system

Abstract

The system according to this embodiment aims to suggest the most suitable candidates, taking into account their skills and cultural fit. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, a pick-up unit, and a proposal unit. The collection unit collects job postings and candidate information. The analysis unit analyzes the information collected by the collection unit. The pick-up unit selects the most suitable candidates based on the information analyzed by the analysis unit. The proposal unit proposes the candidates selected by the pick-up unit to the company.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes 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 prior art, it is difficult to perform matching that fully takes into account skills and cultural fit, and there is a risk of occurrence of mismatches in employment.

[0005] The system according to the embodiment aims to propose an optimal candidate considering skills and cultural fit.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, a pick-up unit, and a proposal unit. The collection unit collects job postings and candidate information. The analysis unit analyzes the information collected by the collection unit. The pick-up unit selects the most suitable candidates based on the information analyzed by the analysis unit. The proposal unit proposes the candidates selected by the pick-up unit to the company. [Effects of the Invention]

[0007] The system according to this embodiment can suggest the most suitable candidates, taking into account their skills and cultural fit. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An AI agent system according to an embodiment of the present invention is a system that deeply analyzes job postings and candidate profiles to perform optimal matching. This AI agent system collects information on job postings and candidates and performs deep analysis using natural language processing to improve the accuracy of corporate recruitment and reduce mismatches. For example, the AI ​​agent system collects information on job postings and candidates. Job postings include detailed information such as job description, required skills, and corporate culture, while candidate information includes resumes, work histories, skill sets, past work experience, and cultural background. Next, the AI ​​agent system performs deep analysis of the collected information using natural language processing. The AI ​​agent analyzes the information on job postings and candidates and performs matching considering skills and cultural fit. For example, the AI ​​agent compares the required skills listed in the job posting with the candidate's skill set and determines the degree of fit. It also compares the corporate culture with the candidate's cultural background and determines the cultural fit. Based on the analyzed information, the AI ​​agent selects the most suitable candidates. The AI ​​agent selects candidates with a high degree of skill and cultural fit and proposes them to the company. For example, an AI agent selects candidates whose skill sets match the requirements of the job posting and who also have a high cultural fit. This mechanism improves the accuracy of a company's recruitment and reduces mismatches. Because matching is performed considering skills and cultural fit, companies can quickly hire the right talent. In addition, employee satisfaction and retention rates improve, preventing a decline in employee retention within the company. For example, by having an AI agent perform matching considering skill sets and cultural fit, companies can hire the right talent and improve employee satisfaction and retention rates. Furthermore, the AI ​​agent optimizes its matching algorithm using machine learning. This improves the accuracy of matching and allows it to propose candidates that meet the company's needs. For example, the AI ​​agent can learn from past matching results and optimize its matching algorithm to propose more suitable candidates. In this way, using an AI agent improves the accuracy of a company's recruitment and reduces mismatches.By enabling optimal talent matching that takes skills and cultural fit into consideration, companies can improve their recruitment success rates and retention rates. This allows AI agent systems to enhance the accuracy of companies' recruitment and reduce mismatches.

[0029] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a pick-up unit, and a proposal unit. The collection unit collects information on job postings and candidates. For example, the collection unit collects detailed information such as job description, required skills, and company culture from job postings, and information on candidates such as resumes, work histories, skill sets, past work experience, and cultural backgrounds from candidate profiles. The collection unit collects this information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit performs deep analysis of the job posting and candidate information using natural language processing. The analysis unit compares the required skills listed in the job posting with the candidate's skill set and determines the degree of fit. The analysis unit also compares the company culture with the candidate's cultural background and determines the cultural fit. The pick-up unit picks the most suitable candidates based on the information analyzed by the analysis unit. For example, the pick-up unit selects candidates with a high degree of skill and cultural fit and proposes them to the company. The pick-up unit selects candidates whose skill sets match the requirements of the job posting and who also have a high cultural fit. The proposal unit proposes candidates selected by the selection unit to companies. For example, the proposal unit proposes candidates to companies. In this way, the AI ​​agent system according to the embodiment collects and analyzes job postings and candidate information, selects the most suitable candidates, and proposes them to companies, thereby improving the accuracy of companies' recruitment and reducing mismatches. Some or all of the above-described processes in the collection unit, analysis unit, selection unit, and proposal unit may be performed using AI, for example, or not using AI. For example, the collection unit collects job postings and candidate information, the analysis unit inputs the information collected by the collection unit into the AI, and the AI ​​analyzes the information. The selection unit inputs the information analyzed by the analysis unit into the AI, and the AI ​​selects the most suitable candidates. The proposal unit inputs the candidates selected by the selection unit into the AI, and the AI ​​proposes them to companies.

[0030] The data collection unit collects job postings and candidate information. For example, job postings include detailed information such as job description, required skills, and company culture, while candidate information includes resumes, work histories, skill sets, past work experience, and cultural background. The data collection unit collects this information. Specifically, the unit has the functionality to automatically retrieve job postings from company recruitment websites and job information sites. This allows for the rapid collection of the latest job information. Candidate information is collected from resumes, work histories, and online profiles registered by candidates. The data collection unit centrally manages this information and stores it in a database. Furthermore, the data collection unit can conduct surveys and interviews with candidates to collect detailed information such as their skill sets, past work experience, and cultural background. This allows the data collection unit to comprehensively collect job posting and candidate information and provide it to the analysis unit. The data collection unit also has functions to verify and cleanse data to ensure the accuracy and reliability of the collected information. For example, it checks for errors and inconsistencies in the collected information and makes corrections or additions as necessary. This allows the data collection unit to provide accurate and reliable information.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses natural language processing to perform in-depth analysis of job postings and candidate information. The analysis unit compares the required skills listed in the job posting with the candidate's skill set to determine the degree of fit. The analysis unit also compares the company culture with the candidate's cultural background to determine cultural fit. Specifically, the analysis unit uses natural language processing technology to analyze the text data of job postings and extract information on required skills, job duties, and company culture. Similarly, it analyzes text data such as candidate resumes, work histories, and online profiles to extract information on skill sets, past work experience, and cultural background. Based on this information, the analysis unit executes algorithms to determine the degree of fit between job postings and candidates. For example, it uses a skill matching algorithm to compare the required skills listed in the job posting with the candidate's skill set and scores the degree of fit. It also compares the company culture with the candidate's cultural background to determine cultural fit and scores the degree of fit. Based on these scores, the analysis unit comprehensively evaluates the candidate's suitability and provides it to the selection unit. Furthermore, the analysis unit utilizes past data and statistical information to learn and improve the accuracy of its analysis results. For example, it learns successful matching patterns based on past recruitment data and incorporates them into future analyses. This allows the analysis unit to provide more accurate analysis results.

[0032] The selection unit selects the most suitable candidates based on the information analyzed by the analysis unit. For example, the selection unit selects candidates with high skill and cultural fit scores and proposes them to companies. The selection unit selects candidates whose skill sets match the job requirements and who also have a high cultural fit. Specifically, the selection unit runs an algorithm to select the most suitable candidates based on the fit scores provided by the analysis unit. For example, it comprehensively evaluates skill fit and cultural fit scores and selects candidates who meet certain criteria. The selection unit can also select candidates considering the specific requirements and priorities of the company. For example, if certain skills or experience are particularly important, it applies an algorithm that prioritizes evaluating those requirements. The selection unit organizes information on the selected candidates and creates a report for proposal to the company. The report includes detailed information on the candidate's skill set, work experience, and cultural background, serving as a reference for the company to evaluate the candidates. Furthermore, the selection unit can provide companies with detailed information on the selection criteria and algorithms to ensure transparency and fairness in the selection process. This allows the selection unit to provide companies with highly reliable candidate selection, supporting the streamlining and accuracy of the recruitment process.

[0033] The Proposal Department proposes candidates selected by the Picking Department to companies. For example, the Proposal Department proposes candidates to companies. Specifically, the Proposal Department makes detailed proposals to companies based on the candidate information provided by the Picking Department. The Proposal Department organizes information on candidates' skill sets, work experience, and cultural backgrounds and provides it in a format that makes it easy for companies to evaluate candidates. For example, it creates presentation materials and reports that highlight the candidate's strengths and suitability and submits them to companies. The Proposal Department also works with company recruiters to address additional information and questions about candidates. The Proposal Department collects company requests and feedback and provides this feedback to the Picking Department and Analysis Department to improve the accuracy and effectiveness of the overall system. Furthermore, the Proposal Department can also assist in coordinating interviews and the selection process with candidates. For example, it coordinates interview schedules between candidates and companies and supports the progress of interviews. The Proposal Department also tracks candidate selection results and manages the progress of the recruitment process. In this way, the Proposal Department can provide companies with quick and accurate candidate proposals, helping to streamline the recruitment process and improve the success rate. The proposal department plays a crucial role in facilitating smooth communication between companies and candidates and achieving optimal matching that meets the needs of both parties.

[0034] The analysis unit can perform deep analysis of job postings and candidate information using natural language processing. For example, the analysis unit can analyze job postings and candidate information using morphological analysis. The analysis unit can also analyze job postings and candidate information using grammatical analysis. The analysis unit can also analyze job postings and candidate information using semantic analysis. This allows for deeper analysis of job postings and candidate information using natural language processing, enabling more accurate matching. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs job postings and candidate information into a generative AI, and the generative AI performs deep analysis of the information.

[0035] The selection unit can select the most suitable candidates by considering their skill set and cultural fit. For example, the selection unit can select the most suitable candidates by considering their skill set. The selection unit can also select the most suitable candidates by considering their cultural fit. The selection unit can also select the most suitable candidates by considering both their skill set and cultural fit. This allows for the selection of more suitable candidates by considering both their skill set and cultural fit. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit inputs information on skill sets and cultural fit into a generative AI, and the generative AI selects the most suitable candidates.

[0036] The proposal department can propose candidates to companies. For example, the proposal department can propose candidates to companies. The proposal department can also provide companies with information on candidates' skill sets and cultural fit. The proposal department can also explain the candidates' suitability to companies. This improves the accuracy of companies' recruitment by proposing candidates to them. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department inputs candidate information into a generative AI, and the generative AI proposes candidates to companies.

[0037] The optimization unit can optimize the matching algorithm using machine learning. For example, the optimization unit can optimize the matching algorithm using a neural network. The optimization unit can also optimize the matching algorithm using a support vector machine. The optimization unit can also optimize the matching algorithm using a decision tree. This allows for the optimization of the matching algorithm using machine learning, enabling more accurate matching. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit inputs the matching algorithm to a generative AI, and the generative AI optimizes the algorithm.

[0038] The fit unit can consider skill sets and cultural fit. For example, the fit unit selects candidates by considering their skill sets. The fit unit can also select candidates by considering their cultural fit. The fit unit can also select candidates by considering both their skill sets and cultural fit. This allows for the selection of more suitable candidates by considering both skill sets and cultural fit. Some or all of the above processing in the fit unit may be performed using, for example, generative AI, or without generative AI. For example, the fit unit inputs skill set and cultural fit information into a generative AI, and the generative AI selects candidates.

[0039] The learning unit can learn past matching results. For example, the learning unit can learn successful matching results. The learning unit can also learn failed matching results. The learning unit can also learn both successful and unsuccessful matching results. This improves the accuracy of matching by learning past matching results. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit inputs past matching results into the AI, and the AI ​​learns the results.

[0040] The data collection unit can analyze past collected data and select the optimal data collection method. For example, the data collection unit can identify the most effective data collection method from past collected data and prioritize its use. The data collection unit can also analyze past collected data to find areas for improvement in data collection methods and optimize the data collection process. For example, the data collection unit can analyze patterns in data collection methods based on past collected data and select the optimal data collection method. This allows for the selection of the optimal data collection method and optimization of the data collection process by analyzing past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into AI, which will analyze the data and select the optimal data collection method.

[0041] The data collection unit can filter information based on a company's current hiring needs and the job-seeking status of candidates. For example, the data collection unit can prioritize collecting information on candidates with the necessary skill sets based on a company's current hiring needs. The data collection unit can also prioritize collecting information on candidates who are actively seeking employment, taking into account their job-seeking status. The data collection unit can also comprehensively assess a company's hiring needs and the job-seeking status of candidates and filter and collect the most relevant information. This allows for the collection of more appropriate information by filtering based on a company's hiring needs and the job-seeking status of candidates. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit inputs information on a company's hiring needs and the job-seeking status of candidates into an AI, which then performs the filtering.

[0042] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of companies. For example, the data collection unit can prioritize the collection of candidate information that is close to the company's location. The data collection unit can also prioritize the collection of information for a specific region based on the company's geographical recruitment needs. The data collection unit can also filter and collect highly relevant information based on the company's geographical location. This allows for the priority collection of highly relevant information by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the company's geographical location information into the AI, and the AI ​​filters and collects highly relevant information.

[0043] The data collection unit can analyze candidates' social media activity and collect relevant information. For example, the data collection unit can analyze candidates' social media activity and collect information on candidates with relevant skills and experience for the job. For example, the data collection unit can also determine cultural fit from candidates' social media activity and collect relevant information. For example, the data collection unit can prioritize collecting information on candidates with high job-seeking motivation based on their social media activity. This allows relevant information to be collected by analyzing candidates' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit inputs data on candidates' social media activity into AI, which analyzes the data and collects relevant information.

[0044] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit can perform a simplified analysis on information of low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the information. 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 inputs the importance of the information into the AI, and the AI ​​adjusts the level of detail of the analysis.

[0045] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a skill matching algorithm to information about skills. For example, the analysis unit can also apply a cultural fit algorithm to information about cultural fit. The analysis unit can also select and apply the most suitable analysis algorithm depending on the category of information. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the category of information into the AI, and the AI ​​selects and applies the most suitable analysis algorithm.

[0046] The analysis unit can determine the priority of analysis based on the information submission date. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may lower the priority of analysis for older information. The analysis unit may also dynamically adjust the analysis priority based on the information submission date. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the information submission date. 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 inputs the information submission date into the AI, and the AI ​​determines the analysis priority.

[0047] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the relevance of the information into the AI, and the AI ​​adjusts the order of analysis.

[0048] The selection unit can improve the accuracy of its selections by considering the interrelationships of information. For example, the selection unit analyzes the interrelationships between job postings and candidate information to select the most suitable candidates. The selection unit can also improve the accuracy of its selections by considering the interrelationships of information. For example, the selection unit can dynamically adjust the selection criteria based on the interrelationships of information. This improves the accuracy of the selections by considering the interrelationships of information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs the interrelationships of information into the AI, and the AI ​​improves the accuracy of the selections.

[0049] The selection unit can perform selections while considering the attribute information of the information submitter. For example, the selection unit can consider the skill set of the information submitter and select the most suitable candidate. For example, the selection unit can consider the cultural background of the information submitter and select candidates with a high cultural fit. For example, the selection unit can dynamically adjust the selection criteria based on the attribute information of the information submitter. This allows for the selection of more appropriate candidates by considering the attribute information of the information submitter. Some or all of the above processes in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit inputs the attribute information of the information submitter into AI, and the AI ​​performs the selection.

[0050] The selection unit can perform selection while considering the geographical distribution of information. For example, the selection unit analyzes the geographical distribution of information and selects the most suitable candidates. The selection unit can also improve the accuracy of selection by considering geographical distribution. For example, the selection unit can dynamically adjust the selection criteria based on geographical distribution. This allows for the selection of more appropriate candidates by considering the geographical distribution of information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs the geographical distribution of information into the AI, and the AI ​​performs the selection.

[0051] The selection unit can improve the accuracy of its selections by referring to relevant literature for the information. For example, the selection unit refers to relevant literature for the information and selects the most suitable candidates. The selection unit can also improve the accuracy of its selections by considering relevant literature. The selection unit can also dynamically adjust the selection criteria based on relevant literature. This improves the accuracy of the selections by referring to relevant literature for the information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs relevant literature for the information into the AI, and the AI ​​improves the accuracy of the selections.

[0052] The proposal unit can adjust the level of detail of its proposals based on the importance of the information. For example, the proposal unit can provide detailed proposals for highly important information. For example, it can also provide simplified proposals for less important information. The proposal unit can also dynamically adjust the level of detail of its proposals according to the importance of the information. This allows for the provision of more appropriate proposals by adjusting the level of detail of proposals based on the importance of the information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit inputs the importance of the information into the AI, and the AI ​​adjusts the level of detail of the proposals.

[0053] The proposal unit can apply different proposal algorithms depending on the category of information. For example, the proposal unit can apply a skill matching algorithm to information about skills. For example, the proposal unit can also apply a cultural fit algorithm to information about cultural fit. The proposal unit can also select and apply the most suitable proposal algorithm depending on the category of information. This improves the accuracy of the proposals by applying the most suitable proposal algorithm according to the category of information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit inputs the category of information into the AI, and the AI ​​selects and applies the most suitable proposal algorithm.

[0054] The proposal department can determine the priority of proposals based on when the information was submitted. For example, the proposal department will prioritize the most recent information. For example, the proposal department may lower the priority of older information. The proposal department can also dynamically adjust the priority of proposals based on when the information was submitted. This allows for the provision of more appropriate proposals by determining the priority of proposals based on when the information was submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department inputs the information submission dates into the AI, and the AI ​​determines the priority of proposals.

[0055] The suggestion unit can adjust the order of suggestions based on the relevance of the information. For example, the suggestion unit will prioritize suggesting highly relevant information. For example, the suggestion unit can also postpone suggesting less relevant information. The suggestion unit can also dynamically adjust the order of suggestions based on the relevance of the information. This allows for the provision of suggestions in a more appropriate order by adjusting the order of suggestions based on the relevance of the information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit inputs the relevance of the information into the AI, and the AI ​​adjusts the order of suggestions.

[0056] The optimization unit can optimize the optimization algorithm by referring to past optimization data. For example, the optimization unit can select and apply the optimal algorithm based on past optimization data. The optimization unit can also analyze past optimization data to find areas for improvement in the algorithm and optimize it. The optimization unit can also dynamically adjust the optimization algorithm based on past optimization data. This optimizes the optimization algorithm and improves accuracy by referring to past optimization data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs past optimization data into the AI, and the AI ​​refers to the data to optimize the optimization algorithm.

[0057] The optimization unit can weight the optimization data based on when the information was submitted. For example, the optimization unit can assign a higher weight to the most recent information to improve the accuracy of the optimization. For example, the optimization unit can assign a lower weight to older information to lower its optimization priority. The optimization unit can also dynamically adjust the weighting of the optimization data based on when the information was submitted. This allows for more appropriate optimization by weighting the optimization data based on when the information was submitted. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs the information submission dates to the AI, and the AI ​​performs the weighting of the optimization data.

[0058] The fitting unit can improve the accuracy of the fit by considering the interrelationships of information. For example, the fitting unit analyzes the interrelationships between job postings and candidate information to provide the optimal fit. The fitting unit can also improve the accuracy of the fit by considering the interrelationships of information. For example, the fitting unit can dynamically adjust the fitting criteria based on the interrelationships of information. This improves the accuracy of the fit by considering the interrelationships of information. Some or all of the above processing in the fitting unit may be performed using AI, for example, or without AI. For example, the fitting unit inputs the interrelationships of information into the AI, and the AI ​​improves the accuracy of the fit.

[0059] The fitting unit can perform fitting while considering the geographical distribution of information. For example, the fitting unit analyzes the geographical distribution of information and provides the optimal fit. The fitting unit can also improve the accuracy of the fit by considering the geographical distribution. The fitting unit can also dynamically adjust the fitting criteria based on the geographical distribution. This allows for a more appropriate fit by considering the geographical distribution of information. Some or all of the above processing in the fitting unit may be performed using AI, for example, or without AI. For example, the fitting unit inputs the geographical distribution of information into the AI, and the AI ​​performs the fitting.

[0060] The learning unit can optimize the learning algorithm by referring to past training data. For example, the learning unit can select and apply the optimal algorithm based on past training data. The learning unit can also analyze past training data to find areas for improvement in the algorithm and optimize it. The learning unit can also dynamically adjust the learning algorithm based on past training data. This optimizes the learning algorithm and improves accuracy by referring to past training data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit inputs past training data into the AI, and the AI ​​refers to the data to optimize the learning algorithm.

[0061] The learning unit can weight the training data based on when the information was submitted. For example, the learning unit can assign a higher weight to the most recent information to improve the accuracy of learning. The learning unit can also assign a lower weight to older information to reduce its learning priority. The learning unit can also dynamically adjust the weighting of the training data based on when the information was submitted. This allows for more appropriate learning by weighting the training data based on when the information was submitted. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit inputs the information submission dates into the AI, and the AI ​​weights the training data.

[0062] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0063] The data collection unit can filter information based on a company's current hiring needs and the job-seeking status of candidates. For example, the data collection unit can prioritize collecting information on candidates with the necessary skill sets based on a company's current hiring needs. The data collection unit can also prioritize collecting information on candidates who are actively seeking employment, taking into account their job-seeking status. The data collection unit can also comprehensively assess a company's hiring needs and the job-seeking status of candidates and filter and collect the most relevant information. This allows for the collection of more appropriate information by filtering based on a company's hiring needs and the job-seeking status of candidates. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit inputs information on a company's hiring needs and the job-seeking status of candidates into an AI, which then performs the filtering.

[0064] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of companies. For example, the data collection unit can prioritize the collection of candidate information that is close to the company's location. The data collection unit can also prioritize the collection of information for a specific region based on the company's geographical recruitment needs. The data collection unit can also filter and collect highly relevant information based on the company's geographical location. This allows for the priority collection of highly relevant information by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the company's geographical location information into the AI, and the AI ​​filters and collects highly relevant information.

[0065] The data collection unit can analyze candidates' social media activity and collect relevant information. For example, the data collection unit can analyze candidates' social media activity and collect information on candidates with relevant skills and experience for the job. For example, the data collection unit can also determine cultural fit from candidates' social media activity and collect relevant information. For example, the data collection unit can prioritize collecting information on candidates with high job-seeking motivation based on their social media activity. This allows relevant information to be collected by analyzing candidates' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit inputs data on candidates' social media activity into AI, which analyzes the data and collects relevant information.

[0066] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a skill matching algorithm to information about skills. For example, the analysis unit can also apply a cultural fit algorithm to information about cultural fit. The analysis unit can also select and apply the most suitable analysis algorithm depending on the category of information. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the category of information into the AI, and the AI ​​selects and applies the most suitable analysis algorithm.

[0067] The analysis unit can determine the priority of analysis based on the information submission date. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may lower the priority of analysis for older information. The analysis unit may also dynamically adjust the analysis priority based on the information submission date. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the information submission date. 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 inputs the information submission date into the AI, and the AI ​​determines the analysis priority.

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

[0069] Step 1: The collection department collects job postings and candidate information. For example, job postings include detailed information such as job description, required skills, and company culture, while candidate information includes resumes, work histories, skill sets, past work experience, and cultural background. The collection department collects this information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses natural language processing to perform deep analysis on job postings and candidate information. The analysis unit compares the required skills listed in the job posting with the candidate's skill set to determine the degree of fit. The analysis unit also compares the company culture with the candidate's cultural background to determine the cultural fit. Step 3: The selection unit selects the most suitable candidates based on the information analyzed by the analysis unit. For example, it selects candidates with a high degree of skill and cultural fit and proposes them to the company. The selection unit selects candidates whose skill set matches the requirements of the job posting and who also have a high cultural fit. Step 4: The proposal department proposes the candidates selected by the selection department to the companies. For example, they propose candidates to companies. This improves the accuracy of the companies' recruitment process and reduces mismatches.

[0070] (Example of form 2) An AI agent system according to an embodiment of the present invention is a system that deeply analyzes job postings and candidate profiles to perform optimal matching. This AI agent system collects information on job postings and candidates and performs deep analysis using natural language processing to improve the accuracy of corporate recruitment and reduce mismatches. For example, the AI ​​agent system collects information on job postings and candidates. Job postings include detailed information such as job description, required skills, and corporate culture, while candidate information includes resumes, work histories, skill sets, past work experience, and cultural background. Next, the AI ​​agent system performs deep analysis of the collected information using natural language processing. The AI ​​agent analyzes the information on job postings and candidates and performs matching considering skills and cultural fit. For example, the AI ​​agent compares the required skills listed in the job posting with the candidate's skill set and determines the degree of fit. It also compares the corporate culture with the candidate's cultural background and determines the cultural fit. Based on the analyzed information, the AI ​​agent selects the most suitable candidates. The AI ​​agent selects candidates with a high degree of skill and cultural fit and proposes them to the company. For example, an AI agent selects candidates whose skill sets match the requirements of the job posting and who also have a high cultural fit. This mechanism improves the accuracy of a company's recruitment and reduces mismatches. Because matching is performed considering skills and cultural fit, companies can quickly hire the right talent. In addition, employee satisfaction and retention rates improve, preventing a decline in employee retention within the company. For example, by having an AI agent perform matching considering skill sets and cultural fit, companies can hire the right talent and improve employee satisfaction and retention rates. Furthermore, the AI ​​agent optimizes its matching algorithm using machine learning. This improves the accuracy of matching and allows it to propose candidates that meet the company's needs. For example, the AI ​​agent can learn from past matching results and optimize its matching algorithm to propose more suitable candidates. In this way, using an AI agent improves the accuracy of a company's recruitment and reduces mismatches.By enabling optimal talent matching that takes skills and cultural fit into consideration, companies can improve their recruitment success rates and retention rates. This allows AI agent systems to enhance the accuracy of companies' recruitment and reduce mismatches.

[0071] The AI ​​agent system according to this embodiment comprises a collection unit, an analysis unit, a pick-up unit, and a proposal unit. The collection unit collects information on job postings and candidates. For example, the collection unit collects detailed information such as job description, required skills, and company culture from job postings, and information on candidates such as resumes, work histories, skill sets, past work experience, and cultural backgrounds from candidate profiles. The collection unit collects this information. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit performs deep analysis of the job posting and candidate information using natural language processing. The analysis unit compares the required skills listed in the job posting with the candidate's skill set and determines the degree of fit. The analysis unit also compares the company culture with the candidate's cultural background and determines the cultural fit. The pick-up unit picks the most suitable candidates based on the information analyzed by the analysis unit. For example, the pick-up unit selects candidates with a high degree of skill and cultural fit and proposes them to the company. The pick-up unit selects candidates whose skill sets match the requirements of the job posting and who also have a high cultural fit. The proposal unit proposes candidates selected by the selection unit to companies. For example, the proposal unit proposes candidates to companies. In this way, the AI ​​agent system according to the embodiment collects and analyzes job postings and candidate information, selects the most suitable candidates, and proposes them to companies, thereby improving the accuracy of companies' recruitment and reducing mismatches. Some or all of the above-described processes in the collection unit, analysis unit, selection unit, and proposal unit may be performed using AI, for example, or not using AI. For example, the collection unit collects job postings and candidate information, the analysis unit inputs the information collected by the collection unit into the AI, and the AI ​​analyzes the information. The selection unit inputs the information analyzed by the analysis unit into the AI, and the AI ​​selects the most suitable candidates. The proposal unit inputs the candidates selected by the selection unit into the AI, and the AI ​​proposes them to companies.

[0072] The data collection unit collects job postings and candidate information. For example, job postings include detailed information such as job description, required skills, and company culture, while candidate information includes resumes, work histories, skill sets, past work experience, and cultural background. The data collection unit collects this information. Specifically, the unit has the functionality to automatically retrieve job postings from company recruitment websites and job information sites. This allows for the rapid collection of the latest job information. Candidate information is collected from resumes, work histories, and online profiles registered by candidates. The data collection unit centrally manages this information and stores it in a database. Furthermore, the data collection unit can conduct surveys and interviews with candidates to collect detailed information such as their skill sets, past work experience, and cultural background. This allows the data collection unit to comprehensively collect job posting and candidate information and provide it to the analysis unit. The data collection unit also has functions to verify and cleanse data to ensure the accuracy and reliability of the collected information. For example, it checks for errors and inconsistencies in the collected information and makes corrections or additions as necessary. This allows the data collection unit to provide accurate and reliable information.

[0073] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit uses natural language processing to perform in-depth analysis of job postings and candidate information. The analysis unit compares the required skills listed in the job posting with the candidate's skill set to determine the degree of fit. The analysis unit also compares the company culture with the candidate's cultural background to determine cultural fit. Specifically, the analysis unit uses natural language processing technology to analyze the text data of job postings and extract information on required skills, job duties, and company culture. Similarly, it analyzes text data such as candidate resumes, work histories, and online profiles to extract information on skill sets, past work experience, and cultural background. Based on this information, the analysis unit executes algorithms to determine the degree of fit between job postings and candidates. For example, it uses a skill matching algorithm to compare the required skills listed in the job posting with the candidate's skill set and scores the degree of fit. It also compares the company culture with the candidate's cultural background to determine cultural fit and scores the degree of fit. Based on these scores, the analysis unit comprehensively evaluates the candidate's suitability and provides it to the selection unit. Furthermore, the analysis unit utilizes past data and statistical information to learn and improve the accuracy of its analysis results. For example, it learns successful matching patterns based on past recruitment data and incorporates them into future analyses. This allows the analysis unit to provide more accurate analysis results.

[0074] The selection unit selects the most suitable candidates based on the information analyzed by the analysis unit. For example, the selection unit selects candidates with high skill and cultural fit scores and proposes them to companies. The selection unit selects candidates whose skill sets match the job requirements and who also have a high cultural fit. Specifically, the selection unit runs an algorithm to select the most suitable candidates based on the fit scores provided by the analysis unit. For example, it comprehensively evaluates skill fit and cultural fit scores and selects candidates who meet certain criteria. The selection unit can also select candidates considering the specific requirements and priorities of the company. For example, if certain skills or experience are particularly important, it applies an algorithm that prioritizes evaluating those requirements. The selection unit organizes information on the selected candidates and creates a report for proposal to the company. The report includes detailed information on the candidate's skill set, work experience, and cultural background, serving as a reference for the company to evaluate the candidates. Furthermore, the selection unit can provide companies with detailed information on the selection criteria and algorithms to ensure transparency and fairness in the selection process. This allows the selection unit to provide companies with highly reliable candidate selection, supporting the streamlining and accuracy of the recruitment process.

[0075] The Proposal Department proposes candidates selected by the Picking Department to companies. For example, the Proposal Department proposes candidates to companies. Specifically, the Proposal Department makes detailed proposals to companies based on the candidate information provided by the Picking Department. The Proposal Department organizes information on candidates' skill sets, work experience, and cultural backgrounds and provides it in a format that makes it easy for companies to evaluate candidates. For example, it creates presentation materials and reports that highlight the candidate's strengths and suitability and submits them to companies. The Proposal Department also works with company recruiters to address additional information and questions about candidates. The Proposal Department collects company requests and feedback and provides this feedback to the Picking Department and Analysis Department to improve the accuracy and effectiveness of the overall system. Furthermore, the Proposal Department can also assist in coordinating interviews and the selection process with candidates. For example, it coordinates interview schedules between candidates and companies and supports the progress of interviews. The Proposal Department also tracks candidate selection results and manages the progress of the recruitment process. In this way, the Proposal Department can provide companies with quick and accurate candidate proposals, helping to streamline the recruitment process and improve the success rate. The proposal department plays a crucial role in facilitating smooth communication between companies and candidates and achieving optimal matching that meets the needs of both parties.

[0076] The analysis unit can perform deep analysis of job postings and candidate information using natural language processing. For example, the analysis unit can analyze job postings and candidate information using morphological analysis. The analysis unit can also analyze job postings and candidate information using grammatical analysis. The analysis unit can also analyze job postings and candidate information using semantic analysis. This allows for deeper analysis of job postings and candidate information using natural language processing, enabling more accurate matching. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit inputs job postings and candidate information into a generative AI, and the generative AI performs deep analysis of the information.

[0077] The selection unit can select the most suitable candidates by considering their skill set and cultural fit. For example, the selection unit can select the most suitable candidates by considering their skill set. The selection unit can also select the most suitable candidates by considering their cultural fit. The selection unit can also select the most suitable candidates by considering both their skill set and cultural fit. This allows for the selection of more suitable candidates by considering both their skill set and cultural fit. Some or all of the above processing in the selection unit may be performed using, for example, a generative AI, or without a generative AI. For example, the selection unit inputs information on skill sets and cultural fit into a generative AI, and the generative AI selects the most suitable candidates.

[0078] The proposal department can propose candidates to companies. For example, the proposal department can propose candidates to companies. The proposal department can also provide companies with information on candidates' skill sets and cultural fit. The proposal department can also explain the candidates' suitability to companies. This improves the accuracy of companies' recruitment by proposing candidates to them. Some or all of the above processes in the proposal department may be performed using, for example, generative AI, or not using generative AI. For example, the proposal department inputs candidate information into a generative AI, and the generative AI proposes candidates to companies.

[0079] The optimization unit can optimize the matching algorithm using machine learning. For example, the optimization unit can optimize the matching algorithm using a neural network. The optimization unit can also optimize the matching algorithm using a support vector machine. The optimization unit can also optimize the matching algorithm using a decision tree. This allows for the optimization of the matching algorithm using machine learning, enabling more accurate matching. Some or all of the above-described processes in the optimization unit may be performed using, for example, a generative AI, or without a generative AI. For example, the optimization unit inputs the matching algorithm to a generative AI, and the generative AI optimizes the algorithm.

[0080] The fit unit can consider skill sets and cultural fit. For example, the fit unit selects candidates by considering their skill sets. The fit unit can also select candidates by considering their cultural fit. The fit unit can also select candidates by considering both their skill sets and cultural fit. This allows for the selection of more suitable candidates by considering both skill sets and cultural fit. Some or all of the above processing in the fit unit may be performed using, for example, generative AI, or without generative AI. For example, the fit unit inputs skill set and cultural fit information into a generative AI, and the generative AI selects candidates.

[0081] The learning unit can learn past matching results. For example, the learning unit can learn successful matching results. The learning unit can also learn failed matching results. The learning unit can also learn both successful and unsuccessful matching results. This improves the accuracy of matching by learning past matching results. Some or all of the above processing in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit inputs past matching results into the AI, and the AI ​​learns the results.

[0082] The data collection unit can estimate the user's emotions and adjust the timing of collecting job postings and candidate information based on the estimated emotions. For example, if the user is stressed, the data collection unit can delay the collection timing so that the user can provide information in a relaxed state. For example, if the user is relaxed, the data collection unit can collect information immediately and start analysis quickly. For example, if the user is in a hurry, the data collection unit can advance the collection timing to collect information quickly. This allows for information to be collected at a more appropriate time by adjusting the collection timing based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the collection timing.

[0083] The data collection unit can analyze past collected data and select the optimal data collection method. For example, the data collection unit can identify the most effective data collection method from past collected data and prioritize its use. The data collection unit can also analyze past collected data to find areas for improvement in data collection methods and optimize the data collection process. For example, the data collection unit can analyze patterns in data collection methods based on past collected data and select the optimal data collection method. This allows for the selection of the optimal data collection method and optimization of the data collection process by analyzing past collected data. Some or all of the above processes in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit can input past collected data into AI, which will analyze the data and select the optimal data collection method.

[0084] The data collection unit can filter information based on a company's current hiring needs and the job-seeking status of candidates. For example, the data collection unit can prioritize collecting information on candidates with the necessary skill sets based on a company's current hiring needs. The data collection unit can also prioritize collecting information on candidates who are actively seeking employment, taking into account their job-seeking status. The data collection unit can also comprehensively assess a company's hiring needs and the job-seeking status of candidates and filter and collect the most relevant information. This allows for the collection of more appropriate information by filtering based on a company's hiring needs and the job-seeking status of candidates. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit inputs information on a company's hiring needs and the job-seeking status of candidates into an AI, which then performs the filtering.

[0085] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority information to reduce the user's burden. For example, if the user is relaxed, the data collection unit can also collect detailed information and perform more accurate analysis. For example, if the user is in a hurry, the data collection unit can also prioritize collecting information that can be collected quickly. This allows for the collection of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit inputs user emotion data into the AI, which estimates the emotions and determines the priority of information.

[0086] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of companies. For example, the data collection unit can prioritize the collection of candidate information that is close to the company's location. The data collection unit can also prioritize the collection of information for a specific region based on the company's geographical recruitment needs. The data collection unit can also filter and collect highly relevant information based on the company's geographical location. This allows for the priority collection of highly relevant information by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the company's geographical location information into the AI, and the AI ​​filters and collects highly relevant information.

[0087] The data collection unit can analyze candidates' social media activity and collect relevant information. For example, the data collection unit can analyze candidates' social media activity and collect information on candidates with relevant skills and experience for the job. For example, the data collection unit can also determine cultural fit from candidates' social media activity and collect relevant information. For example, the data collection unit can prioritize collecting information on candidates with high job-seeking motivation based on their social media activity. This allows relevant information to be collected by analyzing candidates' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit inputs data on candidates' social media activity into AI, which analyzes the data and collects relevant information.

[0088] The analysis unit can estimate the user's emotions and adjust the presentation of the analysis based on the estimated emotions. For example, if the user is tense, the analysis unit can provide a simple and easy-to-understand analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is in a hurry, the analysis unit can also provide a concise analysis result. By adjusting the presentation of the analysis based on the user's emotions, more appropriate analysis results 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 analysis unit may be performed using AI, or not using AI. For example, the analysis unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the presentation of the analysis.

[0089] The analysis unit can adjust the level of detail of the analysis based on the importance of the information. For example, the analysis unit performs a detailed analysis on information of high importance. For example, the analysis unit can perform a simplified analysis on information of low importance. The analysis unit can also dynamically adjust the level of detail of the analysis according to the importance of the information. This allows for the provision of more appropriate analysis results by adjusting the level of detail of the analysis based on the importance of the information. 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 inputs the importance of the information into the AI, and the AI ​​adjusts the level of detail of the analysis.

[0090] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a skill matching algorithm to information about skills. For example, the analysis unit can also apply a cultural fit algorithm to information about cultural fit. The analysis unit can also select and apply the most suitable analysis algorithm depending on the category of information. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the category of information into the AI, and the AI ​​selects and applies the most suitable analysis algorithm.

[0091] The analysis unit can estimate the user's emotions and adjust the length of the analysis based on the estimated emotions. For example, if the user is in a hurry, the analysis unit can provide a short, concise analysis result. For example, if the user is relaxed, the analysis unit can also provide a detailed analysis result. For example, if the user is excited, the analysis unit can also provide a visually stimulating analysis result. By adjusting the length of the analysis based on the user's emotions, more appropriate analysis results 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 analysis unit may be performed using AI or not using AI. For example, the analysis unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the length of the analysis.

[0092] The analysis unit can determine the priority of analysis based on the information submission date. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may lower the priority of analysis for older information. The analysis unit may also dynamically adjust the analysis priority based on the information submission date. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the information submission date. 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 inputs the information submission date into the AI, and the AI ​​determines the analysis priority.

[0093] The analysis unit can adjust the order of analysis based on the relevance of the information. For example, the analysis unit prioritizes the analysis of highly relevant information. For example, the analysis unit can postpone the analysis of less relevant information. The analysis unit can also dynamically adjust the order of analysis based on the relevance of the information. This allows for the provision of more appropriate analysis results by adjusting the order of analysis based on the relevance of the information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the relevance of the information into the AI, and the AI ​​adjusts the order of analysis.

[0094] The selection unit can estimate the user's emotions and adjust the selection criteria based on the estimated emotions. For example, if the user is nervous, the selection unit provides simple and easily understandable selection criteria. For example, if the user is relaxed, the selection unit can also provide detailed selection criteria. For example, if the user is in a hurry, the selection unit can provide concise selection criteria. This allows for the selection of more appropriate candidates by adjusting the selection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the selection criteria.

[0095] The selection unit can improve the accuracy of its selections by considering the interrelationships of information. For example, the selection unit analyzes the interrelationships between job postings and candidate information to select the most suitable candidates. The selection unit can also improve the accuracy of its selections by considering the interrelationships of information. For example, the selection unit can dynamically adjust the selection criteria based on the interrelationships of information. This improves the accuracy of the selections by considering the interrelationships of information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs the interrelationships of information into the AI, and the AI ​​improves the accuracy of the selections.

[0096] The selection unit can perform selections while considering the attribute information of the information submitter. For example, the selection unit can consider the skill set of the information submitter and select the most suitable candidate. For example, the selection unit can consider the cultural background of the information submitter and select candidates with a high cultural fit. For example, the selection unit can dynamically adjust the selection criteria based on the attribute information of the information submitter. This allows for the selection of more appropriate candidates by considering the attribute information of the information submitter. Some or all of the above processes in the selection unit may be performed using AI, for example, or not using AI. For example, the selection unit inputs the attribute information of the information submitter into AI, and the AI ​​performs the selection.

[0097] The pickup unit can estimate the user's emotions and adjust the order in which the pickup results are displayed based on the estimated emotions. For example, if the user is nervous, the pickup unit can display the pickup results in a simple and easily visible order. If the user is relaxed, the pickup unit can also display the pickup results in a more detailed order. If the user is in a hurry, the pickup unit can also display the pickup results in a concise order. By adjusting the order in which the pickup results are displayed based on the user's emotions, the results can be displayed in a more appropriate order. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the pickup unit may be performed using AI or not using AI. For example, the pickup unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and adjusts the order in which the pickup results are displayed.

[0098] The selection unit can perform selection while considering the geographical distribution of information. For example, the selection unit analyzes the geographical distribution of information and selects the most suitable candidates. The selection unit can also improve the accuracy of selection by considering geographical distribution. For example, the selection unit can dynamically adjust the selection criteria based on geographical distribution. This allows for the selection of more appropriate candidates by considering the geographical distribution of information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs the geographical distribution of information into the AI, and the AI ​​performs the selection.

[0099] The selection unit can improve the accuracy of its selections by referring to relevant literature for the information. For example, the selection unit refers to relevant literature for the information and selects the most suitable candidates. The selection unit can also improve the accuracy of its selections by considering relevant literature. The selection unit can also dynamically adjust the selection criteria based on relevant literature. This improves the accuracy of the selections by referring to relevant literature for the information. Some or all of the above processing in the selection unit may be performed using AI, for example, or without AI. For example, the selection unit inputs relevant literature for the information into the AI, and the AI ​​improves the accuracy of the selections.

[0100] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit inputs user emotion data into an AI, the AI ​​estimates the emotion, and adjusts the way suggestions are presented.

[0101] The proposal unit can adjust the level of detail of its proposals based on the importance of the information. For example, the proposal unit can provide detailed proposals for highly important information. For example, it can also provide simplified proposals for less important information. The proposal unit can also dynamically adjust the level of detail of its proposals according to the importance of the information. This allows for the provision of more appropriate proposals by adjusting the level of detail of proposals based on the importance of the information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit inputs the importance of the information into the AI, and the AI ​​adjusts the level of detail of the proposals.

[0102] The proposal unit can apply different proposal algorithms depending on the category of information. For example, the proposal unit can apply a skill matching algorithm to information about skills. For example, the proposal unit can also apply a cultural fit algorithm to information about cultural fit. The proposal unit can also select and apply the most suitable proposal algorithm depending on the category of information. This improves the accuracy of the proposals by applying the most suitable proposal algorithm according to the category of information. Some or all of the above processing in the proposal unit may be performed using AI, for example, or without AI. For example, the proposal unit inputs the category of information into the AI, and the AI ​​selects and applies the most suitable proposal algorithm.

[0103] The suggestion unit can estimate the user's emotions and adjust the length of suggestions based on the estimated emotions. For example, if the user is in a hurry, the suggestion unit can provide short, concise suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is excited, the suggestion unit can also provide visually stimulating suggestions. By adjusting the length of suggestions based on the user's emotions, more appropriate suggestions can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not. For example, the suggestion unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the length of the suggestions.

[0104] The proposal department can determine the priority of proposals based on when the information was submitted. For example, the proposal department will prioritize the most recent information. For example, the proposal department may lower the priority of older information. The proposal department can also dynamically adjust the priority of proposals based on when the information was submitted. This allows for the provision of more appropriate proposals by determining the priority of proposals based on when the information was submitted. Some or all of the above processes in the proposal department may be performed using AI, for example, or not using AI. For example, the proposal department inputs the information submission dates into the AI, and the AI ​​determines the priority of proposals.

[0105] The suggestion unit can adjust the order of suggestions based on the relevance of the information. For example, the suggestion unit will prioritize suggesting highly relevant information. For example, the suggestion unit can also postpone suggesting less relevant information. The suggestion unit can also dynamically adjust the order of suggestions based on the relevance of the information. This allows for the provision of suggestions in a more appropriate order by adjusting the order of suggestions based on the relevance of the information. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit inputs the relevance of the information into the AI, and the AI ​​adjusts the order of suggestions.

[0106] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is nervous, the optimization unit can provide a simple and easy-to-understand optimization method. For example, if the user is relaxed, the optimization unit can also provide a detailed optimization method. For example, if the user is in a hurry, the optimization unit can provide a concise optimization method. This allows for more appropriate optimization by adjusting the optimization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the optimization method.

[0107] The optimization unit can optimize the optimization algorithm by referring to past optimization data. For example, the optimization unit can select and apply the optimal algorithm based on past optimization data. The optimization unit can also analyze past optimization data to find areas for improvement in the algorithm and optimize it. The optimization unit can also dynamically adjust the optimization algorithm based on past optimization data. This optimizes the optimization algorithm and improves accuracy by referring to past optimization data. Some or all of the above processes in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs past optimization data into the AI, and the AI ​​refers to the data to optimize the optimization algorithm.

[0108] The optimization unit can estimate the user's emotions and adjust the optimization frequency based on the estimated emotions. For example, if the user is stressed, the optimization unit can lower the optimization frequency to reduce the user's burden. For example, if the user is relaxed, the optimization unit can increase the optimization frequency to improve accuracy. For example, if the user is in a hurry, the optimization unit can increase the optimization frequency to perform optimization quickly. By adjusting the optimization frequency based on the user's emotions, optimization can be performed at a more appropriate frequency. 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 optimization unit may be performed using AI, or not using AI. For example, the optimization unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the optimization frequency.

[0109] The optimization unit can weight the optimization data based on when the information was submitted. For example, the optimization unit can assign a higher weight to the most recent information to improve the accuracy of the optimization. For example, the optimization unit can assign a lower weight to older information to lower its optimization priority. The optimization unit can also dynamically adjust the weighting of the optimization data based on when the information was submitted. This allows for more appropriate optimization by weighting the optimization data based on when the information was submitted. Some or all of the above processing in the optimization unit may be performed using AI, for example, or without AI. For example, the optimization unit inputs the information submission dates to the AI, and the AI ​​performs the weighting of the optimization data.

[0110] The Fit Unit can estimate the user's emotions and adjust the fit criteria based on the estimated emotions. For example, if the user is tense, the Fit Unit can provide simple and easily understandable fit criteria. For example, if the user is relaxed, the Fit Unit can also provide detailed fit criteria. For example, if the user is in a hurry, the Fit Unit can provide concise fit criteria. This allows for the provision of more appropriate fit criteria by adjusting the fit criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Fit Unit may be performed using AI or not using AI. For example, the Fit Unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the fit criteria.

[0111] The fitting unit can improve the accuracy of the fit by considering the interrelationships of information. For example, the fitting unit analyzes the interrelationships between job postings and candidate information to provide the optimal fit. The fitting unit can also improve the accuracy of the fit by considering the interrelationships of information. For example, the fitting unit can dynamically adjust the fitting criteria based on the interrelationships of information. This improves the accuracy of the fit by considering the interrelationships of information. Some or all of the above processing in the fitting unit may be performed using AI, for example, or without AI. For example, the fitting unit inputs the interrelationships of information into the AI, and the AI ​​improves the accuracy of the fit.

[0112] The Fit Unit can estimate the user's emotions and adjust the order in which the Fit results are displayed based on the estimated emotions. For example, if the user is tense, the Fit Unit can display the Fit results in a simple and easy-to-read order. If the user is relaxed, the Fit Unit can also display the Fit results in a more detailed order. If the user is in a hurry, the Fit Unit can also display the Fit results in a concise order. This allows for the display of results in a more appropriate order by adjusting the order in which the Fit results are displayed based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Fit Unit may be performed using AI or not. For example, the Fit Unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the order in which the Fit results are displayed.

[0113] The fitting unit can perform fitting while considering the geographical distribution of information. For example, the fitting unit analyzes the geographical distribution of information and provides the optimal fit. The fitting unit can also improve the accuracy of the fit by considering the geographical distribution. The fitting unit can also dynamically adjust the fitting criteria based on the geographical distribution. This allows for a more appropriate fit by considering the geographical distribution of information. Some or all of the above processing in the fitting unit may be performed using AI, for example, or without AI. For example, the fitting unit inputs the geographical distribution of information into the AI, and the AI ​​performs the fitting.

[0114] The learning unit can estimate the user's emotions and select training data based on the estimated emotions. For example, if the user is nervous, the learning unit provides simple and highly visual training data. For example, if the user is relaxed, the learning unit can also provide detailed training data. For example, if the user is in a hurry, the learning unit can also provide concise training data. This allows for the provision of more appropriate training data by selecting training data based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the learning unit may be performed using AI or not using AI. For example, the learning unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and selects training data.

[0115] The learning unit can optimize the learning algorithm by referring to past training data. For example, the learning unit can select and apply the optimal algorithm based on past training data. The learning unit can also analyze past training data to find areas for improvement in the algorithm and optimize it. The learning unit can also dynamically adjust the learning algorithm based on past training data. This optimizes the learning algorithm and improves accuracy by referring to past training data. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit inputs past training data into the AI, and the AI ​​refers to the data to optimize the learning algorithm.

[0116] The learning unit can estimate the user's emotions and adjust the learning frequency based on the estimated emotions. For example, if the user is stressed, the learning unit can lower the learning frequency to reduce the user's burden. For example, if the user is relaxed, the learning unit can increase the learning frequency to improve accuracy. For example, if the user is in a hurry, the learning unit can increase the learning frequency to learn quickly. This allows for learning at a more appropriate frequency by adjusting the learning frequency based on the user'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 learning unit may be performed using AI or not using AI. For example, the learning unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the learning frequency.

[0117] The learning unit can weight the training data based on when the information was submitted. For example, the learning unit can assign a higher weight to the most recent information to improve the accuracy of learning. The learning unit can also assign a lower weight to older information to reduce its learning priority. The learning unit can also dynamically adjust the weighting of the training data based on when the information was submitted. This allows for more appropriate learning by weighting the training data based on when the information was submitted. Some or all of the above processes in the learning unit may be performed using AI, for example, or without AI. For example, the learning unit inputs the information submission dates into the AI, and the AI ​​weights the training data.

[0118] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0119] The analysis unit can estimate the user's emotions and adjust the analysis priority based on the estimated emotions. For example, if the user is stressed, the analysis unit can prioritize analyzing high-priority information to reduce the user's burden. For example, if the user is relaxed, the analysis unit can prioritize analyzing detailed information to provide more accurate results. For example, if the user is in a hurry, the analysis unit can prioritize analyzing information that can be analyzed quickly. This allows for more appropriate analysis results by adjusting the analysis priority based on the user'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 analysis unit may be performed using AI or not using AI. For example, the analysis unit inputs the user's emotion data into the AI, the AI ​​estimates the emotions, and adjusts the analysis priority.

[0120] The data collection unit can estimate the user's emotions and determine the priority of information to collect based on the estimated emotions. For example, if the user is stressed, the data collection unit can prioritize collecting high-priority information to reduce the user's burden. For example, if the user is relaxed, the data collection unit can also collect detailed information and perform more accurate analysis. For example, if the user is in a hurry, the data collection unit can also prioritize collecting information that can be collected quickly. This allows for the collection of more appropriate information by prioritizing information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the data collection unit may be performed using AI or not using AI. For example, the data collection unit inputs user emotion data into the AI, which estimates the emotions and determines the priority of information.

[0121] The selection unit can estimate the user's emotions and adjust the selection criteria based on the estimated emotions. For example, if the user is nervous, the selection unit provides simple and easily understandable selection criteria. For example, if the user is relaxed, the selection unit can also provide detailed selection criteria. For example, if the user is in a hurry, the selection unit can provide concise selection criteria. This allows for the selection of more appropriate candidates by adjusting the selection criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the selection unit may be performed using AI or not. For example, the selection unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the selection criteria.

[0122] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can provide simple and easily understandable suggestions. If the user is relaxed, the suggestion unit can also provide detailed suggestions. If the user is in a hurry, the suggestion unit can provide concise suggestions. By adjusting the way suggestions are presented based on the user's emotions, more appropriate suggestions can be provided. 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 suggestion unit may be performed using AI or not. For example, the suggestion unit inputs user emotion data into an AI, the AI ​​estimates the emotion, and adjusts the way suggestions are presented.

[0123] The optimization unit can estimate the user's emotions and adjust the optimization method based on the estimated emotions. For example, if the user is nervous, the optimization unit can provide a simple and easy-to-understand optimization method. For example, if the user is relaxed, the optimization unit can also provide a detailed optimization method. For example, if the user is in a hurry, the optimization unit can provide a concise optimization method. This allows for more appropriate optimization by adjusting the optimization method based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the optimization unit may be performed using AI, or not using AI. For example, the optimization unit inputs user emotion data into the AI, the AI ​​estimates the emotions, and adjusts the optimization method.

[0124] The data collection unit can filter information based on a company's current hiring needs and the job-seeking status of candidates. For example, the data collection unit can prioritize collecting information on candidates with the necessary skill sets based on a company's current hiring needs. The data collection unit can also prioritize collecting information on candidates who are actively seeking employment, taking into account their job-seeking status. The data collection unit can also comprehensively assess a company's hiring needs and the job-seeking status of candidates and filter and collect the most relevant information. This allows for the collection of more appropriate information by filtering based on a company's hiring needs and the job-seeking status of candidates. Some or all of the above processing in the data collection unit may be performed using AI, or not. For example, the data collection unit inputs information on a company's hiring needs and the job-seeking status of candidates into an AI, which then performs the filtering.

[0125] The data collection unit can prioritize the collection of highly relevant information by considering the geographical location of companies. For example, the data collection unit can prioritize the collection of candidate information that is close to the company's location. The data collection unit can also prioritize the collection of information for a specific region based on the company's geographical recruitment needs. The data collection unit can also filter and collect highly relevant information based on the company's geographical location. This allows for the priority collection of highly relevant information by considering the company's geographical location. Some or all of the above processing in the data collection unit may be performed using AI, for example, or without AI. For example, the data collection unit inputs the company's geographical location information into the AI, and the AI ​​filters and collects highly relevant information.

[0126] The data collection unit can analyze candidates' social media activity and collect relevant information. For example, the data collection unit can analyze candidates' social media activity and collect information on candidates with relevant skills and experience for the job. For example, the data collection unit can also determine cultural fit from candidates' social media activity and collect relevant information. For example, the data collection unit can prioritize collecting information on candidates with high job-seeking motivation based on their social media activity. This allows relevant information to be collected by analyzing candidates' social media activity. Some or all of the above processing in the data collection unit may be performed using AI, for example, or not using AI. For example, the data collection unit inputs data on candidates' social media activity into AI, which analyzes the data and collects relevant information.

[0127] The analysis unit can apply different analysis algorithms depending on the category of information. For example, the analysis unit can apply a skill matching algorithm to information about skills. For example, the analysis unit can also apply a cultural fit algorithm to information about cultural fit. The analysis unit can also select and apply the most suitable analysis algorithm depending on the category of information. This improves the accuracy of the analysis by applying the most suitable analysis algorithm according to the category of information. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit inputs the category of information into the AI, and the AI ​​selects and applies the most suitable analysis algorithm.

[0128] The analysis unit can determine the priority of analysis based on the information submission date. For example, the analysis unit may prioritize the analysis of the most recent information. For example, the analysis unit may lower the priority of analysis for older information. The analysis unit may also dynamically adjust the analysis priority based on the information submission date. This allows for the provision of more appropriate analysis results by determining the analysis priority based on the information submission date. 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 inputs the information submission date into the AI, and the AI ​​determines the analysis priority.

[0129] The following briefly describes the processing flow for example form 2.

[0130] Step 1: The collection department collects job postings and candidate information. For example, job postings include detailed information such as job description, required skills, and company culture, while candidate information includes resumes, work histories, skill sets, past work experience, and cultural background. The collection department collects this information. Step 2: The analysis unit analyzes the information collected by the collection unit. For example, it uses natural language processing to perform deep analysis on job postings and candidate information. The analysis unit compares the required skills listed in the job posting with the candidate's skill set to determine the degree of fit. The analysis unit also compares the company culture with the candidate's cultural background to determine the cultural fit. Step 3: The selection unit selects the most suitable candidates based on the information analyzed by the analysis unit. For example, it selects candidates with a high degree of skill and cultural fit and proposes them to the company. The selection unit selects candidates whose skill set matches the requirements of the job posting and who also have a high cultural fit. Step 4: The proposal department proposes the candidates selected by the selection department to the companies. For example, they propose candidates to companies. This improves the accuracy of the companies' recruitment process and reduces mismatches.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] Each of the multiple elements described above, including the collection unit, analysis unit, pick-up unit, proposal unit, optimization unit, fit unit, and learning unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the collection unit collects job postings and candidate information using the camera 42 and microphone 38B of the smart device 14 and transmits the information to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs deep analysis of the collected information using natural language processing. The pick-up unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects the most suitable candidate based on the analyzed information. The proposal unit is implemented, for example, by the control unit 46A of the smart device 14 and proposes the selected candidate to the company. The optimization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and optimizes the matching algorithm using machine learning. The fit unit is implemented, for example, by the control unit 46A of the smart device 14 and selects a candidate considering their skill set and cultural fit. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which learns past matching results and improves the accuracy of the matching. 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.

[0135] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the collection unit, analysis unit, pickup unit, proposal unit, optimization unit, fit unit, and learning unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the collection unit collects job postings and candidate information using the camera 42 and microphone 238 of the smart glasses 214 and transmits the information to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs deep analysis of the collected information using natural language processing. The pickup unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects the most suitable candidate based on the analyzed information. The proposal unit is implemented, for example, by the control unit 46A of the smart glasses 214 and proposes the selected candidate to the company. The optimization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and optimizes the matching algorithm using machine learning. The fit unit is implemented, for example, by the control unit 46A of the smart glasses 214 and selects a candidate considering their skill set and cultural fit. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which learns past matching results and improves the accuracy of the matching. 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.

[0151] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0156] 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).

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] Each of the multiple elements described above, including the collection unit, analysis unit, pickup unit, proposal unit, optimization unit, fit unit, and learning unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit collects job postings and candidate information using the camera 42 and microphone 238 of the headset terminal 314 and transmits the information to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs deep analysis of the collected information using natural language processing. The pickup unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects the most suitable candidate based on the analyzed information. The proposal unit is implemented, for example, by the control unit 46A of the headset terminal 314 and proposes the selected candidate to the company. The optimization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and optimizes the matching algorithm using machine learning. The fit unit is implemented, for example, by the control unit 46A of the headset terminal 314 and selects candidates considering their skill set and cultural fit. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which learns past matching results and improves the accuracy of the matching. 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.

[0167] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0172] 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).

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.).

[0180] 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.

[0181] 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.

[0182] 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.

[0183] Each of the multiple elements described above, including the collection unit, analysis unit, pickup unit, proposal unit, optimization unit, fit unit, and learning unit, is implemented in at least one of the following: the robot 414 and the data processing unit 12. For example, the collection unit collects job postings and candidate information using the camera 42 and microphone 238 of the robot 414 and transmits the information to the data processing unit 12 via the control unit 46A. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and performs deep analysis of the collected information using natural language processing. The pickup unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and selects the most suitable candidate based on the analyzed information. The proposal unit is implemented, for example, by the control unit 46A of the robot 414 and proposes the selected candidate to the company. The optimization unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12 and optimizes the matching algorithm using machine learning. The fit unit is implemented, for example, by the control unit 46A of the robot 414 and selects a candidate considering their skill set and cultural fit. The learning unit is implemented, for example, by the specific processing unit 290 of the data processing device 12, which learns past matching results and improves the accuracy of the matching. 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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."

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] (Note 1) A collection department that collects job postings and candidate information, An analysis unit analyzes the information collected by the aforementioned collection unit, A selection unit that picks out the most suitable candidates based on the information analyzed by the aforementioned analysis unit, The system comprises a proposal unit that proposes candidates selected by the aforementioned pickup unit to companies. A system characterized by the following features. (Note 2) The aforementioned analysis unit, We use natural language processing to perform deep analysis on job postings and candidate information. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned pickup unit is We select the best candidates based on their skill sets and cultural fit. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned proposal section is, Proposing candidates to companies The system described in Appendix 1, characterized by the features described herein. (Note 5) It includes an optimization unit that optimizes the matching algorithm using machine learning. The system described in Appendix 1, characterized by the features described herein. (Note 6) Features a fit section that takes skill set and cultural fit into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 7) It includes a learning unit that learns from past matching results. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of job posting and candidate information collection based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Analyze past collected data and select the optimal collection method. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Filtering is performed based on the company's current hiring needs and the job-seeking status of candidates. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is It estimates the user's emotions and prioritizes the information to collect based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Prioritize the collection of highly relevant information, taking into account the geographical location of companies. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned collection unit is Analyze candidates' social media activity and collect relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, The system estimates the user's emotions and adjusts the representation of the analysis based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, Adjust the level of detail in the analysis based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Apply different analysis algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, It estimates the user's emotions and adjusts the length of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, Prioritize analysis based on when the information was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Adjust the order of analysis based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned pickup unit is We estimate the user's emotions and adjust the selection criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned pickup unit is Improve the accuracy of the selection process by considering the interrelationships of the information. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned pickup unit is The selection process takes into account the attributes of the information submitters. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned pickup unit is It estimates the user's sentiment and adjusts the order in which the pick-up results are displayed based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned pickup unit is The selection process takes into account the geographical distribution of the information. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned pickup unit is Improve the accuracy of information selection by referring to related literature. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, Adjust the level of detail in the proposal based on the importance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, Apply different proposed algorithms depending on the category of information. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Prioritize proposals based on when the information is submitted. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, Adjust the order of suggestions based on the relevance of the information. The system described in Appendix 1, characterized by the features described herein. (Note 32) The optimization unit is, It estimates the user's emotions and adjusts the optimization method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 33) The optimization unit is, Optimize the optimization algorithm by referring to past optimization data. The system described in Appendix 1, characterized by the features described herein. (Note 34) The optimization unit is, It estimates the user's emotions and adjusts the optimization frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The optimization unit is, Weighting of optimization data based on the timing of information submission. The system described in Appendix 1, characterized by the features described herein. (Note 36) The fit part is, It estimates the user's emotions and adjusts the fit criteria based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 37) The fit part is, Improve the accuracy of the fit by considering the interrelationships of the information. The system described in Appendix 1, characterized by the features described herein. (Note 38) The fit part is, It estimates the user's emotions and adjusts the order in which the fit results are displayed based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 39) The fit part is, Perform a fit while considering the geographical distribution of the information. The system described in Appendix 1, characterized by the features described herein. (Note 40) The aforementioned learning unit, The system estimates the user's emotions and selects training data based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 41) The aforementioned learning unit, Optimize the learning algorithm by referring to past training data. The system described in Appendix 1, characterized by the features described herein. (Note 42) The aforementioned learning unit, It estimates the user's emotions and adjusts the learning frequency based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 43) The aforementioned learning unit, Weighting of training data based on the timing of information submission. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0203] 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. A collection department that collects job postings and candidate information, An analysis unit analyzes the information collected by the aforementioned collection unit, A selection unit that picks out the most suitable candidates based on the information analyzed by the aforementioned analysis unit, The system comprises a proposal unit that proposes candidates selected by the aforementioned pickup unit to companies. A system characterized by the following features.

2. The aforementioned analysis unit, We use natural language processing to perform deep analysis on job postings and candidate information. The system according to feature 1.

3. The aforementioned pickup unit is We select the best candidates based on their skill sets and cultural fit. The system according to feature 1.

4. The aforementioned proposal section is, Proposing candidates to companies The system according to feature 1.

5. It includes an optimization unit that optimizes the matching algorithm using machine learning. The system according to feature 1.

6. Features a fit section that takes into account skill set and cultural fit. The system according to feature 1.

7. It includes a learning unit that learns from past matching results. The system according to feature 1.

8. The aforementioned collection unit is The system estimates user sentiment and adjusts the timing of job posting and candidate information collection based on the estimated user sentiment. The system according to feature 1.

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