system

Abstract

The system according to this embodiment aims to efficiently match job postings with the skills and desired conditions of job seekers. [Solution] The system according to this embodiment comprises a reception unit, an analysis unit, a job seeker reception unit, and a proposal unit. The reception unit registers job information. The analysis unit analyzes the job information registered by the reception unit. The job seeker reception unit receives information about job seekers. The proposal unit proposes the most suitable job to the job seeker based on the information analyzed by the analysis unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, it is difficult to efficiently match job offer information with the skills and desired conditions of job seekers, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently match job offer information with the skills and desired conditions of job seekers.

Means for Solving the Problems

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a job seeker reception unit, and a proposal unit. The reception unit registers job offer information. The analysis unit analyzes the job offer information registered by the reception unit. The job seeker reception unit receives information of job seekers. The proposal unit proposes the most suitable job for the job seeker based on the information analyzed by the analysis unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently match job postings with the skills and desired conditions of job seekers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The job matching system according to an embodiment of the present invention is a system that utilizes AI to differentiate job postings into business tasks and matches job seekers with the most suitable tasks according to their skills and desired conditions. In the job matching system, companies register job information, and the AI ​​analyzes this job information and suggests the necessary skills and experience for each task. On the other hand, job seekers input their skills and desired conditions, and the AI ​​analyzes this information and suggests the most suitable tasks. This allows companies to efficiently secure short-term and temporary personnel, and job seekers to flexibly choose a work style that suits them. Through this mechanism, companies can quickly secure the necessary personnel, and job seekers can find tasks that match their skills and preferences. Furthermore, this platform can be applied not only to specific industries but also to various industries. By automatically breaking down job information into business tasks and supporting companies in easily recruiting on a task-by-task basis, it contributes to the revitalization of the entire labor market. As a result, the job matching system allows companies to quickly secure the necessary personnel, and job seekers can find tasks that match their skills and preferences.

[0029] The job matching system according to this embodiment comprises a reception unit, an analysis unit, a job seeker reception unit, and a proposal unit. The reception unit receives job information registered by companies. For example, companies can input job information through a web form into the reception unit. The reception unit can also automatically receive job information through an API provided by companies. Furthermore, the reception unit can manually input job information provided by companies. The analysis unit analyzes the job information registered by the reception unit. For example, the analysis unit analyzes the content of the job information using text analysis technology. Furthermore, the analysis unit can also extract patterns in the job information using data mining technology. Furthermore, the analysis unit can also learn the characteristics of the job information using machine learning algorithms. The job seeker reception unit receives information from job seekers. For example, job seekers can input their skills and desired conditions through a web form into the job seeker reception unit. Furthermore, job seekers can upload resumes and work histories provided by job seekers to the job seeker reception unit. Furthermore, the job seeker reception unit can manually input the skill sets provided by job seekers. The proposal unit proposes the most suitable job to job seekers based on the information analyzed by the analysis unit. For example, the proposal unit proposes the most suitable job based on the job seeker's skills and desired conditions. The proposal unit can also propose jobs considering the job seeker's past application history and evaluations. Furthermore, the proposal unit can also propose jobs based on the job seeker's current skills and desired conditions. As a result, the job matching system according to this embodiment can efficiently analyze job information and propose the most suitable job to job seekers.

[0030] The reception department receives job postings registered by companies. For example, companies can enter job postings through a web form. Specifically, companies access a dedicated web portal and fill in the necessary information on a form for job postings. This form includes detailed fields such as job title, location, salary, required skills, and years of experience, and companies can register job postings by filling in these fields. The reception department can also automatically receive job postings through APIs provided by companies. Using APIs, companies' internal systems and the job matching system are linked, and job postings are automatically transferred. This eliminates the need for companies to manually enter information, allowing for efficient job posting registration. Furthermore, the reception department can also manually enter job postings provided by companies. For example, a reception staff member can manually enter job postings sent by companies via paper or email into the system. This method is particularly effective for companies that cannot utilize APIs or web forms. This allows the reception department to receive job postings in a variety of ways, enabling it to flexibly respond to the needs of companies.

[0031] The analysis unit analyzes job postings registered by the reception unit. For example, the analysis unit uses text analysis techniques to analyze the content of job postings. Specifically, it uses natural language processing (NLP) techniques to analyze the text data of job postings and extract important keywords and phrases. This allows the content of job postings to be organized as structured data. The analysis unit can also extract patterns in job postings using data mining techniques. By using data mining techniques, common patterns and trends can be found in past job posting data and applied to new job postings. Furthermore, the analysis unit can learn the characteristics of job postings using machine learning algorithms. Machine learning algorithms learn from large amounts of job posting data and automatically identify features associated with specific job types and skill sets. This allows the analysis unit to more accurately understand the content of job postings and build a foundation for providing optimal matching to job seekers. Additionally, the analysis unit stores the results of the job posting analysis in a database, making it accessible to other departments. This allows the analysis unit to analyze job postings efficiently and effectively, improving the overall system performance.

[0032] The Job Seeker Reception Department receives information from job seekers. For example, job seekers can input their skills and desired conditions through a web form. Specifically, job seekers access a dedicated web portal and fill out a form with detailed information such as skills, experience, desired job type, work location, and salary. The Job Seeker Reception Department also allows job seekers to upload resumes and work histories. This allows job seekers to register documents detailing their career history and skills in the system. Furthermore, the Job Seeker Reception Department can also manually input the skill sets provided by job seekers. For example, if a job seeker has specific skills or qualifications, this information can be manually entered and registered in the system. This allows the Job Seeker Reception Department to receive information from job seekers in a variety of ways and respond flexibly to the needs of job seekers. In addition, the Job Seeker Reception Department stores the received information in a database, making it accessible to the Analysis Department and the Proposal Department. This allows the Job Seeker Reception Department to manage job seeker information efficiently and effectively, improving the overall performance of the system.

[0033] The Proposal Department proposes the most suitable jobs to job seekers based on information analyzed by the Analysis Department. For example, the Proposal Department proposes the most suitable jobs based on the job seeker's skills and desired conditions. Specifically, it matches the characteristics of the job information extracted by the Analysis Department with the job seeker's skill set and desired conditions, and lists the most suitable job information. The Proposal Department can also propose jobs considering the job seeker's past application history and evaluations. For example, it analyzes the job seeker's aptitude and evaluation based on past job applications and results, and proposes more suitable jobs. Furthermore, the Proposal Department can propose jobs based on the job seeker's current skills and desired conditions. For example, it considers newly acquired qualifications and skills and proposes job information that is appropriate for them. This allows the Proposal Department to quickly and accurately propose the most suitable jobs to job seekers, improving job seeker satisfaction. In addition, the Proposal Department can notify job seekers of the proposed content and collect feedback. For example, if a job seeker shows interest in a proposed job, this information is recorded in the system and reflected in future proposals. This allows the Proposal Department to make flexible proposals that meet the needs of job seekers and improve the overall performance of the system.

[0034] The decomposition unit can break down business tasks. For example, it can subdivide business tasks into project tasks, daily tasks, specific business processes, etc. It can also break down business tasks into steps. For example, it can break down project tasks into planning, execution, and evaluation steps. Furthermore, it can break down business tasks into roles. For example, it can break down project tasks into the roles of project manager, developer, and tester. This allows for more specific job proposals to job seekers by breaking down business tasks. Some or all of the above processing in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can have a generating AI perform the decomposition of business tasks.

[0035] The Skills Proposal Department can propose necessary skills and experience. For example, it can propose specific skills and experience such as programming skills or project management experience. Furthermore, the Skills Proposal Department can propose necessary skills and experience based on the job seeker's skill set. For example, based on the job seeker's programming skills, it can propose experience in specific programming languages ​​or frameworks. Additionally, the Skills Proposal Department can propose necessary skills and experience based on job postings. For example, it can propose necessary skills and experience based on the job description in the job posting. This allows for more appropriate job proposals to job seekers by proposing necessary skills and experience. Some or all of the above processes in the Skills Proposal Department may be performed using AI, or not. For example, the Skills Proposal Department can have a generating AI generate suggestions for necessary skills and experience.

[0036] The proposal department can propose the most suitable job based on the job seeker's skills and desired conditions. For example, the proposal department can propose a job based on the job seeker's technical skills, desired work location, desired salary, etc. Furthermore, the proposal department can also propose a job considering the job seeker's past application history and evaluations. For example, the proposal department can propose the most suitable job based on the evaluations of jobs the job seeker has applied for in the past. In addition, the proposal department can propose a job based on the job seeker's current skills and desired conditions. For example, the proposal department can propose the most suitable job based on the job seeker's current skill set and desired conditions. This allows for more appropriate job proposals to job seekers by proposing the most suitable job based on their skills and desired conditions. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can propose a job using an AI model that takes the job seeker's skills and desired conditions as input and outputs the most suitable job.

[0037] The analysis unit can analyze job postings and propose the necessary skills and experience for each job. For example, the analysis unit can use text analysis technology to analyze the content of job postings and extract the necessary skills and experience. It can also use data mining technology to extract patterns in job postings and propose the necessary skills and experience. Furthermore, the analysis unit can use machine learning algorithms to learn the characteristics of job postings and propose the necessary skills and experience. For example, the analysis unit can propose the necessary skills and experience based on the job description in the job posting. This allows for more appropriate job recommendations to job seekers by analyzing job postings and proposing the necessary skills and experience for each job. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can perform analysis using an AI model that takes job postings as input and outputs the necessary skills and experience.

[0038] The reception department can receive job postings registered by companies. For example, companies can input job postings through a web form. The reception department can also automatically receive job postings through an API provided by the company. Furthermore, the reception department can manually input job postings provided by companies. This streamlines the management of job postings by receiving job postings registered by companies. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can use an AI model that takes job postings provided by companies as input and performs the reception of job postings.

[0039] The reception department can analyze a company's past recruitment history and select the most suitable reception method. For example, the reception department can prioritize suggesting reception methods that the company has frequently used in the past. It can also select the most effective reception method from the company's past recruitment history. Furthermore, the reception department can analyze the company's past recruitment history and suggest reception methods suitable for specific industries or job types. This allows for the selection of the most suitable reception method by analyzing the company's past recruitment history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can select a reception method using an AI model that takes the company's past recruitment history as input and outputs the most suitable reception method.

[0040] The reception department can filter job postings based on the company's current projects and operational status. For example, it can prioritize receiving relevant job postings based on the progress of the company's current projects. It can also prioritize receiving job seekers with the necessary skills and experience, taking into account the company's operational situation. Furthermore, it can receive job postings at an appropriate time based on the company's current workload. This allows for the reception of more relevant job postings by filtering based on the company's current projects and operational status. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can perform filtering using an AI model that takes the company's current projects and operational status as input and outputs the filtered results.

[0041] The reception department can prioritize receiving highly relevant information when receiving job postings, taking into account the geographical location of the company. For example, the reception department can prioritize receiving information from job seekers who are close to the company's location. Furthermore, the reception department can prioritize receiving job postings related to a specific region based on the company's geographical location. In addition, the reception department can prioritize receiving information from job seekers with shorter commute times, taking into account the company's geographical location. This allows for the priority of receiving highly relevant information by considering the company's geographical location. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI. For example, the reception department can receive information using an AI model that takes the company's geographical location as input and outputs highly relevant information.

[0042] The reception department can analyze a company's social media activity when receiving job postings and receive relevant information. For example, the reception department can prioritize receiving job postings related to current projects or campaigns based on a company's social media activity. The reception department can also analyze a company's social media activity and prioritize receiving information on job seekers with specific skills or experience. Furthermore, the reception department can use a company's social media activity as a reference to receive job postings that match the company's brand image. In this way, by analyzing a company's social media activity, relevant information can be prioritized. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can receive information using an AI model that takes a company's social media activity as input and outputs relevant information.

[0043] The analysis unit can adjust the level of detail of the analysis based on the importance of the job postings during the analysis. For example, the analysis unit performs a detailed analysis on highly important job postings. It can also perform a simplified analysis on less important job postings. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance. By adjusting the level of detail of the analysis based on the importance of the job postings, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail using an AI model that takes the importance of the job postings as input and outputs the level of detail of the analysis.

[0044] The analysis unit can apply different analysis algorithms depending on the category of the job posting during analysis. For example, the analysis unit can apply an analysis algorithm specialized in technical skills to job postings for technical positions. It can also apply an analysis algorithm specialized in administrative skills to job postings for administrative positions. Furthermore, it can apply an analysis algorithm specialized in sales skills to job postings for sales positions. By applying different analysis algorithms depending on the category of the job posting, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply an algorithm using an AI model that takes the category of the job posting as input and outputs an analysis algorithm.

[0045] The analysis unit can determine the priority of analysis based on the submission date of job postings during the analysis process. For example, the analysis unit may prioritize the analysis of job postings submitted earlier. It can also postpone the analysis of job postings submitted later. Furthermore, the analysis unit can adjust the analysis priority in stages according to the submission date. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the submission date of job postings. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority using an AI model that takes the submission date of job postings as input and outputs the analysis priority.

[0046] The analysis unit can adjust the order of analysis based on the relevance of the job postings during the analysis process. For example, the analysis unit may prioritize the analysis of job postings with high relevance. It can also postpone the analysis of job postings with low relevance. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the job postings. By adjusting the order of analysis based on the relevance of the job postings, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order using an AI model that takes the relevance of the job postings as input and outputs the order of analysis.

[0047] The job seeker reception department can analyze a job seeker's past application history and select the most suitable application method. For example, the job seeker reception department may prioritize suggesting application methods that the job seeker has frequently used in the past. It can also select the most effective application method based on the job seeker's past application history. Furthermore, the job seeker reception department can analyze a job seeker's past application history and suggest application methods suitable for specific industries or job types. This allows for the selection of the most suitable application method by analyzing the job seeker's past application history. Some or all of the above processes in the job seeker reception department may be performed using AI, for example, or without AI. For example, the job seeker reception department can select an application method using an AI model that takes a job seeker's past application history as input and outputs the most suitable application method.

[0048] The job seeker reception department can filter job seekers' information based on their current skills and desired conditions. For example, the job seeker reception department can prioritize receiving relevant information based on the job seeker's current skills. It can also prioritize receiving appropriate information considering the job seeker's desired conditions. Furthermore, the job seeker reception department can filter based on the job seeker's skills and desired conditions to receive the most suitable information. This allows for the receipt of more appropriate information by filtering based on the job seeker's current skills and desired conditions. Some or all of the above processing in the job seeker reception department may be performed using AI, for example, or without AI. For example, the job seeker reception department can perform filtering using an AI model that takes the job seeker's skills and desired conditions as input and outputs the filtering results.

[0049] The job seeker reception department can prioritize receiving highly relevant information by considering the job seeker's geographical location when receiving job seeker information. For example, the job seeker reception department can prioritize receiving job postings that are close to the job seeker's location. Furthermore, the job seeker reception department can also prioritize receiving information related to a specific region based on the job seeker's geographical location. In addition, the job seeker reception department can prioritize receiving job postings with short commute times by considering the job seeker's geographical location. This allows for the priority of receiving highly relevant information by considering the job seeker's geographical location. Some or all of the above processing in the job seeker reception department may be performed using AI, for example, or without AI. For example, the job seeker reception department can receive information using an AI model that takes the job seeker's geographical location as input and outputs highly relevant information.

[0050] The job seeker reception department can analyze the social media activity of job seekers when receiving their information and receive relevant information. For example, the job seeker reception department can prioritize receiving information related to the job seeker's current skills and interests from their social media activity. The job seeker reception department can also analyze the job seeker's social media activity and prioritize receiving information related to specific industries or job types. Furthermore, the job seeker reception department can use the job seeker's social media activity as a reference to receive information that matches the job seeker's desired conditions. In this way, by analyzing the job seeker's social media activity, relevant information can be prioritized. Some or all of the above processing in the job seeker reception department may be performed using AI, for example, or not. For example, the job seeker reception department can receive information using an AI model that takes the job seeker's social media activity as input and outputs relevant information.

[0051] The proposal department can adjust the level of detail in a proposal based on the importance of the task. For example, the proposal department can provide detailed proposals for high-priority tasks, and concise proposals for low-priority tasks. Furthermore, the proposal department can adjust the level of detail in stages according to importance. This allows for the provision of more appropriate proposals by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can adjust the level of detail using an AI model that takes the importance of the task as input and outputs the level of detail of the proposal.

[0052] The proposal department can apply different proposal algorithms depending on the category of work when making a proposal. For example, the proposal department can apply a proposal algorithm specialized in technical skills to technical work. It can also apply a proposal algorithm specialized in administrative skills to administrative work. Furthermore, it can apply a proposal algorithm specialized in sales skills to sales work. By applying different proposal algorithms depending on the category of work, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can apply an algorithm using an AI model that takes the category of work as input and outputs a proposal algorithm.

[0053] The proposal department can determine the priority of proposals based on the submission deadlines for each task. For example, the proposal department can prioritize tasks with earlier submission deadlines. It can also postpone proposals for tasks with later submission deadlines. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission deadlines. This allows for the provision of more appropriate proposals by determining the priority of proposals based on the submission deadlines for each task. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can determine the priority using an AI model that takes the submission deadlines for tasks as input and outputs the priority of proposals.

[0054] The proposal department can adjust the order of proposals based on their relevance to the business. For example, the proposal department can prioritize proposals with high business relevance. It can also postpone proposals with low business relevance. Furthermore, the proposal department can adjust the order of proposals in stages according to their business relevance. This allows for the provision of more appropriate proposals by adjusting the order of proposals based on business relevance. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can adjust the order using an AI model that takes business relevance as input and outputs the order of proposals.

[0055] The decomposition unit can adjust the level of detail of the decomposition based on the importance of the tasks during the decomposition process. For example, the decomposition unit can perform a detailed decomposition for tasks with high importance. It can also perform a simplified decomposition for tasks with low importance. Furthermore, the decomposition unit can adjust the level of detail of the decomposition in stages according to importance. This allows for a more appropriate decomposition method by adjusting the level of detail of the decomposition based on the importance of the tasks. Some or all of the above-described processes in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can adjust the level of detail using an AI model that takes the importance of the tasks as input and outputs the level of detail of the decomposition.

[0056] The decomposition unit can apply different decomposition algorithms depending on the category of work during the decomposition process. For example, the decomposition unit can apply a decomposition algorithm specialized in technical skills to the work of a technical professional. It can also apply a decomposition algorithm specialized in administrative skills to the work of an administrative professional. Furthermore, it can apply a decomposition algorithm specialized in sales skills to the work of a sales professional. By applying different decomposition algorithms depending on the category of work, a more appropriate decomposition method can be provided. Some or all of the above processing in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can apply an algorithm using an AI model that takes the category of work as input and outputs a decomposition algorithm.

[0057] The decomposition unit can determine the priority of decomposition based on the submission dates of the tasks during the decomposition process. For example, the decomposition unit may prioritize decomposing tasks with earlier submission dates. It can also postpone decomposing tasks with later submission dates. Furthermore, the decomposition unit can adjust the decomposition priority in stages according to the submission dates. This allows for a more appropriate decomposition method by determining the priority of decomposition based on the submission dates of the tasks. Some or all of the above processing in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can determine the priority using an AI model that takes the submission dates of the tasks as input and outputs the decomposition priority.

[0058] The decomposition unit can adjust the order of decomposition based on the relevance of the tasks during the decomposition process. For example, the decomposition unit may prioritize decomposing tasks that are highly relevant to the tasks. It can also postpone the decomposition of tasks that are less relevant to the tasks. Furthermore, the decomposition unit can adjust the order of decomposition step by step according to the relevance of the tasks. This allows for a more appropriate decomposition method by adjusting the order of decomposition based on the relevance of the tasks. Some or all of the above-described processes in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can adjust the order using an AI model that takes the relevance of tasks as input and outputs the order of decomposition.

[0059] The Skill Proposal Department can adjust the level of detail in skill proposals based on the importance of the task. For example, the Skill Proposal Department can provide detailed skill proposals for high-importance tasks, and concise skill proposals for low-importance tasks. Furthermore, the Skill Proposal Department can adjust the level of detail in skill proposals in stages according to importance. This allows for the provision of more appropriate skill proposals by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the Skill Proposal Department may be performed using AI, for example, or without AI. For example, the Skill Proposal Department can adjust the level of detail using an AI model that takes the importance of the task as input and outputs the level of detail in skill proposals.

[0060] The Skill Proposal Department can apply different proposal algorithms depending on the job category when proposing skills. For example, the Skill Proposal Department can apply a proposal algorithm specializing in technical skills to technical jobs. It can also apply a proposal algorithm specializing in administrative skills to administrative jobs. Furthermore, it can apply a proposal algorithm specializing in sales skills to sales jobs. By applying different proposal algorithms depending on the job category, it is possible to provide more appropriate skill proposals. Some or all of the above processing in the Skill Proposal Department may be performed using AI, for example, or without AI. For example, the Skill Proposal Department can apply an algorithm using an AI model that takes the job category as input and outputs a proposal algorithm.

[0061] The Skill Proposal Department can determine the priority of skill proposals based on the submission timing of the tasks. For example, the Skill Proposal Department can prioritize tasks with earlier submission deadlines. It can also postpone the submission of tasks with later submission deadlines. Furthermore, the Skill Proposal Department can adjust the priority of proposals in stages according to the submission timing. This allows for the provision of more appropriate skill proposals by determining the priority of proposals based on the submission timing of the tasks. Some or all of the above processing in the Skill Proposal Department may be performed using AI, for example, or not. For example, the Skill Proposal Department can determine the priority using an AI model that takes the submission timing of tasks as input and outputs the priority of proposals.

[0062] The skill suggestion department can adjust the order of suggestions based on their relevance to the work when making skill suggestions. For example, the skill suggestion department can prioritize suggesting skills with high relevance to the work. It can also postpone suggesting skills with low relevance to the work. Furthermore, the skill suggestion department can adjust the order of suggestions in stages according to their relevance to the work. This allows for the provision of more appropriate skill suggestions by adjusting the order of suggestions based on their relevance to the work. Some or all of the above processing in the skill suggestion department may be performed using AI, for example, or without AI. For example, the skill suggestion department can adjust the order using an AI model that takes the relevance to the work as input and outputs the order of suggestions.

[0063] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0064] The job matching system may further include a filtering unit that filters job postings based on a company's brand image. For example, the filtering unit may prioritize suggesting job seekers who match the company's brand image. It can also prioritize suggesting job seekers with specific skills or experience based on the company's brand image. Furthermore, the filtering unit may consider the job seeker's past work experience and evaluations based on the company's brand image when making suggestions. This improves the accuracy of matching companies with job seekers by suggesting job seekers who align with the company's brand image. Some or all of the above processing in the filtering unit may be performed using AI, or not. For example, the filtering unit can perform filtering using an AI model that takes a company's brand image as input and outputs filtering results.

[0065] The job matching system may also include a health status consideration unit that proposes jobs while taking into account the health status of job seekers. For example, if a job seeker is in good health, the health status consideration unit may propose physically demanding jobs. If a job seeker is in unstable health, the health status consideration unit may also propose remote work or light work. Furthermore, the health status consideration unit may also propose appropriate working hours and leave systems based on the job seeker's health status. This improves the ease of working for job seekers by providing job proposals that are tailored to their health status. Some or all of the above processing in the health status consideration unit may be performed using AI, for example, or without AI. For example, the health status consideration unit may make proposals using an AI model that takes the job seeker's health status as input and outputs job proposals.

[0066] The job matching system may also include a hobby / interest consideration unit that proposes jobs considering the job seeker's hobbies and interests. For example, if a job seeker has a specific hobby or interest, the hobby / interest consideration unit will propose jobs related to that hobby or interest. The hobby / interest consideration unit can also propose job content and environment based on the job seeker's hobbies and interests. Furthermore, the hobby / interest consideration unit can also propose flexibility in the job and working style based on the job seeker's hobbies and interests. This can improve the job seeker's motivation by proposing jobs that match their hobbies and interests. Some or all of the above processing in the hobby / interest consideration unit may be performed using AI, for example, or not. For example, the hobby / interest consideration unit can make suggestions using an AI model that takes the job seeker's hobbies and interests as input and outputs job suggestions.

[0067] The job matching system may also include a career path suggestion unit that analyzes the job seeker's past work history in detail and proposes career paths. The career path suggestion unit can, for example, propose future career paths based on the job seeker's past work history. It can also propose tasks for career advancement based on the job seeker's skills and experience. Furthermore, it can propose the optimal career path based on the job seeker's desired conditions. This allows the system to support the job seeker's career growth by proposing career paths tailored to their past work history. Some or all of the above-described processes in the career path suggestion unit may be performed using AI, for example, or without AI. For example, the career path suggestion unit can use an AI model that takes the job seeker's work history as input and outputs a career path to make suggestions.

[0068] The following briefly describes the processing flow for example form 1.

[0069] Step 1: The reception desk receives job postings registered by companies. Companies can enter job postings via a web form, or they can automatically receive job postings via an API. Furthermore, companies can also manually enter job postings they provide. Step 2: The analysis unit analyzes the job information registered by the reception unit. The analysis unit can analyze the content and patterns of the job information using text analysis and data mining technologies, and learn the characteristics of the job information using machine learning algorithms. Step 3: The job seeker reception department receives information from job seekers. Job seekers can enter their skills and desired conditions through a web form, and can also upload their resume and work history. In addition, they can manually enter the skill set they offer. Step 4: The Proposal Department proposes the most suitable job to the job seeker based on the information analyzed by the Analysis Department. The Proposal Department can propose jobs based on the job seeker's skills and desired conditions, past application history and evaluations, and current skills and desired conditions.

[0070] (Example of form 2) The job matching system according to an embodiment of the present invention is a system that utilizes AI to differentiate job postings into business tasks and matches job seekers with the most suitable tasks according to their skills and desired conditions. In the job matching system, companies register job information, and the AI ​​analyzes this job information and suggests the necessary skills and experience for each task. On the other hand, job seekers input their skills and desired conditions, and the AI ​​analyzes this information and suggests the most suitable tasks. This allows companies to efficiently secure short-term and temporary personnel, and job seekers to flexibly choose a work style that suits them. Through this mechanism, companies can quickly secure the necessary personnel, and job seekers can find tasks that match their skills and preferences. Furthermore, this platform can be applied not only to specific industries but also to various industries. By automatically breaking down job information into business tasks and supporting companies in easily recruiting on a task-by-task basis, it contributes to the revitalization of the entire labor market. As a result, the job matching system allows companies to quickly secure the necessary personnel, and job seekers can find tasks that match their skills and preferences.

[0071] The job matching system according to this embodiment comprises a reception unit, an analysis unit, a job seeker reception unit, and a proposal unit. The reception unit receives job information registered by companies. For example, companies can input job information through a web form into the reception unit. The reception unit can also automatically receive job information through an API provided by companies. Furthermore, the reception unit can manually input job information provided by companies. The analysis unit analyzes the job information registered by the reception unit. For example, the analysis unit analyzes the content of the job information using text analysis technology. Furthermore, the analysis unit can also extract patterns in the job information using data mining technology. Furthermore, the analysis unit can also learn the characteristics of the job information using machine learning algorithms. The job seeker reception unit receives information from job seekers. For example, job seekers can input their skills and desired conditions through a web form into the job seeker reception unit. Furthermore, job seekers can upload resumes and work histories provided by job seekers to the job seeker reception unit. Furthermore, the job seeker reception unit can manually input the skill sets provided by job seekers. The proposal unit proposes the most suitable job to job seekers based on the information analyzed by the analysis unit. For example, the proposal unit proposes the most suitable job based on the job seeker's skills and desired conditions. The proposal unit can also propose jobs considering the job seeker's past application history and evaluations. Furthermore, the proposal unit can also propose jobs based on the job seeker's current skills and desired conditions. As a result, the job matching system according to this embodiment can efficiently analyze job information and propose the most suitable job to job seekers.

[0072] The reception department receives job postings registered by companies. For example, companies can enter job postings through a web form. Specifically, companies access a dedicated web portal and fill in the necessary information on a form for job postings. This form includes detailed fields such as job title, location, salary, required skills, and years of experience, and companies can register job postings by filling in these fields. The reception department can also automatically receive job postings through APIs provided by companies. Using APIs, companies' internal systems and the job matching system are linked, and job postings are automatically transferred. This eliminates the need for companies to manually enter information, allowing for efficient job posting registration. Furthermore, the reception department can also manually enter job postings provided by companies. For example, a reception staff member can manually enter job postings sent by companies via paper or email into the system. This method is particularly effective for companies that cannot utilize APIs or web forms. This allows the reception department to receive job postings in a variety of ways, enabling it to flexibly respond to the needs of companies.

[0073] The analysis unit analyzes job postings registered by the reception unit. For example, the analysis unit uses text analysis techniques to analyze the content of job postings. Specifically, it uses natural language processing (NLP) techniques to analyze the text data of job postings and extract important keywords and phrases. This allows the content of job postings to be organized as structured data. The analysis unit can also extract patterns in job postings using data mining techniques. By using data mining techniques, common patterns and trends can be found in past job posting data and applied to new job postings. Furthermore, the analysis unit can learn the characteristics of job postings using machine learning algorithms. Machine learning algorithms learn from large amounts of job posting data and automatically identify features associated with specific job types and skill sets. This allows the analysis unit to more accurately understand the content of job postings and build a foundation for providing optimal matching to job seekers. Additionally, the analysis unit stores the results of the job posting analysis in a database, making it accessible to other departments. This allows the analysis unit to analyze job postings efficiently and effectively, improving the overall system performance.

[0074] The Job Seeker Reception Department receives information from job seekers. For example, job seekers can input their skills and desired conditions through a web form. Specifically, job seekers access a dedicated web portal and fill out a form with detailed information such as skills, experience, desired job type, work location, and salary. The Job Seeker Reception Department also allows job seekers to upload resumes and work histories. This allows job seekers to register documents detailing their career history and skills in the system. Furthermore, the Job Seeker Reception Department can also manually input the skill sets provided by job seekers. For example, if a job seeker has specific skills or qualifications, this information can be manually entered and registered in the system. This allows the Job Seeker Reception Department to receive information from job seekers in a variety of ways and respond flexibly to the needs of job seekers. In addition, the Job Seeker Reception Department stores the received information in a database, making it accessible to the Analysis Department and the Proposal Department. This allows the Job Seeker Reception Department to manage job seeker information efficiently and effectively, improving the overall performance of the system.

[0075] The Proposal Department proposes the most suitable jobs to job seekers based on information analyzed by the Analysis Department. For example, the Proposal Department proposes the most suitable jobs based on the job seeker's skills and desired conditions. Specifically, it matches the characteristics of the job information extracted by the Analysis Department with the job seeker's skill set and desired conditions, and lists the most suitable job information. The Proposal Department can also propose jobs considering the job seeker's past application history and evaluations. For example, it analyzes the job seeker's aptitude and evaluation based on past job applications and results, and proposes more suitable jobs. Furthermore, the Proposal Department can propose jobs based on the job seeker's current skills and desired conditions. For example, it considers newly acquired qualifications and skills and proposes job information that is appropriate for them. This allows the Proposal Department to quickly and accurately propose the most suitable jobs to job seekers, improving job seeker satisfaction. In addition, the Proposal Department can notify job seekers of the proposed content and collect feedback. For example, if a job seeker shows interest in a proposed job, this information is recorded in the system and reflected in future proposals. This allows the Proposal Department to make flexible proposals that meet the needs of job seekers and improve the overall performance of the system.

[0076] The decomposition unit can break down business tasks. For example, it can subdivide business tasks into project tasks, daily tasks, specific business processes, etc. It can also break down business tasks into steps. For example, it can break down project tasks into planning, execution, and evaluation steps. Furthermore, it can break down business tasks into roles. For example, it can break down project tasks into the roles of project manager, developer, and tester. This allows for more specific job proposals to job seekers by breaking down business tasks. Some or all of the above processing in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can have a generating AI perform the decomposition of business tasks.

[0077] The Skills Proposal Department can propose necessary skills and experience. For example, it can propose specific skills and experience such as programming skills or project management experience. Furthermore, the Skills Proposal Department can propose necessary skills and experience based on the job seeker's skill set. For example, based on the job seeker's programming skills, it can propose experience in specific programming languages ​​or frameworks. Additionally, the Skills Proposal Department can propose necessary skills and experience based on job postings. For example, it can propose necessary skills and experience based on the job description in the job posting. This allows for more appropriate job proposals to job seekers by proposing necessary skills and experience. Some or all of the above processes in the Skills Proposal Department may be performed using AI, or not. For example, the Skills Proposal Department can have a generating AI generate suggestions for necessary skills and experience.

[0078] The proposal department can propose the most suitable job based on the job seeker's skills and desired conditions. For example, the proposal department can propose a job based on the job seeker's technical skills, desired work location, desired salary, etc. Furthermore, the proposal department can also propose a job considering the job seeker's past application history and evaluations. For example, the proposal department can propose the most suitable job based on the evaluations of jobs the job seeker has applied for in the past. In addition, the proposal department can propose a job based on the job seeker's current skills and desired conditions. For example, the proposal department can propose the most suitable job based on the job seeker's current skill set and desired conditions. This allows for more appropriate job proposals to job seekers by proposing the most suitable job based on their skills and desired conditions. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can propose a job using an AI model that takes the job seeker's skills and desired conditions as input and outputs the most suitable job.

[0079] The analysis unit can analyze job postings and propose the necessary skills and experience for each job. For example, the analysis unit can use text analysis technology to analyze the content of job postings and extract the necessary skills and experience. It can also use data mining technology to extract patterns in job postings and propose the necessary skills and experience. Furthermore, the analysis unit can use machine learning algorithms to learn the characteristics of job postings and propose the necessary skills and experience. For example, the analysis unit can propose the necessary skills and experience based on the job description in the job posting. This allows for more appropriate job recommendations to job seekers by analyzing job postings and proposing the necessary skills and experience for each job. Some or all of the above-described processes in the analysis unit may be performed using AI, or not. For example, the analysis unit can perform analysis using an AI model that takes job postings as input and outputs the necessary skills and experience.

[0080] The reception department can receive job postings registered by companies. For example, companies can input job postings through a web form. The reception department can also automatically receive job postings through an API provided by the company. Furthermore, the reception department can manually input job postings provided by companies. This streamlines the management of job postings by receiving job postings registered by companies. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can use an AI model that takes job postings provided by companies as input and performs the reception of job postings.

[0081] The reception desk can estimate a company's sentiment and adjust the timing of job postings based on that sentiment. For example, if a company is in a hurry, the reception desk can immediately accept the job posting and begin processing it quickly. If a company is being cautious, the reception desk can accept the job posting after a detailed verification process. Furthermore, if a company is relaxed, the reception desk can accept the job posting at a flexible time. By adjusting the timing of job postings based on the company's sentiment, job postings can be received at a more appropriate time. Sentiment estimation is achieved using a sentiment estimation function, such as an sentiment engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can adjust the timing of job postings using an AI model that takes a company's sentiment as input and outputs the timing of job posting acceptance.

[0082] The reception department can analyze a company's past recruitment history and select the most suitable reception method. For example, the reception department can prioritize suggesting reception methods that the company has frequently used in the past. It can also select the most effective reception method from the company's past recruitment history. Furthermore, the reception department can analyze the company's past recruitment history and suggest reception methods suitable for specific industries or job types. This allows for the selection of the most suitable reception method by analyzing the company's past recruitment history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can select a reception method using an AI model that takes the company's past recruitment history as input and outputs the most suitable reception method.

[0083] The reception department can filter job postings based on the company's current projects and operational status. For example, it can prioritize receiving relevant job postings based on the progress of the company's current projects. It can also prioritize receiving job seekers with the necessary skills and experience, taking into account the company's operational situation. Furthermore, it can receive job postings at an appropriate time based on the company's current workload. This allows for the reception of more relevant job postings by filtering based on the company's current projects and operational status. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can perform filtering using an AI model that takes the company's current projects and operational status as input and outputs the filtered results.

[0084] The reception desk can estimate a company's sentiment and determine the priority of job postings to receive based on that estimated sentiment. For example, if a company is in a hurry, the reception desk will prioritize receiving urgent job postings. If a company is being cautious, the reception desk may also prioritize receiving job postings that require further review. Furthermore, if a company is relaxed, the reception desk can receive job postings with flexible priorities. This allows for more appropriate prioritization of job postings by determining them based on the company's sentiment. Sentiment estimation is achieved using a sentiment estimation function, such as an sentiment engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can determine priorities using an AI model that takes a company's sentiment as input and outputs the priority of job postings.

[0085] The reception department can prioritize receiving highly relevant information when receiving job postings, taking into account the geographical location of the company. For example, the reception department can prioritize receiving information from job seekers who are close to the company's location. Furthermore, the reception department can prioritize receiving job postings related to a specific region based on the company's geographical location. In addition, the reception department can prioritize receiving information from job seekers with shorter commute times, taking into account the company's geographical location. This allows for the priority of receiving highly relevant information by considering the company's geographical location. Some or all of the above processing in the reception department may be performed using AI, for example, or without AI. For example, the reception department can receive information using an AI model that takes the company's geographical location as input and outputs highly relevant information.

[0086] The reception department can analyze a company's social media activity when receiving job postings and receive relevant information. For example, the reception department can prioritize receiving job postings related to current projects or campaigns based on a company's social media activity. The reception department can also analyze a company's social media activity and prioritize receiving information on job seekers with specific skills or experience. Furthermore, the reception department can use a company's social media activity as a reference to receive job postings that match the company's brand image. In this way, by analyzing a company's social media activity, relevant information can be prioritized. Some or all of the above processing in the reception department may be performed using AI, for example, or not. For example, the reception department can receive information using an AI model that takes a company's social media activity as input and outputs relevant information.

[0087] The analysis unit can estimate a company's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if a company is in a hurry, the analysis unit can provide a concise and to-the-point analysis result. If a company is being cautious, the analysis unit can also provide a detailed and comprehensive analysis result. Furthermore, if a company is relaxed, the analysis unit can provide the analysis result in a flexible presentation. This allows for the provision of more appropriate analysis results by adjusting the presentation of the analysis based on the company's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the presentation using an AI model that takes a company's emotions as input and outputs a presentation of the analysis.

[0088] The analysis unit can adjust the level of detail of the analysis based on the importance of the job postings during the analysis. For example, the analysis unit performs a detailed analysis on highly important job postings. It can also perform a simplified analysis on less important job postings. Furthermore, the analysis unit can adjust the level of detail of the analysis in stages according to the importance. By adjusting the level of detail of the analysis based on the importance of the job postings, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the level of detail using an AI model that takes the importance of the job postings as input and outputs the level of detail of the analysis.

[0089] The analysis unit can apply different analysis algorithms depending on the category of the job posting during analysis. For example, the analysis unit can apply an analysis algorithm specialized in technical skills to job postings for technical positions. It can also apply an analysis algorithm specialized in administrative skills to job postings for administrative positions. Furthermore, it can apply an analysis algorithm specialized in sales skills to job postings for sales positions. By applying different analysis algorithms depending on the category of the job posting, more appropriate analysis results can be provided. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can apply an algorithm using an AI model that takes the category of the job posting as input and outputs an analysis algorithm.

[0090] The analysis unit can estimate the company's sentiment and adjust the length of the analysis based on the estimated sentiment. For example, if the company is in a hurry, the analysis unit can provide a short, concise analysis. If the company is cautious, the analysis unit can also provide a detailed and comprehensive analysis. Furthermore, if the company is relaxed, the analysis unit can provide an analysis of a flexible length. This allows for more appropriate analysis results by adjusting the length of the analysis based on the company's sentiment. Sentiment estimation is achieved using a sentiment estimation function, such as an sentiment engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, or not. For example, the analysis unit can adjust the length using an AI model that takes the company's sentiment as input and outputs the length of the analysis.

[0091] The analysis unit can determine the priority of analysis based on the submission date of job postings during the analysis process. For example, the analysis unit may prioritize the analysis of job postings submitted earlier. It can also postpone the analysis of job postings submitted later. Furthermore, the analysis unit can adjust the analysis priority in stages according to the submission date. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the submission date of job postings. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can determine the priority using an AI model that takes the submission date of job postings as input and outputs the analysis priority.

[0092] The analysis unit can adjust the order of analysis based on the relevance of the job postings during the analysis process. For example, the analysis unit may prioritize the analysis of job postings with high relevance. It can also postpone the analysis of job postings with low relevance. Furthermore, the analysis unit can adjust the order of analysis in stages according to the relevance of the job postings. By adjusting the order of analysis based on the relevance of the job postings, it is possible to provide more appropriate analysis results. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can adjust the order using an AI model that takes the relevance of the job postings as input and outputs the order of analysis.

[0093] The job seeker reception unit can estimate the emotions of job seekers and adjust the timing of information reception based on the estimated emotions. For example, if a job seeker is in a hurry, the job seeker reception unit can immediately receive the information and begin processing it quickly. Alternatively, if a job seeker is cautious, the job seeker reception unit can receive the information after a detailed verification process. Furthermore, if a job seeker is relaxed, the job seeker reception unit can receive the information at a more flexible time. This allows for information to be received at a more appropriate time by adjusting the timing of information reception based on the emotions of the job seeker. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the job seeker reception unit may be performed using AI or not. For example, the job seeker reception unit can adjust the reception timing using an AI model that takes the emotions of job seekers as input and outputs the timing of information reception.

[0094] The job seeker reception department can analyze a job seeker's past application history and select the most suitable application method. For example, the job seeker reception department may prioritize suggesting application methods that the job seeker has frequently used in the past. It can also select the most effective application method based on the job seeker's past application history. Furthermore, the job seeker reception department can analyze a job seeker's past application history and suggest application methods suitable for specific industries or job types. This allows for the selection of the most suitable application method by analyzing the job seeker's past application history. Some or all of the above processes in the job seeker reception department may be performed using AI, for example, or without AI. For example, the job seeker reception department can select an application method using an AI model that takes a job seeker's past application history as input and outputs the most suitable application method.

[0095] The job seeker reception department can filter job seekers' information based on their current skills and desired conditions. For example, the job seeker reception department can prioritize receiving relevant information based on the job seeker's current skills. It can also prioritize receiving appropriate information considering the job seeker's desired conditions. Furthermore, the job seeker reception department can filter based on the job seeker's skills and desired conditions to receive the most suitable information. This allows for the receipt of more appropriate information by filtering based on the job seeker's current skills and desired conditions. Some or all of the above processing in the job seeker reception department may be performed using AI, for example, or without AI. For example, the job seeker reception department can perform filtering using an AI model that takes the job seeker's skills and desired conditions as input and outputs the filtering results.

[0096] The job seeker reception department can estimate the emotions of job seekers and determine the priority of information to receive based on the estimated emotions. For example, if a job seeker is in a hurry, the job seeker reception department will prioritize receiving information of high urgency. If a job seeker is cautious, the job seeker reception department may also prioritize receiving information that requires detailed verification. Furthermore, if a job seeker is relaxed, the job seeker reception department may receive information with flexible priorities. This allows for more appropriate information to be received by determining the priority of information based on the emotions of the job seeker. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the job seeker reception department may be performed using AI, for example, or not using AI. For example, the job seeker reception department can determine priorities using an AI model that takes the emotions of job seekers as input and outputs the priority of information.

[0097] The job seeker reception department can prioritize receiving highly relevant information by considering the job seeker's geographical location when receiving job seeker information. For example, the job seeker reception department can prioritize receiving job postings that are close to the job seeker's location. Furthermore, the job seeker reception department can also prioritize receiving information related to a specific region based on the job seeker's geographical location. In addition, the job seeker reception department can prioritize receiving job postings with short commute times by considering the job seeker's geographical location. This allows for the priority of receiving highly relevant information by considering the job seeker's geographical location. Some or all of the above processing in the job seeker reception department may be performed using AI, for example, or without AI. For example, the job seeker reception department can receive information using an AI model that takes the job seeker's geographical location as input and outputs highly relevant information.

[0098] The job seeker reception department can analyze the social media activity of job seekers when receiving their information and receive relevant information. For example, the job seeker reception department can prioritize receiving information related to the job seeker's current skills and interests from their social media activity. The job seeker reception department can also analyze the job seeker's social media activity and prioritize receiving information related to specific industries or job types. Furthermore, the job seeker reception department can use the job seeker's social media activity as a reference to receive information that matches the job seeker's desired conditions. In this way, by analyzing the job seeker's social media activity, relevant information can be prioritized. Some or all of the above processing in the job seeker reception department may be performed using AI, for example, or not. For example, the job seeker reception department can receive information using an AI model that takes the job seeker's social media activity as input and outputs relevant information.

[0099] The proposal unit can estimate the job seeker's emotions and adjust the presentation of the proposal based on the estimated emotions. For example, if the job seeker is nervous, the proposal unit can provide a simple and highly visible proposal. If the job seeker is relaxed, the proposal unit can also provide a proposal that includes detailed information. Furthermore, if the job seeker is in a hurry, the proposal unit can provide a concise proposal. This allows for the provision of more appropriate proposals by adjusting the presentation based on the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI includes, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can adjust the presentation using an AI model that takes the job seeker's emotions as input and outputs a presentation of the proposal.

[0100] The proposal department can adjust the level of detail in a proposal based on the importance of the task. For example, the proposal department can provide detailed proposals for high-priority tasks, and concise proposals for low-priority tasks. Furthermore, the proposal department can adjust the level of detail in stages according to importance. This allows for the provision of more appropriate proposals by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the proposal department may be performed using AI, for example, or not. For example, the proposal department can adjust the level of detail using an AI model that takes the importance of the task as input and outputs the level of detail of the proposal.

[0101] The proposal department can apply different proposal algorithms depending on the category of work when making a proposal. For example, the proposal department can apply a proposal algorithm specialized in technical skills to technical work. It can also apply a proposal algorithm specialized in administrative skills to administrative work. Furthermore, it can apply a proposal algorithm specialized in sales skills to sales work. By applying different proposal algorithms depending on the category of work, it is possible to provide more appropriate proposals. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can apply an algorithm using an AI model that takes the category of work as input and outputs a proposal algorithm.

[0102] The proposal unit can estimate the job seeker's emotions and adjust the length of the proposal based on the estimated emotions. For example, if the job seeker is in a hurry, the proposal unit will provide a short, concise proposal. If the job seeker is relaxed, the proposal unit can provide a longer proposal with detailed explanations. Furthermore, if the job seeker is excited, the proposal unit can provide a proposal with visually stimulating effects. By adjusting the length of the proposal based on the job seeker's emotions, a more appropriate proposal can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the proposal unit may be performed using AI or not. For example, the proposal unit can adjust the length using an AI model that takes the job seeker's emotions as input and outputs the length of the proposal.

[0103] The proposal department can determine the priority of proposals based on the submission deadlines for each task. For example, the proposal department can prioritize tasks with earlier submission deadlines. It can also postpone proposals for tasks with later submission deadlines. Furthermore, the proposal department can adjust the priority of proposals in stages according to the submission deadlines. This allows for the provision of more appropriate proposals by determining the priority of proposals based on the submission deadlines for each task. Some or all of the above processing in the proposal department may be performed using AI, for example, or not. For example, the proposal department can determine the priority using an AI model that takes the submission deadlines for tasks as input and outputs the priority of proposals.

[0104] The proposal department can adjust the order of proposals based on their relevance to the business. For example, the proposal department can prioritize proposals with high business relevance. It can also postpone proposals with low business relevance. Furthermore, the proposal department can adjust the order of proposals in stages according to their business relevance. This allows for the provision of more appropriate proposals by adjusting the order of proposals based on business relevance. Some or all of the above processing in the proposal department may be performed using AI, for example, or without AI. For example, the proposal department can adjust the order using an AI model that takes business relevance as input and outputs the order of proposals.

[0105] The decomposition unit can estimate the applicant's emotions and adjust the decomposition method of the work tasks based on the estimated emotions. For example, if the applicant is nervous, the decomposition unit can provide a simple and easy-to-understand decomposition method. If the applicant is relaxed, the decomposition unit can also provide a decomposition method that includes detailed information. Furthermore, if the applicant is in a hurry, the decomposition unit can provide a concise decomposition method. This allows for the provision of a more appropriate decomposition method by adjusting the decomposition method of the work tasks based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decomposition unit may be performed using AI, for example, or not using AI. For example, the decomposition unit can adjust the decomposition method using an AI model that takes the applicant's emotions as input and outputs a decomposition method.

[0106] The decomposition unit can adjust the level of detail of the decomposition based on the importance of the tasks during the decomposition process. For example, the decomposition unit can perform a detailed decomposition for tasks with high importance. It can also perform a simplified decomposition for tasks with low importance. Furthermore, the decomposition unit can adjust the level of detail of the decomposition in stages according to importance. This allows for a more appropriate decomposition method by adjusting the level of detail of the decomposition based on the importance of the tasks. Some or all of the above-described processes in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can adjust the level of detail using an AI model that takes the importance of the tasks as input and outputs the level of detail of the decomposition.

[0107] The decomposition unit can apply different decomposition algorithms depending on the category of work during the decomposition process. For example, the decomposition unit can apply a decomposition algorithm specialized in technical skills to the work of a technical professional. It can also apply a decomposition algorithm specialized in administrative skills to the work of an administrative professional. Furthermore, it can apply a decomposition algorithm specialized in sales skills to the work of a sales professional. By applying different decomposition algorithms depending on the category of work, a more appropriate decomposition method can be provided. Some or all of the above processing in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can apply an algorithm using an AI model that takes the category of work as input and outputs a decomposition algorithm.

[0108] The decomposition unit can estimate the applicant's emotions and adjust the length of the decomposition based on the estimated emotions. For example, if the applicant is in a hurry, the decomposition unit will produce a short, concise decomposition. If the applicant is relaxed, the decomposition unit can produce a longer decomposition with more detailed explanations. Furthermore, if the applicant is excited, the decomposition unit can produce a decomposition with visually stimulating effects. This allows for a more appropriate decomposition method by adjusting the length of the decomposition based on the applicant's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the decomposition unit may be performed using AI or not. For example, the decomposition unit can adjust the length using an AI model that takes the applicant's emotions as input and outputs the length of the decomposition.

[0109] The decomposition unit can determine the priority of decomposition based on the submission dates of the tasks during the decomposition process. For example, the decomposition unit may prioritize decomposing tasks with earlier submission dates. It can also postpone decomposing tasks with later submission dates. Furthermore, the decomposition unit can adjust the decomposition priority in stages according to the submission dates. This allows for a more appropriate decomposition method by determining the priority of decomposition based on the submission dates of the tasks. Some or all of the above processing in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can determine the priority using an AI model that takes the submission dates of the tasks as input and outputs the decomposition priority.

[0110] The decomposition unit can adjust the order of decomposition based on the relevance of the tasks during the decomposition process. For example, the decomposition unit may prioritize decomposing tasks that are highly relevant to the tasks. It can also postpone the decomposition of tasks that are less relevant to the tasks. Furthermore, the decomposition unit can adjust the order of decomposition step by step according to the relevance of the tasks. This allows for a more appropriate decomposition method by adjusting the order of decomposition based on the relevance of the tasks. Some or all of the above-described processes in the decomposition unit may be performed using AI, for example, or without AI. For example, the decomposition unit can adjust the order using an AI model that takes the relevance of tasks as input and outputs the order of decomposition.

[0111] The skill suggestion unit can estimate the job seeker's emotions and adjust the presentation of skill suggestions based on those emotions. For example, if the job seeker is nervous, the skill suggestion unit can provide a simple and easily understandable suggestion. If the job seeker is relaxed, the skill suggestion unit can also provide a suggestion that includes detailed information. Furthermore, if the job seeker is in a hurry, the skill suggestion unit can provide a concise suggestion. By adjusting the presentation of skill suggestions based on the job seeker's emotions, more appropriate skill suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the skill suggestion unit may be performed using AI or not. For example, the skill suggestion unit can adjust the presentation using an AI model that takes the job seeker's emotions as input and outputs a presentation of skill suggestions.

[0112] The Skill Proposal Department can adjust the level of detail in skill proposals based on the importance of the task. For example, the Skill Proposal Department can provide detailed skill proposals for high-importance tasks, and concise skill proposals for low-importance tasks. Furthermore, the Skill Proposal Department can adjust the level of detail in skill proposals in stages according to importance. This allows for the provision of more appropriate skill proposals by adjusting the level of detail based on the importance of the task. Some or all of the above processes in the Skill Proposal Department may be performed using AI, for example, or without AI. For example, the Skill Proposal Department can adjust the level of detail using an AI model that takes the importance of the task as input and outputs the level of detail in skill proposals.

[0113] The Skill Proposal Department can apply different proposal algorithms depending on the job category when proposing skills. For example, the Skill Proposal Department can apply a proposal algorithm specializing in technical skills to technical jobs. It can also apply a proposal algorithm specializing in administrative skills to administrative jobs. Furthermore, it can apply a proposal algorithm specializing in sales skills to sales jobs. By applying different proposal algorithms depending on the job category, it is possible to provide more appropriate skill proposals. Some or all of the above processing in the Skill Proposal Department may be performed using AI, for example, or without AI. For example, the Skill Proposal Department can apply an algorithm using an AI model that takes the job category as input and outputs a proposal algorithm.

[0114] The skill suggestion unit can estimate the job seeker's emotions and adjust the length of skill suggestions based on those emotions. For example, if the job seeker is in a hurry, the skill suggestion unit will provide short, concise skill suggestions. If the job seeker is relaxed, the skill suggestion unit can provide longer skill suggestions with more detailed explanations. Furthermore, if the job seeker is excited, the skill suggestion unit can provide skill suggestions with visually stimulating effects. By adjusting the length of skill suggestions based on the job seeker's emotions, more appropriate skill suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the skill suggestion unit may be performed using AI or not. For example, the skill suggestion unit can adjust the length using an AI model that takes the job seeker's emotions as input and outputs the length of the skill suggestion.

[0115] The Skill Proposal Department can determine the priority of skill proposals based on the submission timing of the tasks. For example, the Skill Proposal Department can prioritize tasks with earlier submission deadlines. It can also postpone the submission of tasks with later submission deadlines. Furthermore, the Skill Proposal Department can adjust the priority of proposals in stages according to the submission timing. This allows for the provision of more appropriate skill proposals by determining the priority of proposals based on the submission timing of the tasks. Some or all of the above processing in the Skill Proposal Department may be performed using AI, for example, or not. For example, the Skill Proposal Department can determine the priority using an AI model that takes the submission timing of tasks as input and outputs the priority of proposals.

[0116] The skill suggestion department can adjust the order of suggestions based on their relevance to the work when making skill suggestions. For example, the skill suggestion department can prioritize suggesting skills with high relevance to the work. It can also postpone suggesting skills with low relevance to the work. Furthermore, the skill suggestion department can adjust the order of suggestions in stages according to their relevance to the work. This allows for the provision of more appropriate skill suggestions by adjusting the order of suggestions based on their relevance to the work. Some or all of the above processing in the skill suggestion department may be performed using AI, for example, or without AI. For example, the skill suggestion department can adjust the order using an AI model that takes the relevance to the work as input and outputs the order of suggestions.

[0117] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0118] The job matching system may further include a filtering unit that filters job postings based on a company's brand image. For example, the filtering unit may prioritize suggesting job seekers who match the company's brand image. It can also prioritize suggesting job seekers with specific skills or experience based on the company's brand image. Furthermore, the filtering unit may consider the job seeker's past work experience and evaluations based on the company's brand image when making suggestions. This improves the accuracy of matching companies with job seekers by suggesting job seekers who align with the company's brand image. Some or all of the above processing in the filtering unit may be performed using AI, or not. For example, the filtering unit can perform filtering using an AI model that takes a company's brand image as input and outputs filtering results.

[0119] The job matching system may further include a learning motivation estimation unit that estimates the job seeker's motivation to learn and makes skill suggestions based on the estimated motivation. For example, if the job seeker has high motivation to learn, the learning motivation estimation unit will suggest new skills and experiences. If the job seeker has low motivation to learn, the learning motivation estimation unit can also suggest tasks that utilize existing skills and experience. Furthermore, based on the job seeker's motivation to learn, the learning motivation estimation unit can also suggest training and workshops for skill improvement. In this way, by making skill suggestions that match the job seeker's motivation to learn, the system can support the career growth of job seekers. Some or all of the above processing in the learning motivation estimation unit may be performed using AI, for example, or without AI. For example, the learning motivation estimation unit can make suggestions using an AI model that takes the job seeker's motivation to learn as input and outputs skill suggestions.

[0120] The job matching system may also include a health status consideration unit that proposes jobs while taking into account the health status of job seekers. For example, if a job seeker is in good health, the health status consideration unit may propose physically demanding jobs. If a job seeker is in unstable health, the health status consideration unit may also propose remote work or light work. Furthermore, the health status consideration unit may also propose appropriate working hours and leave systems based on the job seeker's health status. This improves the ease of working for job seekers by providing job proposals that are tailored to their health status. Some or all of the above processing in the health status consideration unit may be performed using AI, for example, or without AI. For example, the health status consideration unit may make proposals using an AI model that takes the job seeker's health status as input and outputs job proposals.

[0121] The job matching system may also include a hobby / interest consideration unit that proposes jobs considering the job seeker's hobbies and interests. For example, if a job seeker has a specific hobby or interest, the hobby / interest consideration unit will propose jobs related to that hobby or interest. The hobby / interest consideration unit can also propose job content and environment based on the job seeker's hobbies and interests. Furthermore, the hobby / interest consideration unit can also propose flexibility in the job and working style based on the job seeker's hobbies and interests. This can improve the job seeker's motivation by proposing jobs that match their hobbies and interests. Some or all of the above processing in the hobby / interest consideration unit may be performed using AI, for example, or not. For example, the hobby / interest consideration unit can make suggestions using an AI model that takes the job seeker's hobbies and interests as input and outputs job suggestions.

[0122] The job matching system may also include a career path suggestion unit that analyzes the job seeker's past work history in detail and proposes career paths. The career path suggestion unit can, for example, propose future career paths based on the job seeker's past work history. It can also propose tasks for career advancement based on the job seeker's skills and experience. Furthermore, it can propose the optimal career path based on the job seeker's desired conditions. This allows the system to support the job seeker's career growth by proposing career paths tailored to their past work history. Some or all of the above-described processes in the career path suggestion unit may be performed using AI, for example, or without AI. For example, the career path suggestion unit can use an AI model that takes the job seeker's work history as input and outputs a career path to make suggestions.

[0123] The job matching system may further include an interview timing adjustment unit that estimates the emotions of job seekers and adjusts the timing of interviews based on the estimated emotions. For example, if a job seeker is nervous, the interview timing adjustment unit will schedule the interview at a time when the job seeker can relax. The interview timing adjustment unit can also schedule the interview immediately if the job seeker is relaxed. Furthermore, if a job seeker is in a hurry, the interview timing adjustment unit can quickly schedule the interview. In this way, by adjusting the interview timing according to the emotions of the job seeker, interviews can be conducted at a more appropriate time. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview timing adjustment unit may be performed using AI, for example, or without AI. For example, the interview timing adjustment unit can adjust the timing using an AI model that takes the emotions of the job seeker as input and outputs the timing of the interview.

[0124] The job matching system may further include a feedback adjustment unit that estimates the job seeker's emotions and adjusts the content of the feedback based on the estimated emotions. For example, if the job seeker is nervous, the feedback adjustment unit may prioritize providing positive feedback. It may also provide detailed feedback if the job seeker is relaxed. Furthermore, if the job seeker is in a hurry, the feedback adjustment unit may provide concise and to-the-point feedback. This can improve the job seeker's motivation by providing feedback that is tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback adjustment unit may be performed using AI, for example, or not using AI. For example, the feedback adjustment unit may adjust the content using an AI model that takes the job seeker's emotions as input and outputs the content of the feedback.

[0125] The job matching system may further include a display method adjustment unit that estimates the job seeker's emotions and adjusts the way job information is displayed based on the estimated emotions. For example, if the job seeker is nervous, the display method adjustment unit may provide a simple and highly visible display method. If the job seeker is relaxed, the display method adjustment unit may also provide a display method that includes detailed information. Furthermore, if the job seeker is in a hurry, the display method adjustment unit may also provide a display method that gets straight to the point. This allows for the provision of more appropriate job information by providing a display method that responds to the job seeker's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the display method adjustment unit may be performed using AI, for example, or without AI. For example, the display method adjustment unit can adjust the display method using an AI model that takes the job seeker's emotions as input and outputs a display method.

[0126] The job matching system may further include a difficulty adjustment unit that estimates the job seeker's emotions and adjusts the difficulty of tasks based on the estimated emotions. For example, if the job seeker is nervous, the difficulty adjustment unit may prioritize suggesting easy tasks. Conversely, if the job seeker is relaxed, the difficulty adjustment unit may also suggest more difficult tasks. Furthermore, if the job seeker is in a hurry, the difficulty adjustment unit may also suggest tasks that can be completed in a short period of time. In this way, by adjusting the difficulty of tasks according to the job seeker's emotions, more appropriate tasks can be suggested. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the difficulty adjustment unit may be performed using AI, for example, or without AI. For example, the difficulty adjustment unit can adjust the difficulty using an AI model that takes the job seeker's emotions as input and outputs the difficulty of tasks.

[0127] The job matching system may further include a feedback adjustment unit that estimates the emotions of job seekers and adjusts the feedback based on those emotions. For example, if a job seeker is nervous, the feedback adjustment unit may prioritize providing positive feedback. It may also provide detailed feedback if the job seeker is relaxed. Furthermore, if the job seeker is in a hurry, the feedback adjustment unit may provide concise and to-the-point feedback. This can improve the motivation of job seekers by providing feedback that is tailored to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback adjustment unit may be performed using AI or not. For example, the feedback adjustment unit may adjust the content using an AI model that takes the job seeker's emotions as input and outputs the content of the feedback.

[0128] The following briefly describes the processing flow for example form 2.

[0129] Step 1: The reception desk receives job postings registered by companies. Companies can enter job postings via a web form, or they can automatically receive job postings via an API. Furthermore, companies can also manually enter job postings they provide. Step 2: The analysis unit analyzes the job information registered by the reception unit. The analysis unit can analyze the content and patterns of the job information using text analysis and data mining technologies, and learn the characteristics of the job information using machine learning algorithms. Step 3: The job seeker reception department receives information from job seekers. Job seekers can enter their skills and desired conditions through a web form, and can also upload their resume and work history. In addition, they can manually enter the skill set they offer. Step 4: The Proposal Department proposes the most suitable job to the job seeker based on the information analyzed by the Analysis Department. The Proposal Department can propose jobs based on the job seeker's skills and desired conditions, past application history and evaluations, and current skills and desired conditions.

[0130] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0131] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0132] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0133] Each of the multiple elements described above, including the reception unit, analysis unit, job seeker reception unit, proposal unit, breakdown unit, and skill proposal unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, allowing companies to input job information via a web form. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the content of job information using text analysis technology. The job seeker reception unit is implemented by, for example, the control unit 46A of the smart device 14, allowing job seekers to input their skills and desired conditions via a web form. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most suitable work based on the job seeker's skills and desired conditions. The breakdown unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which subdivides work tasks into project tasks, daily tasks, specific work processes, etc. The skill proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the necessary skills and experience. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0134] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0135] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0136] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0137] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0138] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0139] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0140] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0141] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0142] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0143] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0144] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0145] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0146] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0147] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0148] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0149] Each of the multiple elements described above, including the reception unit, analysis unit, job seeker reception unit, proposal unit, breakdown unit, and skill proposal unit, is implemented by, for example, at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, allowing companies to input job information via a web form. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the content of job information using text analysis technology. The job seeker reception unit is implemented by, for example, the control unit 46A of the smart glasses 214, allowing job seekers to input their skills and desired conditions via a web form. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most suitable work based on the job seeker's skills and desired conditions. The breakdown unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which subdivides work tasks into project tasks, daily tasks, specific work processes, etc. The skill proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the necessary skills and experience. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0150] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0151] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0152] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0153] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0154] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0155] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0156] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0157] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0158] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0159] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0160] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0161] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0162] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0163] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0164] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0165] Each of the multiple elements described above, including the reception unit, analysis unit, job seeker reception unit, proposal unit, breakdown unit, and skill proposal unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, allowing companies to input job information via a web form. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the content of job information using text analysis technology. The job seeker reception unit is implemented by, for example, the control unit 46A of the headset terminal 314, allowing job seekers to input their skills and desired conditions via a web form. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most suitable work based on the job seeker's skills and desired conditions. The breakdown unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which subdivides work tasks into project tasks, daily tasks, specific work processes, etc. The skill suggestion unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, and suggests the necessary skills and experience. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0166] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0167] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0168] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0169] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0170] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0171] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0172] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0173] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0174] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0175] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0176] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0177] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0178] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0179] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0180] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0181] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0182] Each of the multiple elements described above, including the reception unit, analysis unit, job seeker reception unit, proposal unit, breakdown unit, and skill proposal unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, allowing companies to input job information via a web form. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the content of job information using text analysis technology. The job seeker reception unit is implemented by, for example, the control unit 46A of the robot 414, allowing job seekers to input their skills and desired conditions via a web form. The proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the most suitable work based on the job seeker's skills and desired conditions. The breakdown unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which subdivides work tasks into project tasks, daily tasks, specific work processes, etc. The skill proposal unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which proposes the necessary skills and experience. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0183] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0184] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0185] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0186] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0187] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0188] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0189] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0190] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0191] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0192] 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.

[0193] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0194] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0195] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0196] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0197] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0198] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0199] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0200] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0201] (Note 1) The reception area for registering job postings, An analysis unit analyzes the job information registered by the aforementioned reception unit, The Job Seeker Reception Department receives information from job seekers, Based on the information analyzed by the aforementioned analysis unit, the proposal unit proposes the most suitable job to the job seeker. Equipped with A system characterized by the following features. (Note 2) It includes a decomposition unit for breaking down business tasks. The system described in Appendix 1, characterized by the features described herein. (Note 3) We have a skills suggestion department that proposes necessary skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, We propose the most suitable job based on the job seeker's skills and desired conditions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned analysis unit, We analyze job postings and propose the necessary skills and experience for each position. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is We accept job postings registered by companies. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is We estimate the sentiment of companies and adjust the timing of job postings based on the estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze a company's past recruitment history and select the most suitable application method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving job postings, filtering is performed based on the company's current projects and operational status. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the sentiment of companies and prioritizes the job postings it accepts based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving job postings, the system prioritizes receiving information that is highly relevant, taking into account the geographical location of the companies. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving job postings, the system analyzes the company's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the sentiment of companies and adjust the representation of the analysis based on the estimated sentiment of the companies. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the level of detail of the analysis is adjusted based on the importance of the job postings. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, different analysis algorithms are applied depending on the category of the job posting. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the sentiment of companies and adjusts the length of the analysis based on the estimated sentiment of the companies. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During analysis, the priority of the analysis is determined based on when the job postings were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the order of analysis is adjusted based on the relevance of the job postings. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned job seeker registration department is, The system estimates the emotions of job seekers and adjusts the timing of information submission based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned job seeker registration department is, Analyze job seekers' past application history to select the most suitable application method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned job seeker registration department is, When receiving job seeker information, filtering is performed based on the job seeker's current skills and desired conditions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned job seeker registration department is, The system estimates the emotions of job seekers and prioritizes the information to be received based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned job seeker registration department is, When receiving job seeker information, the system prioritizes receiving information that is highly relevant, taking into account the job seeker's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned job seeker registration department is, When receiving job seeker information, the system analyzes the job seeker's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned proposal section is, We estimate the job seeker's emotions and adjust the way the proposal is presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the task. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the work. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, The system estimates the applicant's emotions and adjusts the length of the proposal based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, When submitting proposals, prioritize them based on the submission deadline. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, When making proposals, adjust the order of proposals based on their relevance to the work. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned disassembly unit is The system estimates the emotions of job seekers and adjusts the breakdown of work tasks based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 32) The aforementioned disassembly unit is During the breakdown process, adjust the level of detail based on the importance of the task. The system described in Appendix 2, characterized by the features described herein. (Note 33) The aforementioned disassembly unit is During the decomposition process, different decomposition algorithms are applied depending on the category of the task. The system described in Appendix 2, characterized by the features described herein. (Note 34) The aforementioned disassembly unit is The system estimates the job seeker's emotions and adjusts the length of the decomposition based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 35) The aforementioned disassembly unit is When disassembling a task, prioritize the tasks based on their submission deadlines. The system described in Appendix 2, characterized by the features described herein. (Note 36) The aforementioned disassembly unit is During the disassembly process, adjust the order of disassembly based on the relevance of the tasks. The system described in Appendix 2, characterized by the features described herein. (Note 37) The aforementioned skill proposal unit, The system estimates the job seeker's emotions and adjusts the way skill suggestions are presented based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 38) The aforementioned skill proposal unit, When proposing skills, adjust the level of detail in your proposal based on the importance of the task. The system described in Appendix 3, characterized by the features described herein. (Note 39) The aforementioned skill proposal unit, When proposing skills, different proposal algorithms are applied depending on the job category. The system described in Appendix 3, characterized by the features described herein. (Note 40) The aforementioned skill proposal unit, The system estimates the job seeker's emotions and adjusts the length of skill suggestions based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 41) The aforementioned skill proposal unit, When submitting skill proposals, prioritize them based on the submission deadline for the work. The system described in Appendix 3, characterized by the features described herein. (Note 42) The aforementioned skill proposal unit, When proposing skills, adjust the order of suggestions based on their relevance to the job. The system described in Appendix 3, characterized by the features described herein. [Explanation of symbols]

[0202] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception desk for registering job postings, An analysis unit analyzes the job information registered by the aforementioned reception unit, The job seeker registration department receives information from job seekers, Based on the information analyzed by the aforementioned analysis unit, the proposal unit proposes the most suitable job to the job seeker. Equipped with A system characterized by the following features.

2. It includes a decomposition unit for breaking down business tasks. The system according to feature 1.

3. We have a skills suggestion department that proposes necessary skills and experience. The system according to feature 1.

4. The aforementioned proposal section is, We propose the most suitable job based on the job seeker's skills and desired conditions. The system according to feature 1.

5. The aforementioned analysis unit, We analyze job postings and propose the necessary skills and experience for each position. The system according to feature 1.

6. The aforementioned reception unit is We accept job postings registered by companies. The system according to feature 1.

7. The aforementioned reception unit is We estimate the sentiment of companies and adjust the timing of job postings based on the estimated sentiment. The system according to feature 1.

8. The aforementioned reception unit is We analyze a company's past recruitment history and select the most suitable application method. The system according to feature 1.

9. The aforementioned reception unit is When receiving job postings, filtering is performed based on the company's current projects and operational status. The system according to feature 1.

10. The aforementioned reception unit is The system estimates the sentiment of companies and prioritizes the job postings it accepts based on that estimated sentiment. The system according to feature 1.

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