly relevant job postings by filtering based on the job seeker's current employment status and areas of interest. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs data on the job seeker's employment status and areas of interest into the AI, which then performs the filtering.

[0081] The reception desk can estimate the emotions of job seekers and prioritize the resumes and work histories submitted based on the estimated emotions. For example, if a job seeker is stressed, the reception desk will prioritize submitting important information. If a job seeker is relaxed, the reception desk may also prioritize submitting detailed information. If a job seeker is in a hurry, the reception desk may also prioritize submitting the most important information. This allows for the prioritization of resumes and work histories submitted according to the job seeker's emotions, ensuring that more relevant information is submitted first. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the job seeker's emotion data into the AI, and the AI ​​determines the priorities.

[0082] The reception desk can prioritize retrieving highly relevant information by considering the job seeker's geographical location when they submit their resume and work history. For example, the reception desk can prioritize displaying nearby job postings based on the job seeker's current location. The reception desk can also prioritize displaying relevant job postings based on the job seeker's desired work location. The reception desk can even display the most suitable job postings based on both the job seeker's geographical location and desired work location. This allows the reception desk to provide highly relevant job postings by considering the job seeker's geographical location. Some or all of the above processing in the reception desk is performed using AI. For example, the reception desk inputs the job seeker's geographical location into the AI, and the AI ​​retrieves highly relevant information.

[0083] The reception department can analyze a job seeker's social media activity and retrieve relevant information when they submit their resume and work history. For example, the reception department can identify areas of interest from the job seeker's social media activity and display relevant job postings. The reception department can also identify skills and experience from the job seeker's social media activity and display relevant job postings. The reception department can also identify desired conditions from the job seeker's social media activity and display relevant job postings. In this way, by analyzing the job seeker's social media activity, it is possible to provide highly relevant job postings. Some or all of the above processing in the reception department is performed using AI. For example, the reception department inputs the job seeker's social media data into the AI, and the AI ​​retrieves relevant information.

[0084] The interviewing unit can estimate the job seeker's emotions and adjust the way the conversation is expressed based on the estimated emotions. For example, if the job seeker is nervous, the interviewing unit will use a way of expressing emotions that helps them relax. If the job seeker is relaxed, the interviewing unit may also use a way of expressing emotions that helps elicit detailed information. If the job seeker is in a hurry, the interviewing unit may also use a way of expressing emotions that helps gather information quickly. By adjusting the way the conversation is expressed according to the job seeker's emotions, a more appropriate conversation becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewing unit is performed using generative AI. For example, the interviewing unit inputs the job seeker's emotion data into the generative AI, and the generative AI adjusts the way the conversation is expressed.

[0085] The interviewing unit can adjust the level of detail in the conversation based on the importance of the job seeker during the interview. For example, the interviewing unit can conduct a detailed conversation to prioritize the collection of important information. The interviewing unit can also collect less important information concisely. The interviewing unit can also dynamically adjust the level of detail in the conversation according to the importance of the information. This allows for efficient information collection by adjusting the level of detail in the conversation according to the importance of the job seeker. Some or all of the above processing in the interviewing unit is performed using a generative AI. For example, the interviewing unit inputs the importance data of the job seeker into the generative AI, and the generative AI adjusts the level of detail in the conversation.

[0086] The interviewing unit can apply different dialogue algorithms to job seekers depending on their occupational category during the interview. For example, the interviewing unit can conduct detailed conversations about technical skills with job seekers in the IT industry. It can also conduct detailed conversations about professional experience with job seekers in the medical industry. It can also conduct detailed conversations about educational experience and qualifications with job seekers in the education industry. By applying a dialogue algorithm tailored to the job seeker's occupational category, more appropriate information can be collected. Some or all of the above processing in the interviewing unit is performed using generative AI. For example, the interviewing unit inputs the job seeker's occupational category data into the generative AI, and the generative AI applies a dialogue algorithm.

[0087] The interviewing unit can estimate the job seeker's emotions and adjust the length of the conversation based on the estimated emotions. For example, if the job seeker is nervous, the interviewing unit can collect information through a short conversation. If the job seeker is relaxed, the interviewing unit can also collect detailed information through a longer conversation. If the job seeker is in a hurry, the interviewing unit can adjust the length of the conversation to quickly collect information. This allows for efficient information gathering by adjusting the length of the conversation according to the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewing unit is performed using generative AI. For example, the interviewing unit inputs the job seeker's emotion data into the generative AI, and the generative AI adjusts the length of the conversation.

[0088] The interviewing department can prioritize conversations based on the timing of job applicants' submissions. For example, the interviewing department will prioritize interviews with resumes and work histories submitted early. It can also prioritize interviews with resumes and work histories that are nearing their deadlines. The interviewing department can also dynamically adjust the priority of conversations based on the submission timing. This enables efficient information gathering by prioritizing conversations based on the timing of job applicants' submissions. Some or all of the above processing in the interviewing department is performed using a generative AI. For example, the interviewing department inputs job applicant submission timing data into the generative AI, and the generative AI determines the priority of conversations.

[0089] The interviewing unit can adjust the order of conversations based on the relevance of job seekers during interviews. For example, the interviewing unit will prioritize interviews with job seekers whose skills and experience are relevant to the job posting. It can also prioritize interviews with job seekers whose desired conditions are relevant to the job posting. The interviewing unit can also dynamically adjust the order of conversations based on the relevance of job seekers. This allows for efficient information gathering by adjusting the order of conversations based on the relevance of job seekers. Some or all of the above processing in the interviewing unit is performed using a generative AI. For example, the interviewing unit inputs the relevance data of job seekers into the generative AI, and the generative AI adjusts the order of conversations.

[0090] The recommendation system can estimate the job seeker's emotions and adjust its recommendation criteria based on those emotions. For example, if a job seeker is stressed, the recommendation system will prioritize recommending relaxing job postings. If a job seeker is relaxed, the recommendation system can also prioritize recommending more detailed job postings. If a job seeker is in a hurry, the recommendation system can also prioritize recommending job postings that can be applied for quickly. By adjusting the recommendation criteria according to the job seeker's emotions, the system can provide more appropriate job information. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation system is performed using generative AI. For example, the recommendation system inputs the job seeker's emotion data into the generative AI, and the generative AI adjusts the recommendation criteria.

[0091] The recommendation unit can improve the accuracy of its recommendations by considering the relationships between job seekers. For example, the recommendation unit can make recommendations by considering the evaluations of job seekers' past colleagues and supervisors. The recommendation unit can also make recommendations by considering the evaluations of job seekers' past project members. The recommendation unit can also make recommendations by considering the evaluations of job seekers' past team members. This improves the accuracy of recommendations by considering the relationships between job seekers. Some or all of the above processing in the recommendation unit is performed using a generative AI. For example, the recommendation unit inputs the relationship data of job seekers into the generative AI, and the generative AI improves the accuracy of the recommendations.

[0092] The recommendation unit can make recommendations while considering the job seeker's attribute information. For example, the recommendation unit can make recommendations considering the job seeker's age and gender. The recommendation unit can also make recommendations considering the job seeker's educational background and work experience. The recommendation unit can also make recommendations considering the job seeker's skills and qualifications. In this way, by considering the job seeker's attribute information, more appropriate job information can be provided. Some or all of the above processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit inputs the job seeker's attribute information into the generation AI, and the generation AI makes recommendations.

[0093] The recommendation unit can estimate the job seeker's emotions and adjust the order in which it displays recommendations based on the estimated emotions. For example, if a job seeker is feeling stressed, the recommendation unit will prioritize displaying relaxing job postings. If a job seeker is relaxed, the recommendation unit can also prioritize displaying detailed job postings. If a job seeker is in a hurry, the recommendation unit can also prioritize displaying job postings that can be applied for quickly. This allows the recommendation unit to provide more appropriate job information by adjusting the order in which it displays recommendations according to the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recommendation unit is performed using generative AI. For example, the recommendation unit inputs the job seeker's emotion data into the generative AI, and the generative AI adjusts the order in which it displays the results.

[0094] The recommendation unit can make recommendations while considering the geographical distribution of job seekers. For example, the recommendation unit can prioritize recommending nearby job postings based on the job seeker's current location. The recommendation unit can also prioritize recommending relevant job postings based on the job seeker's desired work location. The recommendation unit can even recommend the most suitable job postings based on both the job seeker's geographical distribution and desired work location. This allows for the provision of more appropriate job postings by considering the job seeker's geographical distribution. Some or all of the above processing in the recommendation unit is performed using a generation AI. For example, the recommendation unit inputs the job seeker's geographical distribution data into the generation AI, and the generation AI makes recommendations.

[0095] The recommendation system can improve the accuracy of its recommendations by referring to the job seeker's relevant literature. For example, the recommendation system can make recommendations by referring to the job seeker's past papers and articles. It can also make recommendations by referring to the job seeker's past project reports. It can also make recommendations by referring to the job seeker's past presentation materials. This improves the accuracy of recommendations by referring to the job seeker's relevant literature. Some or all of the above processing in the recommendation system is performed using a generative AI. For example, the recommendation system inputs the job seeker's relevant literature data into the generative AI, which then improves the accuracy of its recommendations.

[0096] The customization unit can estimate the job seeker's emotions and adjust the customization method based on the estimated emotions. For example, if the job seeker is stressed, the customization unit can provide a simple customization method. If the job seeker is relaxed, the customization unit can also provide a detailed customization method. If the job seeker is in a hurry, the customization unit can also provide a method that allows for quick customization. This allows for more appropriate customization by adjusting the customization method according to the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit is performed using generative AI. For example, the customization unit inputs the job seeker's emotion data into the generative AI, and the generative AI adjusts the customization method.

[0097] The customization department can analyze a job seeker's past resumes and work histories to select the optimal customization method during the customization process. For example, the customization department can select a customization method that emphasizes the job seeker's strengths from their past resumes and work histories. The customization department can also select a customization method that compensates for the job seeker's weaknesses from their past resumes and work histories. The customization department can also select a customization method that matches the job seeker's desired conditions from their past resumes and work histories. In this way, by analyzing a job seeker's past resumes and work histories, the optimal customization method can be provided. Some or all of the above processes in the customization department are performed using a generation AI. For example, the customization department inputs the job seeker's past resume and work histories data into the generation AI, and the generation AI selects the optimal customization method.

[0098] The customization unit can customize the means of customization based on the job seeker's current occupational status during the customization process. For example, the customization unit can provide a customization method that emphasizes relevant skills and experience based on the current occupational status. The customization unit can also provide a customization method that matches the job seeker's desired conditions based on the current occupational status. The customization unit can also provide a customization method that maximizes the job seeker's strengths based on the current occupational status. This allows for more appropriate customization by providing customization means based on the job seeker's current occupational status. Some or all of the above processing in the customization unit is performed using a generative AI. For example, the customization unit inputs the job seeker's current occupational status data into the generative AI, and the generative AI customizes the means of customization.

[0099] The customization unit can estimate the job seeker's emotions and determine customization priorities based on the estimated emotions. For example, if the job seeker is stressed, the customization unit will prioritize important information. If the job seeker is relaxed, the customization unit can also prioritize detailed information. If the job seeker is in a hurry, the customization unit can also prioritize the most important information. This allows for the prioritization of more appropriate information by determining customization priorities according to the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the customization unit is performed using generative AI. For example, the customization unit inputs the job seeker's emotion data into the generative AI, and the generative AI determines the customization priorities.

[0100] The customization unit can select the optimal customization method by considering the job seeker's geographical location information during the customization process. For example, the customization unit can prioritize customizing relevant job postings based on the job seeker's current location. The customization unit can also prioritize customizing relevant job postings based on the job seeker's desired work location. The customization unit can also select the optimal customization method based on both the job seeker's geographical location information and their desired work location. This allows for the provision of a more appropriate customization method by considering the job seeker's geographical location information. Some or all of the above processing in the customization unit is performed using a generation AI. For example, the customization unit inputs the job seeker's geographical location information data into the generation AI, and the generation AI selects the optimal customization method.

[0101] The customization unit can analyze the job seeker's social media activity during the customization process and propose customization methods. For example, the customization unit can identify areas of interest from the job seeker's social media activity and customize relevant information. The customization unit can also identify skills and experience from the job seeker's social media activity and customize relevant information. The customization unit can also identify desired conditions from the job seeker's social media activity and customize relevant information. This allows for the provision of more appropriate customization methods by analyzing the job seeker's social media activity. Some or all of the above processing in the customization unit is performed using generative AI. For example, the customization unit inputs the job seeker's social media data into the generative AI, which then proposes customization methods.

[0102] The optimization unit can estimate the job seeker's emotions and adjust the optimization method based on the estimated emotions. For example, if the job seeker is stressed, the optimization unit can provide a simple optimization method. If the job seeker is relaxed, the optimization unit can also provide a detailed optimization method. If the job seeker is in a hurry, the optimization unit can also provide a method that allows for rapid optimization. This allows for more appropriate optimization by adjusting the optimization method according to the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit is performed using AI. For example, the optimization unit inputs the job seeker's emotion data into the AI, and the AI ​​adjusts the optimization method.

[0103] The optimization unit can analyze a company's past job postings to select the optimal optimization method during the optimization process. For example, the optimization unit can identify the skills and experience a company is looking for from past job postings and provide the optimal optimization method. The optimization unit can also identify the conditions a company is looking for from past job postings and provide the optimal optimization method. The optimization unit can also identify the ideal candidate profile a company is looking for from past job postings and provide the optimal optimization method. In this way, by analyzing a company's past job postings, the optimal optimization method can be provided. Some or all of the above processes in the optimization unit are performed using AI. For example, the optimization unit inputs the company's past job posting data into the AI, and the AI ​​selects the optimal optimization method.

[0104] The optimization unit can customize the optimization methods based on the company's current recruitment situation during the optimization process. For example, the optimization unit can provide an optimization method that emphasizes relevant skills and experience based on the current recruitment situation. The optimization unit can also provide an optimization method that matches the company's requirements based on the current recruitment situation. The optimization unit can also provide an optimization method that maximizes the ideal candidate profile the company is looking for based on the current recruitment situation. This enables more appropriate optimization by providing optimization methods based on the company's current recruitment situation. Some or all of the above processes in the optimization unit are performed using AI. For example, the optimization unit inputs the company's current recruitment situation data into the AI, and the AI ​​customizes the optimization methods.

[0105] The optimization unit can estimate the job seeker's emotions and determine optimization priorities based on the estimated emotions. For example, if the job seeker is stressed, the optimization unit will prioritize optimizing important information. If the job seeker is relaxed, the optimization unit can also prioritize optimizing detailed information. If the job seeker is in a hurry, the optimization unit can also prioritize optimizing the most important information. This allows for prioritizing more appropriate information by determining optimization priorities according to the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the optimization unit is performed using AI. For example, the optimization unit inputs the job seeker's emotion data into the AI, and the AI ​​determines the optimization priorities.

[0106] The optimization unit can select the optimal optimization method by considering the geographical location information of companies during the optimization process. For example, the optimization unit can prioritize optimizing relevant job postings based on the company's location. The optimization unit can also prioritize optimizing relevant job postings based on the company's preferred work location. The optimization unit can also select the optimal optimization method based on both the company's geographical location information and the preferred work location. This allows for the provision of a more appropriate optimization method by considering the company's geographical location information. Some or all of the above processing in the optimization unit is performed using AI. For example, the optimization unit inputs the company's geographical location information data into the AI, and the AI ​​selects the optimal optimization method.

[0107] The optimization unit can analyze a company's social media activities and propose optimization methods during the optimization process. For example, the optimization unit can identify the desired candidate profile from a company's social media activities and optimize the relevant information. The optimization unit can also identify the desired skills and experience from a company's social media activities and optimize the relevant information. The optimization unit can also identify the desired conditions from a company's social media activities and optimize the relevant information. In this way, by analyzing a company's social media activities, it can provide more appropriate optimization methods. Some or all of the above processing in the optimization unit is performed using AI. For example, the optimization unit inputs the company's social media data into the AI, and the AI ​​proposes optimization methods.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The job matching assistant system can also include a "feedback department." This department collects feedback from both job seekers and companies to improve the system's accuracy. For example, it collects feedback from job seekers about their interview experiences after applying, and from companies about their evaluations of the job seekers' interview results. The feedback department analyzes this feedback and provides it to the recommendation and customization departments, thereby improving the accuracy of future matching. Furthermore, the feedback department can survey job seekers' satisfaction with the job postings they've applied for, contributing to overall system improvements. This allows for more satisfying matches for both job seekers and companies.

[0110] The job matching assistant system can also include a "training department." This department provides job seekers with training for interview preparation and skill development. For example, it could allow job seekers to participate in mock interviews before their actual interviews. The training department can also use generative AI to create training programs based on job seekers' skills and experience. Furthermore, the training department can offer online courses to help job seekers acquire new skills. This allows job seekers to improve their skills and apply for a wider range of job postings.

[0111] The job matching assistant system can also include a "Networking Department." This department plans and manages networking events between job seekers and companies. For example, it could host online career fairs or networking events focused on specific industries. The Networking Department provides job seekers with opportunities to interact directly with company representatives, enabling them to gain a deeper understanding. Furthermore, the Networking Department can facilitate interaction among job seekers, facilitating information sharing and mutual support. This allows job seekers to advance their careers more effectively.

[0112] The job matching assistant system can also include a "career advice department." This department provides career path advice to job seekers. For example, it can offer specific advice on what skills job seekers should acquire and which industries they should enter. The career advice department can also use generative AI to create career plans based on job seekers' skills, experience, and desired conditions. Furthermore, the career advice department can clearly outline the steps job seekers need to take to achieve their career goals. This allows job seekers to plan their careers more effectively.

[0113] The job matching assistant system can also include a "Data Security Department." This department provides functions to securely protect the data of job seekers and companies. For example, it can encrypt and store job seekers' personal information and resume / work history data. The Data Security Department can also formulate the system's overall security policy and conduct regular security checks. Furthermore, the Data Security Department can strengthen access controls to prevent unauthorized access to job seeker and company data. This ensures that job seeker and company data is securely protected, allowing users to use the system with peace of mind.

[0114] The job matching assistant system can also be equipped with an "emotional feedback unit." This unit monitors the emotions of both job seekers and companies in real time and provides feedback. For example, if a job seeker is nervous during an interview, it can offer advice on how to relax. The emotional feedback unit can also use generative AI to analyze emotional data from both job seekers and companies and provide appropriate feedback. Furthermore, the emotional feedback unit can collect satisfaction and dissatisfaction levels felt by job seekers after applying, and use this information to improve the system. This allows for the provision of feedback based on the emotions of both job seekers and companies, leading to better matching.

[0115] The job matching assistant system can also be equipped with an "emotional support unit." This unit provides functions to support the emotional well-being of job seekers. For example, if a job seeker is feeling stressed, it can provide mental health support to help them relax. The emotional support unit can also use generative AI to analyze the job seeker's emotional data and provide appropriate support. Furthermore, if a job seeker is nervous before an interview, the emotional support unit can provide advice to help them relax. This helps support the emotional well-being of job seekers, enabling them to perform better.

[0116] The job matching assistant system can also be equipped with an "emotional analysis unit." This unit provides data to improve matching accuracy by thoroughly analyzing the emotions of job seekers and companies. For example, it analyzes the emotions a job seeker feels towards a particular job posting. The emotional analysis unit can also use generative AI to collect and analyze emotional data from both job seekers and companies. Furthermore, it can analyze the emotions a job seeker felt during an interview and provide suggestions for improvement for future interviews. This allows for more accurate matching by providing data based on the emotions of both job seekers and companies.

[0117] The job matching assistant system can also be equipped with an "emotion tracking unit." The emotion tracking unit continuously tracks the emotions of job seekers and companies, monitoring long-term emotional changes. For example, it tracks how job seekers experience emotional changes from application to hiring. The emotion tracking unit can also use generative AI to continuously collect and analyze emotional data from job seekers and companies. Furthermore, the emotion tracking unit can track the satisfaction and dissatisfaction felt by job seekers after being hired, and use this information to improve the system. This allows for continuous monitoring of emotional changes in job seekers and companies, leading to better matching.

[0118] The job matching assistant system can also be equipped with an "emotion prediction unit." This unit predicts the emotions of job seekers and companies, providing data to improve the success rate of future matches. For example, it predicts how a job seeker will feel about a particular job posting. The emotion prediction unit can also use generative AI to collect emotional data from job seekers and companies and build predictive models. Furthermore, the emotion prediction unit can predict the tension and anxiety job seekers feel before an interview and provide appropriate support. This allows for more accurate matching by predicting the emotions of both job seekers and companies.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives the applicant's resume and work history. The applicant's resume and work history include a format, required fields, and necessary information. The reception desk accepts resumes and work histories submitted online, as well as those sent by mail or delivered in person. Those submitted by mail are scanned and saved as digital data, while those delivered in person are manually converted into digital data. Step 2: The interviewing department uses a generative AI to chat with job seekers based on the information received by the reception department, and gathers detailed information such as skills, experience, and desired conditions. The generative AI collects detailed information by asking questions to job seekers and analyzing their answers. It can also gather information by referring to past resumes and work histories. Step 3: The recommendation department uses a generation AI to analyze the information gathered by the interview department and recommend the most suitable job postings. The generation AI recommends job postings based on the job seeker's skills, experience, and desired conditions, and also refers to their past application history. Step 4: The customization section uses a generation AI to customize resumes and work histories based on job postings recommended by the recommendation section. The generation AI customizes resumes and work histories based on the job seeker's skills and experience, desired conditions, and the requirements of the hiring company. Step 5: The optimization unit uses AI to optimize the job postings customized by the customization unit. The AI ​​analyzes the company's conditions, benefits, requirements, and past job postings to provide optimized job postings.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, interview unit, recommendation unit, customization unit, and optimization unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and receives the job seeker's resume and work history. The interview unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generated AI to chat with the job seeker and understand details such as skills, experience, and desired conditions. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the job seeker's information and recommends the most suitable job information. The customization unit is implemented by, for example, the control unit 46A of the smart device 14 and customizes the resume and work history based on the recommended job information. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes the customized job information. 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.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, interview unit, recommendation unit, customization unit, and optimization unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and receives the job seeker's resume and work history. The interview unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generated AI to chat with the job seeker and understand details such as skills, experience, and desired conditions. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the job seeker's information and recommends the most suitable job information. The customization unit is implemented by, for example, the control unit 46A of the smart glasses 214 and customizes the resume and work history based on the recommended job information. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes the customized job information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the reception unit, interview unit, recommendation unit, customization unit, and optimization unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and receives the job seeker's resume and work history. The interview unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generated AI to chat with the job seeker and understand details such as skills, experience, and desired conditions. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the job seeker's information and recommends the most suitable job information. The customization unit is implemented by, for example, the control unit 46A of the headset terminal 314 and customizes the resume and work history based on the recommended job information. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes the customized job information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0167] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0168] In 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.

[0169] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0170] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0171] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0172] The data processing system 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.

[0173] Each of the multiple elements described above, including the reception unit, interview unit, recommendation unit, customization unit, and optimization unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and receives the job seeker's resume and work history. The interview unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses generated AI to chat with the job seeker and understand details such as skills, experience, and desired conditions. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the job seeker's information and recommends the most suitable job information. The customization unit is implemented by, for example, the control unit 46A of the robot 414 and customizes the resume and work history based on the recommended job information. The optimization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and optimizes the customized job information. 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The reception area accepts resumes and work histories from job seekers, Based on the information received by the aforementioned reception department, the interviewing department communicates with job seekers via chat to understand details such as their skills, experience, and desired conditions. The recommendation department analyzes the information gathered by the aforementioned hearing department and recommends the most suitable job postings. Based on the job information recommended by the aforementioned recommendation unit, a customization unit customizes resumes and work history documents. The system includes an optimization unit that optimizes the job postings customized by the customization unit. A system characterized by the following features. (Note 2) The aforementioned hearing section is, The AI ​​generates conversations with job seekers via chat to understand their skills, experience, and desired conditions in detail. The system described in Appendix 1, characterized by the features described herein. (Note 3) The recommendation unit is, The AI ​​generates and analyzes job seekers' information to recommend the most suitable job postings. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned customization unit is AI-generated resumes and work histories are customized for each hiring company. The system described in Appendix 1, characterized by the features described herein. (Note 5) The optimization unit, AI analyzes companies' requirements and benefits to provide optimized job postings. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the emotions of job seekers and adjusts the timing of resume and CV submission based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We analyze the past resume and work history submission history of job seekers and select the most suitable submission method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When submitting resumes and work histories, applicants are filtered based on their current employment status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is It estimates the emotions of job seekers and determines the priority of resumes and work histories to be submitted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When applicants submit their resumes and work histories, the system prioritizes retrieving highly relevant information by considering their geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When applicants submit their resumes and work histories, we analyze their social media activity and obtain relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned hearing section is, The system estimates the job seeker's emotions and adjusts the way the dialogue is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned hearing section is, During the interview, adjust the level of detail in the conversation based on the importance of the job seeker. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned hearing section is, During the interview, different dialogue algorithms are applied depending on the job seeker's occupation category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned hearing section is, The system estimates the job seeker's emotions and adjusts the length of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned hearing section is, During the initial consultation, we will prioritize discussions based on when the applicants submitted their applications. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned hearing section is, During the interview, the order of the conversation will be adjusted based on the relevance of the job seeker. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recommendation unit is, The system estimates the sentiments of job seekers and adjusts recommendation criteria based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 19) The recommendation unit is, When making recommendations, we improve the accuracy of recommendations by considering the relationships between job seekers. The system described in Appendix 1, characterized by the features described herein. (Note 20) The recommendation unit is, When making recommendations, the recommendation system takes into account the job seeker's attribute information. The system described in Appendix 1, characterized by the features described herein. (Note 21) The recommendation unit is, The system estimates the job seeker's emotions and adjusts the order in which recommendations are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The recommendation unit is, When making recommendations, the geographical distribution of job seekers should be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 23) The recommendation unit is, When making recommendations, we refer to relevant literature for job seekers to improve the accuracy of the recommendations. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned customization unit is It estimates the job seeker's emotions and adjusts the customization method based on the estimated job seeker's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned customization unit is During customization, the system analyzes the applicant's past resumes and work histories to select the most suitable customization method. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned customization unit is During customization, the customization method is customized based on the job seeker's current employment status. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned customization unit is It estimates the emotions of job seekers and determines customization priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned customization unit is During customization, the optimal customization method is selected by considering the job seeker's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned customization unit is During the customization process, we analyze the job seeker's social media activity and propose customization methods. The system described in Appendix 1, characterized by the features described herein. (Note 30) The optimization unit, The system estimates the emotions of job seekers and adjusts the optimization method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The optimization unit, During optimization, the company's past job postings are analyzed to select the most suitable optimization method. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, During optimization, the optimization methods are customized based on the company's current job market situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, The system estimates the emotions of job seekers and determines optimization priorities based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, During optimization, the optimal optimization method is selected by considering the company's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The optimization unit, During the optimization process, we analyze a company's social media activities and propose optimization strategies. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area accepts resumes and work histories from job seekers, Based on the information received by the aforementioned reception department, the interviewing department communicates with job seekers via chat to understand details such as their skills, experience, and desired conditions. The recommendation department analyzes the information gathered by the aforementioned hearing department and recommends the most suitable job postings. Based on the job information recommended by the aforementioned recommendation unit, a customization unit customizes resumes and work history documents. The system includes an optimization unit that optimizes the job postings customized by the customization unit. A system characterized by the following features.

2. The aforementioned hearing section is, The AI ​​generates profiles and engages in chat conversations with job seekers to understand their skills, experience, and desired conditions in detail. The system according to feature 1.

3. The recommendation unit is, The system uses AI to analyze job seekers' information and recommend the most suitable job postings. The system according to feature 1.

4. The aforementioned customization unit is AI-generated resumes and work histories are customized for each recruiting company. The system according to feature 1.

5. The optimization unit, Using AI, we analyze companies' requirements and benefits and provide optimized job postings. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the emotions of job seekers and adjusts the timing of resume and work history submissions based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is We analyze the past resume and work history submission history of job seekers and select the most suitable submission method. The system according to feature 1.

8. The aforementioned reception unit is When submitting resumes and work histories, applicants are filtered based on their current employment status and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the emotions of job seekers and determines the priority of resumes and work histories to be submitted based on those estimated emotions. The system according to feature 1.

10. The aforementioned reception unit is When applicants submit their resumes and work histories, the system prioritizes obtaining highly relevant information by considering their geographical location. The system according to feature 1.

Images