system

Abstract

The system according to this embodiment aims to analyze job seekers' resumes and work histories and provide them with the most suitable job information. [Solution] The system according to the embodiment comprises an analysis unit, a recommendation unit, an interview support unit, a career planning unit, and an optimization unit. The analysis unit analyzes the resume and work history of job seekers. The recommendation unit recommends the most suitable job information based on the information analyzed by the analysis unit. The interview support unit provides real-time interview support based on the job information recommended by the recommendation unit. The career planning unit performs personalized career planning based on the information obtained by the interview support unit. The optimization unit automatically optimizes the job information based on the information obtained by the career planning unit.

Description

Technical Field

[0003]

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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, the resumes and work experience records of job seekers have not been sufficiently analyzed to provide optimal job information, and there is room for improvement.

[0005] <​​​​​​​The system according to this embodiment comprises an analysis unit, a recommendation unit, an interview support unit, a career planning unit, and an optimization unit. The analysis unit analyzes the resumes and work histories of job seekers. The recommendation unit recommends the most suitable job postings based on the information analyzed by the analysis unit. The interview support unit provides real-time interview support based on the job postings recommended by the recommendation unit. The career planning unit performs personalized career planning based on the information obtained by the interview support unit. The optimization unit automatically optimizes job postings based on the information obtained by the career planning unit. [Effects of the Invention]

[0007] The system according to this embodiment can analyze job seekers' resumes and work histories and provide them with the most suitable job information. [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 numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between multiple computers. Examples of communication standards applicable to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The job matching platform according to an embodiment of the present invention is a system that realizes next-generation job matching by utilizing generative AI technology. This system analyzes the resumes and work histories of job seekers and recommends the most suitable job information. Furthermore, it improves the accuracy of matching job seekers with companies through real-time interview support, personalized career planning, and automatic optimization of job information. For example, when a job seeker submits a resume and work history, the generative AI analyzes these documents. The generative AI grasps the job seeker's skills, experience, and desired conditions in detail and recommends the most suitable job information in a personalized manner. For example, if a job seeker has a particular skill, it will prioritize displaying job information related to that skill. Next, the generative AI provides real-time interview support. During the interview, the generative AI analyzes the job seeker's statements and facial expressions and provides real-time feedback on areas for improvement. This allows job seekers to improve their interview skills. Furthermore, the generative AI provides personalized career planning. The generative AI proposes a growth plan tailored to the job seeker's career goals. For example, if a job seeker wants to change jobs to a specific occupation, it proposes a plan to acquire the skills and experience necessary for that occupation. Furthermore, the generation AI automatically optimizes job postings. The generation AI analyzes the company's requirements and benefits, and makes attractive and appropriate suggestions to target job seekers. For example, if a company is looking for job seekers with specific skills, it optimizes and displays job postings related to those skills. In this way, the job matching platform is a system that utilizes generation AI technology to improve the accuracy of matching job seekers and companies. Job seekers can efficiently find the most suitable job postings in a short amount of time, and companies can efficiently attract the job seekers they need. This allows the job matching platform to improve the accuracy of matching job seekers and companies.

[0029] The job matching platform according to this embodiment comprises an analysis unit, a recommendation unit, an interview support unit, a career planning unit, and an optimization unit. The analysis unit analyzes the resumes and work histories of job seekers. The analysis unit extracts the skills, experience, and desired conditions of job seekers, for example, using text analysis technology. The analysis unit can also analyze the contents of resumes and work histories, for example, using natural language processing technology. The analysis unit can also analyze the past work history of job seekers, for example, using data mining technology. The recommendation unit recommends the most suitable job information based on the information analyzed by the analysis unit. The recommendation unit personalizes and recommends the most suitable job information to job seekers, for example, using machine learning algorithms. The recommendation unit can also recommend the most suitable job information to job seekers, for example, using collaborative filtering technology. The recommendation unit can also recommend the most suitable job information to job seekers, for example, using content-based filtering technology. The Interview Support Department provides real-time interview support based on job postings recommended by the Recommendation Department. The Interview Support Department can, for example, use voice analysis technology to analyze the job seeker's statements during the interview. It can also analyze the job seeker's facial expressions during the interview using facial expression analysis technology. Furthermore, it can analyze the job seeker's emotions during the interview using emotion analysis technology. The Career Planning Department provides personalized career planning based on the information obtained by the Interview Support Department. For example, the Career Planning Department proposes growth plans tailored to the job seeker's career goals. It can also propose training plans for skill development. Finally, it can propose career paths. The Optimization Department automatically optimizes job postings based on the information obtained by the Career Planning Department. For example, the Optimization Department uses algorithms to analyze company conditions and benefits. For example, the Optimization Department makes attractive and appropriate proposals to target job seekers. The Optimization Department can also optimize the display order of job postings.This allows the job matching platform according to the embodiment to improve the accuracy of matching job seekers with companies.

[0030] The analysis department analyzes job seekers' resumes and work histories. For example, it uses text analysis techniques to extract job seekers' skills, experience, and desired conditions. Specifically, it uses natural language processing techniques to analyze the contents of resumes and work histories in detail. Natural language processing techniques extract keywords and phrases within documents and evaluate their relationships, allowing for an accurate understanding of job seekers' skill sets and work experience. For example, it analyzes what projects job seekers have been involved in and what roles they have played in the past, clarifying their expertise and strengths. Furthermore, data mining techniques can be used to analyze job seekers' past work history. Data mining techniques extract useful patterns and trends from large amounts of data, helping to identify key points in job seekers' career paths and work histories. This allows the analysis department to gain a detailed understanding of job seekers' skills and experience, improving the accuracy of matching them with job postings. In addition, the analysis department can analyze the results of questionnaires and interviews to extract job seekers' desired conditions and career goals. This creates a foundation for providing job postings that meet job seekers' needs.

[0031] The recommendation unit recommends the most suitable job postings based on the information analyzed by the analysis unit. For example, the recommendation unit uses machine learning algorithms to personalize and recommend the most suitable job postings to job seekers. Specifically, it uses collaborative filtering technology to recommend job postings that similar job seekers have been interested in, based on past behavioral and evaluation data of job seekers. Collaborative filtering technology evaluates the similarity between job seekers and prioritizes recommending job postings that have received high ratings from other job seekers, thereby providing job postings that match the job seeker's interests. Furthermore, content-based filtering technology can also be used to recommend job postings based on the job seeker's skills and experience. Content-based filtering technology compares the content of job postings with the content of the job seeker's resume and work history to identify job postings that match the job seeker's skill set. This allows the recommendation unit to provide job postings that are best suited to the job seeker's needs and skills. In addition, the recommendation unit can collect feedback from job seekers and continuously improve the accuracy of its recommendation algorithms. For example, by analyzing how job seekers react to recommended job postings and adjusting the parameters of the recommendation algorithm, more accurate recommendations can be achieved. This allows the recommendation system to consistently provide job seekers with the most suitable job postings, improving matching accuracy.

[0032] The Interview Support Department provides real-time interview support based on job postings recommended by the Recommendation Department. For example, the Interview Support Department uses voice analysis technology to analyze the applicant's statements during the interview. Specifically, it uses speech recognition technology to transcribe the applicant's statements into text and analyzes the content to evaluate the applicant's communication skills and expertise. It can also analyze the applicant's facial expressions during the interview using facial expression analysis technology. This technology detects changes in the applicant's facial expressions in real time, helping to evaluate their emotional state and stress level. Furthermore, it can analyze the applicant's emotions during the interview using emotion analysis technology. This technology detects changes in emotions from the applicant's voice and facial expressions, helping to evaluate their level of confidence and nervousness. This allows the Interview Support Department to comprehensively evaluate the applicant's interview performance and provide appropriate feedback to the interviewer. In addition, the Interview Support Department can provide applicants with interview advice and suggestions for improvement. For example, based on the analysis of the applicant's statements and facial expressions, it can advise on more effective communication methods and key points for self-promotion. This allows the interview support department to improve job seekers' interview skills and increase their interview success rate.

[0033] The Career Planning Department conducts personalized career planning based on information obtained by the Interview Support Department. For example, the Career Planning Department proposes growth plans tailored to the job seeker's career goals. Specifically, based on the job seeker's skill set and work experience, it designs future career paths and proposes concrete steps to acquire the necessary skills and experience. It can also propose training plans for skill development. For instance, it introduces online courses and workshops to help job seekers acquire the skills required for their desired job, supporting them in efficiently improving their skills. Furthermore, it can propose career paths. These career path proposals specifically indicate what types of jobs and positions job seekers should aim for, in line with their long-term career goals, helping them clarify their career direction. This allows the Career Planning Department to comprehensively support job seekers' career development and provide concrete plans for them to achieve their career goals. Additionally, the Career Planning Department collects feedback from job seekers and continuously improves the accuracy and effectiveness of its proposals. This enables the Career Planning Department to consistently provide job seekers with the optimal career plan and support their career development.

[0034] The Optimization Unit automatically optimizes job postings based on information obtained by the Career Planning Unit. For example, the Optimization Unit uses algorithms to analyze company conditions and benefits. Specifically, it analyzes in detail the conditions offered by companies, such as salary, benefits, location, and working hours, and compares them with the job seeker's desired conditions to identify the most suitable job postings. It also makes attractive and appropriate suggestions to target job seekers. For example, it prioritizes displaying job postings that match the job seeker's skill set and career goals, providing information that is likely to interest them. Furthermore, it can optimize the display order of job postings. By optimizing the display order, the information most likely to interest job seekers is displayed first, increasing their motivation to apply. This allows the Optimization Unit to improve the accuracy of matching job seekers with companies. Additionally, the Optimization Unit collects feedback from job seekers and continuously improves the accuracy of the optimization algorithm. For example, by analyzing what types of job postings job seekers are interested in and what conditions they prioritize, the optimization unit adjusts the parameters of the optimization algorithm to achieve more accurate optimization. This allows the Optimization Unit to consistently provide job seekers with the most suitable job postings and improve matching accuracy.

[0035] The analysis unit can gain a detailed understanding of job seekers' skills, experience, and desired conditions. For example, the analysis unit can gain a detailed understanding of job seekers' skills, experience, and desired conditions through interviews. For example, the analysis unit can also gain a detailed understanding of job seekers' skills, experience, and desired conditions by conducting questionnaires. For example, the analysis unit can also gain a detailed understanding of job seekers' skills, experience, and desired conditions using data analysis technology. This allows for the provision of more appropriate job information by gaining a detailed understanding of job seekers' skills, experience, and desired conditions. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit inputs the job seeker's resume and work history into the generative AI, which then gains a detailed understanding of their skills, experience, and desired conditions. The generative AI uses natural language processing technology to analyze the content of the resume and work history and extracts the job seeker's skills, experience, and desired conditions. For example, the generative AI uses morphological analysis to analyze the text of the resume and work history and extract skills and experience. The generative AI, for example, uses grammatical analysis to analyze the sentence structure of resumes and work histories to understand the job seeker's desired conditions. The generative AI also uses semantic analysis to understand the content of resumes and work histories, gaining a detailed understanding of the job seeker's skills, experience, and desired conditions. This allows the analysis unit to use the generative AI to gain a detailed understanding of the job seeker's skills, experience, and desired conditions.

[0036] The recommendation unit can personalize and recommend the most suitable job information to job seekers. For example, the recommendation unit can analyze a user's past behavioral history and personalize and recommend the most suitable job information. The recommendation unit can also analyze a user's preferences and personalize and recommend the most suitable job information. The recommendation unit can also analyze user feedback and personalize and recommend the most suitable job information. This improves job seeker satisfaction by personalizing and recommending the most suitable job information to job seekers. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit inputs the job seeker's behavioral history data into a generative AI, and the generative AI personalizes and recommends the most suitable job information. The generative AI uses a machine learning algorithm to analyze the behavioral history data and recommend the most suitable job information to the job seeker. The generative AI can, for example, use K-means clustering to cluster job seekers' behavioral history data and recommend the most suitable job postings. It can also, for example, use hierarchical clustering to cluster job seekers' preference data and recommend the most suitable job postings. Furthermore, it can, for example, use collaborative filtering to analyze job seekers' feedback data and recommend the most suitable job postings. This allows the recommendation unit to use the generative AI to personalize and recommend the most suitable job postings to job seekers.

[0037] The interview support department can analyze the applicant's statements and facial expressions during the interview and provide real-time feedback on areas for improvement. For example, the interview support department can use speech recognition technology to analyze the applicant's statements during the interview. The interview support department can also use facial recognition technology to analyze the applicant's facial expressions during the interview. The interview support department can also use emotion analysis technology to analyze the applicant's emotions during the interview. This allows the department to analyze the applicant's statements and facial expressions during the interview and provide real-time feedback on areas for improvement, thereby improving the applicant's interview skills. Some or all of the above processing in the interview support department may be performed using a generative AI, or it may be performed without a generative AI. For example, the interview support department inputs the applicant's voice data during the interview into a generative AI, which analyzes the statements. The generative AI uses speech recognition technology to convert the voice data into text data and analyzes the content of the statements. The generative AI uses facial recognition technology to analyze the applicant's facial data during the interview and detect changes in facial expressions. The generating AI, for example, uses emotion analysis technology to analyze the emotional data of job seekers during interviews and detect changes in their emotions. The generating AI then provides feedback on the analysis results in real time, suggesting areas for improvement to the job seekers. This allows the interview support department to use the generating AI to analyze the job seekers' statements and facial expressions during interviews and provide real-time feedback on areas for improvement.

[0038] The Career Planning Department can propose growth plans tailored to job seekers' career goals. For example, the Career Planning Department sets career goals for job seekers and proposes a growth plan based on them. The Career Planning Department can also propose training plans for skill development. The Career Planning Department can also propose career paths. In this way, by proposing growth plans tailored to job seekers' career goals, it supports job seekers' career development. Some or all of the above processes in the Career Planning Department may be performed using or without a Generative AI. For example, the Career Planning Department inputs the job seeker's career goal data into a Generative AI, and the Generative AI proposes a growth plan. The Generative AI generates a training plan for skill development based on the career goals. The Generative AI proposes career paths, for example, by referring to past success stories. The Generative AI analyzes the job seeker's skill set and proposes a plan to acquire the necessary skills. The Generative AI customizes the growth plan to match the job seeker's career goals. In this way, the Career Planning Department can propose growth plans tailored to job seekers' career goals using a Generative AI.

[0039] The optimization unit can analyze a company's conditions and benefits and make attractive and appropriate proposals to target job seekers. For example, the optimization unit can analyze a company's salary conditions and make attractive proposals to target job seekers. The optimization unit can also analyze a company's employee benefits and make appropriate proposals to target job seekers. The optimization unit can also analyze a company's culture and make attractive proposals to target job seekers. By analyzing a company's conditions and benefits and making attractive and appropriate proposals to target job seekers, the company can efficiently attract the talent it needs. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit inputs company condition data into a generation AI, and the generation AI makes attractive proposals to target job seekers. The generation AI analyzes a company's salary conditions and generates attractive proposals to job seekers. The generation AI analyzes company employee benefits data and generates appropriate proposals to job seekers. The generation AI analyzes company culture data and generates attractive proposals to job seekers. The generation AI, for example, analyzes the profile of a target job seeker and makes optimal suggestions based on the company's requirements and benefits. This allows the optimization unit to use the generation AI to analyze the company's requirements and benefits and make attractive and appropriate suggestions to target job seekers.

[0040] The analysis unit can analyze a job seeker's past resume submission history and select the optimal analysis method. For example, the analysis unit can analyze the content of resumes previously submitted by the job seeker and select the optimal analysis method for similar resumes. The analysis unit can also analyze the frequency of resume submissions by the job seeker and adjust the analysis method according to the frequency. The analysis unit can also analyze the trends of companies to which resumes previously submitted by the job seeker have been submitted and select an analysis method based on those trends. In this way, the optimal analysis method can be selected by analyzing a job seeker's past resume submission history. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs the job seeker's past resume submission history data into a generation AI, and the generation AI selects the optimal analysis method. The generation AI uses data mining techniques to analyze the submission history data and select the optimal analysis method. The generation AI can cluster the past submission history data and select the optimal analysis method for similar resumes. The generating AI, for example, analyzes submission frequency data and adjusts the analysis method according to the frequency. The generating AI also analyzes trend data of the companies to which applications are submitted and selects an analysis method based on that trend. As a result, the analysis unit can use the generating AI to analyze the past application history of job seekers and select the optimal analysis method.

[0041] The analysis unit can filter data based on the job seeker's current work situation and areas of interest during analysis. For example, the analysis unit can analyze the job seeker's current work situation and prioritize the analysis of relevant information from their resume. The analysis unit can also analyze the job seeker's areas of interest and prioritize the analysis of information related to those areas. The analysis unit can also combine the job seeker's current work situation and areas of interest to provide the most relevant information. This allows for the provision of highly relevant information by filtering based on the job seeker's current work situation and areas of interest. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the job seeker's current work situation data into a generative AI, which then performs filtering. The generative AI uses keyword matching technology to analyze the current work situation data and prioritize the analysis of relevant information. The generative AI can also analyze the areas of interest data and prioritize the analysis of information related to those areas. The generative AI can also combine the current work situation data and areas of interest data to provide the most relevant information. The generating AI, for example, uses condition setting technology to establish filtering criteria and provide highly relevant information. This allows the analysis unit to use the generating AI to filter job seekers based on their current work situation and areas of interest.

[0042] The analysis unit can prioritize analyzing highly relevant information by considering the job seeker's geographical location during the analysis. For example, the analysis unit can analyze the job seeker's current location and prioritize analyzing job postings related to that area. The analysis unit can also analyze the job seeker's desired work location and prioritize analyzing job postings related to that area. The analysis unit can also analyze the job seeker's past work locations and prioritize analyzing job postings related to those areas. By prioritizing the analysis of highly relevant information while considering the job seeker's geographical location, the analysis unit can provide useful information to job seekers. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit inputs the job seeker's geographical location data into a generating AI, and the generating AI prioritizes analyzing highly relevant information. The generating AI uses distance calculation technology to compare the job seeker's current location with the location of the job postings and prioritizes analyzing highly relevant information. The generating AI, for example, analyzes desired work location data and prioritizes analyzing job postings related to that area. The generating AI, for example, analyzes past work location data and prioritizes analyzing job postings related to that region. The generating AI also uses regional characteristic analysis techniques to perform analysis that takes geographical location information into account. As a result, the analysis unit can use the generating AI to prioritize the analysis of highly relevant information while considering the job seeker's geographical location.

[0043] The analysis unit can analyze a job seeker's social media activity and analyze relevant information during the analysis process. For example, the analysis unit can analyze the content of a job seeker's social media posts and prioritize the analysis of relevant job postings. The analysis unit can also analyze a job seeker's social media followers and followed accounts and prioritize the analysis of relevant information. The analysis unit can also analyze a job seeker's social media activity history and prioritize the analysis of relevant information. This allows the analysis of a job seeker's social media activity to provide relevant information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the job seeker's social media activity data into a generative AI, and the generative AI analyzes the relevant information. The generative AI uses post content analysis technology to analyze the content of social media posts and prioritizes the analysis of relevant job postings. The generative AI, for example, analyzes follower data and prioritizes the analysis of information related to followed accounts. The generative AI, for example, analyzes activity history data and prioritizes the analysis of information related to past activities. The generative AI, for example, analyzes trends on social media and provides relevant information. This allows the analysis unit to use the generative AI to analyze job seekers' social media activities and analyze relevant information.

[0044] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the job postings. For example, the recommendation unit can provide detailed information for highly important job postings. For example, the recommendation unit can also provide concise information for less important job postings. The recommendation unit can also adjust the level of detail in its recommendations according to the importance of the job postings. This allows the recommendation unit to provide job seekers with important information by adjusting the level of detail in its recommendations based on the importance of the job postings. Some or all of the above processing in the recommendation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the recommendation unit inputs job posting importance data into a generating AI, and the generating AI adjusts the level of detail in its recommendations. The generating AI evaluates the depth of the information and provides detailed information for highly important job postings. The generating AI may, for example, adjust the amount of information presented and provide concise information for less important job postings. The generating AI may, for example, set criteria for evaluating importance and adjust the level of detail in its recommendations according to the importance of the job postings. The generating AI optimizes how information is presented, for example, based on the importance of the job postings. This allows the recommendation unit to use the generating AI to adjust the level of detail in recommendations based on the importance of the job postings.

[0045] The recommendation unit can apply different recommendation algorithms depending on the category of the job posting when making recommendations. For example, the recommendation unit can apply a recommendation algorithm that emphasizes technical skills to job postings for technical positions. For example, the recommendation unit can apply a recommendation algorithm that emphasizes communication skills to job postings for sales positions. For example, the recommendation unit can apply a recommendation algorithm that emphasizes leadership skills to job postings for management positions. By applying different recommendation algorithms depending on the category of the job posting, the recommendation unit can provide job seekers with the most suitable information. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit inputs job posting category data into a generative AI, and the generative AI applies different recommendation algorithms. The generative AI uses collaborative filtering technology to make recommendations that emphasize technical skills for job postings for technical positions. The generative AI uses content-based filtering technology to make recommendations that emphasize communication skills for job postings for sales positions. The generating AI, for example, applies an algorithm that emphasizes leadership skills to recommend job postings for management positions. The generating AI also selects the optimal recommendation algorithm based on the category of the job posting. This allows the recommendation unit to use the generating AI to apply different recommendation algorithms depending on the category of the job posting.

[0046] The recommendation unit can determine the priority of recommendations based on when the job postings were submitted. For example, the recommendation unit may prioritize the most recent job postings. The recommendation unit may also postpone older information based on when the job postings were submitted. The recommendation unit may also adjust the priority of recommendations according to when the job postings were submitted. This allows the system to provide the latest information by prioritizing recommendations based on when the job postings were submitted. Some or all of the above processing in the recommendation unit may be performed using a generating AI, or not. For example, the recommendation unit inputs the job posting submission date data into the generating AI, which then determines the recommendation priority. The generating AI evaluates the priority of the latest information and prioritizes recommending the most recent job postings. The generating AI may, for example, set criteria for postponing older information and adjust the recommendation priority based on the submission date. The generating AI may, for example, analyze the submission date data and determine the recommendation priority according to the recency of the job postings. The generating AI evaluates the importance of job postings based on factors such as the submission date and adjusts their priority accordingly. This allows the recommendation department to use the generating AI to determine the priority of recommendations based on the submission date of job postings.

[0047] The recommendation unit can adjust the order of recommendations based on the relevance of the job postings. For example, the recommendation unit may prioritize recommending job postings that are most relevant to the job seeker's skills. The recommendation unit may also prioritize recommending job postings that are most relevant to the job seeker's desired conditions. The recommendation unit may also prioritize recommending job postings that are most relevant to the job seeker's experience. By adjusting the order of recommendations based on the relevance of the job postings, the recommendation unit can prioritize providing job seekers with the most relevant information. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or not. For example, the recommendation unit inputs relevance data of the job postings into a generative AI, and the generative AI adjusts the order of recommendations. The generative AI uses keyword matching technology to prioritize recommending job postings that are most relevant to the job seeker's skills. The generative AI may also calculate a relevance score and prioritize recommending job postings that are most relevant to the job seeker's desired conditions. The generating AI, for example, analyzes experience data and prioritizes recommending job postings that are most relevant to the job seeker's experience. The generating AI also evaluates the relevance of job postings and prioritizes providing the most relevant information. This allows the recommendation unit to use the generating AI to adjust the order of recommendations based on the relevance of job postings.

[0048] The interview support department can select the optimal support method by referring to the job seeker's past interview history during interview support. For example, the interview support department can analyze the job seeker's past interview history and select the optimal support method for similar interviews. For example, the interview support department can extract areas for improvement from the job seeker's past interview history and select a support method based on those points. For example, the interview support department can analyze the job seeker's past interview history and select a support method by referring to successful interview patterns. In this way, the optimal support method can be selected by referring to the job seeker's past interview history. Some or all of the above processing in the interview support department may be performed using a generation AI, or it may be performed without a generation AI. For example, the interview support department inputs the job seeker's past interview history data into a generation AI, and the generation AI selects the optimal support method. The generation AI analyzes the history data and selects the optimal support method for similar interviews. The generation AI, for example, extracts areas for improvement and selects a support method based on those points. The generation AI, for example, selects a support method by referring to successful interview patterns. The generating AI, for example, analyzes past interview history data and selects the optimal support method. This allows the interview support department to use the generating AI to refer to the job seeker's past interview history and select the most suitable support method.

[0049] The interview support department can customize the means of support during interviews based on the job seeker's current work situation. For example, the interview support department can analyze the job seeker's current work situation and provide support means appropriate to that situation. For example, the interview support department can also suggest appropriate interview strategies based on the job seeker's current work situation. For example, the interview support department can customize the points to emphasize during the interview, taking into account the job seeker's current work situation. By customizing the means of support based on the job seeker's current work situation, the department can provide the job seeker with the best possible support. Some or all of the above processes in the interview support department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the interview support department inputs the job seeker's current work situation data into a generation AI, and the generation AI customizes the means of support. The generation AI analyzes the current work situation data and provides support means appropriate to that situation. For example, the generation AI suggests appropriate interview strategies. For example, the generation AI customizes the points to emphasize during the interview. For example, the generation AI selects the optimal means of support based on the current work situation data. This allows the interview support department to use generating AI to customize support methods based on the job seeker's current work situation.

[0050] The interview support department can select the optimal support method during interviews by considering the job seeker's geographical location. For example, the interview support department can analyze the job seeker's current location and provide interview preparation relevant to that area. For example, the interview support department can also analyze the job seeker's desired work location and provide interview preparation relevant to that area. For example, the interview support department can analyze the job seeker's past work locations and provide interview preparation relevant to those areas. By selecting the optimal support method while considering the job seeker's geographical location, the department can provide beneficial support to the job seeker. Some or all of the above processing in the interview support department may be performed using a generative AI, or it may be performed without a generative AI. For example, the interview support department inputs the job seeker's geographical location data into a generative AI, and the generative AI selects the optimal support method. The generative AI uses distance calculation technology to compare the job seeker's current location with the interview location and provides relevant interview preparation. For example, the generative AI analyzes desired work location data and provides interview preparation relevant to that area. The generating AI, for example, analyzes past work location data and provides interview preparation tailored to that region. The generating AI also uses regional characteristic analysis techniques to select support methods that consider geographical location information. This allows the interview support department to use the generating AI to select the optimal support method while considering the job seeker's geographical location.

[0051] The interview support department can analyze a job seeker's social media activity during interview support and propose means of support. For example, the interview support department can analyze the content of a job seeker's social media posts and provide relevant interview preparation. The interview support department can also analyze a job seeker's social media followers and followed accounts and provide relevant information. The interview support department can also analyze a job seeker's social media activity history and provide relevant interview preparation. In this way, by analyzing a job seeker's social media activity, relevant means of support can be provided. Some or all of the above processing in the interview support department may be performed using a generative AI, or not. For example, the interview support department inputs the job seeker's social media activity data into a generative AI, and the generative AI proposes means of support. The generative AI uses content analysis technology to analyze the content of social media posts and provides relevant interview preparation. The generative AI, for example, analyzes follower data and provides information related to followed accounts. The generative AI, for example, analyzes activity history data and provides interview preparation related to past activities. The generative AI, for example, analyzes trends on social media and provides relevant information. This allows the interview support department to use the generative AI to analyze job seekers' social media activities and propose support measures.

[0052] The career planning unit can select the optimal planning method by referring to the job seeker's past career history during the career planning process. For example, the career planning unit can analyze the job seeker's past career history and select the optimal planning method for similar career paths. For example, the career planning unit can also select a planning method by referring to successful career paths from the job seeker's past career history. For example, the career planning unit can analyze the job seeker's past career history and select a planning method considering turning points in their career. This allows the optimal planning method to be selected by referring to the job seeker's past career history. Some or all of the above processes in the career planning unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the career planning unit inputs the job seeker's past career history data into a generation AI, and the generation AI selects the optimal planning method. The generation AI analyzes the history data and selects the optimal planning method for similar career paths. For example, the generation AI selects a planning method by referring to successful career paths. The generating AI, for example, selects a planning method by considering turning points in a person's career. The generating AI also analyzes past career history data to select the optimal planning method. This allows the career planning department to use the generating AI to refer to the job seeker's past career history and select the most suitable planning method.

[0053] The career planning unit can customize the planning methods based on the job seeker's current job situation during career planning. For example, the career planning unit can analyze the job seeker's current job situation and provide career planning methods appropriate to that situation. For example, the career planning unit can also propose appropriate career goals based on the job seeker's current job situation. For example, the career planning unit can customize a career growth plan taking into account the job seeker's current job situation. By customizing the planning methods based on the job seeker's current job situation, the unit can provide the job seeker with the optimal career plan. Some or all of the above processes in the career planning unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the career planning unit inputs the job seeker's current job situation data into a generative AI, and the generative AI customizes the planning methods. The generative AI analyzes the current job situation data and provides career planning methods appropriate to that situation. The generative AI proposes appropriate career goals. The generative AI customizes a career growth plan. The generating AI, for example, selects the optimal planning method based on current job status data. This allows the career planning department to customize the planning method based on the job seeker's current job status using the generating AI.

[0054] The career planning department can select the optimal planning method when creating a career plan, taking into account the job seeker's geographical location information. For example, the career planning department can analyze the job seeker's current location and provide a career planning method relevant to that region. The career planning department can also analyze the job seeker's desired work location and provide a career planning method relevant to that region. The career planning department can also analyze the job seeker's past work locations and provide a career planning method relevant to those regions. By selecting the optimal planning method while considering the job seeker's geographical location information, it is possible to provide a plan that is beneficial to the job seeker. Some or all of the above processing in the career planning department may be performed using a generation AI, or it may be performed without a generation AI. For example, the career planning department inputs the job seeker's geographical location information data into a generation AI, and the generation AI selects the optimal planning method. The generation AI uses distance calculation technology to compare the job seeker's current location with the target area for career planning and provides a relevant planning method. The generating AI, for example, analyzes desired work location data and provides career planning methods relevant to that region. The generating AI, for example, analyzes past work location data and provides career planning methods relevant to that region. The generating AI, for example, uses regional characteristic analysis techniques to select a planning method that takes geographical location information into consideration. This allows the career planning department to use the generating AI to select the optimal planning method while considering the job seeker's geographical location information.

[0055] The Career Planning Department can analyze a job seeker's social media activity during career planning and propose planning methods. For example, the Career Planning Department can analyze the content of a job seeker's social media posts and provide relevant career planning methods. The Career Planning Department can also analyze a job seeker's social media followers and followed accounts and provide relevant information. The Career Planning Department can also analyze a job seeker's social media activity history and provide relevant career planning methods. In this way, by analyzing a job seeker's social media activity, relevant planning methods can be provided. Some or all of the above processing in the Career Planning Department may be performed using a generative AI, or not. For example, the Career Planning Department inputs the job seeker's social media activity data into a generative AI, and the generative AI proposes planning methods. The generative AI analyzes the content of social media posts using post content analysis technology and provides relevant career planning methods. The generative AI analyzes follower data and provides information related to followed accounts. The generating AI can, for example, analyze activity history data and provide career planning methods related to past activities. It can also, for example, analyze trends on social media and provide relevant information. This allows the career planning department to use the generating AI to analyze job seekers' social media activities and propose planning methods.

[0056] The optimization unit can select the optimal optimization method by referring to the history of a company's past job postings during the optimization process. For example, the optimization unit can analyze the history of a company's past job postings and select the optimal optimization method for similar job postings. For example, the optimization unit can also select an optimization method by referring to successful job postings from the company's past job posting history. For example, the optimization unit can analyze the history of a company's past job postings and select an optimization method considering areas for improvement in the job postings. This allows the optimal optimization method to be selected by referring to the company's past job posting history. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit inputs the history data of a company's past job postings into a generation AI, and the generation AI selects the optimal optimization method. The generation AI analyzes the history data and selects the optimal optimization method for similar job postings. For example, the generation AI selects an optimization method by referring to successful job postings. For example, the generation AI selects an optimization method considering areas for improvement in the job postings. For example, the generation AI analyzes the history data of past job postings and selects the optimal optimization method. This allows the optimization unit to use generating AI to refer to the history of a company's past job postings and select the optimal optimization method.

[0057] The optimization unit can customize the optimization methods based on the company's current situation during optimization. For example, the optimization unit can analyze the company's current situation and provide job posting optimization methods appropriate to that situation. For example, the optimization unit can also propose an appropriate job posting optimization method based on the company's current situation. For example, the optimization unit can customize areas for improvement in job postings, taking into account the company's current situation. This allows the optimization unit to provide optimal job postings for the company by customizing the optimization methods based on the company's current situation. Some or all of the above-described processes in the optimization unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the optimization unit inputs the company's current situation data into a generation AI, and the generation AI customizes the optimization methods. The generation AI analyzes the current situation data and provides job posting optimization methods appropriate to that situation. The generation AI proposes an appropriate job posting optimization method. The generation AI customizes areas for improvement in job postings. The generation AI selects the optimal optimization method based on the current situation data. This allows the optimization unit to customize the optimization methods based on the company's current situation using a generation AI.

[0058] The optimization unit can select the optimal optimization method by considering the geographical location information of companies during optimization. For example, the optimization unit can analyze the current location of a company and provide an optimization method for job postings related to that region. For example, the optimization unit can also analyze the desired work location of a company and provide an optimization method for job postings related to that region. For example, the optimization unit can analyze the past work locations of a company and provide an optimization method for job postings related to that region. By selecting the optimal optimization method by considering the geographical location information of companies, it is possible to provide job postings that are beneficial to companies. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit inputs the geographical location information data of companies into a generation AI, and the generation AI selects the optimal optimization method. The generation AI uses distance calculation technology to compare the current location of companies with the location of job postings and provides an appropriate optimization method. For example, the generation AI analyzes desired work location data and provides an optimization method for job postings related to that region. For example, the generation AI analyzes past work location data and provides an optimization method for job postings related to that region. The generation AI, for example, uses regional characteristic analysis techniques to select an optimization method that takes geographical location information into account. This allows the optimization unit to select the optimal optimization method by considering the company's geographical location information using the generation AI.

[0059] The optimization unit can analyze a company's social media activities during optimization and propose optimization methods. For example, the optimization unit can analyze the content of a company's social media posts and provide methods for optimizing related job postings. The optimization unit can also analyze a company's followers and followed accounts on social media and provide relevant information. The optimization unit can also analyze a company's social media activity history and provide methods for optimizing related job postings. In this way, by analyzing a company's social media activities, it is possible to provide relevant optimization methods. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit inputs the company's social media activity data into a generative AI, and the generative AI proposes optimization methods. The generative AI uses content analysis technology to analyze the content of social media posts and provides methods for optimizing related job postings. The generative AI can analyze follower data and provide information related to followed accounts. The generative AI can analyze activity history data and provide methods for optimizing job postings related to past activities. The generative AI can analyze social media trends and provide relevant information. This allows the optimization unit to analyze a company's social media activities using generative AI and propose optimization methods.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The analytics department can analyze job seekers' online portfolios and project histories to gain a detailed understanding of their skills, experience, and desired conditions. For example, it can analyze projects that job seekers have published on platforms such as code sharing and portfolios to extract skills and experience. Furthermore, it can analyze the history of online courses and workshops that job seekers have participated in to understand their skill acquisition status. This allows for a more accurate understanding of job seekers' skills and experience, enabling the provision of more suitable job information.

[0062] The recommendation system can dynamically change the display order of job postings based on a job seeker's skills and experience. For example, if a job seeker possesses a specific skill, job postings related to that skill will be displayed at the top. Furthermore, the recommendation system can analyze a job seeker's past application history and prioritize displaying job postings related to companies and job types they have previously applied to. This allows the system to provide job seekers with the most relevant job information.

[0063] The interview support department can provide a mock interview function to assist job seekers in preparing for interviews. For example, when a job seeker conducts a mock interview, the interview support department uses AI to act as the interviewer and provide real-time feedback. Furthermore, the interview support department can analyze recordings of the job seeker's mock interviews and specifically point out areas for improvement. This allows job seekers to effectively prepare for actual interviews.

[0064] The Career Planning Department can consider industry trends and future demand when proposing growth plans tailored to job seekers' career goals. For example, the Career Planning Department can forecast the demand for skills in specific industries and propose the necessary skills to job seekers based on that information. Furthermore, the Career Planning Department can also propose new career paths to job seekers based on industry trends. This allows job seekers to create effective plans for their future career development.

[0065] The optimization unit can consider a company's brand image and corporate culture when optimizing its job postings. For example, the optimization unit can analyze the company's website and social media content to generate job postings that match the company's brand image and culture. Furthermore, the optimization unit can provide attractive job postings to job seekers based on the company's vision and mission. This allows companies to efficiently attract job seekers who align with their brand image.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The analysis unit analyzes the job seeker's resume and work history. The analysis unit uses text analysis technology, natural language processing technology, and data mining technology to extract and analyze the job seeker's skills, experience, desired conditions, and past work history. Step 2: The recommendation unit recommends the most suitable job postings based on the information analyzed by the analysis unit. The recommendation unit uses machine learning algorithms, collaborative filtering technology, and content-based filtering technology to personalize and recommend the most suitable job postings to job seekers. Step 3: The Interview Support Department provides real-time interview support based on job postings recommended by the Recommendation Department. The Interview Support Department uses voice analysis technology, facial expression analysis technology, and emotion analysis technology to analyze the job seeker's statements, facial expressions, and emotions during the interview. Step 4: The Career Planning Department conducts personalized career planning based on the information obtained by the Interview Support Department. The Career Planning Department proposes growth plans, skill development training plans, and career paths tailored to the job seeker's career goals. Step 5: The optimization unit automatically optimizes job postings based on the information obtained by the career planning unit. The optimization unit uses algorithms to analyze the company's conditions and benefits, makes attractive and appropriate proposals to target job seekers, and optimizes the display order of job postings.

[0068] (Example of form 2) The job matching platform according to an embodiment of the present invention is a system that realizes next-generation job matching by utilizing generative AI technology. This system analyzes the resumes and work histories of job seekers and recommends the most suitable job information. Furthermore, it improves the accuracy of matching job seekers with companies through real-time interview support, personalized career planning, and automatic optimization of job information. For example, when a job seeker submits a resume and work history, the generative AI analyzes these documents. The generative AI grasps the job seeker's skills, experience, and desired conditions in detail and recommends the most suitable job information in a personalized manner. For example, if a job seeker has a particular skill, it will prioritize displaying job information related to that skill. Next, the generative AI provides real-time interview support. During the interview, the generative AI analyzes the job seeker's statements and facial expressions and provides real-time feedback on areas for improvement. This allows job seekers to improve their interview skills. Furthermore, the generative AI provides personalized career planning. The generative AI proposes a growth plan tailored to the job seeker's career goals. For example, if a job seeker wants to change jobs to a specific occupation, it proposes a plan to acquire the skills and experience necessary for that occupation. Furthermore, the generation AI automatically optimizes job postings. The generation AI analyzes the company's requirements and benefits, and makes attractive and appropriate suggestions to target job seekers. For example, if a company is looking for job seekers with specific skills, it optimizes and displays job postings related to those skills. In this way, the job matching platform is a system that utilizes generation AI technology to improve the accuracy of matching job seekers and companies. Job seekers can efficiently find the most suitable job postings in a short amount of time, and companies can efficiently attract the job seekers they need. This allows the job matching platform to improve the accuracy of matching job seekers and companies.

[0069] The job matching platform according to this embodiment comprises an analysis unit, a recommendation unit, an interview support unit, a career planning unit, and an optimization unit. The analysis unit analyzes the resumes and work histories of job seekers. The analysis unit extracts the skills, experience, and desired conditions of job seekers, for example, using text analysis technology. The analysis unit can also analyze the contents of resumes and work histories, for example, using natural language processing technology. The analysis unit can also analyze the past work history of job seekers, for example, using data mining technology. The recommendation unit recommends the most suitable job information based on the information analyzed by the analysis unit. The recommendation unit personalizes and recommends the most suitable job information to job seekers, for example, using machine learning algorithms. The recommendation unit can also recommend the most suitable job information to job seekers, for example, using collaborative filtering technology. The recommendation unit can also recommend the most suitable job information to job seekers, for example, using content-based filtering technology. The Interview Support Department provides real-time interview support based on job postings recommended by the Recommendation Department. The Interview Support Department can, for example, use voice analysis technology to analyze the job seeker's statements during the interview. It can also analyze the job seeker's facial expressions during the interview using facial expression analysis technology. Furthermore, it can analyze the job seeker's emotions during the interview using emotion analysis technology. The Career Planning Department provides personalized career planning based on the information obtained by the Interview Support Department. For example, the Career Planning Department proposes growth plans tailored to the job seeker's career goals. It can also propose training plans for skill development. Finally, it can propose career paths. The Optimization Department automatically optimizes job postings based on the information obtained by the Career Planning Department. For example, the Optimization Department uses algorithms to analyze company conditions and benefits. For example, the Optimization Department makes attractive and appropriate proposals to target job seekers. The Optimization Department can also optimize the display order of job postings.This allows the job matching platform according to the embodiment to improve the accuracy of matching job seekers with companies.

[0070] The analysis department analyzes job seekers' resumes and work histories. For example, it uses text analysis techniques to extract job seekers' skills, experience, and desired conditions. Specifically, it uses natural language processing techniques to analyze the contents of resumes and work histories in detail. Natural language processing techniques extract keywords and phrases within documents and evaluate their relationships, allowing for an accurate understanding of job seekers' skill sets and work experience. For example, it analyzes what projects job seekers have been involved in and what roles they have played in the past, clarifying their expertise and strengths. Furthermore, data mining techniques can be used to analyze job seekers' past work history. Data mining techniques extract useful patterns and trends from large amounts of data, helping to identify key points in job seekers' career paths and work histories. This allows the analysis department to gain a detailed understanding of job seekers' skills and experience, improving the accuracy of matching them with job postings. In addition, the analysis department can analyze the results of questionnaires and interviews to extract job seekers' desired conditions and career goals. This creates a foundation for providing job postings that meet job seekers' needs.

[0071] The recommendation unit recommends the most suitable job postings based on the information analyzed by the analysis unit. For example, the recommendation unit uses machine learning algorithms to personalize and recommend the most suitable job postings to job seekers. Specifically, it uses collaborative filtering technology to recommend job postings that similar job seekers have been interested in, based on past behavioral and evaluation data of job seekers. Collaborative filtering technology evaluates the similarity between job seekers and prioritizes recommending job postings that have received high ratings from other job seekers, thereby providing job postings that match the job seeker's interests. Furthermore, content-based filtering technology can also be used to recommend job postings based on the job seeker's skills and experience. Content-based filtering technology compares the content of job postings with the content of the job seeker's resume and work history to identify job postings that match the job seeker's skill set. This allows the recommendation unit to provide job postings that are best suited to the job seeker's needs and skills. In addition, the recommendation unit can collect feedback from job seekers and continuously improve the accuracy of its recommendation algorithms. For example, by analyzing how job seekers react to recommended job postings and adjusting the parameters of the recommendation algorithm, more accurate recommendations can be achieved. This allows the recommendation system to consistently provide job seekers with the most suitable job postings, improving matching accuracy.

[0072] The Interview Support Department provides real-time interview support based on job postings recommended by the Recommendation Department. For example, the Interview Support Department uses voice analysis technology to analyze the applicant's statements during the interview. Specifically, it uses speech recognition technology to transcribe the applicant's statements into text and analyzes the content to evaluate the applicant's communication skills and expertise. It can also analyze the applicant's facial expressions during the interview using facial expression analysis technology. This technology detects changes in the applicant's facial expressions in real time, helping to evaluate their emotional state and stress level. Furthermore, it can analyze the applicant's emotions during the interview using emotion analysis technology. This technology detects changes in emotions from the applicant's voice and facial expressions, helping to evaluate their level of confidence and nervousness. This allows the Interview Support Department to comprehensively evaluate the applicant's interview performance and provide appropriate feedback to the interviewer. In addition, the Interview Support Department can provide applicants with interview advice and suggestions for improvement. For example, based on the analysis of the applicant's statements and facial expressions, it can advise on more effective communication methods and key points for self-promotion. This allows the interview support department to improve job seekers' interview skills and increase their interview success rate.

[0073] The Career Planning Department conducts personalized career planning based on information obtained by the Interview Support Department. For example, the Career Planning Department proposes growth plans tailored to the job seeker's career goals. Specifically, based on the job seeker's skill set and work experience, it designs future career paths and proposes concrete steps to acquire the necessary skills and experience. It can also propose training plans for skill development. For instance, it introduces online courses and workshops to help job seekers acquire the skills required for their desired job, supporting them in efficiently improving their skills. Furthermore, it can propose career paths. These career path proposals specifically indicate what types of jobs and positions job seekers should aim for, in line with their long-term career goals, helping them clarify their career direction. This allows the Career Planning Department to comprehensively support job seekers' career development and provide concrete plans for them to achieve their career goals. Additionally, the Career Planning Department collects feedback from job seekers and continuously improves the accuracy and effectiveness of its proposals. This enables the Career Planning Department to consistently provide job seekers with the optimal career plan and support their career development.

[0074] The Optimization Unit automatically optimizes job postings based on information obtained by the Career Planning Unit. For example, the Optimization Unit uses algorithms to analyze company conditions and benefits. Specifically, it analyzes in detail the conditions offered by companies, such as salary, benefits, location, and working hours, and compares them with the job seeker's desired conditions to identify the most suitable job postings. It also makes attractive and appropriate suggestions to target job seekers. For example, it prioritizes displaying job postings that match the job seeker's skill set and career goals, providing information that is likely to interest them. Furthermore, it can optimize the display order of job postings. By optimizing the display order, the information most likely to interest job seekers is displayed first, increasing their motivation to apply. This allows the Optimization Unit to improve the accuracy of matching job seekers with companies. Additionally, the Optimization Unit collects feedback from job seekers and continuously improves the accuracy of the optimization algorithm. For example, by analyzing what types of job postings job seekers are interested in and what conditions they prioritize, the optimization unit adjusts the parameters of the optimization algorithm to achieve more accurate optimization. This allows the Optimization Unit to consistently provide job seekers with the most suitable job postings and improve matching accuracy.

[0075] The analysis unit can gain a detailed understanding of job seekers' skills, experience, and desired conditions. For example, the analysis unit can gain a detailed understanding of job seekers' skills, experience, and desired conditions through interviews. For example, the analysis unit can also gain a detailed understanding of job seekers' skills, experience, and desired conditions by conducting questionnaires. For example, the analysis unit can also gain a detailed understanding of job seekers' skills, experience, and desired conditions using data analysis technology. This allows for the provision of more appropriate job information by gaining a detailed understanding of job seekers' skills, experience, and desired conditions. Some or all of the above-described processes in the analysis unit may be performed using or without a generative AI. For example, the analysis unit inputs the job seeker's resume and work history into the generative AI, which then gains a detailed understanding of their skills, experience, and desired conditions. The generative AI uses natural language processing technology to analyze the content of the resume and work history and extracts the job seeker's skills, experience, and desired conditions. For example, the generative AI uses morphological analysis to analyze the text of the resume and work history and extract skills and experience. The generative AI, for example, uses grammatical analysis to analyze the sentence structure of resumes and work histories to understand the job seeker's desired conditions. The generative AI also uses semantic analysis to understand the content of resumes and work histories, gaining a detailed understanding of the job seeker's skills, experience, and desired conditions. This allows the analysis unit to use the generative AI to gain a detailed understanding of the job seeker's skills, experience, and desired conditions.

[0076] The recommendation unit can personalize and recommend the most suitable job information to job seekers. For example, the recommendation unit can analyze a user's past behavioral history and personalize and recommend the most suitable job information. The recommendation unit can also analyze a user's preferences and personalize and recommend the most suitable job information. The recommendation unit can also analyze user feedback and personalize and recommend the most suitable job information. This improves job seeker satisfaction by personalizing and recommending the most suitable job information to job seekers. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit inputs the job seeker's behavioral history data into a generative AI, and the generative AI personalizes and recommends the most suitable job information. The generative AI uses a machine learning algorithm to analyze the behavioral history data and recommend the most suitable job information to the job seeker. The generative AI can, for example, use K-means clustering to cluster job seekers' behavioral history data and recommend the most suitable job postings. It can also, for example, use hierarchical clustering to cluster job seekers' preference data and recommend the most suitable job postings. Furthermore, it can, for example, use collaborative filtering to analyze job seekers' feedback data and recommend the most suitable job postings. This allows the recommendation unit to use the generative AI to personalize and recommend the most suitable job postings to job seekers.

[0077] The interview support department can analyze the applicant's statements and facial expressions during the interview and provide real-time feedback on areas for improvement. For example, the interview support department can use speech recognition technology to analyze the applicant's statements during the interview. The interview support department can also use facial recognition technology to analyze the applicant's facial expressions during the interview. The interview support department can also use emotion analysis technology to analyze the applicant's emotions during the interview. This allows the department to analyze the applicant's statements and facial expressions during the interview and provide real-time feedback on areas for improvement, thereby improving the applicant's interview skills. Some or all of the above processing in the interview support department may be performed using a generative AI, or it may be performed without a generative AI. For example, the interview support department inputs the applicant's voice data during the interview into a generative AI, which analyzes the statements. The generative AI uses speech recognition technology to convert the voice data into text data and analyzes the content of the statements. The generative AI uses facial recognition technology to analyze the applicant's facial data during the interview and detect changes in facial expressions. The generating AI, for example, uses emotion analysis technology to analyze the emotional data of job seekers during interviews and detect changes in their emotions. The generating AI then provides feedback on the analysis results in real time, suggesting areas for improvement to the job seekers. This allows the interview support department to use the generating AI to analyze the job seekers' statements and facial expressions during interviews and provide real-time feedback on areas for improvement.

[0078] The Career Planning Department can propose growth plans tailored to job seekers' career goals. For example, the Career Planning Department sets career goals for job seekers and proposes a growth plan based on them. The Career Planning Department can also propose training plans for skill development. The Career Planning Department can also propose career paths. In this way, by proposing growth plans tailored to job seekers' career goals, it supports job seekers' career development. Some or all of the above processes in the Career Planning Department may be performed using or without a Generative AI. For example, the Career Planning Department inputs the job seeker's career goal data into a Generative AI, and the Generative AI proposes a growth plan. The Generative AI generates a training plan for skill development based on the career goals. The Generative AI proposes career paths, for example, by referring to past success stories. The Generative AI analyzes the job seeker's skill set and proposes a plan to acquire the necessary skills. The Generative AI customizes the growth plan to match the job seeker's career goals. In this way, the Career Planning Department can propose growth plans tailored to job seekers' career goals using a Generative AI.

[0079] The optimization unit can analyze a company's conditions and benefits and make attractive and appropriate proposals to target job seekers. For example, the optimization unit can analyze a company's salary conditions and make attractive proposals to target job seekers. The optimization unit can also analyze a company's employee benefits and make appropriate proposals to target job seekers. The optimization unit can also analyze a company's culture and make attractive proposals to target job seekers. By analyzing a company's conditions and benefits and making attractive and appropriate proposals to target job seekers, the company can efficiently attract the talent it needs. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit inputs company condition data into a generation AI, and the generation AI makes attractive proposals to target job seekers. The generation AI analyzes a company's salary conditions and generates attractive proposals to job seekers. The generation AI analyzes company employee benefits data and generates appropriate proposals to job seekers. The generation AI analyzes company culture data and generates attractive proposals to job seekers. The generation AI, for example, analyzes the profile of a target job seeker and makes optimal suggestions based on the company's requirements and benefits. This allows the optimization unit to use the generation AI to analyze the company's requirements and benefits and make attractive and appropriate suggestions to target job seekers.

[0080] The analysis unit can estimate the emotions of job seekers and adjust the accuracy of the analysis based on the estimated emotions. The analysis unit can estimate the emotions of job seekers using, for example, facial expression analysis technology. The analysis unit can also estimate the emotions of job seekers using, for example, voice analysis technology. The analysis unit can also estimate the emotions of job seekers using, for example, text analysis technology. By adjusting the accuracy of the analysis based on the emotions of job seekers, more appropriate information can be provided. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI or not using a generative AI. For example, the analysis unit inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI analyzes the facial expression data using facial expression analysis technology and estimates the emotions. The generative AI, for example, analyzes audio data and estimates emotions based on voice tone and speed. It also analyzes text data and estimates emotions based on word choice and sentence structure. The generative AI adjusts the accuracy of its analysis based on the estimated emotions. This allows the analysis unit to use the generative AI to estimate the emotions of job seekers and adjust the accuracy of its analysis based on the estimated emotions of the job seekers.

[0081] The analysis unit can analyze a job seeker's past resume submission history and select the optimal analysis method. For example, the analysis unit can analyze the content of resumes previously submitted by the job seeker and select the optimal analysis method for similar resumes. The analysis unit can also analyze the frequency of resume submissions by the job seeker and adjust the analysis method according to the frequency. The analysis unit can also analyze the trends of companies to which resumes previously submitted by the job seeker have been submitted and select an analysis method based on those trends. In this way, the optimal analysis method can be selected by analyzing a job seeker's past resume submission history. Some or all of the above processing in the analysis unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the analysis unit inputs the job seeker's past resume submission history data into a generation AI, and the generation AI selects the optimal analysis method. The generation AI uses data mining techniques to analyze the submission history data and select the optimal analysis method. The generation AI can cluster the past submission history data and select the optimal analysis method for similar resumes. The generating AI, for example, analyzes submission frequency data and adjusts the analysis method according to the frequency. The generating AI also analyzes trend data of the companies to which applications are submitted and selects an analysis method based on that trend. As a result, the analysis unit can use the generating AI to analyze the past application history of job seekers and select the optimal analysis method.

[0082] The analysis unit can filter data based on the job seeker's current work situation and areas of interest during analysis. For example, the analysis unit can analyze the job seeker's current work situation and prioritize the analysis of relevant information from their resume. The analysis unit can also analyze the job seeker's areas of interest and prioritize the analysis of information related to those areas. The analysis unit can also combine the job seeker's current work situation and areas of interest to provide the most relevant information. This allows for the provision of highly relevant information by filtering based on the job seeker's current work situation and areas of interest. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the job seeker's current work situation data into a generative AI, which then performs filtering. The generative AI uses keyword matching technology to analyze the current work situation data and prioritize the analysis of relevant information. The generative AI can also analyze the areas of interest data and prioritize the analysis of information related to those areas. The generative AI can also combine the current work situation data and areas of interest data to provide the most relevant information. The generating AI, for example, uses condition setting technology to establish filtering criteria and provide highly relevant information. This allows the analysis unit to use the generating AI to filter job seekers based on their current work situation and areas of interest.

[0083] The analysis unit can estimate the emotions of job seekers and determine the priority of analysis results based on the estimated emotions. The analysis unit can estimate the emotions of job seekers using, for example, facial expression analysis technology. The analysis unit can also estimate the emotions of job seekers using, for example, voice analysis technology. The analysis unit can also estimate the emotions of job seekers using, for example, text analysis technology. By determining the priority of analysis results based on the emotions of job seekers, information important to job seekers can be provided preferentially. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the analysis unit may be performed using a generative AI or not using a generative AI. For example, the analysis unit inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI analyzes the facial expression data using facial expression analysis technology and estimates the emotions. The generative AI, for example, analyzes audio data and estimates emotions based on voice tone and speed. It also analyzes text data and estimates emotions based on word choice and sentence structure. Furthermore, the generative AI determines the priority of analysis results based on the estimated emotions. This allows the analysis unit to use the generative AI to estimate the emotions of job seekers and determine the priority of analysis results based on the estimated emotions of the job seekers.

[0084] The analysis unit can prioritize analyzing highly relevant information by considering the job seeker's geographical location during the analysis. For example, the analysis unit can analyze the job seeker's current location and prioritize analyzing job postings related to that area. The analysis unit can also analyze the job seeker's desired work location and prioritize analyzing job postings related to that area. The analysis unit can also analyze the job seeker's past work locations and prioritize analyzing job postings related to those areas. By prioritizing the analysis of highly relevant information while considering the job seeker's geographical location, the analysis unit can provide useful information to job seekers. Some or all of the above processing in the analysis unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the analysis unit inputs the job seeker's geographical location data into a generating AI, and the generating AI prioritizes analyzing highly relevant information. The generating AI uses distance calculation technology to compare the job seeker's current location with the location of the job postings and prioritizes analyzing highly relevant information. The generating AI, for example, analyzes desired work location data and prioritizes analyzing job postings related to that area. The generating AI, for example, analyzes past work location data and prioritizes analyzing job postings related to that region. The generating AI also uses regional characteristic analysis techniques to perform analysis that takes geographical location information into account. As a result, the analysis unit can use the generating AI to prioritize the analysis of highly relevant information while considering the job seeker's geographical location.

[0085] The analysis unit can analyze a job seeker's social media activity and analyze relevant information during the analysis process. For example, the analysis unit can analyze the content of a job seeker's social media posts and prioritize the analysis of relevant job postings. The analysis unit can also analyze a job seeker's social media followers and followed accounts and prioritize the analysis of relevant information. The analysis unit can also analyze a job seeker's social media activity history and prioritize the analysis of relevant information. This allows the analysis of a job seeker's social media activity to provide relevant information. Some or all of the above processing in the analysis unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the analysis unit inputs the job seeker's social media activity data into a generative AI, and the generative AI analyzes the relevant information. The generative AI uses post content analysis technology to analyze the content of social media posts and prioritizes the analysis of relevant job postings. The generative AI, for example, analyzes follower data and prioritizes the analysis of information related to followed accounts. The generative AI, for example, analyzes activity history data and prioritizes the analysis of information related to past activities. The generative AI, for example, analyzes trends on social media and provides relevant information. This allows the analysis unit to use the generative AI to analyze job seekers' social media activities and analyze relevant information.

[0086] The recommendation unit can estimate the emotions of job seekers and adjust the way recommendations are presented based on the estimated emotions. For example, the recommendation unit can estimate the emotions of job seekers using facial expression analysis technology. The recommendation unit can also estimate the emotions of job seekers using voice analysis technology. The recommendation unit can also estimate the emotions of job seekers using text analysis technology. By adjusting the way recommendations are presented based on the emotions of job seekers, information that is easy for job seekers to understand can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the recommendation unit may be performed using a generative AI or not. For example, the recommendation unit inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI uses facial expression analysis technology to analyze facial expression data and estimate emotions. For example, the generative AI analyzes audio data and estimates emotions based on voice tone and speed. For example, the generative AI analyzes text data and estimates emotions based on word choice and sentence structure. For example, the generative AI adjusts the way recommendations are presented based on the estimated emotions. As a result, the recommendation unit can use the generative AI to estimate the emotions of job seekers and adjust the way recommendations are presented based on the estimated emotions of job seekers.

[0087] The recommendation unit can adjust the level of detail in its recommendations based on the importance of the job postings. For example, the recommendation unit can provide detailed information for highly important job postings. For example, the recommendation unit can also provide concise information for less important job postings. The recommendation unit can also adjust the level of detail in its recommendations according to the importance of the job postings. This allows the recommendation unit to provide job seekers with important information by adjusting the level of detail in its recommendations based on the importance of the job postings. Some or all of the above processing in the recommendation unit may be performed using a generating AI, or it may be performed without a generating AI. For example, the recommendation unit inputs job posting importance data into a generating AI, and the generating AI adjusts the level of detail in its recommendations. The generating AI evaluates the depth of the information and provides detailed information for highly important job postings. The generating AI may, for example, adjust the amount of information presented and provide concise information for less important job postings. The generating AI may, for example, set criteria for evaluating importance and adjust the level of detail in its recommendations according to the importance of the job postings. The generating AI optimizes how information is presented, for example, based on the importance of the job postings. This allows the recommendation unit to use the generating AI to adjust the level of detail in recommendations based on the importance of the job postings.

[0088] The recommendation unit can apply different recommendation algorithms depending on the category of the job posting when making recommendations. For example, the recommendation unit can apply a recommendation algorithm that emphasizes technical skills to job postings for technical positions. For example, the recommendation unit can apply a recommendation algorithm that emphasizes communication skills to job postings for sales positions. For example, the recommendation unit can apply a recommendation algorithm that emphasizes leadership skills to job postings for management positions. By applying different recommendation algorithms depending on the category of the job posting, the recommendation unit can provide job seekers with the most suitable information. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the recommendation unit inputs job posting category data into a generative AI, and the generative AI applies different recommendation algorithms. The generative AI uses collaborative filtering technology to make recommendations that emphasize technical skills for job postings for technical positions. The generative AI uses content-based filtering technology to make recommendations that emphasize communication skills for job postings for sales positions. The generating AI, for example, applies an algorithm that emphasizes leadership skills to recommend job postings for management positions. The generating AI also selects the optimal recommendation algorithm based on the category of the job posting. This allows the recommendation unit to use the generating AI to apply different recommendation algorithms depending on the category of the job posting.

[0089] The recommendation unit can estimate the emotions of job seekers and adjust the length of recommendations based on the estimated emotions. The recommendation unit can estimate the emotions of job seekers using, for example, facial expression analysis technology. The recommendation unit can also estimate the emotions of job seekers using, for example, voice analysis technology. The recommendation unit can also estimate the emotions of job seekers using, for example, text analysis technology. By adjusting the length of recommendations based on the emotions of job seekers, information that is easy for job seekers to understand can be provided. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the recommendation unit may be performed using a generative AI or not using a generative AI. For example, the recommendation unit inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI analyzes the facial expression data using facial expression analysis technology and estimates the emotions. The generative AI can, for example, analyze audio data and estimate emotions based on tone and speed of voice. It can also analyze text data and estimate emotions based on word choice and sentence structure. The generative AI can adjust the length of recommendations based on the estimated emotions. This allows the recommendation unit to use the generative AI to estimate the emotions of job seekers and adjust the length of recommendations based on those estimated emotions.

[0090] The recommendation unit can determine the priority of recommendations based on when the job postings were submitted. For example, the recommendation unit may prioritize the most recent job postings. The recommendation unit may also postpone older information based on when the job postings were submitted. The recommendation unit may also adjust the priority of recommendations according to when the job postings were submitted. This allows the system to provide the latest information by prioritizing recommendations based on when the job postings were submitted. Some or all of the above processing in the recommendation unit may be performed using a generating AI, or not. For example, the recommendation unit inputs the job posting submission date data into the generating AI, which then determines the recommendation priority. The generating AI evaluates the priority of the latest information and prioritizes recommending the most recent job postings. The generating AI may, for example, set criteria for postponing older information and adjust the recommendation priority based on the submission date. The generating AI may, for example, analyze the submission date data and determine the recommendation priority according to the recency of the job postings. The generating AI evaluates the importance of job postings based on factors such as the submission date and adjusts their priority accordingly. This allows the recommendation department to use the generating AI to determine the priority of recommendations based on the submission date of job postings.

[0091] The recommendation unit can adjust the order of recommendations based on the relevance of the job postings. For example, the recommendation unit may prioritize recommending job postings that are most relevant to the job seeker's skills. The recommendation unit may also prioritize recommending job postings that are most relevant to the job seeker's desired conditions. The recommendation unit may also prioritize recommending job postings that are most relevant to the job seeker's experience. By adjusting the order of recommendations based on the relevance of the job postings, the recommendation unit can prioritize providing job seekers with the most relevant information. Some or all of the above processing in the recommendation unit may be performed using a generative AI, or not. For example, the recommendation unit inputs relevance data of the job postings into a generative AI, and the generative AI adjusts the order of recommendations. The generative AI uses keyword matching technology to prioritize recommending job postings that are most relevant to the job seeker's skills. The generative AI may also calculate a relevance score and prioritize recommending job postings that are most relevant to the job seeker's desired conditions. The generating AI, for example, analyzes experience data and prioritizes recommending job postings that are most relevant to the job seeker's experience. The generating AI also evaluates the relevance of job postings and prioritizes providing the most relevant information. This allows the recommendation unit to use the generating AI to adjust the order of recommendations based on the relevance of job postings.

[0092] The interview support department can estimate the emotions of job seekers and adjust the interview support method based on the estimated emotions. The interview support department can estimate the emotions of job seekers using, for example, facial expression analysis technology. The interview support department can also estimate the emotions of job seekers using, for example, voice analysis technology. The interview support department can also estimate the emotions of job seekers using, for example, text analysis technology. By adjusting the interview support method based on the emotions of job seekers, the department can provide the most suitable interview support for each job seeker. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the interview support department may be performed using a generative AI or not using a generative AI. For example, the interview support department inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI analyzes the facial expression data using facial expression analysis technology and estimates the emotions. Generative AI can, for example, analyze audio data and estimate emotions based on tone and speed of voice. Generative AI can, for example, analyze text data and estimate emotions based on word choice and sentence structure. Generative AI can, for example, adjust interview support methods based on the estimated emotions. This allows the interview support department to use generative AI to estimate the emotions of job seekers and adjust interview support methods based on the estimated emotions of the job seekers.

[0093] The interview support department can select the optimal support method by referring to the job seeker's past interview history during interview support. For example, the interview support department can analyze the job seeker's past interview history and select the optimal support method for similar interviews. For example, the interview support department can extract areas for improvement from the job seeker's past interview history and select a support method based on those points. For example, the interview support department can analyze the job seeker's past interview history and select a support method by referring to successful interview patterns. In this way, the optimal support method can be selected by referring to the job seeker's past interview history. Some or all of the above processing in the interview support department may be performed using a generation AI, or it may be performed without a generation AI. For example, the interview support department inputs the job seeker's past interview history data into a generation AI, and the generation AI selects the optimal support method. The generation AI analyzes the history data and selects the optimal support method for similar interviews. The generation AI, for example, extracts areas for improvement and selects a support method based on those points. The generation AI, for example, selects a support method by referring to successful interview patterns. The generating AI, for example, analyzes past interview history data and selects the optimal support method. This allows the interview support department to use the generating AI to refer to the job seeker's past interview history and select the most suitable support method.

[0094] The interview support department can customize the means of support during interviews based on the job seeker's current work situation. For example, the interview support department can analyze the job seeker's current work situation and provide support means appropriate to that situation. For example, the interview support department can also suggest appropriate interview strategies based on the job seeker's current work situation. For example, the interview support department can customize the points to emphasize during the interview, taking into account the job seeker's current work situation. By customizing the means of support based on the job seeker's current work situation, the department can provide the job seeker with the best possible support. Some or all of the above processes in the interview support department may be performed using a generation AI, or they may not be performed using a generation AI. For example, the interview support department inputs the job seeker's current work situation data into a generation AI, and the generation AI customizes the means of support. The generation AI analyzes the current work situation data and provides support means appropriate to that situation. For example, the generation AI suggests appropriate interview strategies. For example, the generation AI customizes the points to emphasize during the interview. For example, the generation AI selects the optimal means of support based on the current work situation data. This allows the interview support department to use generating AI to customize support methods based on the job seeker's current work situation.

[0095] The interview support department can estimate the emotions of job seekers and determine the priority of interview support based on the estimated emotions. The interview support department can estimate the emotions of job seekers using, for example, facial expression analysis technology. The interview support department can also estimate the emotions of job seekers using, for example, voice analysis technology. The interview support department can also estimate the emotions of job seekers using, for example, text analysis technology. This allows the department to prioritize the support that is important to the job seeker by determining the priority of interview support based on the emotions of the job seeker. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the interview support department may be performed using a generative AI or not using a generative AI. For example, the interview support department inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI analyzes the facial expression data using facial expression analysis technology and estimates the emotions. Generative AI can, for example, analyze audio data and estimate emotions based on tone and speed of voice. Generative AI can, for example, analyze text data and estimate emotions based on word choice and sentence structure. Generative AI can, for example, determine the priority of interview support based on the estimated emotions. This allows the interview support department to use generative AI to estimate the emotions of job seekers and determine the priority of interview support based on the estimated emotions of the job seekers.

[0096] The interview support department can select the optimal support method during interviews by considering the job seeker's geographical location. For example, the interview support department can analyze the job seeker's current location and provide interview preparation relevant to that area. For example, the interview support department can also analyze the job seeker's desired work location and provide interview preparation relevant to that area. For example, the interview support department can analyze the job seeker's past work locations and provide interview preparation relevant to those areas. By selecting the optimal support method while considering the job seeker's geographical location, the department can provide beneficial support to the job seeker. Some or all of the above processing in the interview support department may be performed using a generative AI, or it may be performed without a generative AI. For example, the interview support department inputs the job seeker's geographical location data into a generative AI, and the generative AI selects the optimal support method. The generative AI uses distance calculation technology to compare the job seeker's current location with the interview location and provides relevant interview preparation. For example, the generative AI analyzes desired work location data and provides interview preparation relevant to that area. The generating AI, for example, analyzes past work location data and provides interview preparation tailored to that region. The generating AI also uses regional characteristic analysis techniques to select support methods that consider geographical location information. This allows the interview support department to use the generating AI to select the optimal support method while considering the job seeker's geographical location.

[0097] The interview support department can analyze a job seeker's social media activity during interview support and propose means of support. For example, the interview support department can analyze the content of a job seeker's social media posts and provide relevant interview preparation. The interview support department can also analyze a job seeker's social media followers and followed accounts and provide relevant information. The interview support department can also analyze a job seeker's social media activity history and provide relevant interview preparation. In this way, by analyzing a job seeker's social media activity, relevant means of support can be provided. Some or all of the above processing in the interview support department may be performed using a generative AI, or not. For example, the interview support department inputs the job seeker's social media activity data into a generative AI, and the generative AI proposes means of support. The generative AI uses content analysis technology to analyze the content of social media posts and provides relevant interview preparation. The generative AI, for example, analyzes follower data and provides information related to followed accounts. The generative AI, for example, analyzes activity history data and provides interview preparation related to past activities. The generative AI, for example, analyzes trends on social media and provides relevant information. This allows the interview support department to use the generative AI to analyze job seekers' social media activities and propose support measures.

[0098] The career planning unit can estimate the emotions of job seekers and adjust the career planning method based on the estimated emotions. The career planning unit can estimate the emotions of job seekers using, for example, facial expression analysis technology. The career planning unit can also estimate the emotions of job seekers using, for example, voice analysis technology. The career planning unit can also estimate the emotions of job seekers using, for example, text analysis technology. By adjusting the career planning method based on the emotions of job seekers, the unit can provide the optimal career plan for the job seeker. Emotion estimation is achieved using an emotion estimation function with an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the career planning unit may be performed using a generative AI or not using a generative AI. For example, the career planning unit inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI analyzes the facial expression data using facial expression analysis technology and estimates the emotions. The generative AI can, for example, analyze audio data and estimate emotions based on tone and speed of voice. It can also, for example, analyze text data and estimate emotions based on word choice and sentence structure. The generative AI can, for example, adjust the career planning method based on the estimated emotions. This allows the career planning unit to use the generative AI to estimate the emotions of job seekers and adjust the career planning method based on those estimated emotions.

[0099] The career planning unit can select the optimal planning method by referring to the job seeker's past career history during the career planning process. For example, the career planning unit can analyze the job seeker's past career history and select the optimal planning method for similar career paths. For example, the career planning unit can also select a planning method by referring to successful career paths from the job seeker's past career history. For example, the career planning unit can analyze the job seeker's past career history and select a planning method considering turning points in their career. This allows the optimal planning method to be selected by referring to the job seeker's past career history. Some or all of the above processes in the career planning unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the career planning unit inputs the job seeker's past career history data into a generation AI, and the generation AI selects the optimal planning method. The generation AI analyzes the history data and selects the optimal planning method for similar career paths. For example, the generation AI selects a planning method by referring to successful career paths. The generating AI, for example, selects a planning method by considering turning points in a person's career. The generating AI also analyzes past career history data to select the optimal planning method. This allows the career planning department to use the generating AI to refer to the job seeker's past career history and select the most suitable planning method.

[0100] The career planning unit can customize the planning methods based on the job seeker's current job situation during career planning. For example, the career planning unit can analyze the job seeker's current job situation and provide career planning methods appropriate to that situation. For example, the career planning unit can also propose appropriate career goals based on the job seeker's current job situation. For example, the career planning unit can customize a career growth plan taking into account the job seeker's current job situation. By customizing the planning methods based on the job seeker's current job situation, the unit can provide the job seeker with the optimal career plan. Some or all of the above processes in the career planning unit may be performed using a generative AI, or they may not be performed using a generative AI. For example, the career planning unit inputs the job seeker's current job situation data into a generative AI, and the generative AI customizes the planning methods. The generative AI analyzes the current job situation data and provides career planning methods appropriate to that situation. The generative AI proposes appropriate career goals. The generative AI customizes a career growth plan. The generating AI, for example, selects the optimal planning method based on current job status data. This allows the career planning department to customize the planning method based on the job seeker's current job status using the generating AI.

[0101] The career planning unit can estimate the emotions of job seekers and determine the priority of career planning based on the estimated emotions. The career planning unit can estimate the emotions of job seekers using, for example, facial expression analysis technology. The career planning unit can also estimate the emotions of job seekers using, for example, voice analysis technology. The career planning unit can also estimate the emotions of job seekers using, for example, text analysis technology. By determining the priority of career planning based on the emotions of job seekers, the unit can prioritize providing planning that is important to the job seeker. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the career planning unit may be performed using a generative AI or not using a generative AI. For example, the career planning unit inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI uses facial expression analysis technology to analyze facial expression data and estimate emotions. For example, the generative AI analyzes voice data and estimates emotions based on voice tone and speed. For example, the generative AI analyzes text data and estimates emotions based on word choice and sentence structure. For example, the generative AI determines career planning priorities based on the estimated emotions. This allows the career planning department to estimate the emotions of job seekers using the generative AI and determine career planning priorities based on the estimated emotions of the job seekers.

[0102] The career planning department can select the optimal planning method when creating a career plan, taking into account the job seeker's geographical location information. For example, the career planning department can analyze the job seeker's current location and provide a career planning method relevant to that region. The career planning department can also analyze the job seeker's desired work location and provide a career planning method relevant to that region. The career planning department can also analyze the job seeker's past work locations and provide a career planning method relevant to those regions. By selecting the optimal planning method while considering the job seeker's geographical location information, it is possible to provide a plan that is beneficial to the job seeker. Some or all of the above processing in the career planning department may be performed using a generation AI, or it may be performed without a generation AI. For example, the career planning department inputs the job seeker's geographical location information data into a generation AI, and the generation AI selects the optimal planning method. The generation AI uses distance calculation technology to compare the job seeker's current location with the target area for career planning and provides a relevant planning method. The generating AI, for example, analyzes desired work location data and provides career planning methods relevant to that region. The generating AI, for example, analyzes past work location data and provides career planning methods relevant to that region. The generating AI, for example, uses regional characteristic analysis techniques to select a planning method that takes geographical location information into consideration. This allows the career planning department to use the generating AI to select the optimal planning method while considering the job seeker's geographical location information.

[0103] The Career Planning Department can analyze a job seeker's social media activity during career planning and propose planning methods. For example, the Career Planning Department can analyze the content of a job seeker's social media posts and provide relevant career planning methods. The Career Planning Department can also analyze a job seeker's social media followers and followed accounts and provide relevant information. The Career Planning Department can also analyze a job seeker's social media activity history and provide relevant career planning methods. In this way, by analyzing a job seeker's social media activity, relevant planning methods can be provided. Some or all of the above processing in the Career Planning Department may be performed using a generative AI, or not. For example, the Career Planning Department inputs the job seeker's social media activity data into a generative AI, and the generative AI proposes planning methods. The generative AI analyzes the content of social media posts using post content analysis technology and provides relevant career planning methods. The generative AI analyzes follower data and provides information related to followed accounts. The generating AI can, for example, analyze activity history data and provide career planning methods related to past activities. It can also, for example, analyze trends on social media and provide relevant information. This allows the career planning department to use the generating AI to analyze job seekers' social media activities and propose planning methods.

[0104] The optimization unit can estimate the emotions of job seekers and adjust the optimization method of job postings based on the estimated emotions. The optimization unit can estimate the emotions of job seekers using, for example, facial expression analysis technology. The optimization unit can also estimate the emotions of job seekers using, for example, voice analysis technology. The optimization unit can also estimate the emotions of job seekers using, for example, text analysis technology. By adjusting the optimization method of job postings based on the emotions of job seekers, the system can provide job postings that are optimal for job seekers. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using a generative AI or not using a generative AI. For example, the optimization unit inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI analyzes the facial expression data using facial expression analysis technology and estimates the emotions. The generative AI, for example, analyzes audio data and estimates emotions based on tone and speed of voice. It also analyzes text data and estimates emotions based on word choice and sentence structure. Furthermore, the generative AI adjusts the optimization method for job postings based on the estimated emotions. This allows the optimization unit to use the generative AI to estimate the emotions of job seekers and adjust the optimization method for job postings based on the estimated emotions.

[0105] The optimization unit can select the optimal optimization method by referring to the history of a company's past job postings during the optimization process. For example, the optimization unit can analyze the history of a company's past job postings and select the optimal optimization method for similar job postings. For example, the optimization unit can also select an optimization method by referring to successful job postings from the company's past job posting history. For example, the optimization unit can analyze the history of a company's past job postings and select an optimization method considering areas for improvement in the job postings. This allows the optimal optimization method to be selected by referring to the company's past job posting history. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit inputs the history data of a company's past job postings into a generation AI, and the generation AI selects the optimal optimization method. The generation AI analyzes the history data and selects the optimal optimization method for similar job postings. For example, the generation AI selects an optimization method by referring to successful job postings. For example, the generation AI selects an optimization method considering areas for improvement in the job postings. For example, the generation AI analyzes the history data of past job postings and selects the optimal optimization method. This allows the optimization unit to use generating AI to refer to the history of a company's past job postings and select the optimal optimization method.

[0106] The optimization unit can customize the optimization methods based on the company's current situation during optimization. For example, the optimization unit can analyze the company's current situation and provide job posting optimization methods appropriate to that situation. For example, the optimization unit can also propose an appropriate job posting optimization method based on the company's current situation. For example, the optimization unit can customize areas for improvement in job postings, taking into account the company's current situation. This allows the optimization unit to provide optimal job postings for the company by customizing the optimization methods based on the company's current situation. Some or all of the above-described processes in the optimization unit may be performed using a generation AI, or they may be performed without a generation AI. For example, the optimization unit inputs the company's current situation data into a generation AI, and the generation AI customizes the optimization methods. The generation AI analyzes the current situation data and provides job posting optimization methods appropriate to that situation. The generation AI proposes an appropriate job posting optimization method. The generation AI customizes areas for improvement in job postings. The generation AI selects the optimal optimization method based on the current situation data. This allows the optimization unit to customize the optimization methods based on the company's current situation using a generation AI.

[0107] The optimization unit can estimate the emotions of job seekers and determine the priority of job posting optimization based on the estimated emotions. The optimization unit can estimate the emotions of job seekers using, for example, facial expression analysis technology. The optimization unit can also estimate the emotions of job seekers using, for example, voice analysis technology. The optimization unit can also estimate the emotions of job seekers using, for example, text analysis technology. By determining the priority of job posting optimization based on the emotions of job seekers, important information can be provided to job seekers on a priority basis. Emotion estimation is achieved using an emotion estimation function using an emotion engine or generative AI. Generative AI is, for example, a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the optimization unit may be performed using a generative AI or not using a generative AI. For example, the optimization unit inputs the job seeker's facial expression data into a generative AI, and the generative AI estimates the emotions. The generative AI analyzes the facial expression data using facial expression analysis technology and estimates the emotions. The generative AI, for example, analyzes audio data and estimates emotions based on tone and speed of voice. It also analyzes text data and estimates emotions based on word choice and sentence structure. Based on the estimated emotions, the generative AI determines the priority for optimizing job postings. This allows the optimization unit to use the generative AI to estimate the emotions of job seekers and determine the priority for optimizing job postings based on the estimated emotions.

[0108] The optimization unit can select the optimal optimization method by considering the geographical location information of companies during optimization. For example, the optimization unit can analyze the current location of a company and provide an optimization method for job postings related to that region. For example, the optimization unit can also analyze the desired work location of a company and provide an optimization method for job postings related to that region. For example, the optimization unit can analyze the past work locations of a company and provide an optimization method for job postings related to that region. By selecting the optimal optimization method by considering the geographical location information of companies, it is possible to provide job postings that are beneficial to companies. Some or all of the above processing in the optimization unit may be performed using a generation AI, or it may be performed without a generation AI. For example, the optimization unit inputs the geographical location information data of companies into a generation AI, and the generation AI selects the optimal optimization method. The generation AI uses distance calculation technology to compare the current location of companies with the location of job postings and provides an appropriate optimization method. For example, the generation AI analyzes desired work location data and provides an optimization method for job postings related to that region. For example, the generation AI analyzes past work location data and provides an optimization method for job postings related to that region. The generation AI, for example, uses regional characteristic analysis techniques to select an optimization method that takes geographical location information into account. This allows the optimization unit to select the optimal optimization method by considering the company's geographical location information using the generation AI.

[0109] The optimization unit can analyze a company's social media activities during optimization and propose optimization methods. For example, the optimization unit can analyze the content of a company's social media posts and provide methods for optimizing related job postings. The optimization unit can also analyze a company's followers and followed accounts on social media and provide relevant information. The optimization unit can also analyze a company's social media activity history and provide methods for optimizing related job postings. In this way, by analyzing a company's social media activities, it is possible to provide relevant optimization methods. Some or all of the above processing in the optimization unit may be performed using a generative AI, or it may be performed without a generative AI. For example, the optimization unit inputs the company's social media activity data into a generative AI, and the generative AI proposes optimization methods. The generative AI uses content analysis technology to analyze the content of social media posts and provides methods for optimizing related job postings. The generative AI can analyze follower data and provide information related to followed accounts. The generative AI can analyze activity history data and provide methods for optimizing job postings related to past activities. The generative AI can analyze social media trends and provide relevant information. This allows the optimization unit to analyze a company's social media activities using generative AI and propose optimization methods.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The analytics department can analyze job seekers' online portfolios and project histories to gain a detailed understanding of their skills, experience, and desired conditions. For example, it can analyze projects that job seekers have published on platforms such as code sharing and portfolios to extract skills and experience. Furthermore, it can analyze the history of online courses and workshops that job seekers have participated in to understand their skill acquisition status. This allows for a more accurate understanding of job seekers' skills and experience, enabling the provision of more suitable job information.

[0112] The recommendation system can dynamically change the display order of job postings based on a job seeker's skills and experience. For example, if a job seeker possesses a specific skill, job postings related to that skill will be displayed at the top. Furthermore, the recommendation system can analyze a job seeker's past application history and prioritize displaying job postings related to companies and job types they have previously applied to. This allows the system to provide job seekers with the most relevant job information.

[0113] The interview support department can provide a mock interview function to assist job seekers in preparing for interviews. For example, when a job seeker conducts a mock interview, the interview support department uses AI to act as the interviewer and provide real-time feedback. Furthermore, the interview support department can analyze recordings of the job seeker's mock interviews and specifically point out areas for improvement. This allows job seekers to effectively prepare for actual interviews.

[0114] The Career Planning Department can consider industry trends and future demand when proposing growth plans tailored to job seekers' career goals. For example, the Career Planning Department can forecast the demand for skills in specific industries and propose the necessary skills to job seekers based on that information. Furthermore, the Career Planning Department can also propose new career paths to job seekers based on industry trends. This allows job seekers to create effective plans for their future career development.

[0115] The optimization unit can consider a company's brand image and corporate culture when optimizing its job postings. For example, the optimization unit can analyze the company's website and social media content to generate job postings that match the company's brand image and culture. Furthermore, the optimization unit can provide attractive job postings to job seekers based on the company's vision and mission. This allows companies to efficiently attract job seekers who align with their brand image.

[0116] The analysis unit can estimate the emotions of job seekers and adjust the accuracy of the analysis based on the estimated emotions. For example, the analysis unit can estimate the emotions of a job seeker when submitting a resume and adjust the analysis to provide more detailed information if the job seeker is nervous. Furthermore, the analysis unit can estimate the emotions of a job seeker during an interview and adjust the analysis to ask more advanced questions if the job seeker is relaxed. This allows for the provision of appropriate information tailored to the emotions of the job seeker.

[0117] The recommendation system can estimate the job seeker's emotions and adjust the timing of recommendations based on those emotions. For example, if the job seeker is stressed, the recommendation system will reduce the frequency of recommendations, and if they are relaxed, it will increase the frequency. Furthermore, if the job seeker is excited, the recommendation system can immediately provide recommendations, and if they are calm, it can delay recommendations. This allows for recommendations to be made at the optimal timing according to the job seeker's emotions.

[0118] The interview support department can estimate the emotions of job seekers and adjust the interview process based on those estimates. For example, if a job seeker is nervous, the department will ask questions to help them relax, and if they are relaxed, it will ask more in-depth questions. Furthermore, if a job seeker is excited, the department can ask questions to encourage them to calm down, and if they are calm, it can ask questions to elicit their emotions. This allows the department to provide optimal interview support tailored to the emotions of each job seeker.

[0119] The Career Planning Department can estimate the emotions of job seekers and adjust the content of their career plans based on those estimated emotions. For example, if a job seeker is feeling anxious, the Career Planning Department can propose a career plan that provides a sense of security, and if they are confident, it can propose a challenging career plan. Furthermore, if a job seeker is excited, the Career Planning Department can propose a career plan that encourages them to calm down, and if they are calm, it can propose a career plan that evokes emotions. This allows the department to provide the most suitable career plan for each job seeker, tailored to their emotions.

[0120] The optimization unit can estimate the job seeker's emotions and adjust how job postings are displayed based on those emotions. For example, if a job seeker is stressed, the optimization unit will display simple and easy-to-understand job postings, while if they are relaxed, it will display more detailed job postings. Furthermore, if a job seeker is excited, the optimization unit can display job postings that encourage immediate application, while if they are calm, it can display job postings that allow for careful consideration. This allows the system to provide job postings that are best suited to the job seeker's emotions.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The analysis unit analyzes the job seeker's resume and work history. The analysis unit uses text analysis technology, natural language processing technology, and data mining technology to extract and analyze the job seeker's skills, experience, desired conditions, and past work history. Step 2: The recommendation unit recommends the most suitable job postings based on the information analyzed by the analysis unit. The recommendation unit uses machine learning algorithms, collaborative filtering technology, and content-based filtering technology to personalize and recommend the most suitable job postings to job seekers. Step 3: The Interview Support Department provides real-time interview support based on job postings recommended by the Recommendation Department. The Interview Support Department uses voice analysis technology, facial expression analysis technology, and emotion analysis technology to analyze the job seeker's statements, facial expressions, and emotions during the interview. Step 4: The Career Planning Department conducts personalized career planning based on the information obtained by the Interview Support Department. The Career Planning Department proposes growth plans, skill development training plans, and career paths tailored to the job seeker's career goals. Step 5: The optimization unit automatically optimizes job postings based on the information obtained by the career planning unit. The optimization unit uses algorithms to analyze the company's conditions and benefits, makes attractive and appropriate proposals to target job seekers, and optimizes the display order of job postings.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the analysis unit, recommendation unit, interview support unit, career planning unit, and optimization unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the smart device 14 and analyzes the job seeker's resume and work history. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the most suitable job information based on the analyzed information. The interview support unit is implemented by the control unit 46A of the smart device 14 and provides real-time interview support. The career planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides personalized career planning. The optimization unit is implemented by the control unit 46A of the smart device 14 and automatically optimizes 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the analysis unit, recommendation unit, interview support unit, career planning 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 analysis unit is implemented by the control unit 46A of the smart glasses 214 and analyzes the job seeker's resume and work history. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends the most suitable job information based on the analyzed information. The interview support unit is implemented by, for example, the control unit 46A of the smart glasses 214 and provides real-time interview support. The career planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs personalized career planning. The optimization unit is implemented by, for example, the control unit 46A of the smart glasses 214 and automatically optimizes 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the analysis unit, recommendation unit, interview support unit, career planning unit, and optimization unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit is implemented by the control unit 46A of the headset terminal 314 and analyzes the job seeker's resume and work history. The recommendation unit is implemented by the specific processing unit 290 of the data processing unit 12 and recommends the most suitable job information based on the analyzed information. The interview support unit is implemented by the control unit 46A of the headset terminal 314 and provides real-time interview support. The career planning unit is implemented by the specific processing unit 290 of the data processing unit 12 and performs personalized career planning. The optimization unit is implemented by the control unit 46A of the headset terminal 314 and automatically optimizes 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the analysis unit, recommendation unit, interview support unit, career planning 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 analysis unit is implemented by the control unit 46A of the robot 414 and analyzes the resume and work history of job seekers. The recommendation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and recommends the most suitable job information based on the analyzed information. The interview support unit is implemented by, for example, the control unit 46A of the robot 414 and provides real-time interview support. The career planning unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and performs personalized career planning. The optimization unit is implemented by, for example, the control unit 46A of the robot 414 and automatically optimizes 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) The analysis department analyzes the resumes and work histories of job seekers, A recommendation unit recommends the most suitable job postings based on the information analyzed by the aforementioned analysis unit, The interview support department provides real-time interview support based on the job information recommended by the aforementioned recommendation department, Based on the information obtained by the aforementioned interview support department, the career planning department conducts personalized career planning. The system includes an optimization unit that automatically optimizes job postings based on information obtained by the career planning unit. A system characterized by the following features. (Note 2) The aforementioned analysis unit, Gain a detailed understanding of the job seeker's skills, experience, and desired conditions. The system described in Appendix 1, characterized by the features described herein. (Note 3) The recommendation unit is, Personalize and recommend the most suitable job information to job seekers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned interview support department, The system analyzes the applicant's statements and facial expressions during the interview and provides real-time feedback on areas for improvement. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned career planning department, We propose a growth plan tailored to the job seeker's career goals. The system described in Appendix 1, characterized by the features described herein. (Note 6) The optimization unit, We analyze the conditions and benefits of companies and make attractive and appropriate proposals to target job seekers. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, The system estimates the emotions of job seekers and adjusts the accuracy of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, We analyze the past resume submission history of job seekers and select the most suitable analysis method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, During the analysis, filtering is performed based on the job seeker's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit, The system estimates the emotions of job seekers and prioritizes the analysis results based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, During analysis, the system prioritizes analyzing highly relevant information, taking into account the geographical location of job seekers. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned analysis unit, During the analysis, the social media activity of job seekers is analyzed, and relevant information is analyzed. The system described in Appendix 1, characterized by the features described herein. (Note 13) The recommendation unit is, The system estimates the emotions of job seekers and adjusts the way recommendations are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The recommendation unit is, When making recommendations, adjust the level of detail based on the importance of the job information. The system described in Appendix 1, characterized by the features described herein. (Note 15) The recommendation unit is, When making recommendations, different recommendation algorithms are applied depending on the category of the job posting. The system described in Appendix 1, characterized by the features described herein. (Note 16) The recommendation unit is, The system estimates the job seeker's emotions and adjusts the length of recommendations based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The recommendation unit is, When making recommendations, the priority of recommendations is determined based on when the job postings were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The recommendation unit is, When making recommendations, the order of recommendations is adjusted based on the relevance of the job postings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned interview support department, We estimate the emotions of job seekers and adjust the interview support methods based on the estimated emotions of the job seekers. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned interview support department, During interview support, we select the most appropriate support method by referring to the job seeker's past interview history. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned interview support department, During interview support, customize the support methods based on the job seeker's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned interview support department, The system estimates the emotions of job seekers and prioritizes interview support based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned interview support department, When providing interview support, we select the most appropriate support method by considering the job seeker's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned interview support department, During interview support, we analyze the job seeker's social media activity and propose support methods. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned career planning department, We estimate the emotions of job seekers and adjust career planning methods based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned career planning department, When planning a career, the optimal planning method is selected by referring to the job seeker's past career history. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned career planning department, When planning a career, customize the planning method based on the job seeker's current work situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned career planning department, Estimate the emotions of job seekers and determine career planning priorities based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned career planning department, When planning a career, the most suitable planning method is selected by considering the job seeker's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned career planning department, When providing career planning advice, we analyze job seekers' social media activity and propose planning strategies based on that analysis. The system described in Appendix 1, characterized by the features described herein. (Note 31) The optimization unit, We estimate the sentiments of job seekers and adjust the optimization method of job postings based on the estimated sentiments of job seekers. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit, During optimization, the system selects the most suitable optimization method by referring to the company's past job posting history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit, During optimization, the optimization methods are customized based on the company's current situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit, The system estimates the sentiments of job seekers and determines the optimization priorities for job postings based on these estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 35) 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 36) 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]

[0195] 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 analysis department analyzes the resumes and work histories of job seekers, A recommendation unit recommends the most suitable job postings based on the information analyzed by the aforementioned analysis unit, The interview support department provides real-time interview support based on the job information recommended by the aforementioned recommendation department, Based on the information obtained by the aforementioned interview support department, the career planning department conducts personalized career planning. The system includes an optimization unit that automatically optimizes job postings based on information obtained by the career planning unit. A system characterized by the following features.

2. The aforementioned analysis unit, Gain a detailed understanding of the job seeker's skills, experience, and desired conditions. The system according to feature 1.

3. The recommendation unit is, Personalize and recommend the most suitable job information to job seekers. The system according to feature 1.

4. The aforementioned interview support department, The system analyzes the applicant's statements and facial expressions during the interview and provides real-time feedback on areas for improvement. The system according to feature 1.

5. The aforementioned career planning department, We propose a growth plan tailored to the job seeker's career goals. The system according to feature 1.

6. The optimization unit, We analyze the conditions and benefits of companies and make attractive and appropriate proposals to target job seekers. The system according to feature 1.

7. The aforementioned analysis unit, The system estimates the emotions of job seekers and adjusts the accuracy of the analysis based on the estimated emotions. The system according to feature 1.

8. The aforementioned analysis unit, We analyze the past resume submission history of job seekers and select the most suitable analysis method. The system according to feature 1.

Images