system

Abstract

The system according to this embodiment aims to enable job seekers to find the most suitable job, create resumes and work histories, match with job postings, and efficiently practice for interviews. [Solution] The system according to this embodiment comprises a reception unit, a job aptitude assessment unit, a resume creation unit, a job matching unit, and an interview practice unit. The reception unit inputs the job seeker's skills and experience. The job aptitude assessment unit analyzes the information input by the reception unit and proposes the most suitable job. The resume creation unit creates a resume and work history based on the job proposed by the job aptitude assessment unit. The job matching unit proposes job openings based on the resume and work history created by the resume creation unit. The interview practice unit conducts a mock interview based on the job opening proposed by the job matching unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there is a problem that it is difficult for job seekers to find the most suitable job type for themselves, efficiently create resumes and work experience documents, match job offers, and practice interviews.

[0005] The system according to the embodiment aims to enable job seekers to efficiently find the most suitable job type for themselves, create resumes and work experience documents, match job offers, and practice interviews.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a job aptitude assessment unit, a resume creation unit, a job matching unit, and an interview practice unit. The reception unit inputs the job seeker's skills and experience. The job aptitude assessment unit analyzes the information entered by the reception unit and proposes the most suitable job. The resume creation unit creates a resume and work history based on the job proposed by the job aptitude assessment unit. The job matching unit proposes job openings based on the resume and work history created by the resume creation unit. The interview practice unit conducts a mock interview based on the job openings proposed by the job matching unit. [Effects of the Invention]

[0007] The system according to this embodiment allows job seekers to find the most suitable job for them, create resumes and work histories, match with job openings, and efficiently practice for interviews. [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 a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

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

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

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

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

[0019] The smart device 14 comprises a computer 36, a reception 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 reception 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-changing support agent system according to an embodiment of the present invention is a system that analyzes the skills and experiences of job seekers, proposes optimal job types, creates resumes and work histories, proposes job offers, and conducts mock interviews. This job-changing support agent system inputs the skills and experiences of job seekers, and AI analyzes this information to propose optimal job types. For example, for job seekers with programming skills, it proposes the job type of software engineer. Next, based on the history and skills of the job seeker, AI automatically creates an effective resume and work history format. For example, for a job seeker with experience as a project manager, it creates a format that emphasizes that experience. Furthermore, based on the desired conditions and career goals of the job seeker, AI proposes job offers. For example, for job seekers who desire remote work, it proposes job offers that allow remote work. Finally, AI conducts a mock interview with the job seeker, analyzes the answers, and provides feedback. For example, for a job seeker who has introduced themselves, it evaluates the content and points out areas for improvement. Thereby, the job-changing support agent system can support the job-changing activities of job seekers, find options that match their career goals, and significantly reduce the time spent on job-changing activities. Also, for companies, the matching accuracy with job seekers can be improved, and an efficient recruitment process can be realized. Thereby, the job-changing support agent system can efficiently analyze the skills and experiences of job seekers, enabling optimal job type proposals, resume creation, job offer proposals, and mock interviews.

[0029] The job placement support agent system according to this embodiment comprises a reception unit, a job aptitude assessment unit, a resume creation unit, a job matching unit, and an interview practice unit. The reception unit inputs the skills and experience of job seekers. The reception unit provides, for example, an interface for job seekers to input their skills and experience. The reception unit can also store the information entered by job seekers in a database. The job aptitude assessment unit analyzes the information entered by the reception unit and proposes the most suitable job. The job aptitude assessment unit analyzes the skills and experience of job seekers using, for example, AI and proposes the most suitable job. The job aptitude assessment unit can also identify the most suitable job by comparing the job seeker's skill set with industry demands. The resume creation unit creates resumes and work history documents based on the job suggested by the job aptitude assessment unit. The resume creation unit automatically creates effective resume and work history document formats based on the job seeker's career and skills using, for example, AI. The resume creation unit can also create formats that highlight the job seeker's strengths and achievements. The Job Matching Department proposes job openings based on resumes and work histories created by the Resume Creation Department. The Job Matching Department can, for example, use AI to propose job openings based on the job seeker's desired conditions and career goals. The Job Matching Department can also identify the most suitable job openings by comparing the job seeker's skill set with job information. The Interview Practice Department conducts mock interviews based on the job openings proposed by the Job Matching Department. The Interview Practice Department can, for example, use AI to conduct mock interviews with job seekers, analyze their responses, and provide feedback. The Interview Practice Department can also evaluate the content and expression of the job seeker's responses and point out areas for improvement. As a result, the career support agent system according to this embodiment can efficiently analyze the skills and experience of job seekers and enable optimal job type proposals, resume creation, job proposals, and mock interviews.

[0030] The reception department inputs the skills and experience of job seekers. For example, the reception department provides an interface for job seekers to input their skills and experience. Specifically, the reception department allows job seekers to easily input information through web-based forms or mobile applications. Job seekers can input detailed information such as past work experience, education, qualifications, skill sets, and desired job type and location. Furthermore, the reception department has a function to verify the entered information in real time and automatically correct input errors or incomplete information. For example, if the entered data is incomplete, the system displays a message prompting the job seeker to enter the correct information. The reception department can also save the information entered by job seekers to a database. Since the saved data is used by subsequent processing departments, it saves job seekers the trouble of re-entering information they have already entered. In addition, to protect job seekers' privacy, the reception department has security features that encrypt and store the entered information, protecting it from unauthorized access. This allows the reception department to provide an environment where job seekers can enter information with peace of mind, enabling a smooth job placement support process.

[0031] The Job Aptitude Assessment Department analyzes the information entered by the reception department and proposes the most suitable job. For example, the Job Aptitude Assessment Department uses AI to analyze the job seeker's skills and experience and propose the most suitable job. Specifically, the AI ​​uses natural language processing technology to analyze the job seeker's input information and extract patterns of skill sets and experience. Furthermore, the AI ​​considers industry demand and trends to identify job types that match the job seeker's skill set. For example, if a job seeker has programming skills, the AI ​​analyzes the current demand in the IT industry and proposes job types such as software engineer or data scientist. The Job Aptitude Assessment Department also takes into account the job seeker's career goals and desired conditions. For example, if a job seeker has leadership experience and desires a management position, the AI ​​compares the job seeker's skill set with the requirements of a management position and proposes the most suitable job. In addition, the Job Aptitude Assessment Department provides job seekers with detailed feedback, explaining the reasons for the proposed job and the job seeker's strengths. This allows job seekers to receive job suggestions based on their skills and experience, enabling them to effectively advance their job search.

[0032] The Resume Creation Department creates resumes and work history documents based on the job types suggested by the Job Aptitude Assessment Department. For example, the Resume Creation Department uses AI to automatically create effective resume and work history document formats based on the job seeker's background and skills. Specifically, the AI ​​analyzes the job seeker's input information and selects the optimal format and layout. For instance, if a job seeker is seeking an engineering position, the AI ​​selects a format that emphasizes technical skills and project experience. The AI ​​also automatically generates wording to effectively highlight the job seeker's strengths and achievements. For example, it generates wording that emphasizes the results and specific figures of projects the job seeker has completed in the past, and incorporates this into the resume. Furthermore, the Resume Creation Department automatically creates a work history document based on the information entered by the job seeker. This work history document details the job seeker's past work experience, roles, and achievements. This allows job seekers to create effective resumes and work history documents quickly, enabling them to proceed smoothly with their job search. Furthermore, the resume creation section includes a function that allows job seekers to save their created resumes and work histories, enabling them to edit and update them later. This allows job seekers to flexibly update their resumes and work histories as their job search progresses.

[0033] The Job Matching Department proposes job openings based on resumes and work histories created by the Resume Creation Department. The Job Matching Department also uses AI to propose job openings based on job seekers' desired conditions and career goals. Specifically, the AI ​​analyzes the job seeker's skill set, experience, and desired conditions, and extracts the most suitable job information from the job database. For example, if a job seeker desires a specific industry or job type, the AI ​​prioritizes suggesting job openings related to that industry or job type. The AI ​​also takes into account the job seeker's career goals and long-term career plans. For example, if a job seeker aims for a management position in the future, the AI ​​suggests job openings that will allow them to hone their management skills. Furthermore, the Job Matching Department provides job seekers with detailed job information and supports the application process. For example, job information includes company overview, job description, application requirements, and salary information, allowing job seekers to consider applying based on this information. The Job Matching Department also tracks the progress of job applications in real time and provides appropriate feedback to job seekers. This allows job seekers to efficiently find the most suitable job postings and effectively advance their job search.

[0034] The Interview Practice Department conducts mock interviews based on job postings suggested by the Job Matching Department. For example, the Interview Practice Department uses AI to conduct mock interviews with job seekers, analyzes their responses, and provides feedback. Specifically, the AI ​​uses speech recognition technology to analyze the job seeker's responses and evaluates their content and expression. For instance, it converts the job seeker's answers into text and evaluates their logic and consistency. The AI ​​also evaluates the job seeker's expression and attitude. For example, it analyzes the job seeker's tone of voice, speaking speed, and facial expressions to assess their impression on the interviewer. Furthermore, the Interview Practice Department provides specific feedback to job seekers, pointing out areas for improvement. For example, if an answer is insufficient, it provides specific methods and examples for improvement, supporting the job seeker in giving better answers in future interviews. The Interview Practice Department also provides an environment where job seekers can repeatedly conduct mock interviews and provides training to improve their interview skills. This allows job seekers to approach actual interviews with confidence and succeed in their job search. Furthermore, the interview practice section also has a function to save the history of job seekers' interview practice and track their progress. This allows job seekers to see their own growth and feel the improvement in their interview skills.

[0035] The Job Aptitude Assessment Department can analyze a job seeker's skills and experience and propose the most suitable job. For example, the Department can use AI to analyze a job seeker's skills and experience and propose the most suitable job. The Department can also identify the most suitable job by comparing a job seeker's skill set with industry demand. For example, it can propose a software engineer position to a job seeker with programming skills. It can also propose a project manager position to a job seeker with project management experience. Furthermore, it can propose a marketing specialist position to a job seeker with marketing skills. In this way, the Department can propose the most suitable job by analyzing a job seeker's skills and experience. Some or all of the above processes in the Job Aptitude Assessment Department may be performed using AI, or not. For example, the Department can input data on a job seeker's skills and experience into a generating AI and have the generating AI propose the most suitable job.

[0036] The resume creation department can automatically generate effective resume and CV formats based on the job seeker's career history and skills. For example, the resume creation department can use AI to automatically generate effective resume and CV formats based on the job seeker's career history and skills. The resume creation department can also create formats that highlight the job seeker's strengths and achievements. For example, for a job seeker with experience as a project manager, the resume creation department can create a format that emphasizes that experience. Similarly, for a job seeker with skills as a software engineer, the resume creation department can create a format that emphasizes that skill. Furthermore, for a job seeker with experience as a marketing specialist, the resume creation department can create a format that emphasizes that experience. This allows the resume creation department to automatically generate effective resumes and CVs based on the job seeker's career history and skills. Some or all of the above processes in the resume creation department may be performed using AI, for example, or without AI. For example, the resume creation department can input data on job seekers' work history and skills into a generating AI, which can then create resume and work history formats.

[0037] The job matching department can propose job openings based on job seekers' desired conditions and career goals. For example, the job matching department can use AI to propose job openings based on job seekers' desired conditions and career goals. The job matching department can also identify the most suitable job by comparing job seekers' skill sets with job information. For example, the job matching department can propose remote work options to job seekers who desire remote work. It can also propose high-paying jobs to job seekers who desire high income. Furthermore, it can propose jobs that offer career advancement opportunities to job seekers aiming for career advancement. In this way, the job matching department can propose the most suitable job openings based on job seekers' desired conditions and career goals. Some or all of the above processes in the job matching department may be performed using AI, for example, or without AI. For example, the job matching department can input data on job seekers' desired conditions and career goals into a generating AI and have the generating AI propose the most suitable job openings.

[0038] The interview practice department can conduct mock interviews with AI, analyze job seekers' responses, and provide feedback. For example, the interview practice department can use AI to conduct mock interviews with job seekers, analyze their responses, and provide feedback. The interview practice department can also evaluate the content and expression of job seekers' responses and point out areas for improvement. For example, the interview practice department can evaluate the content of a job seeker's self-introduction and point out areas for improvement. It can also evaluate the responses of job seekers who answer questions about their work experience and point out areas for improvement. Furthermore, it can evaluate the responses of job seekers who answer questions about their career goals and point out areas for improvement. In this way, the interview practice department can effectively prepare job seekers for interviews by conducting mock interviews with AI, analyzing their responses, and providing feedback. Some or all of the above processes in the interview practice department may be performed using AI, for example, or without AI. For example, the interview practice department can input job seeker response data into a generating AI and have the generating AI provide feedback.

[0039] The reception desk can analyze a job seeker's past work history and select the optimal input method. For example, the reception desk can automatically display frequently entered skills and experience as suggestions based on past work history. The reception desk can also prompt the job seeker to prioritize entering relevant work experience based on past work history. The reception desk can also analyze past work history and provide an easy-to-use interface for job seekers. This allows the reception desk to select the optimal input method by analyzing past work history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the job seeker's past work history data into a generating AI and have the generating AI select the optimal input method.

[0040] The reception desk can filter the input of skills and experience based on the job seeker's current work situation and areas of interest. For example, the reception desk may prompt the job seeker to prioritize inputting relevant skills and experience based on their current work situation. The reception desk can also automatically display relevant skills and experience as suggestions based on the job seeker's areas of interest. The reception desk can also determine the priority of the information to be entered, taking into account the current work situation and areas of interest. This allows the reception desk to input more relevant information by filtering based on the current work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input data on the job seeker's current work situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The reception desk can prioritize the input of highly relevant information when job seekers enter their skills and experience, taking into account their geographical location. For example, the reception desk may prompt job seekers to prioritize the input of relevant work experience based on their geographical location. The reception desk can also determine the priority of information to be entered, taking geographical location into consideration. The reception desk can also automatically display relevant skills and experience as suggestions based on the job seeker's geographical location. This allows the reception desk to prioritize the input of highly relevant information by considering geographical location. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input job seekers' geographical location data into a generating AI and have the generating AI prioritize the input of highly relevant information.

[0042] The reception desk can analyze a job seeker's social media activity and input relevant information when they input their skills and experience. For example, the reception desk can analyze social media activity and automatically display relevant skills and experience as suggestions. The reception desk can also prompt the job seeker to prioritize inputting relevant work experience based on their social media activity. The reception desk can also analyze social media activity and determine the priority of the information to be entered. This allows the reception desk to input relevant information by analyzing social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on a job seeker's social media activity into a generating AI and have the generating AI input relevant information.

[0043] The Job Aptitude Assessment Department can adjust the level of detail in job suggestions based on the importance of the job seeker's skills during the job aptitude assessment. For example, the Job Aptitude Assessment Department can provide detailed job suggestions to job seekers with important skills. The Job Aptitude Assessment Department can also adjust the level of detail in suggestions according to the importance of skills. The Job Aptitude Assessment Department can also provide concise job suggestions to job seekers with less important skills. This allows the Job Aptitude Assessment Department to provide more appropriate job suggestions by adjusting the level of detail in suggestions based on the importance of skills. Some or all of the above processes in the Job Aptitude Assessment Department may be performed using AI, for example, or not using AI. For example, the Job Aptitude Assessment Department can input data on the importance of the job seeker's skills into a generating AI and have the generating AI perform the adjustment of the level of detail in suggestions.

[0044] The Job Aptitude Assessment Department can apply different diagnostic algorithms to job seekers depending on their job category during the job aptitude assessment. For example, the Job Aptitude Assessment Department can apply an IT-specific diagnostic algorithm to job seekers in IT positions. It can also apply a medical-specific diagnostic algorithm to job seekers in medical positions. It can also apply a sales-specific diagnostic algorithm to job seekers in sales positions. By applying different diagnostic algorithms according to job category, the Job Aptitude Assessment Department can make more appropriate job suggestions. Some or all of the above processing in the Job Aptitude Assessment Department may be performed using AI, for example, or not using AI. For example, the Job Aptitude Assessment Department can input job seeker job category data into a generating AI and have the generating AI execute the application of the diagnostic algorithm.

[0045] The Job Aptitude Assessment Department can determine the priority of job suggestions based on the timing of the job seeker's work history submission during the job aptitude assessment. For example, the Job Aptitude Assessment Department can prioritize job suggestions for job seekers with recent work histories. The Job Aptitude Assessment Department can also determine the priority of suggestions based on the timing of the work history submission. The Job Aptitude Assessment Department can also postpone job suggestions for job seekers with older work histories. This allows the Job Aptitude Assessment Department to make more appropriate job suggestions by prioritizing suggestions based on the timing of the work history submission. Some or all of the above processes in the Job Aptitude Assessment Department may be performed using AI, for example, or not using AI. For example, the Job Aptitude Assessment Department can input data on the timing of the job seeker's work history submission into a generating AI and have the generating AI determine the priority of suggestions.

[0046] The Job Aptitude Assessment Department can adjust the order of suggestions based on the job seeker's relevance during the job aptitude assessment. For example, the Job Aptitude Assessment Department can prioritize suggesting highly relevant job types. The Job Aptitude Assessment Department can also adjust the order of suggestions based on relevance. The Job Aptitude Assessment Department can also postpone suggesting less relevant job types. This allows the Job Aptitude Assessment Department to suggest more appropriate job types by adjusting the order of suggestions based on relevance. Some or all of the above processes in the Job Aptitude Assessment Department may be performed using AI, for example, or not using AI. For example, the Job Aptitude Assessment Department can input the job seeker's relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0047] The resume creation unit can adjust the level of detail in a resume based on the importance of the job seeker's experience. For example, the resume creation unit will create a detailed resume for a job seeker with important experience. The resume creation unit can also adjust the level of detail in a resume according to the importance of the experience. The resume creation unit can also create a concise resume for a job seeker with less important experience. In this way, the resume creation unit can create a more appropriate resume by adjusting the level of detail in a resume based on the importance of the experience. Some or all of the above processes in the resume creation unit may be performed using AI, for example, or not using AI. For example, the resume creation unit can input data on the importance of the job seeker's experience into a generating AI and have the generating AI perform the adjustment of the level of detail in the resume.

[0048] The resume creation unit can apply different resume formats depending on the job category of the job seeker when creating a resume. For example, the resume creation unit can apply an IT-specific resume format to job seekers in IT positions. It can also apply a medical-specific resume format to job seekers in medical positions. It can also apply a sales-specific resume format to job seekers in sales positions. In this way, the resume creation unit can create more appropriate resumes by applying different resume formats according to the job category. Some or all of the above processing in the resume creation unit may be performed using AI, for example, or not using AI. For example, the resume creation unit can input the job category data of the job seeker into a generating AI and have the generating AI perform the application of the resume format.

[0049] The resume creation unit can prioritize resumes based on the timing of job seekers' work history submissions. For example, the resume creation unit prioritizes resumes for job seekers with recent work histories. The resume creation unit can also prioritize resumes based on the timing of work history submissions. The resume creation unit can also postpone the creation of resumes for job seekers with older work histories. This allows the resume creation unit to create more appropriate resumes by prioritizing them based on the timing of work history submissions. Some or all of the above processes in the resume creation unit may be performed using AI, for example, or not. For example, the resume creation unit can input data on the timing of job seekers' work history submissions into a generating AI and have the generating AI perform the task of determining resume priorities.

[0050] The resume creation unit can adjust the order of resumes based on the applicant's relevance during the resume creation process. For example, the resume creation unit can prioritize listing highly relevant work experience in the resume. The resume creation unit can also adjust the order of resumes based on relevance. The resume creation unit can also list less relevant work experience later in the resume. This allows the resume creation unit to create a more appropriate resume by adjusting the order of resumes based on relevance. Some or all of the above processes in the resume creation unit may be performed using AI, for example, or not using AI. For example, the resume creation unit can input the applicant's relevance data into a generating AI and have the generating AI perform the adjustment of the resume order.

[0051] The job matching department can adjust the level of detail in job proposals based on the importance of the job seeker's desired conditions during the job matching process. For example, the job matching department can provide detailed job proposals to job seekers with important desired conditions. The job matching department can also adjust the level of detail in proposals according to the importance of the desired conditions. The job matching department can also provide concise job proposals to job seekers with less important desired conditions. This allows the job matching department to provide more appropriate job proposals by adjusting the level of detail in proposals based on the importance of the desired conditions. Some or all of the above processes in the job matching department may be performed using AI, for example, or not using AI. For example, the job matching department can input data on the importance of the job seeker's desired conditions into a generating AI and have the generating AI perform the adjustment of the level of detail in proposals.

[0052] The job matching unit can apply different matching algorithms to job seekers depending on their job category when matching them with jobs. For example, the job matching unit can apply an IT-specific matching algorithm to job seekers in IT positions. It can also apply a medical-specific matching algorithm to job seekers in medical positions. It can also apply a sales-specific matching algorithm to job seekers in sales positions. By applying different matching algorithms depending on the job category, the job matching unit can provide more appropriate job suggestions. Some or all of the above processing in the job matching unit may be performed using AI, for example, or without AI. For example, the job matching unit can input job seeker job category data into a generating AI and have the generating AI execute the application of the matching algorithm.

[0053] The job matching department can prioritize job offers based on when job seekers submit their desired conditions. For example, the job matching department can prioritize job offers to job seekers with recently submitted desired conditions. The job matching department can also prioritize offers based on when the desired conditions were submitted. The job matching department can also postpone job offers to job seekers with older desired conditions. This allows the job matching department to make more appropriate job offers by prioritizing offers based on when the desired conditions were submitted. Some or all of the above processes in the job matching department may be performed using AI, for example, or not. For example, the job matching department can input data on when job seekers submitted their desired conditions into a generating AI and have the generating AI determine the priority of offers.

[0054] The job matching unit can adjust the order of job suggestions based on the relevance of the job seeker during the job matching process. For example, the job matching unit can prioritize suggesting highly relevant jobs. The job matching unit can also adjust the order of suggestions based on relevance. The job matching unit can also postpone suggesting less relevant jobs. This allows the job matching unit to make more appropriate job suggestions by adjusting the order of suggestions based on relevance. Some or all of the above processes in the job matching unit may be performed using AI, for example, or not using AI. For example, the job matching unit can input the relevance data of job seekers into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0055] The interview practice unit can adjust the level of detail in feedback based on the importance of the job seeker's answers during interview practice. For example, the interview practice unit provides detailed feedback to job seekers with important answers. The interview practice unit can also adjust the level of detail in feedback according to the importance of the answers. The interview practice unit can also provide concise feedback to job seekers with less important answers. This allows the interview practice unit to provide more appropriate feedback by adjusting the level of detail in feedback based on the importance of the answers. Some or all of the above processing in the interview practice unit may be performed using AI, for example, or without AI. For example, the interview practice unit can input data on the importance of job seekers' answers into a generating AI and have the generating AI perform the adjustment of the level of detail in feedback.

[0056] The interview practice unit can apply different interview algorithms to job seekers depending on their job category during interview practice. For example, the interview practice unit can apply an IT-specific interview algorithm to job seekers in IT positions. It can also apply a medical-specific interview algorithm to job seekers in medical positions. It can also apply a sales-specific interview algorithm to job seekers in sales positions. This allows the interview practice unit to provide more appropriate feedback by applying different interview algorithms according to job category. Some or all of the above processing in the interview practice unit may be performed using AI, for example, or without AI. For example, the interview practice unit can input job seeker job category data into a generating AI and have the generating AI execute the application of interview algorithms.

[0057] The interview practice unit can prioritize feedback during interview practice based on when job seekers submit their answers. For example, the interview practice unit can prioritize feedback for job seekers with recent answers. The interview practice unit can also prioritize feedback based on when answers are submitted. The interview practice unit can postpone providing feedback to job seekers with older answers. This allows the interview practice unit to provide more appropriate feedback by prioritizing feedback based on when answers are submitted. Some or all of the above processes in the interview practice unit may be performed using AI, for example, or not using AI. For example, the interview practice unit can input data on when job seekers submitted their answers into a generating AI and have the generating AI determine the priority of feedback.

[0058] The interview practice unit can adjust the order of feedback based on the relevance of the job seeker during interview practice. For example, the interview practice unit will prioritize feedback on highly relevant answers. The interview practice unit can also adjust the order of feedback based on relevance. The interview practice unit can also postpone feedback on less relevant answers. This allows the interview practice unit to provide more appropriate feedback by adjusting the order of feedback based on relevance. Some or all of the above processes in the interview practice unit may be performed using AI, for example, or not using AI. For example, the interview practice unit can input the job seeker's relevance data into a generating AI and have the generating AI perform the adjustment of the feedback order.

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

[0060] The Job Aptitude Assessment Department can analyze a job seeker's skills and experience, evaluating the degree of skill growth based on their past work history. For example, by analyzing work history over the past five years, if skill improvement is observed, it can suggest job types that take this growth into account. Furthermore, if a particular skill has been maintained over a long period, it can suggest job types that can utilize that skill. In addition, if skill growth is rapid, it can suggest job types that offer opportunities for career advancement. In this way, the Job Aptitude Assessment Department can suggest job types that take into account the degree of skill growth of the job seeker.

[0061] The resume creation department can customize resume designs based on job seekers' work history and skills. For example, it can provide a visually appealing design for job seekers seeking creative positions, a simple design that emphasizes technical skills for job seekers seeking technical positions, and a design that highlights leadership and management skills for job seekers seeking management positions. In this way, the resume creation department can create resumes with designs tailored to the job seeker's desired position.

[0062] The job matching department can evaluate the reliability of job postings based on job seekers' desired conditions and career goals. For example, it can evaluate the reliability of the job posting provider and prioritize suggesting highly reliable job postings. It can also analyze past job posting history and prioritize suggesting job postings from reliable companies. Furthermore, it can analyze the content of job postings and evaluate whether they match the job seeker's desired conditions. As a result, the job matching department can provide job seekers with highly reliable job postings.

[0063] The reception desk can check the consistency of input information when job seekers enter their skills and experience, based on their past work history. For example, it can compare past work history with current input and display a warning if there is inconsistency. It can also evaluate the accuracy of input based on past work history. Furthermore, it can supplement input based on past work history. In this way, the reception desk can ensure the consistency and accuracy of job seekers' input.

[0064] The resume creation system can automatically update the content of a job seeker's resume based on their work history and skills. For example, if a job seeker acquires a new skill, that skill can be added to their resume. Similarly, if a job seeker gains new work experience, that experience can be reflected in their resume. Furthermore, if a job seeker changes their career goals, the resume can be updated to reflect those goals. This allows the resume creation system to provide resumes that reflect the job seeker's most up-to-date information.

[0065] The interview practice department can customize interview questions based on the results of mock interviews with job seekers. For example, if a job seeker has difficulty with a particular question, they can practice that question repeatedly. Furthermore, more advanced questions can be added for questions the job seeker excels at. In addition, related questions can be added based on the job seeker's answers. This allows the interview practice department to provide questions tailored to the job seeker's level and abilities.

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

[0067] Step 1: The reception desk inputs the job seeker's skills and experience. The reception desk provides an interface for job seekers to input their skills and experience, and can save the entered information in a database. Step 2: The Job Aptitude Assessment Department analyzes the information entered by the reception department and proposes the most suitable job. The Job Aptitude Assessment Department uses AI to analyze the job seeker's skills and experience, and identifies the most suitable job by comparing the job seeker's skill set with industry demand. Step 3: The Resume Creation Department creates resumes and work history documents based on the job categories suggested by the Job Aptitude Assessment Department. The Resume Creation Department uses AI to automatically create effective resume and work history document formats based on the job seeker's experience and skills, creating formats that highlight the job seeker's strengths and achievements. Step 4: The job matching department proposes job openings based on the resume and work history created by the resume creation department. The job matching department uses AI to propose job openings based on the job seeker's desired conditions and career goals, and identifies the most suitable job openings by comparing the job seeker's skill set with the job information. Step 5: The interview practice department conducts mock interviews based on the job openings proposed by the job matching department. The interview practice department uses AI to conduct mock interviews with the job seeker, analyzes the answers to provide feedback, evaluates the content and expression methods of the job seeker's answers, and points out areas for improvement.

[0068] (Form Example 2) The job change support agent system according to an embodiment of the present invention is a system that analyzes the skills and experiences of job seekers, proposes the most suitable job types, creates resumes and work histories, proposes job openings, and conducts mock interviews. This job change support agent system inputs the skills and experiences of job seekers, and AI analyzes this information to propose the most suitable job types. For example, for job seekers with programming skills, the job type of software engineer is proposed. Next, based on the job seeker's history and skills, AI automatically creates an effective resume and work history format. For example, for job seekers with experience as a project manager, a format that emphasizes that experience is created. Furthermore, AI proposes job openings based on the job seeker's desired conditions and career goals. For example, for job seekers who desire remote work, job openings that allow remote work are proposed. Finally, AI conducts mock interviews with the job seeker, analyzes the answers, and provides feedback. For example, for job seekers who have introduced themselves, the content is evaluated and areas for improvement are pointed out. As a result, the job change support agent system can support the job change activities of job seekers, find options that match their career goals, and significantly reduce the time required for job change activities. Also, for companies, the matching accuracy with job seekers is improved, and an efficient recruitment process can be realized. As a result, the job change support agent system can efficiently analyze the skills and experiences of job seekers, and enable the proposal of the most suitable job types, resume creation, job opening proposal, and mock interviews.

[0069] The job placement support agent system according to this embodiment comprises a reception unit, a job aptitude assessment unit, a resume creation unit, a job matching unit, and an interview practice unit. The reception unit inputs the skills and experience of job seekers. The reception unit provides, for example, an interface for job seekers to input their skills and experience. The reception unit can also store the information entered by job seekers in a database. The job aptitude assessment unit analyzes the information entered by the reception unit and proposes the most suitable job. The job aptitude assessment unit analyzes the skills and experience of job seekers using, for example, AI and proposes the most suitable job. The job aptitude assessment unit can also identify the most suitable job by comparing the job seeker's skill set with industry demands. The resume creation unit creates resumes and work history documents based on the job suggested by the job aptitude assessment unit. The resume creation unit automatically creates effective resume and work history document formats based on the job seeker's career and skills using, for example, AI. The resume creation unit can also create formats that highlight the job seeker's strengths and achievements. The Job Matching Department proposes job openings based on resumes and work histories created by the Resume Creation Department. The Job Matching Department can, for example, use AI to propose job openings based on the job seeker's desired conditions and career goals. The Job Matching Department can also identify the most suitable job openings by comparing the job seeker's skill set with job information. The Interview Practice Department conducts mock interviews based on the job openings proposed by the Job Matching Department. The Interview Practice Department can, for example, use AI to conduct mock interviews with job seekers, analyze their responses, and provide feedback. The Interview Practice Department can also evaluate the content and expression of the job seeker's responses and point out areas for improvement. As a result, the career support agent system according to this embodiment can efficiently analyze the skills and experience of job seekers and enable optimal job type proposals, resume creation, job proposals, and mock interviews.

[0070] The reception department inputs the skills and experience of job seekers. For example, the reception department provides an interface for job seekers to input their skills and experience. Specifically, the reception department allows job seekers to easily input information through web-based forms or mobile applications. Job seekers can input detailed information such as past work experience, education, qualifications, skill sets, and desired job type and location. Furthermore, the reception department has a function to verify the entered information in real time and automatically correct input errors or incomplete information. For example, if the entered data is incomplete, the system displays a message prompting the job seeker to enter the correct information. The reception department can also save the information entered by job seekers to a database. Since the saved data is used by subsequent processing departments, it saves job seekers the trouble of re-entering information they have already entered. In addition, to protect job seekers' privacy, the reception department has security features that encrypt and store the entered information, protecting it from unauthorized access. This allows the reception department to provide an environment where job seekers can enter information with peace of mind, enabling a smooth job placement support process.

[0071] The Job Aptitude Assessment Department analyzes the information entered by the reception department and proposes the most suitable job. For example, the Job Aptitude Assessment Department uses AI to analyze the job seeker's skills and experience and propose the most suitable job. Specifically, the AI ​​uses natural language processing technology to analyze the job seeker's input information and extract patterns of skill sets and experience. Furthermore, the AI ​​considers industry demand and trends to identify job types that match the job seeker's skill set. For example, if a job seeker has programming skills, the AI ​​analyzes the current demand in the IT industry and proposes job types such as software engineer or data scientist. The Job Aptitude Assessment Department also takes into account the job seeker's career goals and desired conditions. For example, if a job seeker has leadership experience and desires a management position, the AI ​​compares the job seeker's skill set with the requirements of a management position and proposes the most suitable job. In addition, the Job Aptitude Assessment Department provides job seekers with detailed feedback, explaining the reasons for the proposed job and the job seeker's strengths. This allows job seekers to receive job suggestions based on their skills and experience, enabling them to effectively advance their job search.

[0072] The Resume Creation Department creates resumes and work history documents based on the job types suggested by the Job Aptitude Assessment Department. For example, the Resume Creation Department uses AI to automatically create effective resume and work history document formats based on the job seeker's background and skills. Specifically, the AI ​​analyzes the job seeker's input information and selects the optimal format and layout. For instance, if a job seeker is seeking an engineering position, the AI ​​selects a format that emphasizes technical skills and project experience. The AI ​​also automatically generates wording to effectively highlight the job seeker's strengths and achievements. For example, it generates wording that emphasizes the results and specific figures of projects the job seeker has completed in the past, and incorporates this into the resume. Furthermore, the Resume Creation Department automatically creates a work history document based on the information entered by the job seeker. This work history document details the job seeker's past work experience, roles, and achievements. This allows job seekers to create effective resumes and work history documents quickly, enabling them to proceed smoothly with their job search. Furthermore, the resume creation section includes a function that allows job seekers to save their created resumes and work histories, enabling them to edit and update them later. This allows job seekers to flexibly update their resumes and work histories as their job search progresses.

[0073] The Job Matching Department proposes job openings based on resumes and work histories created by the Resume Creation Department. The Job Matching Department also uses AI to propose job openings based on job seekers' desired conditions and career goals. Specifically, the AI ​​analyzes the job seeker's skill set, experience, and desired conditions, and extracts the most suitable job information from the job database. For example, if a job seeker desires a specific industry or job type, the AI ​​prioritizes suggesting job openings related to that industry or job type. The AI ​​also takes into account the job seeker's career goals and long-term career plans. For example, if a job seeker aims for a management position in the future, the AI ​​suggests job openings that will allow them to hone their management skills. Furthermore, the Job Matching Department provides job seekers with detailed job information and supports the application process. For example, job information includes company overview, job description, application requirements, and salary information, allowing job seekers to consider applying based on this information. The Job Matching Department also tracks the progress of job applications in real time and provides appropriate feedback to job seekers. This allows job seekers to efficiently find the most suitable job postings and effectively advance their job search.

[0074] The Interview Practice Department conducts mock interviews based on job postings suggested by the Job Matching Department. For example, the Interview Practice Department uses AI to conduct mock interviews with job seekers, analyzes their responses, and provides feedback. Specifically, the AI ​​uses speech recognition technology to analyze the job seeker's responses and evaluates their content and expression. For instance, it converts the job seeker's answers into text and evaluates their logic and consistency. The AI ​​also evaluates the job seeker's expression and attitude. For example, it analyzes the job seeker's tone of voice, speaking speed, and facial expressions to assess their impression on the interviewer. Furthermore, the Interview Practice Department provides specific feedback to job seekers, pointing out areas for improvement. For example, if an answer is insufficient, it provides specific methods and examples for improvement, supporting the job seeker in giving better answers in future interviews. The Interview Practice Department also provides an environment where job seekers can repeatedly conduct mock interviews and provides training to improve their interview skills. This allows job seekers to approach actual interviews with confidence and succeed in their job search. Furthermore, the interview practice section also has a function to save the history of job seekers' interview practice and track their progress. This allows job seekers to see their own growth and feel the improvement in their interview skills.

[0075] The Job Aptitude Assessment Department can analyze a job seeker's skills and experience and propose the most suitable job. For example, the Department can use AI to analyze a job seeker's skills and experience and propose the most suitable job. The Department can also identify the most suitable job by comparing a job seeker's skill set with industry demand. For example, it can propose a software engineer position to a job seeker with programming skills. It can also propose a project manager position to a job seeker with project management experience. Furthermore, it can propose a marketing specialist position to a job seeker with marketing skills. In this way, the Department can propose the most suitable job by analyzing a job seeker's skills and experience. Some or all of the above processes in the Job Aptitude Assessment Department may be performed using AI, or not. For example, the Department can input data on a job seeker's skills and experience into a generating AI and have the generating AI propose the most suitable job.

[0076] The resume creation department can automatically generate effective resume and CV formats based on the job seeker's career history and skills. For example, the resume creation department can use AI to automatically generate effective resume and CV formats based on the job seeker's career history and skills. The resume creation department can also create formats that highlight the job seeker's strengths and achievements. For example, for a job seeker with experience as a project manager, the resume creation department can create a format that emphasizes that experience. Similarly, for a job seeker with skills as a software engineer, the resume creation department can create a format that emphasizes that skill. Furthermore, for a job seeker with experience as a marketing specialist, the resume creation department can create a format that emphasizes that experience. This allows the resume creation department to automatically generate effective resumes and CVs based on the job seeker's career history and skills. Some or all of the above processes in the resume creation department may be performed using AI, for example, or without AI. For example, the resume creation department can input data on job seekers' work history and skills into a generating AI, which can then create resume and work history formats.

[0077] The job matching department can propose job openings based on job seekers' desired conditions and career goals. For example, the job matching department can use AI to propose job openings based on job seekers' desired conditions and career goals. The job matching department can also identify the most suitable job by comparing job seekers' skill sets with job information. For example, the job matching department can propose remote work options to job seekers who desire remote work. It can also propose high-paying jobs to job seekers who desire high income. Furthermore, it can propose jobs that offer career advancement opportunities to job seekers aiming for career advancement. In this way, the job matching department can propose the most suitable job openings based on job seekers' desired conditions and career goals. Some or all of the above processes in the job matching department may be performed using AI, for example, or without AI. For example, the job matching department can input data on job seekers' desired conditions and career goals into a generating AI and have the generating AI propose the most suitable job openings.

[0078] The interview practice department can conduct mock interviews with AI, analyze job seekers' responses, and provide feedback. For example, the interview practice department can use AI to conduct mock interviews with job seekers, analyze their responses, and provide feedback. The interview practice department can also evaluate the content and expression of job seekers' responses and point out areas for improvement. For example, the interview practice department can evaluate the content of a job seeker's self-introduction and point out areas for improvement. It can also evaluate the responses of job seekers who answer questions about their work experience and point out areas for improvement. Furthermore, it can evaluate the responses of job seekers who answer questions about their career goals and point out areas for improvement. In this way, the interview practice department can effectively prepare job seekers for interviews by conducting mock interviews with AI, analyzing their responses, and providing feedback. Some or all of the above processes in the interview practice department may be performed using AI, for example, or without AI. For example, the interview practice department can input job seeker response data into a generating AI and have the generating AI provide feedback.

[0079] The reception desk can estimate the applicant's emotions and adjust the timing of skill and experience input based on the estimated emotions. For example, if the applicant is nervous, the reception desk can simplify the interface and provide step-by-step input to help them relax. If the applicant is anxious, the reception desk can prioritize voice input to allow for quick input. If the applicant is relaxed, the reception desk can provide detailed input options and suggest customizable input methods. This allows the reception desk to provide more appropriate input by adjusting the timing of input based on the applicant's emotions. Emotion estimation is achieved using emotion estimation functions, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the applicant's emotion data into a generative AI and have the generative AI adjust the timing of input.

[0080] The reception desk can analyze a job seeker's past work history and select the optimal input method. For example, the reception desk can automatically display frequently entered skills and experience as suggestions based on past work history. The reception desk can also prompt the job seeker to prioritize entering relevant work experience based on past work history. The reception desk can also analyze past work history and provide an easy-to-use interface for job seekers. This allows the reception desk to select the optimal input method by analyzing past work history. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the job seeker's past work history data into a generating AI and have the generating AI select the optimal input method.

[0081] The reception desk can filter the input of skills and experience based on the job seeker's current work situation and areas of interest. For example, the reception desk may prompt the job seeker to prioritize inputting relevant skills and experience based on their current work situation. The reception desk can also automatically display relevant skills and experience as suggestions based on the job seeker's areas of interest. The reception desk can also determine the priority of the information to be entered, taking into account the current work situation and areas of interest. This allows the reception desk to input more relevant information by filtering based on the current work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk may input data on the job seeker's current work situation and areas of interest into a generating AI and have the generating AI perform the filtering.

[0082] The reception desk can estimate the applicant's emotions and determine the priority of the information to be entered based on the estimated emotions. For example, if the applicant is nervous, the reception desk may prompt them to prioritize entering important information. If the applicant is relaxed, the reception desk may also prompt them to enter detailed information. If the applicant is anxious, the reception desk may also prompt them to prioritize entering information that can be entered quickly. This allows the reception desk to enter more appropriate information by prioritizing the information to be entered based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not using AI. For example, the reception desk may input the applicant's emotion data into a generative AI and have the generative AI perform the determination of information priority.

[0083] The reception desk can prioritize the input of highly relevant information when job seekers enter their skills and experience, taking into account their geographical location. For example, the reception desk may prompt job seekers to prioritize the input of relevant work experience based on their geographical location. The reception desk can also determine the priority of information to be entered, taking geographical location into consideration. The reception desk can also automatically display relevant skills and experience as suggestions based on the job seeker's geographical location. This allows the reception desk to prioritize the input of highly relevant information by considering geographical location. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input job seekers' geographical location data into a generating AI and have the generating AI prioritize the input of highly relevant information.

[0084] The reception desk can analyze a job seeker's social media activity and input relevant information when they input their skills and experience. For example, the reception desk can analyze social media activity and automatically display relevant skills and experience as suggestions. The reception desk can also prompt the job seeker to prioritize inputting relevant work experience based on their social media activity. The reception desk can also analyze social media activity and determine the priority of the information to be entered. This allows the reception desk to input relevant information by analyzing social media activity. Some or all of the above processes in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input data on a job seeker's social media activity into a generating AI and have the generating AI input relevant information.

[0085] The Job Aptitude Assessment Department can estimate the emotions of job seekers and adjust the way job suggestions are presented based on those estimated emotions. For example, if a job seeker is nervous, the Job Aptitude Assessment Department can provide a simple and easily understandable suggestion. If a job seeker is relaxed, the Job Aptitude Assessment Department can also provide a suggestion that includes detailed information. If a job seeker is anxious, the Job Aptitude Assessment Department can also provide a suggestion that is quickly understandable. This allows the Job Aptitude Assessment Department to provide more appropriate suggestions by adjusting the way job suggestions are presented based on the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Job Aptitude Assessment Department may be performed using AI or not using AI. For example, the Job Aptitude Assessment Department can input the job seeker's emotion data into a generative AI and have the generative AI adjust the way job suggestions are presented.

[0086] The Job Aptitude Assessment Department can adjust the level of detail in job suggestions based on the importance of the job seeker's skills during the job aptitude assessment. For example, the Job Aptitude Assessment Department can provide detailed job suggestions to job seekers with important skills. The Job Aptitude Assessment Department can also adjust the level of detail in suggestions according to the importance of skills. The Job Aptitude Assessment Department can also provide concise job suggestions to job seekers with less important skills. This allows the Job Aptitude Assessment Department to provide more appropriate job suggestions by adjusting the level of detail in suggestions based on the importance of skills. Some or all of the above processes in the Job Aptitude Assessment Department may be performed using AI, for example, or not using AI. For example, the Job Aptitude Assessment Department can input data on the importance of the job seeker's skills into a generating AI and have the generating AI perform the adjustment of the level of detail in suggestions.

[0087] The Job Aptitude Assessment Department can apply different diagnostic algorithms to job seekers depending on their job category during the job aptitude assessment. For example, the Job Aptitude Assessment Department can apply an IT-specific diagnostic algorithm to job seekers in IT positions. It can also apply a medical-specific diagnostic algorithm to job seekers in medical positions. It can also apply a sales-specific diagnostic algorithm to job seekers in sales positions. By applying different diagnostic algorithms according to job category, the Job Aptitude Assessment Department can make more appropriate job suggestions. Some or all of the above processing in the Job Aptitude Assessment Department may be performed using AI, for example, or not using AI. For example, the Job Aptitude Assessment Department can input job seeker job category data into a generating AI and have the generating AI execute the application of the diagnostic algorithm.

[0088] The job aptitude assessment unit can estimate the emotions of job seekers and adjust the length of job suggestions based on the estimated emotions. For example, if a job seeker is nervous, the job aptitude assessment unit will provide short, concise job suggestions. If a job seeker is relaxed, the job aptitude assessment unit can also provide detailed job suggestions. If a job seeker is anxious, the job aptitude assessment unit can provide job suggestions that can be quickly understood. In this way, the job aptitude assessment unit can provide more appropriate suggestions by adjusting the length of job suggestions based on the emotions of job seekers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the job aptitude assessment unit may be performed using AI or not using AI. For example, the job aptitude assessment unit can input the job seeker's emotion data into a generative AI and have the generative AI adjust the length of job suggestions.

[0089] The Job Aptitude Assessment Department can determine the priority of job suggestions based on the timing of the job seeker's work history submission during the job aptitude assessment. For example, the Job Aptitude Assessment Department can prioritize job suggestions for job seekers with recent work histories. The Job Aptitude Assessment Department can also determine the priority of suggestions based on the timing of the work history submission. The Job Aptitude Assessment Department can also postpone job suggestions for job seekers with older work histories. This allows the Job Aptitude Assessment Department to make more appropriate job suggestions by prioritizing suggestions based on the timing of the work history submission. Some or all of the above processes in the Job Aptitude Assessment Department may be performed using AI, for example, or not using AI. For example, the Job Aptitude Assessment Department can input data on the timing of the job seeker's work history submission into a generating AI and have the generating AI determine the priority of suggestions.

[0090] The Job Aptitude Assessment Department can adjust the order of suggestions based on the job seeker's relevance during the job aptitude assessment. For example, the Job Aptitude Assessment Department can prioritize suggesting highly relevant job types. The Job Aptitude Assessment Department can also adjust the order of suggestions based on relevance. The Job Aptitude Assessment Department can also postpone suggesting less relevant job types. This allows the Job Aptitude Assessment Department to suggest more appropriate job types by adjusting the order of suggestions based on relevance. Some or all of the above processes in the Job Aptitude Assessment Department may be performed using AI, for example, or not using AI. For example, the Job Aptitude Assessment Department can input the job seeker's relevance data into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0091] The resume creation unit can estimate the applicant's emotions and adjust the resume's presentation based on those emotions. For example, if the applicant is nervous, the resume creation unit can create a simple and highly legible resume. If the applicant is relaxed, the resume creation unit can also create a resume that includes detailed information. If the applicant is anxious, the resume creation unit can provide a resume that can be quickly prepared. In this way, the resume creation unit can create a more appropriate resume by adjusting its presentation based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the resume creation unit may be performed using AI or not. For example, the resume creation unit can input the applicant's emotion data into the generative AI and have the generative AI adjust the resume's presentation.

[0092] The resume creation unit can adjust the level of detail in a resume based on the importance of the job seeker's experience. For example, the resume creation unit will create a detailed resume for a job seeker with important experience. The resume creation unit can also adjust the level of detail in a resume according to the importance of the experience. The resume creation unit can also create a concise resume for a job seeker with less important experience. In this way, the resume creation unit can create a more appropriate resume by adjusting the level of detail in a resume based on the importance of the experience. Some or all of the above processes in the resume creation unit may be performed using AI, for example, or not using AI. For example, the resume creation unit can input data on the importance of the job seeker's experience into a generating AI and have the generating AI perform the adjustment of the level of detail in the resume.

[0093] The resume creation unit can apply different resume formats depending on the job category of the job seeker when creating a resume. For example, the resume creation unit can apply an IT-specific resume format to job seekers in IT positions. It can also apply a medical-specific resume format to job seekers in medical positions. It can also apply a sales-specific resume format to job seekers in sales positions. In this way, the resume creation unit can create more appropriate resumes by applying different resume formats according to the job category. Some or all of the above processing in the resume creation unit may be performed using AI, for example, or not using AI. For example, the resume creation unit can input the job category data of the job seeker into a generating AI and have the generating AI perform the application of the resume format.

[0094] The resume creation unit can estimate the applicant's emotions and adjust the length of the resume based on the estimated emotions. For example, if the applicant is nervous, the resume creation unit can create a short, concise resume. If the applicant is relaxed, the resume creation unit can also create a detailed resume. If the applicant is anxious, the resume creation unit can provide a resume that can be quickly prepared. In this way, the resume creation unit can create a more appropriate resume by adjusting the length based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the resume creation unit may be performed using AI or not using AI. For example, the resume creation unit can input the applicant's emotion data into the generative AI and have the generative AI adjust the length of the resume.

[0095] The resume creation unit can prioritize resumes based on the timing of job seekers' work history submissions. For example, the resume creation unit prioritizes resumes for job seekers with recent work histories. The resume creation unit can also prioritize resumes based on the timing of work history submissions. The resume creation unit can also postpone the creation of resumes for job seekers with older work histories. This allows the resume creation unit to create more appropriate resumes by prioritizing them based on the timing of work history submissions. Some or all of the above processes in the resume creation unit may be performed using AI, for example, or not. For example, the resume creation unit can input data on the timing of job seekers' work history submissions into a generating AI and have the generating AI perform the task of determining resume priorities.

[0096] The resume creation unit can adjust the order of resumes based on the applicant's relevance during the resume creation process. For example, the resume creation unit can prioritize listing highly relevant work experience in the resume. The resume creation unit can also adjust the order of resumes based on relevance. The resume creation unit can also list less relevant work experience later in the resume. This allows the resume creation unit to create a more appropriate resume by adjusting the order of resumes based on relevance. Some or all of the above processes in the resume creation unit may be performed using AI, for example, or not using AI. For example, the resume creation unit can input the applicant's relevance data into a generating AI and have the generating AI perform the adjustment of the resume order.

[0097] The job matching department can estimate the emotions of job seekers and adjust the way job offers are presented based on those estimated emotions. For example, if a job seeker is nervous, the job matching department can present simple and highly visible job offers. If a job seeker is relaxed, the job matching department can also present job offers that include more detailed information. If a job seeker is anxious, the job matching department can also present job offers that are easy to understand quickly. This allows the job matching department to make more appropriate offers by adjusting the way job offers are presented based on the emotions of job seekers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the job matching department may be performed using AI or not. For example, the job matching department can input job seeker emotion data into a generative AI and have the generative AI adjust the way job offers are presented.

[0098] The job matching department can adjust the level of detail in job proposals based on the importance of the job seeker's desired conditions during the job matching process. For example, the job matching department can provide detailed job proposals to job seekers with important desired conditions. The job matching department can also adjust the level of detail in proposals according to the importance of the desired conditions. The job matching department can also provide concise job proposals to job seekers with less important desired conditions. This allows the job matching department to provide more appropriate job proposals by adjusting the level of detail in proposals based on the importance of the desired conditions. Some or all of the above processes in the job matching department may be performed using AI, for example, or not using AI. For example, the job matching department can input data on the importance of the job seeker's desired conditions into a generating AI and have the generating AI perform the adjustment of the level of detail in proposals.

[0099] The job matching unit can apply different matching algorithms to job seekers depending on their job category when matching them with jobs. For example, the job matching unit can apply an IT-specific matching algorithm to job seekers in IT positions. It can also apply a medical-specific matching algorithm to job seekers in medical positions. It can also apply a sales-specific matching algorithm to job seekers in sales positions. By applying different matching algorithms depending on the job category, the job matching unit can provide more appropriate job suggestions. Some or all of the above processing in the job matching unit may be performed using AI, for example, or without AI. For example, the job matching unit can input job seeker job category data into a generating AI and have the generating AI execute the application of the matching algorithm.

[0100] The job matching unit can estimate the emotions of job seekers and adjust the length of job proposals based on the estimated emotions. For example, if a job seeker is nervous, the job matching unit will provide short, to-the-point job proposals. If a job seeker is relaxed, the job matching unit can also provide detailed job proposals. If a job seeker is anxious, the job matching unit can provide job proposals that can be quickly understood. In this way, the job matching unit can provide more appropriate proposals by adjusting the length of job proposals based on the emotions of job seekers. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the job matching unit may be performed using AI, for example, or not using AI. For example, the job matching unit can input job seeker emotion data into a generative AI and have the generative AI adjust the length of job proposals.

[0101] The job matching department can prioritize job offers based on when job seekers submit their desired conditions. For example, the job matching department can prioritize job offers to job seekers with recently submitted desired conditions. The job matching department can also prioritize offers based on when the desired conditions were submitted. The job matching department can also postpone job offers to job seekers with older desired conditions. This allows the job matching department to make more appropriate job offers by prioritizing offers based on when the desired conditions were submitted. Some or all of the above processes in the job matching department may be performed using AI, for example, or not. For example, the job matching department can input data on when job seekers submitted their desired conditions into a generating AI and have the generating AI determine the priority of offers.

[0102] The job matching unit can adjust the order of job suggestions based on the relevance of the job seeker during the job matching process. For example, the job matching unit can prioritize suggesting highly relevant jobs. The job matching unit can also adjust the order of suggestions based on relevance. The job matching unit can also postpone suggesting less relevant jobs. This allows the job matching unit to make more appropriate job suggestions by adjusting the order of suggestions based on relevance. Some or all of the above processes in the job matching unit may be performed using AI, for example, or not using AI. For example, the job matching unit can input the relevance data of job seekers into a generating AI and have the generating AI perform the adjustment of the suggestion order.

[0103] The interview practice unit can estimate the applicant's emotions and adjust the expression of the mock interview based on the estimated emotions. For example, if the applicant is nervous, the interview practice unit can provide a calm expression to help them relax. If the applicant is relaxed, the interview practice unit can also provide an expression that includes detailed feedback. If the applicant is anxious, the interview practice unit can also provide an expression that is easy to understand quickly. This allows the interview practice unit to provide more appropriate feedback by adjusting the expression of the mock interview based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview practice unit may be performed using AI or not using AI. For example, the interview practice unit can input the applicant's emotion data into a generative AI and have the generative AI adjust the expression of the mock interview.

[0104] The interview practice unit can adjust the level of detail in feedback based on the importance of the job seeker's answers during interview practice. For example, the interview practice unit provides detailed feedback to job seekers with important answers. The interview practice unit can also adjust the level of detail in feedback according to the importance of the answers. The interview practice unit can also provide concise feedback to job seekers with less important answers. This allows the interview practice unit to provide more appropriate feedback by adjusting the level of detail in feedback based on the importance of the answers. Some or all of the above processing in the interview practice unit may be performed using AI, for example, or without AI. For example, the interview practice unit can input data on the importance of job seekers' answers into a generating AI and have the generating AI perform the adjustment of the level of detail in feedback.

[0105] The interview practice unit can apply different interview algorithms to job seekers depending on their job category during interview practice. For example, the interview practice unit can apply an IT-specific interview algorithm to job seekers in IT positions. It can also apply a medical-specific interview algorithm to job seekers in medical positions. It can also apply a sales-specific interview algorithm to job seekers in sales positions. This allows the interview practice unit to provide more appropriate feedback by applying different interview algorithms according to job category. Some or all of the above processing in the interview practice unit may be performed using AI, for example, or without AI. For example, the interview practice unit can input job seeker job category data into a generating AI and have the generating AI execute the application of interview algorithms.

[0106] The interview practice unit can estimate the applicant's emotions and adjust the length of the mock interview based on the estimated emotions. For example, if the applicant is nervous, the interview practice unit can conduct a short, to-the-point mock interview. If the applicant is relaxed, the interview practice unit can also conduct a detailed mock interview. If the applicant is anxious, the interview practice unit can conduct a mock interview that can be quickly understood. This allows the interview practice unit to provide more appropriate feedback by adjusting the length of the mock interview based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview practice unit may be performed using AI or not using AI. For example, the interview practice unit can input the applicant's emotion data into the generative AI and have the generative AI adjust the length of the mock interview.

[0107] The interview practice unit can prioritize feedback during interview practice based on when job seekers submit their answers. For example, the interview practice unit can prioritize feedback for job seekers with recent answers. The interview practice unit can also prioritize feedback based on when answers are submitted. The interview practice unit can postpone providing feedback to job seekers with older answers. This allows the interview practice unit to provide more appropriate feedback by prioritizing feedback based on when answers are submitted. Some or all of the above processes in the interview practice unit may be performed using AI, for example, or not using AI. For example, the interview practice unit can input data on when job seekers submitted their answers into a generating AI and have the generating AI determine the priority of feedback.

[0108] The interview practice unit can adjust the order of feedback based on the relevance of the job seeker during interview practice. For example, the interview practice unit will prioritize feedback on highly relevant answers. The interview practice unit can also adjust the order of feedback based on relevance. The interview practice unit can also postpone feedback on less relevant answers. This allows the interview practice unit to provide more appropriate feedback by adjusting the order of feedback based on relevance. Some or all of the above processes in the interview practice unit may be performed using AI, for example, or not using AI. For example, the interview practice unit can input the job seeker's relevance data into a generating AI and have the generating AI perform the adjustment of the feedback order.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The Job Aptitude Assessment Department can analyze a job seeker's skills and experience, evaluating the degree of skill growth based on their past work history. For example, by analyzing work history over the past five years, if skill improvement is observed, it can suggest job types that take this growth into account. Furthermore, if a particular skill has been maintained over a long period, it can suggest job types that can utilize that skill. In addition, if skill growth is rapid, it can suggest job types that offer opportunities for career advancement. In this way, the Job Aptitude Assessment Department can suggest job types that take into account the degree of skill growth of the job seeker.

[0111] The resume creation department can customize resume designs based on job seekers' work history and skills. For example, it can provide a visually appealing design for job seekers seeking creative positions, a simple design that emphasizes technical skills for job seekers seeking technical positions, and a design that highlights leadership and management skills for job seekers seeking management positions. In this way, the resume creation department can create resumes with designs tailored to the job seeker's desired position.

[0112] The job matching department can evaluate the reliability of job postings based on job seekers' desired conditions and career goals. For example, it can evaluate the reliability of the job posting provider and prioritize suggesting highly reliable job postings. It can also analyze past job posting history and prioritize suggesting job postings from reliable companies. Furthermore, it can analyze the content of job postings and evaluate whether they match the job seeker's desired conditions. As a result, the job matching department can provide job seekers with highly reliable job postings.

[0113] The interview practice department can customize the interview process based on the results of mock interviews with job seekers. For example, if a job seeker is nervous, the interview can be conducted slowly to help them relax. If a job seeker is confident, the interview can be conducted with more challenging questions. Furthermore, if a job seeker is anxious, the interview can be conducted quickly to match their pace. In this way, the interview practice department can provide an interview process tailored to the job seeker's state of mind.

[0114] The reception desk can check the consistency of input information when job seekers enter their skills and experience, based on their past work history. For example, it can compare past work history with current input and display a warning if there is inconsistency. It can also evaluate the accuracy of input based on past work history. Furthermore, it can supplement input based on past work history. In this way, the reception desk can ensure the consistency and accuracy of job seekers' input.

[0115] The Job Aptitude Assessment Department can estimate a job seeker's emotions and adjust the timing of job suggestions based on those estimates. For example, if a job seeker is nervous, it can provide information to help them relax before suggesting jobs. If the job seeker is relaxed, it can immediately suggest jobs. Furthermore, if the job seeker is anxious, it can quickly suggest jobs to match the job seeker's pace. In this way, the Job Aptitude Assessment Department can provide job suggestions at a timing that is appropriate to the job seeker's emotions.

[0116] The resume creation system can automatically update the content of a job seeker's resume based on their work history and skills. For example, if a job seeker acquires a new skill, that skill can be added to their resume. Similarly, if a job seeker gains new work experience, that experience can be reflected in their resume. Furthermore, if a job seeker changes their career goals, the resume can be updated to reflect those goals. This allows the resume creation system to provide resumes that reflect the job seeker's most up-to-date information.

[0117] The job matching department can estimate the emotions of job seekers and adjust the frequency of job suggestions based on those estimates. For example, if a job seeker is feeling anxious, the frequency of job suggestions can be reduced to help them relax. Conversely, if a job seeker is relaxed, the frequency of job suggestions can be increased to provide more options. Furthermore, if a job seeker is feeling anxious, job suggestions can be made quickly to match the job seeker's pace. In this way, the job matching department can provide a frequency of job suggestions that is appropriate to the emotions of job seekers.

[0118] The interview practice department can customize interview questions based on the results of mock interviews with job seekers. For example, if a job seeker has difficulty with a particular question, they can practice that question repeatedly. Furthermore, more advanced questions can be added for questions the job seeker excels at. In addition, related questions can be added based on the job seeker's answers. This allows the interview practice department to provide questions tailored to the job seeker's level and abilities.

[0119] The reception desk can estimate the applicant's emotions and adjust the design of the input interface based on those estimates. For example, if the applicant is nervous, a simple and highly visible interface can be provided. If the applicant is relaxed, an interface that allows for detailed information input can be provided. Furthermore, if the applicant is anxious, an interface that allows for quick input can be provided. In this way, the reception desk can provide an input interface that is tailored to the applicant's emotions.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The reception desk inputs the job seeker's skills and experience. The reception desk provides an interface for job seekers to input their skills and experience, and can save the entered information in a database. Step 2: The Job Aptitude Assessment Department analyzes the information entered by the reception department and proposes the most suitable job. The Job Aptitude Assessment Department uses AI to analyze the job seeker's skills and experience, and identifies the most suitable job by comparing the job seeker's skill set with industry demand. Step 3: The Resume Creation Department creates resumes and work history documents based on the job categories suggested by the Job Aptitude Assessment Department. The Resume Creation Department uses AI to automatically create effective resume and work history document formats based on the job seeker's experience and skills, creating formats that highlight the job seeker's strengths and achievements. Step 4: The Job Matching Department proposes job openings based on the resumes and work histories created by the Resume Creation Department. The Job Matching Department uses AI to propose job openings based on the job seeker's desired conditions and career goals, and identifies the most suitable job by comparing the job seeker's skill set with the job information. Step 5: The interview practice department conducts mock interviews based on job postings suggested by the job matching department. The interview practice department uses AI to conduct mock interviews with job seekers, analyzes their responses, provides feedback, evaluates the content and expression of the job seekers' answers, and points out areas for improvement.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] Each of the multiple elements described above, including the reception unit, job suitability assessment unit, resume creation unit, job matching unit, and interview practice unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14 and provides an interface for job seekers to input their skills and experience. The job suitability assessment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the job seeker's skills and experience and propose the most suitable job. The resume creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates an effective resume and work history format based on the job seeker's career history and skills. The job matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes job openings based on the job seeker's desired conditions and career goals. The interview practice unit is implemented by, for example, the control unit 46A of the smart device 14 and conducts a mock interview with the job seeker, analyzes the answers, and provides feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.).

[0138] 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.

[0139] 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.

[0140] 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.

[0141] Each of the multiple elements described above, including the reception unit, job suitability assessment unit, resume creation unit, job matching unit, and interview practice unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214 and provides an interface for job seekers to input their skills and experience. The job suitability assessment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the job seeker's skills and experience and propose the most suitable job. The resume creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates an effective resume and work history format based on the job seeker's career history and skills. The job matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes job openings based on the job seeker's desired conditions and career goals. The interview practice unit is implemented by, for example, the control unit 46A of the smart glasses 214 and conducts a mock interview with the job seeker, analyzes the answers, and provides feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] 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.

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

[0145] The 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.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

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

[0149] Figure 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.

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

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

[0152] In the 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.

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

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

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

[0156] The data processing system 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.

[0157] Each of the multiple elements described above, including the reception unit, job suitability assessment unit, resume creation unit, job matching unit, and interview practice unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314 and provides an interface for job seekers to input their skills and experience. The job suitability assessment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the job seeker's skills and experience and propose the most suitable job. The resume creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates an effective resume and work history format based on the job seeker's career history and skills. The job matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes job openings based on the job seeker's desired conditions and career goals. The interview practice unit is implemented by, for example, the control unit 46A of the headset terminal 314 and conducts mock interviews with job seekers, analyzes their answers, and provides feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] 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.

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

[0161] The 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.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

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

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

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.).

[0171] 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.

[0172] 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.

[0173] 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.

[0174] Each of the multiple elements described above, including the reception unit, job suitability assessment unit, resume creation unit, job matching unit, and interview practice unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414 and provides an interface for job seekers to input their skills and experience. The job suitability assessment unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and uses AI to analyze the job seeker's skills and experience and propose the most suitable job. The resume creation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and automatically creates an effective resume and work history format based on the job seeker's career history and skills. The job matching unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and proposes job openings based on the job seeker's desired conditions and career goals. The interview practice unit is implemented by, for example, the control unit 46A of the robot 414 and conducts a mock interview with the job seeker, analyzes the answers, and provides feedback. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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."

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] (Note 1) The reception desk enters the job seeker's skills and experience, The job aptitude assessment department analyzes the information entered by the reception department and proposes the most suitable job type, A resume creation department that creates resumes and work history documents based on the job categories proposed by the aforementioned job aptitude assessment department, The Job Matching Department proposes job openings based on resumes and work history documents created by the aforementioned Resume Creation Department, The facility comprises an interview practice unit that conducts mock interviews based on job postings proposed by the aforementioned job matching unit. A system characterized by the following features. (Note 2) The aforementioned job suitability assessment department, We analyze job seekers' skills and experience and suggest the most suitable job for them. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned resume creation unit, Automatically creates effective resume and CV formats based on job seekers' experience and skills. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned job matching department, We propose job openings based on the job seeker's desired conditions and career goals. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned interview practice department The system uses AI to conduct mock interviews, analyzes job seekers' responses, and provides feedback. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the job seeker's emotions and adjusts the timing of skill and experience input based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the job seeker's past work history and select the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When entering skills and experience, the system filters 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 9) The aforementioned reception unit is The system estimates the emotions of job seekers and determines the priority of the information to be entered based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When entering skills and experience, the system prioritizes inputting highly relevant information, taking into account the job seeker's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When entering skills and experience, the system analyzes the job seeker's social media activity and inputs relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned job suitability assessment department, The system estimates the emotions of job seekers and adjusts the way job suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned job suitability assessment department, During the job suitability assessment, the level of detail in the suggestions is adjusted based on the importance of the job seeker's skills. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned job suitability assessment department, During job aptitude assessments, different diagnostic algorithms are applied depending on the job category of the job seeker. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned job suitability assessment department, The system estimates the job seeker's emotions and adjusts the length of job suggestions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned job suitability assessment department, During the job suitability assessment, the priority of proposals is determined based on when the job seeker submitted their work history. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned job suitability assessment department, During the job suitability assessment, the order of suggestions will be adjusted based on the applicant's relevance. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned resume creation unit, We estimate the emotions of job seekers and adjust the wording of their resumes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned resume creation unit, When creating a resume, adjust the level of detail based on the importance of the applicant's work history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned resume creation unit, When creating a resume, apply different resume formats depending on the job category of the applicant. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned resume creation unit, The system estimates the applicant's emotions and adjusts the length of the resume based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned resume creation unit, When creating a resume, prioritize the resume based on when the applicant submitted their work history. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned resume creation unit, When creating a resume, adjust the order of the resume based on the relevance of the job applicant. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned job matching department, The system estimates the emotions of job seekers and adjusts the way job offers are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned job matching department, When matching job seekers with suitable positions, the level of detail in the suggestions is adjusted based on the importance of the job seeker's desired conditions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned job matching department, When matching job seekers with positions, different matching algorithms are applied depending on the job category of the job seeker. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned job matching department, The system estimates the job seeker's emotions and adjusts the length of the job offer based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned job matching department, When matching job seekers with suitable positions, the priority of proposals is determined based on when the job seekers submitted their desired conditions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned job matching department, When matching job seekers with positions, the order of suggestions is adjusted based on their relevance. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned interview practice department We estimate the emotions of job seekers and adjust the expression of the mock interview based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned interview practice department During interview practice, adjust the level of detail in the feedback based on the importance of the applicant's answers. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned interview practice department During interview practice, different interview algorithms are applied depending on the job seeker's job category. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned interview practice department The system estimates the applicant's emotions and adjusts the length of the mock interview based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned interview practice department During interview practice, prioritize feedback based on when the applicant submits their answers. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned interview practice department During interview practice, adjust the order of feedback based on the applicant's relevance. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk enters the job seeker's skills and experience, The job aptitude assessment department analyzes the information entered by the reception department and proposes the most suitable job type, A resume creation department that creates resumes and work history documents based on the job categories proposed by the aforementioned job aptitude assessment department, The Job Matching Department proposes job openings based on resumes and work history documents created by the aforementioned Resume Creation Department, The facility comprises an interview practice unit that conducts mock interviews based on job postings proposed by the aforementioned job matching unit. A system characterized by the following features.

2. The aforementioned job suitability assessment department, We analyze job seekers' skills and experience and suggest the most suitable job for them. The system according to feature 1.

3. The aforementioned resume creation unit, Automatically creates effective resume and CV formats based on job seekers' experience and skills. The system according to feature 1.

4. The aforementioned job matching department, We propose job openings based on the job seeker's desired conditions and career goals. The system according to feature 1.

5. The aforementioned interview practice department The system uses AI to conduct mock interviews, analyzes job seekers' responses, and provides feedback. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the job seeker's emotions and adjusts the timing of skill and experience input based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the job seeker's past work history and select the optimal input method. The system according to feature 1.

8. The aforementioned reception unit is When entering skills and experience, the system filters based on the job seeker's current work situation and areas of interest. The system according to feature 1.

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