System

Abstract

An object of a system according to an embodiment is to provide individualized career advice and job offer information to a job change applicant.SOLUTION: A system includes a skill analysis part, an occupation proposal part, a resume generation part, an interview support part, and a network provision part. A skill analysis part analyzes the skill, experience and interest of the job change applicant. The occupation proposal unit proposes an optimum occupation or company based on the information analyzed by the skill analysis unit. The resume creation unit creates a resume based on the job or company suggested by the job suggestion unit. The interview support unit enhances interview skills based on the personal history generated by the personal history generation unit. The network providing unit provides a job changer network.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to a description of the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2022-180282 Summary of the Invention [Problem to be solved by the invention]

[0004] Conventional technologies do not adequately provide job seekers with personalized career advice and job information, and there is room for improvement.

[0005] The system according to the embodiment aims to provide job seekers with personalized career advice and job information. [Means for solving the problem]

[0006] The system according to the embodiment includes a skill analysis unit, a job type suggestion unit, a resume generation unit, an interview support unit, and a network provision unit. The skill analysis unit analyzes the skills, experience, and interests of job seekers. The job type suggestion unit suggests the most suitable job type and company based on the information analyzed by the skill analysis unit. The resume generation unit generates a resume based on the job type and company suggested by the job type suggestion unit. The interview support unit improves interview skills based on the resume generated by the resume generation unit. The network provision unit provides a network of job seekers. [Effects of the Invention]

[0007] The system according to the embodiment can provide job seekers with personalized career advice and job information. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a conceptual diagram showing an example of the configuration of a data processing system according to a first embodiment. [Figure 2] 1 is a conceptual diagram showing an example of main functions of a data processing device and a smart device according to a first embodiment. [Figure 3] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a second embodiment. [Figure 4] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and smart glasses according to a second embodiment. [Figure 5] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a third embodiment. [Figure 6] FIG. 11 is a conceptual diagram showing an example of main functions of a data processing device and a headset-type terminal according to a third embodiment. [Figure 7] FIG. 10 is a conceptual diagram showing an example of the configuration of a data processing system according to a fourth embodiment. [Figure 8] FIG. 10 is a conceptual diagram showing an example of main functions of a data processing device and a robot according to a fourth embodiment. [Figure 9] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 10] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE INVENTION

[0009] An example of an embodiment of a system according to the technology of the present disclosure will be described below with reference to the accompanying drawings.

[0010] First, the terms used in the following description will be explained.

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

[0012] In the following embodiments, a coded RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by a processor.

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

[0014] In the following embodiments, a communication I / F (Interface) with a symbol is an interface including a communication processor, an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), and Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" is also applied when three or more things are expressed connected by "and / or."

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

[0017] 1, a 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN (Wide Area Network) and / or a LAN (Local Area Network).

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

[0020] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (for example, a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

[0021] Output device 40 includes a display 40A and a speaker 40B, and presents data to a user by outputting the data in a form of expression that the user can perceive (e.g., audio and / or text). Display 40A displays visible information such as text and images in accordance with instructions from processor 46. Speaker 40B outputs audio in accordance with instructions from processor 46. Camera 42 is a compact digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 control the exchange of various information between the processor 46 and the processor 28 via the network 54.

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

[0024] 2, in the data processing device 12, a specific process 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" according to the technology of the present 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 process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0025] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0026] In the smart device 14, the specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used together 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart device 14 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0027] Note that a device other than the data processing device 12 may 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 a processing result (prediction result, etc.) using the data generation model 58 by communicating with the server device having the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device owned by a user (e.g., a mobile phone, a robot, a home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example 1) A career advice platform according to an embodiment of the present invention is a system that analyzes the skills, experience, and interests of job seekers, uses a generation AI to suggest the most suitable jobs and companies, supports resume creation, improves interview skills, and provides a network of job seekers. As a result, the career advice platform can provide job seekers with customized career advice and job information, helping them succeed in changing jobs.

[0029] A career advice platform according to an embodiment includes a skill analysis unit, a job type suggestion unit, a resume generation unit, an interview support unit, and a network provision unit. The skill analysis unit analyzes the skills, experience, and interests of a job seeker. For example, the skill analysis unit analyzes the job seeker's work history, educational background, and skill set to identify the job seeker's strengths and weaknesses. The skill analysis unit can also analyze the job seeker's interests and areas of interest and suggest career directions. The job type suggestion unit suggests optimal job types and companies based on the information analyzed by the skill analysis unit. For example, the job type suggestion unit lists and suggests job types and companies that match the job seeker's skill set. The job type suggestion unit can also suggest companies suitable for the job seeker based on the company's culture and working style. The resume generation unit generates a resume based on the job types and companies suggested by the job type suggestion unit. For example, the resume generation unit analyzes the job seeker's past work history and generates a resume that highlights the job seeker's most important achievements. The resume generation unit can also generate a resume that reflects future objectives based on the career goals of the job seeker. The interview support unit improves interview skills based on the resume generated by the resume generation unit. For example, the interview support unit conducts mock interviews with the job seeker and provides feedback. The interview support unit can also analyze the job seeker's non-verbal communication skills and provide feedback to identify areas for improvement. The network provision unit provides a network of job seekers. For example, the network provision unit provides introductions and interview articles of successful job seekers. The network provision unit can also provide a mentoring program between members of the job seeker network. As a result, the career advice platform according to the embodiment can provide customized career advice and job information to job seekers and support them in successfully changing jobs.

[0030] The skill analysis unit can incorporate an algorithm that analyzes the job seeker's past career path and predicts their future career path. For example, the skill analysis unit analyzes the job seeker's past work history to identify career path patterns. For example, it predicts their future career path based on past job content and the timing of promotions. The skill analysis unit also analyzes the job seeker's past career path and predicts their future career path by referring to data on other job seekers with similar career paths. For example, it makes predictions based on success stories of job seekers with career paths in the same industry. The skill analysis unit also analyzes the job seeker's past career path and builds a machine learning model for predicting their future career path. For example, it predicts future job roles and promotion possibilities based on past data. This makes it possible to predict the job seeker's future career path and provide more appropriate career advice.

[0031] The job type suggestion unit can provide career advice that takes into consideration the lifestyle and values ​​of job seekers. For example, the job type suggestion unit conducts a survey on the lifestyle and values ​​of job seekers and provides career advice based on the results. For example, for job seekers who prioritize work-life balance, the job type suggestion unit suggests jobs that allow remote work. The job type suggestion unit also analyzes the lifestyle and values ​​of job seekers and builds a system that provides career advice based on that. For example, if a job seeker is interested in environmental protection, the job type suggestion unit suggests environmentally related companies. The job type suggestion unit also builds a database for providing career advice that takes into consideration the lifestyle and values ​​of job seekers. For example, the job type suggestion unit creates and suggests a list of companies that match the values ​​of job seekers. This makes it possible to provide career advice based on the lifestyle and values ​​of job seekers.

[0032] The resume generation unit can analyze the job seeker's past work history and generate a resume that emphasizes their most important achievements. The resume generation unit, for example, analyzes the job seeker's past work history and generates a resume that emphasizes their most important achievements. For example, it emphasizes episodes that contributed to the success of a project or improved business performance. The resume generation unit also builds a system that analyzes the job seeker's past work history and generates a resume that emphasizes their most important achievements. For example, it automatically extracts particularly important achievements from the work history and reflects them in the resume. The resume generation unit also analyzes the job seeker's past work history and develops an algorithm for emphasizing their most important achievements. For example, it scores particularly important achievements from the work history and reflects them in the resume. This makes it possible to generate a resume that emphasizes the job seeker's most important achievements.

[0033] The resume generation unit can generate a resume that reflects future goals based on the career goals of a job seeker. For example, the resume generation unit may hold a detailed interview about the career goals of the job seeker and generate a resume that reflects future goals based on that information. For example, the resume may include goals for five or ten years from now. The resume generation unit also builds a system that generates a resume that reflects future goals based on the career goals of the job seeker. For example, it may reflect job content and skills that match the career goals in the resume. The resume generation unit also analyzes the career goals of the job seeker and, based on the results, develops an algorithm for generating a resume that reflects future goals. For example, it may reflect optimal job content and skills in the resume based on the career goals. This makes it possible to generate a resume that reflects the future goals of the job seeker.

[0034] The interview support unit can analyze the job seeker's past interview experiences and provide feedback that identifies areas for improvement. The interview support unit, for example, analyzes the job seeker's past interview experiences and provides feedback that identifies areas for improvement. For example, it points out areas for improvement based on the answers given in past interviews and the interviewer's evaluations. The interview support unit also builds a system that analyzes the job seeker's past interview experiences and provides feedback that identifies areas for improvement. For example, it analyzes recorded interview data and suggests specific areas for improvement. The interview support unit also develops an algorithm for analyzing the job seeker's past interview experiences and identifying areas for improvement. For example, it scores the answers given in interviews and the interviewer's evaluations and identifies areas for improvement. This makes it possible to analyze the job seeker's past interview experiences and provide specific areas for improvement.

[0035] The interview support unit can analyze the nonverbal communication skills of job seekers and provide feedback that identifies areas for improvement. The interview support unit, for example, analyzes the nonverbal communication skills of job seekers and provides feedback that identifies areas for improvement. For example, it analyzes body language and facial expressions to point out specific areas for improvement. The interview support unit also builds a system that analyzes the nonverbal communication skills of job seekers and provides feedback that identifies areas for improvement. For example, it analyzes video recording data of interviews to suggest areas for improvement in nonverbal communication. The interview support unit also develops an algorithm for analyzing the nonverbal communication skills of job seekers and identifying areas for improvement. For example, it scores body language and facial expressions to identify areas for improvement. This makes it possible to analyze the nonverbal communication skills of job seekers and provide specific areas for improvement.

[0036] The network providing unit can analyze success cases within the network of job changers and identify common success factors. The network providing unit, for example, analyzes success cases within the network of job changers and identifies common success factors. For example, it analyzes the career paths and skill sets of successful job changers to extract common success factors. The network providing unit also analyzes success cases within the network of job changers and builds a system to identify common success factors. For example, it creates a database of success cases and develops an algorithm to identify common success factors. The network providing unit also analyzes success cases within the network of job changers and builds a database for identifying common success factors. For example, it collects detailed data on success cases and identifies common success factors. This makes it possible to analyze success cases within the network of job changers and identify common success factors.

[0037] The network providing unit can provide a mentoring program between members within the network of job changers. The network providing unit, for example, provides a mentoring program between members within the network of job changers. For example, successful job changers mentor new job seekers. The network providing unit also builds a system for providing a mentoring program between members within the network of job changers. For example, it develops an algorithm for matching mentors and mentees. The network providing unit also develops a platform for providing a mentoring program between members within the network of job changers. For example, it implements a function for providing online mentoring sessions. This makes it possible to provide a mentoring program between members within the network of job changers.

[0038] The network providing unit can provide a real-time chat function within the job changer network. The network providing unit, for example, provides a real-time chat function within the job changer network. For example, job seekers can exchange information and consult with each other in real time. The network providing unit also builds a system that provides a real-time chat function within the job changer network. For example, it creates a chat room, allowing members to communicate with each other in real time. The network providing unit also develops a platform for providing a real-time chat function within the job changer network. For example, it implements a chat function, allowing members to exchange information with each other in real time. This makes it possible to provide a real-time chat function within the job changer network.

[0039] The network providing department can regularly hold online events and webinars within the job changer network. The network providing department, for example, regularly holds online events and webinars within the job changer network. For example, it holds lectures by successful job changers and sessions offering career advice. The network providing department also builds a system for regularly holding online events and webinars within the job changer network. For example, it provides a function for managing event schedules and registering participants. The network providing department also develops a platform for regularly holding online events and webinars within the job changer network. For example, it provides a function for live streaming and archiving recorded data. This makes it possible to regularly hold online events and webinars within the job changer network.

[0040] The skill analysis unit can analyze the social media activity of job seekers and collect information that complements their skills and interests. For example, the skill analysis unit analyzes the social media activity of job seekers and collects information about their skills and interests. For example, it analyzes LinkedIn profiles and post content to identify skills and interests. The skill analysis unit also builds a system that analyzes the social media activity of job seekers and collects information that complements their skills and interests. For example, it analyzes Twitter follow lists and tweet content to identify interests. The skill analysis unit also analyzes the social media activity of job seekers and builds a database for complementing their skills and interests. For example, it analyzes Facebook group participation status and post content to identify skills and interests. In this way, it is possible to analyze the social media activity of job seekers and collect information that complements their skills and interests.

[0041] The skill analysis unit can analyze the learning history and qualification acquisition history of job seekers to identify skill growth patterns. The skill analysis unit, for example, analyzes the learning history and qualification acquisition history of job seekers to identify skill growth patterns. For example, skill growth is evaluated based on online course attendance history and acquired qualifications. The skill analysis unit also analyzes the learning history and qualification acquisition history of job seekers to build a system to identify skill growth patterns. For example, it analyzes data from a learning platform to evaluate skill growth. The skill analysis unit also analyzes the learning history and qualification acquisition history of job seekers to build a database for identifying skill growth patterns. For example, it creates a list of acquired qualifications and courses taken and evaluates skill growth. In this way, the learning history and qualification acquisition history of job seekers can be analyzed to identify skill growth patterns.

[0042] The job type suggestion unit can suggest long-term career paths based on the career goals of job seekers. For example, the job type suggestion unit may listen in detail to the career goals of job seekers and suggest long-term career paths based on that information. For example, the job type suggestion unit may set goals for five or ten years from now and suggest job types and companies that are aligned with those goals. The job type suggestion unit may also build a system that suggests long-term career paths based on the career goals of job seekers. For example, it may create and suggest a list of job types and companies that match the career goals. The job type suggestion unit may also analyze the career goals of job seekers and develop an algorithm for suggesting long-term career paths based on the results. For example, it may predict the most suitable job types and companies based on the career goals. This makes it possible to suggest long-term career paths based on the career goals of job seekers.

[0043] The job type proposal department can evaluate a company's culture fit based on the cultural background and values ​​of job seekers. For example, the job type proposal department collects information about the job seeker's cultural background and values ​​and evaluates a company's culture fit based on that information. For example, it compares the company's mission and vision with the job seeker's values. The job type proposal department also analyzes the job seeker's cultural background and values ​​and builds a system to evaluate a company's culture fit based on that information. For example, it evaluates a company's corporate culture and working style and suggests companies that suit the job seeker. The job type proposal department also builds a database for evaluating a company's culture fit that takes into account the job seeker's cultural background and values. For example, it collects and evaluates information about a company's culture and values. This makes it possible to evaluate a company's culture fit based on the job seeker's cultural background and values.

[0044] The job type suggestion unit can suggest job types and companies that take into account the geographical constraints of the job seeker. For example, the job type suggestion unit collects information about the geographical constraints of the job seeker and suggests job types and companies based on that information. For example, it suggests job types that allow remote work, taking into account constraints such as commuting time and work location. The job type suggestion unit also analyzes the geographical constraints of the job seeker and builds a system that suggests job types and companies based on that analysis. For example, it suggests companies that are close to the job seeker's place of residence. The job type suggestion unit also builds a database for suggesting job types and companies that take into account the geographical constraints of the job seeker. For example, it collects and suggests information about work location and commuting time. This makes it possible to suggest job types and companies that take into account the geographical constraints of the job seeker.

[0045] The job type proposal unit can propose job types and companies based on the job change candidate's desire for work-life balance. For example, the job type proposal unit listens to the job change candidate's desire for work-life balance and proposes job types and companies based on that. For example, it proposes companies that offer flextime or remote work. The job type proposal unit also analyzes the job change candidate's desire for work-life balance and builds a system that proposes job types and companies based on that. For example, it creates and proposes a list of companies that emphasize work-life balance. The job type proposal unit also builds a database for proposing job types and companies that take into account the job change candidate's desire for work-life balance. For example, it collects and proposes information on company working styles and employee benefits. This makes it possible to propose job types and companies based on the job change candidate's desire for work-life balance.

[0046] The resume generation unit can generate a resume that includes a portfolio and project samples of the job seeker. The resume generation unit, for example, collects portfolios and project samples of the job seeker and generates a resume that includes them. For example, design or programming samples are attached to the resume. The resume generation unit also analyzes the portfolios and project samples of the job seeker and builds a system that generates a resume that includes them. For example, it automatically extracts particularly important samples from the portfolio and reflects them in the resume. The resume generation unit also builds a database for generating a resume that includes a portfolio and project samples of the job seeker. For example, it creates a list of portfolios and project samples and reflects them in the resume. In this way, a resume that includes a portfolio and project samples of the job seeker can be generated.

[0047] The resume generation unit can generate a resume that includes recommendation letters and evaluations for the job seeker. The resume generation unit, for example, collects recommendation letters and evaluations for the job seeker and generates a resume that includes them. For example, recommendation letters from past superiors or colleagues are attached to the resume. The resume generation unit also analyzes the recommendation letters and evaluations for the job seeker and builds a system that generates a resume that includes them. For example, it automatically extracts particularly important parts from the recommendation letters and evaluations and reflects them in the resume. The resume generation unit also builds a database for generating a resume that includes recommendation letters and evaluations for the job seeker. For example, it creates a list of recommendation letters and evaluations and reflects them in the resume. In this way, a resume that includes recommendation letters and evaluations for the job seeker can be generated.

[0048] The interview support unit can record mock interviews for job seekers and enable them to perform self-evaluations later. The interview support unit, for example, records mock interviews for job seekers and enables them to perform self-evaluations later on the recorded data. For example, the job seeker can review their answers and attitudes while playing back the recorded interview. The interview support unit also analyzes the recorded data of the mock interviews and builds a system that provides feedback for self-evaluation. For example, it suggests specific areas for improvement based on the recorded data. The interview support unit also records mock interviews for job seekers and develops an algorithm to utilize the recorded data for self-evaluation. For example, it scores the recorded data and suggests key points for self-evaluation. In this way, the job seeker can record mock interviews and enable them to perform self-evaluations later on.

[0049] The interview support department can provide group practice sessions to improve the interview skills of job seekers. The interview support department provides, for example, group practice sessions to improve the interview skills of job seekers. For example, a plurality of job seekers gather together to conduct mock interviews and share feedback. The interview support department also builds a system to improve the interview skills of job seekers through group practice sessions. For example, online group practice sessions are provided, and participants give each other feedback. The interview support department also develops a platform for providing group practice sessions to improve the interview skills of job seekers. For example, a function is provided for participants to conduct mock interviews and share feedback. This makes it possible to provide group practice sessions to improve the interview skills of job seekers.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The career advice platform can further include a health management unit. The health management unit analyzes the health status of job seekers and provides advice on maintaining a healthy lifestyle. For example, the health management unit analyzes the job seekers' dietary and exercise habits and suggests areas for improvement. The health management unit can also provide relaxation methods for stress management and mental health support. Furthermore, the health management unit can monitor the job seekers' health status and conduct regular health checks. This allows job seekers to aim for career success while maintaining a healthy lifestyle.

[0052] The career advice platform can further include a hobby analysis unit. The hobby analysis unit analyzes the hobbies and leisure activities of job seekers and provides career advice based on the results. For example, the hobby analysis unit suggests job types and companies related to the job seeker's hobbies. The hobby analysis unit can also suggest ways to utilize the skills and experience gained through hobbies in one's career. Furthermore, the hobby analysis unit can introduce networking events and communities related to the job seeker's hobbies. This allows job seekers to aim for career success while making use of their hobbies.

[0053] The career advice platform can also include an education support department. The education support department supports job seekers in upskilling and reskilling. For example, the education support department can introduce job seekers to online courses and certification programs. The education support department can also identify job seekers' skill gaps and provide them with learning plans to fill them. The education support department can also provide advice on study methods and time management so that job seekers can make the most of their learning outcomes. This allows job seekers to aim for career success while improving their skills.

[0054] The career advice platform may further include a volunteer activity support department. The volunteer activity support department supports job seekers in gaining skills and experience through volunteer activities. For example, the volunteer activity support department may introduce suitable volunteer activities to job seekers. The volunteer activity support department may also suggest ways to utilize the skills and experience gained through volunteer activities in one's career. Furthermore, the volunteer activity support department may provide job seekers with opportunities to network through volunteer activities. This allows job seekers to aim for career success while gaining skills and experience through volunteer activities.

[0055] The career advice platform may further include an international career support department, which provides support to job seekers to build international careers. For example, the international career support department may guide job seekers to overseas job information and visa acquisition procedures. The international career support department may also provide training and support to help job seekers adapt to different cultures. Furthermore, the international career support department may provide job seekers with opportunities to network overseas. This allows job seekers to aim for career success while building an international career.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The Skills Analysis Unit analyzes the job seeker's skills, experience, and interests. For example, the Skills Analysis Unit analyzes the job seeker's work history, educational background, and skill set to identify their strengths and weaknesses. The Skills Analysis Unit can also analyze the job seeker's interests and areas of concern and suggest career directions. Step 2: The job suggestion unit suggests the most suitable job types and companies based on the information analyzed by the skill analysis unit. For example, the job suggestion unit lists and suggests job types and companies that match the job seeker's skill set. The job suggestion unit can also suggest companies that are suitable for job seekers based on the company's culture and working style. Step 3: The resume generator generates a resume based on the job types and companies suggested by the job type suggester. For example, the resume generator analyzes the job seeker's past work history and generates a resume that highlights the job seeker's most important achievements. The resume generator can also generate a resume that reflects the job seeker's future goals based on the job seeker's career goals. Step 4: The interview support unit improves interview skills based on the resume generated by the resume generation unit. For example, the interview support unit conducts mock interviews with job seekers and provides feedback. The interview support unit can also analyze the job seeker's non-verbal communication skills and provide feedback to identify areas for improvement. Step 5: The network provider provides a network of job-changers. For example, the network provider may provide introductions and interview articles of successful job-changers. The network provider may also provide a mentoring program among members of the network.

[0058] (Example 2) A career advice platform according to an embodiment of the present invention is a system that analyzes the skills, experience, and interests of job seekers, uses a generation AI to suggest the most suitable jobs and companies, supports resume creation, improves interview skills, and provides a network of job seekers. As a result, the career advice platform can provide job seekers with customized career advice and job information, helping them succeed in changing jobs.

[0059] A career advice platform according to an embodiment includes a skill analysis unit, a job type suggestion unit, a resume generation unit, an interview support unit, and a network provision unit. The skill analysis unit analyzes the skills, experience, and interests of a job seeker. For example, the skill analysis unit analyzes the job seeker's work history, educational background, and skill set to identify the job seeker's strengths and weaknesses. The skill analysis unit can also analyze the job seeker's interests and areas of interest and suggest career directions. The job type suggestion unit suggests optimal job types and companies based on the information analyzed by the skill analysis unit. For example, the job type suggestion unit lists and suggests job types and companies that match the job seeker's skill set. The job type suggestion unit can also suggest companies suitable for the job seeker based on the company's culture and working style. The resume generation unit generates a resume based on the job types and companies suggested by the job type suggestion unit. For example, the resume generation unit analyzes the job seeker's past work history and generates a resume that highlights the job seeker's most important achievements. The resume generation unit can also generate a resume that reflects future objectives based on the career goals of the job seeker. The interview support unit improves interview skills based on the resume generated by the resume generation unit. For example, the interview support unit conducts mock interviews with the job seeker and provides feedback. The interview support unit can also analyze the job seeker's non-verbal communication skills and provide feedback to identify areas for improvement. The network provision unit provides a network of job seekers. For example, the network provision unit provides introductions and interview articles of successful job seekers. The network provision unit can also provide a mentoring program between members of the job seeker network. As a result, the career advice platform according to the embodiment can provide customized career advice and job information to job seekers and support them in successfully changing jobs.

[0060] The skill analysis unit can incorporate an algorithm that analyzes the job seeker's past career path and predicts their future career path. For example, the skill analysis unit analyzes the job seeker's past work history to identify career path patterns. For example, it predicts their future career path based on past job content and the timing of promotions. The skill analysis unit also analyzes the job seeker's past career path and predicts their future career path by referring to data on other job seekers with similar career paths. For example, it makes predictions based on success stories of job seekers with career paths in the same industry. The skill analysis unit also analyzes the job seeker's past career path and builds a machine learning model for predicting their future career path. For example, it predicts future job roles and promotion possibilities based on past data. This makes it possible to predict the job seeker's future career path and provide more appropriate career advice.

[0061] The job type suggestion unit can provide career advice that takes into consideration the lifestyle and values ​​of job seekers. For example, the job type suggestion unit conducts a survey on the lifestyle and values ​​of job seekers and provides career advice based on the results. For example, for job seekers who prioritize work-life balance, the job type suggestion unit suggests jobs that allow remote work. The job type suggestion unit also analyzes the lifestyle and values ​​of job seekers and builds a system that provides career advice based on that. For example, if a job seeker is interested in environmental protection, the job type suggestion unit suggests environmentally related companies. The job type suggestion unit also builds a database for providing career advice that takes into consideration the lifestyle and values ​​of job seekers. For example, the job type suggestion unit creates and suggests a list of companies that match the values ​​of job seekers. This makes it possible to provide career advice based on the lifestyle and values ​​of job seekers.

[0062] The job type suggestion unit can provide career advice according to the emotional state of the job seeker. For example, the job type suggestion unit analyzes the emotional state of the job seeker in real time and provides career advice based on the results. For example, if stress is high, it suggests a job with less stress. The job type suggestion unit also uses an emotion estimation function to build a system that provides career advice according to the emotional state of the job seeker. For example, if positive emotions are strong, it suggests a challenging job. The job type suggestion unit also analyzes the emotional state of the job seeker and develops an algorithm for providing career advice based on the results. For example, it suggests the most suitable job based on the emotion score. This makes it possible to provide career advice based on the emotional state of the job seeker.

[0063] The resume generation unit can analyze the job seeker's past work history and generate a resume that emphasizes their most important achievements. The resume generation unit, for example, analyzes the job seeker's past work history and generates a resume that emphasizes their most important achievements. For example, it emphasizes episodes that contributed to the success of a project or improved business performance. The resume generation unit also builds a system that analyzes the job seeker's past work history and generates a resume that emphasizes their most important achievements. For example, it automatically extracts particularly important achievements from the work history and reflects them in the resume. The resume generation unit also analyzes the job seeker's past work history and develops an algorithm for emphasizing their most important achievements. For example, it scores particularly important achievements from the work history and reflects them in the resume. This makes it possible to generate a resume that emphasizes the job seeker's most important achievements.

[0064] The resume generation unit can generate a resume that reflects future goals based on the career goals of a job seeker. For example, the resume generation unit may hold a detailed interview about the career goals of the job seeker and generate a resume that reflects future goals based on that information. For example, the resume may include goals for five or ten years from now. The resume generation unit also builds a system that generates a resume that reflects future goals based on the career goals of the job seeker. For example, it may reflect job content and skills that match the career goals in the resume. The resume generation unit also analyzes the career goals of the job seeker and, based on the results, develops an algorithm for generating a resume that reflects future goals. For example, it may reflect optimal job content and skills in the resume based on the career goals. This makes it possible to generate a resume that reflects the future goals of the job seeker.

[0065] The resume generation unit can use the emotion estimation function to generate resume text based on the emotions of the job seeker. The resume generation unit, for example, analyzes the emotional state of the job seeker in real time and generates resume text based on the results. For example, if the emotion is strong, it generates text using positive expressions. The resume generation unit also uses the emotion estimation function to build a system that generates resume text based on the emotions of the job seeker. For example, if the emotion is negative, it generates text including encouraging words. The resume generation unit also analyzes the emotional state of the job seeker and develops an algorithm for generating resume text based on the results. For example, it generates text using optimal expressions based on the emotion score. This makes it possible to generate resume text based on the emotions of the job seeker.

[0066] The interview support unit can analyze the job seeker's past interview experiences and provide feedback that identifies areas for improvement. The interview support unit, for example, analyzes the job seeker's past interview experiences and provides feedback that identifies areas for improvement. For example, it points out areas for improvement based on the answers given in past interviews and the interviewer's evaluations. The interview support unit also builds a system that analyzes the job seeker's past interview experiences and provides feedback that identifies areas for improvement. For example, it analyzes recorded interview data and suggests specific areas for improvement. The interview support unit also develops an algorithm for analyzing the job seeker's past interview experiences and identifying areas for improvement. For example, it scores the answers given in interviews and the interviewer's evaluations and identifies areas for improvement. This makes it possible to analyze the job seeker's past interview experiences and provide specific areas for improvement.

[0067] The interview support unit can analyze the nonverbal communication skills of job seekers and provide feedback that identifies areas for improvement. The interview support unit, for example, analyzes the nonverbal communication skills of job seekers and provides feedback that identifies areas for improvement. For example, it analyzes body language and facial expressions to point out specific areas for improvement. The interview support unit also builds a system that analyzes the nonverbal communication skills of job seekers and provides feedback that identifies areas for improvement. For example, it analyzes video recording data of interviews to suggest areas for improvement in nonverbal communication. The interview support unit also develops an algorithm for analyzing the nonverbal communication skills of job seekers and identifying areas for improvement. For example, it scores body language and facial expressions to identify areas for improvement. This makes it possible to analyze the nonverbal communication skills of job seekers and provide specific areas for improvement.

[0068] The interview support unit can use the emotion estimation function to provide interview feedback based on the emotions of the job seeker. For example, the interview support unit analyzes the emotional state of the job seeker in real time and provides interview feedback based on the results. For example, if the job seeker is nervous, it suggests ways to relax. The interview support unit also uses the emotion estimation function to build a system that provides interview feedback based on the emotions of the job seeker. For example, if the job seeker has strong positive emotions, it provides advice on how to maintain those emotions. The interview support unit also analyzes the emotional state of the job seeker and develops an algorithm for providing interview feedback based on the results. For example, it provides optimal feedback based on the emotion score. This makes it possible to provide interview feedback based on the emotions of the job seeker.

[0069] The network providing unit can analyze success cases within the network of job changers and identify common success factors. The network providing unit, for example, analyzes success cases within the network of job changers and identifies common success factors. For example, it analyzes the career paths and skill sets of successful job changers to extract common success factors. The network providing unit also analyzes success cases within the network of job changers and builds a system to identify common success factors. For example, it creates a database of success cases and develops an algorithm to identify common success factors. The network providing unit also analyzes success cases within the network of job changers and builds a database for identifying common success factors. For example, it collects detailed data on success cases and identifies common success factors. This makes it possible to analyze success cases within the network of job changers and identify common success factors.

[0070] The network providing unit can provide a mentoring program between members within the network of job changers. The network providing unit, for example, provides a mentoring program between members within the network of job changers. For example, successful job changers mentor new job seekers. The network providing unit also builds a system for providing a mentoring program between members within the network of job changers. For example, it develops an algorithm for matching mentors and mentees. The network providing unit also develops a platform for providing a mentoring program between members within the network of job changers. For example, it implements a function for providing online mentoring sessions. This makes it possible to provide a mentoring program between members within the network of job changers.

[0071] The network providing unit can use the emotion estimation function to provide emotional support within the job changer network. For example, the network providing unit analyzes the emotional states of members within the job changer network in real time and provides emotional support based on the results. For example, it suggests ways to relax to members who are highly stressed. The network providing unit also uses the emotion estimation function to build a system for providing emotional support within the job changer network. For example, it provides advice to members with strong positive emotions on how to maintain those emotions. The network providing unit also analyzes the emotional states of members within the job changer network and develops an algorithm for providing emotional support based on the results. For example, it provides optimal support based on the emotion score. This makes it possible to provide emotional support within the job changer network.

[0072] The network providing unit can provide a real-time chat function within the job changer network. The network providing unit, for example, provides a real-time chat function within the job changer network. For example, job seekers can exchange information and consult with each other in real time. The network providing unit also builds a system that provides a real-time chat function within the job changer network. For example, it creates a chat room, allowing members to communicate with each other in real time. The network providing unit also develops a platform for providing a real-time chat function within the job changer network. For example, it implements a chat function, allowing members to exchange information with each other in real time. This makes it possible to provide a real-time chat function within the job changer network.

[0073] The network providing department can regularly hold online events and webinars within the job changer network. The network providing department, for example, regularly holds online events and webinars within the job changer network. For example, it holds lectures by successful job changers and sessions offering career advice. The network providing department also builds a system for regularly holding online events and webinars within the job changer network. For example, it provides a function for managing event schedules and registering participants. The network providing department also develops a platform for regularly holding online events and webinars within the job changer network. For example, it provides a function for live streaming and archiving recorded data. This makes it possible to regularly hold online events and webinars within the job changer network.

[0074] The network providing unit can use the emotion estimation function to provide emotional support within the job changer network in real time. For example, the network providing unit analyzes the emotional states of members within the job changer network in real time and provides emotional support based on the results. For example, it suggests ways to relax to members who are highly stressed. The network providing unit also uses the emotion estimation function to build a system that provides emotional support within the job changer network in real time. For example, it provides advice to members with strong positive emotions on how to maintain those emotions. The network providing unit also analyzes the emotional states of members within the job changer network and develops an algorithm for providing emotional support based on the results. For example, it provides optimal support based on the emotion score. This makes it possible to provide emotional support within the job changer network in real time.

[0075] The skill analysis unit can analyze the social media activity of job seekers and collect information that complements their skills and interests. For example, the skill analysis unit analyzes the social media activity of job seekers and collects information about their skills and interests. For example, it analyzes LinkedIn profiles and post content to identify skills and interests. The skill analysis unit also builds a system that analyzes the social media activity of job seekers and collects information that complements their skills and interests. For example, it analyzes Twitter follow lists and tweet content to identify interests. The skill analysis unit also analyzes the social media activity of job seekers and builds a database for complementing their skills and interests. For example, it analyzes Facebook group participation status and post content to identify skills and interests. In this way, it is possible to analyze the social media activity of job seekers and collect information that complements their skills and interests.

[0076] The skill analysis unit can analyze the learning history and qualification acquisition history of job seekers to identify skill growth patterns. The skill analysis unit, for example, analyzes the learning history and qualification acquisition history of job seekers to identify skill growth patterns. For example, skill growth is evaluated based on online course attendance history and acquired qualifications. The skill analysis unit also analyzes the learning history and qualification acquisition history of job seekers to build a system to identify skill growth patterns. For example, it analyzes data from a learning platform to evaluate skill growth. The skill analysis unit also analyzes the learning history and qualification acquisition history of job seekers to build a database for identifying skill growth patterns. For example, it creates a list of acquired qualifications and courses taken and evaluates skill growth. In this way, the learning history and qualification acquisition history of job seekers can be analyzed to identify skill growth patterns.

[0077] The skill analysis unit can use the emotion estimation function to analyze the emotional reactions of job seekers to their skills and experience. The skill analysis unit, for example, analyzes the emotional reactions of job seekers to their skills and experience, and evaluates their skills and experience based on the results. For example, it prioritizes evaluation of skills that evoke strong positive emotions. The skill analysis unit also uses the emotion estimation function to build a system that analyzes the emotional reactions of job seekers to their skills and experience. For example, it evaluates based on the emotion scores for the skills and experience. The skill analysis unit also analyzes the emotional reactions of job seekers to their skills and experience, and develops an algorithm for evaluating their skills and experience based on the results. For example, it evaluates their skills and experience based on the emotion scores. This makes it possible to analyze the emotional reactions of job seekers to their skills and experience.

[0078] The job type suggestion unit can suggest long-term career paths based on the career goals of job seekers. For example, the job type suggestion unit may listen in detail to the career goals of job seekers and suggest long-term career paths based on that information. For example, the job type suggestion unit may set goals for five or ten years from now and suggest job types and companies that are aligned with those goals. The job type suggestion unit may also build a system that suggests long-term career paths based on the career goals of job seekers. For example, it may create and suggest a list of job types and companies that match the career goals. The job type suggestion unit may also analyze the career goals of job seekers and develop an algorithm for suggesting long-term career paths based on the results. For example, it may predict the most suitable job types and companies based on the career goals. This makes it possible to suggest long-term career paths based on the career goals of job seekers.

[0079] The job type proposal department can evaluate a company's culture fit based on the cultural background and values ​​of job seekers. For example, the job type proposal department collects information about the job seeker's cultural background and values ​​and evaluates a company's culture fit based on that information. For example, it compares the company's mission and vision with the job seeker's values. The job type proposal department also analyzes the job seeker's cultural background and values ​​and builds a system to evaluate a company's culture fit based on that information. For example, it evaluates a company's corporate culture and working style and suggests companies that suit the job seeker. The job type proposal department also builds a database for evaluating a company's culture fit that takes into account the job seeker's cultural background and values. For example, it collects and evaluates information about a company's culture and values. This makes it possible to evaluate a company's culture fit based on the job seeker's cultural background and values.

[0080] The job type suggestion unit can use the emotion estimation function to suggest job types and companies based on the emotions of the job seeker. The job type suggestion unit, for example, analyzes the emotional state of the job seeker in real time and suggests job types and companies based on the results. For example, if the emotion is strong positive, it suggests challenging job types. The job type suggestion unit also uses the emotion estimation function to build a system that suggests job types and companies based on the emotions of the job seeker. For example, if the emotion is negative, it suggests job types with less stress. The job type suggestion unit also analyzes the emotional state of the job seeker and develops an algorithm for suggesting job types and companies based on the results. For example, it suggests optimal job types and companies based on the emotion score. This makes it possible to suggest job types and companies based on the emotions of the job seeker.

[0081] The job type suggestion unit can suggest job types and companies that take into account the geographical constraints of the job seeker. For example, the job type suggestion unit collects information about the geographical constraints of the job seeker and suggests job types and companies based on that information. For example, it suggests job types that allow remote work, taking into account constraints such as commuting time and work location. The job type suggestion unit also analyzes the geographical constraints of the job seeker and builds a system that suggests job types and companies based on that analysis. For example, it suggests companies that are close to the job seeker's place of residence. The job type suggestion unit also builds a database for suggesting job types and companies that take into account the geographical constraints of the job seeker. For example, it collects and suggests information about work location and commuting time. This makes it possible to suggest job types and companies that take into account the geographical constraints of the job seeker.

[0082] The job type proposal unit can propose job types and companies based on the job change candidate's desire for work-life balance. For example, the job type proposal unit listens to the job change candidate's desire for work-life balance and proposes job types and companies based on that. For example, it proposes companies that offer flextime or remote work. The job type proposal unit also analyzes the job change candidate's desire for work-life balance and builds a system that proposes job types and companies based on that. For example, it creates and proposes a list of companies that emphasize work-life balance. The job type proposal unit also builds a database for proposing job types and companies that take into account the job change candidate's desire for work-life balance. For example, it collects and proposes information on company working styles and employee benefits. This makes it possible to propose job types and companies based on the job change candidate's desire for work-life balance.

[0083] The job type suggestion unit can use the emotion estimation function to suggest job types and companies based on the emotions of the job seeker in real time. The job type suggestion unit, for example, analyzes the emotional state of the job seeker in real time and suggests job types and companies based on the results. For example, if the emotion is positive, it suggests challenging job types. The job type suggestion unit also uses the emotion estimation function to build a system that suggests job types and companies based on the emotions of the job seeker in real time. For example, if the emotion is negative, it suggests job types with less stress. The job type suggestion unit also analyzes the emotional state of the job seeker in real time and develops an algorithm for suggesting job types and companies based on the results. For example, it suggests optimal job types and companies based on the emotion score. This makes it possible to suggest job types and companies based on the emotions of the job seeker in real time.

[0084] The resume generation unit can generate a resume that includes a portfolio and project samples of the job seeker. The resume generation unit, for example, collects portfolios and project samples of the job seeker and generates a resume that includes them. For example, design or programming samples are attached to the resume. The resume generation unit also analyzes the portfolios and project samples of the job seeker and builds a system that generates a resume that includes them. For example, it automatically extracts particularly important samples from the portfolio and reflects them in the resume. The resume generation unit also builds a database for generating a resume that includes a portfolio and project samples of the job seeker. For example, it creates a list of portfolios and project samples and reflects them in the resume. In this way, a resume that includes a portfolio and project samples of the job seeker can be generated.

[0085] The resume generation unit can generate a resume that includes recommendation letters and evaluations for the job seeker. The resume generation unit, for example, collects recommendation letters and evaluations for the job seeker and generates a resume that includes them. For example, recommendation letters from past superiors or colleagues are attached to the resume. The resume generation unit also analyzes the recommendation letters and evaluations for the job seeker and builds a system that generates a resume that includes them. For example, it automatically extracts particularly important parts from the recommendation letters and evaluations and reflects them in the resume. The resume generation unit also builds a database for generating a resume that includes recommendation letters and evaluations for the job seeker. For example, it creates a list of recommendation letters and evaluations and reflects them in the resume. In this way, a resume that includes recommendation letters and evaluations for the job seeker can be generated.

[0086] The resume generation unit can use the emotion estimation function to generate resume text based on the emotions of the job seeker in real time. The resume generation unit, for example, analyzes the emotional state of the job seeker in real time and generates resume text based on the results. For example, if the emotion is strong, it generates text using positive expressions. The resume generation unit also uses the emotion estimation function to build a system that generates resume text based on the emotions of the job seeker in real time. For example, if the emotion is negative, it generates text including encouraging words. The resume generation unit also analyzes the emotional state of the job seeker and develops an algorithm for generating resume text in real time based on the results. For example, it generates text using optimal expressions based on the emotion score. This makes it possible to generate resume text based on the emotions of the job seeker in real time.

[0087] The interview support unit can record mock interviews for job seekers and enable them to perform self-evaluations later. The interview support unit, for example, records mock interviews for job seekers and enables them to perform self-evaluations later on the recorded data. For example, the job seeker can review their answers and attitudes while playing back the recorded interview. The interview support unit also analyzes the recorded data of the mock interviews and builds a system that provides feedback for self-evaluation. For example, it suggests specific areas for improvement based on the recorded data. The interview support unit also records mock interviews for job seekers and develops an algorithm to utilize the recorded data for self-evaluation. For example, it scores the recorded data and suggests key points for self-evaluation. In this way, the job seeker can record mock interviews and enable them to perform self-evaluations later on.

[0088] The interview support department can provide group practice sessions to improve the interview skills of job seekers. The interview support department provides, for example, group practice sessions to improve the interview skills of job seekers. For example, a plurality of job seekers gather together to conduct mock interviews and share feedback. The interview support department also builds a system to improve the interview skills of job seekers through group practice sessions. For example, online group practice sessions are provided, and participants give each other feedback. The interview support department also develops a platform for providing group practice sessions to improve the interview skills of job seekers. For example, a function is provided for participants to conduct mock interviews and share feedback. This makes it possible to provide group practice sessions to improve the interview skills of job seekers.

[0089] The interview support unit can use the emotion estimation function to provide interview feedback based on the emotions of the job seeker in real time. The interview support unit, for example, analyzes the emotional state of the job seeker in real time and provides interview feedback based on the results. For example, if the job seeker is nervous, it suggests ways to relax. The interview support unit also uses the emotion estimation function to build a system that provides interview feedback based on the emotions of the job seeker in real time. For example, if the job seeker has strong positive emotions, it provides advice on how to maintain those emotions. The interview support unit also analyzes the emotional state of the job seeker in real time and develops an algorithm for providing interview feedback based on the results. For example, it provides optimal feedback based on the emotion score. This makes it possible to provide interview feedback based on the emotions of the job seeker in real time.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The career advice platform can further include a health management unit. The health management unit analyzes the health status of job seekers and provides advice on maintaining a healthy lifestyle. For example, the health management unit analyzes the job seekers' dietary and exercise habits and suggests areas for improvement. The health management unit can also provide relaxation methods for stress management and mental health support. Furthermore, the health management unit can monitor the job seekers' health status and conduct regular health checks. This allows job seekers to aim for career success while maintaining a healthy lifestyle.

[0092] The career advice platform can further include a hobby analysis unit. The hobby analysis unit analyzes the hobbies and leisure activities of job seekers and provides career advice based on the results. For example, the hobby analysis unit suggests job types and companies related to the job seeker's hobbies. The hobby analysis unit can also suggest ways to utilize the skills and experience gained through hobbies in one's career. Furthermore, the hobby analysis unit can introduce networking events and communities related to the job seeker's hobbies. This allows job seekers to aim for career success while making use of their hobbies.

[0093] The career advice platform can also include an education support department. The education support department supports job seekers in upskilling and reskilling. For example, the education support department can introduce job seekers to online courses and certification programs. The education support department can also identify job seekers' skill gaps and provide them with learning plans to fill them. The education support department can also provide advice on study methods and time management so that job seekers can make the most of their learning outcomes. This allows job seekers to aim for career success while improving their skills.

[0094] The career advice platform may further include a volunteer activity support department. The volunteer activity support department supports job seekers in gaining skills and experience through volunteer activities. For example, the volunteer activity support department may introduce suitable volunteer activities to job seekers. The volunteer activity support department may also suggest ways to utilize the skills and experience gained through volunteer activities in one's career. Furthermore, the volunteer activity support department may provide job seekers with opportunities to network through volunteer activities. This allows job seekers to aim for career success while gaining skills and experience through volunteer activities.

[0095] The career advice platform may further include an international career support department, which provides support to job seekers to build international careers. For example, the international career support department may guide job seekers to overseas job information and visa acquisition procedures. The international career support department may also provide training and support to help job seekers adapt to different cultures. Furthermore, the international career support department may provide job seekers with opportunities to network overseas. This allows job seekers to aim for career success while building an international career.

[0096] The career advice platform can further use the emotion estimation function to provide stress management advice based on the job seeker's emotions. For example, the emotion estimation function can be used to analyze the job seeker's stress level in real time and suggest relaxation and stress relief methods based on the results. The emotion estimation function can also be used to provide mental health support according to the job seeker's emotional state. Furthermore, the emotion estimation function can be used to monitor the job seeker's emotional state and conduct regular emotion checks. This allows job seekers to aim for career success while receiving stress management advice based on their emotional state.

[0097] The career advice platform can further use the emotion estimation function to provide motivation improvement advice based on the emotions of job seekers. For example, the emotion estimation function can be used to analyze the motivation level of job seekers in real time and suggest ways to increase motivation based on the results. The emotion estimation function can also be used to provide encouraging messages and positive feedback according to the job seeker's emotional state. Furthermore, the emotion estimation function can be used to monitor the job seeker's emotional state and conduct regular motivation checks. This allows job seekers to aim for career success while receiving motivation improvement advice based on their emotional state.

[0098] The career advice platform can further use the emotion estimation function to provide career goal setting advice based on the job seeker's emotions. For example, the emotion estimation function can be used to analyze the job seeker's emotional state and, based on the results, suggest ways to set realistic and achievable career goals. The emotion estimation function can also be used to provide a step-by-step guide for achieving career goals according to the job seeker's emotional state. Furthermore, the emotion estimation function can be used to monitor the job seeker's emotional state and check their progress toward achieving their career goals. This allows job seekers to aim for career success while receiving career goal setting advice based on their emotional state.

[0099] The career advice platform can further use the emotion estimation function to provide networking advice based on the emotions of job seekers. For example, the emotion estimation function can be used to analyze the emotional state of job seekers and suggest networking events or communities to join based on the results. The emotion estimation function can also be used to provide networking methods and approaches that correspond to the job seeker's emotional state. Furthermore, the emotion estimation function can be used to monitor the job seeker's emotional state and check the progress of networking. This allows job seekers to aim for career success while receiving networking advice based on their emotional state.

[0100] The career advice platform can further use the emotion estimation function to provide feedback based on the emotions of job seekers. For example, the emotion estimation function can be used to analyze the emotional state of job seekers in real time and provide feedback based on the results. The emotion estimation function can also be used to provide positive feedback or constructive advice according to the emotional state of job seekers. The emotion estimation function can also be used to monitor the emotional state of job seekers and provide regular feedback. This allows job seekers to aim for career success while receiving feedback based on their emotional state.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The Skills Analysis Unit analyzes the job seeker's skills, experience, and interests. For example, the Skills Analysis Unit analyzes the job seeker's work history, educational background, and skill set to identify their strengths and weaknesses. The Skills Analysis Unit can also analyze the job seeker's interests and areas of concern and suggest career directions. Step 2: The job suggestion unit suggests the most suitable job types and companies based on the information analyzed by the skill analysis unit. For example, the job suggestion unit lists and suggests job types and companies that match the job seeker's skill set. The job suggestion unit can also suggest companies that are suitable for job seekers based on the company's culture and working style. Step 3: The resume generator generates a resume based on the job types and companies suggested by the job type suggester. For example, the resume generator analyzes the job seeker's past work history and generates a resume that highlights the job seeker's most important achievements. The resume generator can also generate a resume that reflects the job seeker's future goals based on the job seeker's career goals. Step 4: The interview support unit improves interview skills based on the resume generated by the resume generation unit. For example, the interview support unit conducts mock interviews with job seekers and provides feedback. The interview support unit can also analyze the job seeker's non-verbal communication skills and provide feedback to identify areas for improvement. Step 5: The network provider provides a network of job-changers. For example, the network provider may provide introductions and interview articles of successful job-changers. The network provider may also provide a mentoring program among members of the network.

[0103] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the 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.

[0104] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0105] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, 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.

[0106] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0107] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0108] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0109] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0110] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0111] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0112] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0113] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0116] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0117] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] 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 a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0119] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0122] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0123] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0124] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0125] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0127] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0128] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0129] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0130] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0131] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0132] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0133] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0135] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 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.

[0138] 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, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0139] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0140] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0141] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0142] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0143] The control 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 emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0144] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0145] 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0146] The storage 32 stores a data generation model 58 and an 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 a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0147] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. 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 the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0148] Note that a device other than the data processing device 12 may 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 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0149] 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 control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0150] The data generation model 58 is a so-called generative AI. An example of the 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 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0151] 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 executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0152] The emotion identification model 59 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 an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0153] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0154] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0155] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0156] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0157] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs 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 a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0158] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0159] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0160] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0161] 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.

[0162] It is not necessary to store all 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 all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0163] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0164] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with 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). Also, the hardware resource that executes the specific process may be a single processor.

[0165] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0166] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0167] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0168] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0169] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0170] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. Skills Analysis Department, which analyzes the skills, experience, and interests of job seekers; a job suggestion unit that suggests the most suitable job and company based on the information analyzed by the skill analysis unit; a resume generation unit that generates a resume based on the job type and company proposed by the job type proposal unit; an interview support unit that improves interview skills based on the resume generated by the resume generation unit; A network providing unit that provides a network for job-changers. A system characterized by:

2. The skill analysis unit Introducing an algorithm that analyzes job seekers' past career paths and predicts their future career paths 2. The system of claim 1.

3. The job type proposal unit Providing career advice that takes into account the lifestyles and values ​​of job seekers 2. The system of claim 1.

4. The job type proposal unit Providing career advice tailored to job seekers' emotional state 2. The system of claim 1.

5. The resume generation unit Analyze job applicants' past work history and generate resumes that highlight their most important achievements 2. The system of claim 1.

6. The resume generation unit Generate a resume that reflects future goals based on job seekers' career goals 2. The system of claim 1.

Images