system

Abstract

The system according to this embodiment aims to objectively and efficiently evaluate the suitability of candidates in the recruitment process and propose the most suitable role and assignment. [Solution] The system according to the embodiment comprises a collection unit, an analysis unit, an evaluation unit, a proposal unit, a recording unit, and a comparison unit. The collection unit collects information on candidates. The analysis unit analyzes the information collected by the collection unit. The evaluation unit evaluates the suitability of candidates based on the analysis results obtained by the analysis unit. The proposal unit proposes suitable roles and assignments for candidates based on the evaluation results obtained by the evaluation unit. The recording unit records conversations with candidates. The comparison unit performs re-evaluation and comparison based on the information recorded by the recording unit.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems that objectivity and consistency are lacking in the adoption process, resulting in a decrease in efficiency and an increased risk of adoption errors.

[0005] The system according to the embodiment aims to objectively and efficiently evaluate the suitability of candidates in the adoption process and propose the optimal role and assignment destination.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, a proposal unit, a recording unit, and a comparison unit. The collection unit collects information on candidates. The analysis unit analyzes the information collected by the collection unit. The evaluation unit evaluates the suitability of candidates based on the analysis results obtained by the analysis unit. The proposal unit proposes suitable roles and assignments for candidates based on the evaluation results obtained by the evaluation unit. The recording unit records conversations with candidates. The comparison unit performs re-evaluation and comparison based on the information recorded by the recording unit. [Effects of the Invention]

[0007] The system according to this embodiment can objectively and efficiently evaluate the suitability of candidates in the recruitment process and propose the most suitable role and assignment. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The intelligent interview system according to an embodiment of the present invention is an innovative recruitment process utilizing generative AI. This intelligent interview system eliminates the lack of objectivity and consistency that has been problematic in conventional recruitment processes, thereby achieving efficient and fair recruitment. Through dialogue between the AI ​​and the candidate, the intelligent interview system collects information such as thinking style, personality, facial expressions, and tone of voice, and objectively and accurately evaluates the candidate's strengths and characteristics. This process supports the selection of suitable roles and assignments for candidates, enabling effective recruitment decisions. Furthermore, the intelligent interview system automatically records and analyzes dialogue with candidates, allowing for later re-evaluation and comparison. In addition, consistent evaluation by AI realizes a fair and impartial recruitment process, significantly reducing recruitment errors and job suitability mistakes for companies. The introduction of the intelligent interview system can be expected to improve the efficiency and reduce costs of the recruitment process. By using AI automation and a data-driven evaluation system, it is possible to reduce the recruitment cost per person and shorten the recruitment time. Moreover, by objectively evaluating the suitability of candidates without relying on conventional subjective judgments, fair recruitment is achieved. This system offers multilingual support from a global perspective, facilitating seamless communication with candidates from different regions. This enhances corporate diversity and inclusion, contributing to improved overall organizational performance and enhanced competitiveness. The IntelliInterview system is a new approach to revolutionizing corporate recruitment processes, enabling optimal talent selection and placement. It provides an efficient and fair recruitment experience, powerfully supporting organizational growth and success. The IntelliInterview system allows for efficient collection, analysis, evaluation, proposal, recording, and comparison of candidate information.

[0029] The intelligent interview system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, a proposal unit, a recording unit, and a comparison unit. The collection unit collects information about candidates. For example, the collection unit can collect information such as thinking style, personality, facial expressions, and tone of voice through dialogue with candidates. The collection unit can also collect the candidate's work history and skill set through interviews. Furthermore, the collection unit can collect the candidate's personality traits using questionnaires. In addition, the collection unit can obtain candidate information from a database. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the candidate's strengths and characteristics using data mining techniques. For example, the analysis unit can evaluate the candidate's skill set using statistical analysis. Furthermore, the analysis unit can evaluate the candidate's suitability using machine learning algorithms. The evaluation unit evaluates the candidate's suitability based on the analysis results obtained by the analysis unit. For example, the evaluation unit can evaluate the candidate's suitability using a scoring system. For example, the evaluation unit can evaluate the candidate's job suitability using an evaluation model. The evaluation unit can also assess team suitability. The proposal unit proposes suitable roles and assignments for candidates based on the evaluation results obtained by the evaluation unit. For example, the proposal unit can propose specific roles based on the candidate's skill set. For example, the proposal unit can propose appropriate assignments based on the candidate's work history. The proposal unit can also propose assignments that take team suitability into account based on the candidate's personality traits. The recording unit records conversations with candidates. For example, the recording unit can record conversations with candidates using audio recordings. For example, the recording unit can record conversations with candidates using text recordings. The recording unit can also record conversations with candidates using video recordings. The comparison unit performs re-evaluations and comparisons based on the information recorded by the recording unit. For example, the comparison unit can compare with past evaluation results. For example, the comparison unit can compare with other candidates. The comparison unit can also re-evaluate candidates based on the recorded information.This enables the intelligent interview system according to the embodiment to efficiently collect, analyze, evaluate, propose, record, and compare candidate information.

[0030] The data collection unit collects information about candidates. For example, it can collect information such as thinking style, personality, facial expressions, and tone of voice through conversations with candidates. Specifically, the data collection unit uses high-precision microphones and cameras during interviews to capture changes in the candidate's tone of voice and facial expressions in real time. This allows for a detailed understanding of the candidate's emotions and reactions. The data collection unit can also collect the candidate's work history and skill set through interviews. For example, it can obtain detailed information about the projects the candidate has been involved in in the past and the technologies and tools they have used. Furthermore, the data collection unit can collect the candidate's personality traits using questionnaires. The questionnaires include psychological questions and questions about situational judgment, and are designed to reveal the candidate's personality and values. This allows for a deeper understanding of the candidate's inner characteristics. In addition, the data collection unit can retrieve candidate information from databases. For example, it can automatically collect and integrate information such as resumes, letters of recommendation, and online profiles that the candidate has previously submitted. This allows the data collection unit to efficiently collect multifaceted information about candidates and improve the quality of interviews.

[0031] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can use data mining techniques to analyze a candidate's strengths and characteristics. Specifically, it statistically analyzes the collected data to extract patterns in the candidate's skills and experience. For example, it can use text analysis techniques to extract keywords from a candidate's responses and resume and evaluate their frequency and relevance. The analysis unit can also use statistical analysis to evaluate a candidate's skill set. For example, it can quantify the level of a candidate's skills and how frequently those skills are used, and compare them with other candidates. Furthermore, the analysis unit can use machine learning algorithms to evaluate a candidate's suitability. For example, it can use a model trained on past interview results and work history data to predict a candidate's future performance. This allows the analysis unit to analyze the collected data from multiple perspectives and clearly identify a candidate's strengths and weaknesses. In addition, the analysis unit can use sentiment analysis techniques to detect changes in emotion from a candidate's facial expressions and tone of voice, and evaluate their stress level and confidence level during the interview. This allows the analysis unit to more accurately grasp the candidate's inner characteristics and emotional state, enabling a comprehensive evaluation.

[0032] The evaluation department assesses a candidate's suitability based on the analysis results obtained by the analysis department. For example, the evaluation department can use a scoring system to assess a candidate's suitability. Specifically, it quantifies the candidate's skills, experience, and personality traits and calculates an overall score. The evaluation department can also use an evaluation model to assess a candidate's job suitability. For example, it can use a model based on the skills and experience required for a specific job to evaluate how well-suited a candidate is for that job. Furthermore, the evaluation department can assess team suitability. For example, it can evaluate compatibility with existing team members based on a candidate's personality traits and communication style to determine whether they will contribute to improving the overall team performance. This allows the evaluation department to comprehensively evaluate a candidate's suitability and provide information for selecting the most suitable talent. Additionally, the evaluation department can relatively evaluate a candidate's evaluation results by comparing them to past evaluation data and industry standards. This allows the evaluation department to evaluate a candidate's suitability more objectively and fairly, supporting the selection of the most suitable talent.

[0033] The Proposal Department proposes suitable roles and assignments for candidates based on the evaluation results obtained by the Evaluation Department. For example, the Proposal Department can propose specific roles based on a candidate's skill set. Specifically, it proposes the most suitable duties and projects based on the candidate's skills and experience. The Proposal Department can also propose appropriate assignments based on a candidate's work history. For example, it can identify departments or teams where a candidate can contribute most effectively based on their past work experience and project results. Furthermore, the Proposal Department can propose assignments that consider team suitability based on the candidate's personality traits. For example, it can evaluate compatibility with existing team members based on the candidate's communication style and values ​​and propose the optimal team composition. In this way, the Proposal Department can propose assignments that maximize the candidate's strengths and contribute to improving the overall performance of the organization. In addition, the Proposal Department can propose assignments from a long-term perspective, taking into account the candidate's career path and growth potential. In this way, the Proposal Department can make optimal proposals that balance the growth of the candidate with the development of the organization.

[0034] The recording unit records the conversation with the candidate. For example, the recording unit can record the conversation using voice recording. Specifically, it uses a high-quality microphone to clearly record all audio from the interview. The recording unit can also record the conversation with the candidate using text recording. For example, it can use speech recognition technology to transcribe the interview content in real time, making it easier to search and analyze later. Furthermore, the recording unit can record the conversation with the candidate using video recording. For example, it can use a high-resolution camera to record the candidate's facial expressions and gestures in detail, which can then be used for analysis and evaluation. This allows the recording unit to record all aspects of the interview in detail, making re-evaluation and comparison easier later. In addition, the recording unit is equipped with a data management system to securely store the recorded data and make it accessible as needed. This allows the recording unit to efficiently and securely manage all interview information, improving the overall reliability of the system.

[0035] The comparison unit performs re-evaluation and comparison based on the information recorded by the recording unit. For example, the comparison unit can compare current evaluation results with past evaluation results. Specifically, it relatively evaluates the current candidate's evaluation results based on past interview results and evaluation data. This allows for understanding the candidate's growth and changes, and enables more accurate evaluation. The comparison unit can also compare candidates with other candidates. For example, it can compare the evaluation results of multiple candidates who applied for the same position and select the most suitable candidate. Furthermore, the comparison unit can re-evaluate candidates based on the recorded information. For example, it can re-analyze changes in interview content, facial expressions, and tone of voice to confirm and revise the initial evaluation results. This allows the comparison unit to improve the accuracy of evaluations and support the selection of the most suitable personnel. In addition, the comparison unit can visualize the evaluation results and display them clearly using graphs and charts. This makes it easier for evaluators to grasp the characteristics and aptitudes of candidates at a glance, enabling more effective decision-making.

[0036] The data collection unit can collect information such as thinking style, personality, facial expressions, and voice tone through dialogue with candidates. For example, the data collection unit can capture the facial expressions a candidate displays during an interview with a camera and analyze them using facial recognition technology. For example, the data collection unit can analyze the candidate's voice tone using voice analysis technology and estimate their emotions. The data collection unit can also analyze the candidate's thinking style using text analysis technology. For example, the data collection unit can record what the candidate says during an interview as text data and analyze it using natural language processing technology. This allows the data collection unit to collect diverse information about the candidate, enabling a more accurate evaluation. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input the candidate's facial expression data into a generative AI and have the generative AI perform facial expression analysis.

[0037] The analysis unit can analyze the collected information and evaluate the strengths and characteristics of candidates. For example, the analysis unit can analyze a candidate's strengths using data mining techniques. The analysis unit can also evaluate a candidate's characteristics using statistical analysis. Furthermore, the analysis unit can evaluate a candidate's suitability using machine learning algorithms. For example, the analysis unit can input a candidate's skill set into a machine learning algorithm to evaluate their suitability. This allows the analysis unit to accurately evaluate a candidate's strengths and characteristics and propose appropriate roles and assignments. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected candidate data into a generative AI and have the generative AI perform the evaluation of strengths and characteristics.

[0038] The evaluation unit can assess a candidate's suitability based on the analysis results. The evaluation unit can, for example, use a scoring system to assess a candidate's suitability. The evaluation unit can also, for example, use an evaluation model to assess a candidate's job suitability. Furthermore, the evaluation unit can assess team suitability. For example, the evaluation unit can input the candidate's analysis results into a scoring system and evaluate their suitability. This allows the evaluation unit to perform a more accurate assessment by evaluating the candidate's suitability based on the analysis results. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the analysis results into a generative AI and have the generative AI perform the suitability evaluation.

[0039] The proposal department can suggest suitable roles and assignments for candidates based on evaluation results. For example, the proposal department can suggest specific roles based on a candidate's skill set. For example, the proposal department can suggest appropriate assignments based on a candidate's work history. Furthermore, the proposal department can suggest assignments that take team suitability into account based on a candidate's personality traits. For example, the proposal department can use generative AI to suggest the optimal roles and assignments based on evaluation results. This allows the proposal department to maximize the potential of candidates by suggesting appropriate roles and assignments based on evaluation results. Some or all of the above-described processes in the proposal department may be performed using generative AI, or not. For example, the proposal department can input evaluation results into generative AI and have the generative AI propose roles and assignments.

[0040] The recording unit can record conversations with candidates so that they can be re-evaluated and compared later. The recording unit can record conversations with candidates using, for example, audio recording. The recording unit can also record conversations with candidates using, for example, text recording. Furthermore, the recording unit can record conversations with candidates using video recording. For example, the recording unit can automatically record conversations with candidates using generative AI. This allows the recording unit to record conversations with candidates so that they can be re-evaluated and compared later. Some or all of the above processing in the recording unit may be performed using, for example, generative AI, or not using generative AI. For example, the recording unit can input the audio data of the conversation into the generative AI and have the generative AI perform the transcription of the audio into text.

[0041] The comparison unit can re-evaluate and compare candidates based on the information recorded by the recording unit. The comparison unit can, for example, compare candidates with past evaluation results. The comparison unit can also, for example, compare candidates with other candidates. Furthermore, the comparison unit can re-evaluate candidates based on the recorded information. For example, the comparison unit can input the recorded information into a generating AI and have the generating AI perform the re-evaluation and comparison. This allows the comparison unit to perform a more accurate evaluation by re-evaluating and comparing based on the recorded information. Some or all of the above processing in the comparison unit may be performed using a generating AI, for example, or without using a generating AI.

[0042] The data collection unit can automatically collect a candidate's past work history and skill set, and customize the content of the conversation. For example, the data collection unit can automatically generate relevant questions based on the candidate's past work history. The data collection unit can also add specific technical questions based on the candidate's skill set. Furthermore, the data collection unit can combine the candidate's work history and skill set to construct the optimal conversation scenario. For example, the data collection unit can retrieve the candidate's work history from a database and generate relevant questions using generative AI. This allows the data collection unit to ask more appropriate questions by customizing the conversation content based on the candidate's past work history and skill set. Some or all of the above processing in the data collection unit may be performed using generative AI, or not.

[0043] The data collection unit can prioritize the information to be collected based on the candidate's current job status and areas of interest. For example, the data collection unit can prioritize the collection of relevant information based on the candidate's current job status. For example, the data collection unit can prioritize the collection of information on specific topics based on the candidate's areas of interest. The data collection unit can also combine the candidate's job status and areas of interest to set an optimal information collection strategy. For example, the data collection unit can obtain the candidate's job status from a database and prioritize the information using generative AI. This enables efficient information collection by prioritizing information based on the candidate's current job status and areas of interest. Some or all of the above processing in the data collection unit may be performed using generative AI, for example, or without generative AI.

[0044] The data collection unit can add region-specific questions, taking into account the candidate's geographical location. For example, the data collection unit can add questions about the local labor market based on the candidate's geographical location. The data collection unit can also ask questions about local culture and customs, taking into account the candidate's geographical location. Furthermore, the data collection unit can add questions about local companies and industries based on the candidate's geographical location. For example, the data collection unit can obtain the candidate's geographical location from a database and generate region-specific questions using generative AI. This enables the data collection unit to ask region-specific questions by taking the candidate's geographical location into account. Some or all of the above processing in the data collection unit may be performed using generative AI, for example, or without using generative AI.

[0045] The data collection unit can analyze a candidate's social media activity and collect relevant information. For example, the data collection unit can analyze a candidate's social media activity and collect posts related to the job. For example, the data collection unit can also collect information about a candidate's areas of interest and hobbies based on their social media activity. Furthermore, the data collection unit can analyze a candidate's social media activity and collect skills and experience related to the job. For example, the data collection unit can input a candidate's social media activity into a generative AI and collect relevant information. This allows the data collection unit to collect information from a more multifaceted perspective by analyzing a candidate's social media activity. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI.

[0046] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, the analysis unit can evaluate the source of the collected information and prioritize the analysis of highly reliable information. For example, the analysis unit can evaluate the consistency of the information and prioritize the analysis of consistent information. Furthermore, the analysis unit can evaluate the timeliness of the information and prioritize the analysis of the latest information. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the reliability evaluation. This enables the analysis unit to perform highly reliable analysis by evaluating the reliability of the collected information. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, for example, or without using a generating AI.

[0047] The analysis unit can customize the focus of its analysis based on the candidate's work history and skill set. For example, the analysis unit can analyze relevant skills and experience based on the candidate's work history. For example, the analysis unit can analyze specific technical skills based on the candidate's skill set. The analysis unit can also combine the candidate's work history and skill set to select the optimal analysis method. For example, the analysis unit can retrieve the candidate's work history from a database and customize the focus of its analysis using generative AI. This allows the analysis unit to perform more appropriate analysis by customizing the focus of its analysis based on the candidate's work history and skill set. Some or all of the above-described processes in the analysis unit may be performed using generative AI, for example, or without using generative AI.

[0048] The analysis unit can perform region-specific analyses by taking into account the candidate's geographical location information. For example, the analysis unit can perform analyses of the regional labor market based on the candidate's geographical location information. The analysis unit can also perform analyses of region-specific cultures and customs by taking into account the candidate's geographical location information. Furthermore, the analysis unit can perform analyses of regional companies and industries based on the candidate's geographical location information. For example, the analysis unit can obtain the candidate's geographical location information from a database and perform region-specific analyses using generative AI. This enables the analysis unit to perform region-specific analyses by taking into account the candidate's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using generative AI, for example, or without using generative AI.

[0049] The analysis unit can analyze a candidate's social media activity and reflect it in the analysis results. For example, the analysis unit can analyze a candidate's social media activity and reflect job-related posts in the analysis results. For example, the analysis unit can also reflect information about a candidate's areas of interest and hobbies in the analysis results based on a candidate's social media activity. Furthermore, the analysis unit can analyze a candidate's social media activity and reflect job-related skills and experience in the analysis results. For example, the analysis unit can input a candidate's social media activity into a generating AI and reflect the relevant information in the analysis results. This allows the analysis unit to perform a more multifaceted analysis by analyzing a candidate's social media activity. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI.

[0050] The evaluation unit can assess the reliability of the analysis results and prioritize evaluation of highly reliable results. For example, the evaluation unit can assess the source of the analysis results and prioritize evaluation of highly reliable results. For example, the evaluation unit can assess the consistency of the analysis results and prioritize evaluation of consistent results. Furthermore, the evaluation unit can assess the timeliness of the analysis results and prioritize evaluation of the latest results. For example, the evaluation unit can input the analysis results into a generating AI and have the generating AI perform the reliability assessment. This allows the evaluation unit to perform a highly reliable assessment by evaluating the reliability of the analysis results. Some or all of the above-described processes in the evaluation unit may be performed using a generating AI, for example, or without using a generating AI.

[0051] The evaluation unit can customize the focus of its evaluation based on the candidate's work history and skill set. For example, the evaluation unit can assess relevant skills and experience based on the candidate's work history. For example, the evaluation unit can assess specific technical skills based on the candidate's skill set. The evaluation unit can also combine the candidate's work history and skill set to select the optimal evaluation method. For example, the evaluation unit can retrieve the candidate's work history from a database and customize the focus of its evaluation using generative AI. This allows the evaluation unit to perform more appropriate evaluations by customizing the focus of its evaluation based on the candidate's work history and skill set. Some or all of the above processes in the evaluation unit may be performed using generative AI, for example, or without generative AI.

[0052] The evaluation unit can conduct region-specific evaluations by taking into account the candidate's geographical location information. For example, the evaluation unit can conduct evaluations of the local labor market based on the candidate's geographical location information. The evaluation unit can also conduct evaluations of region-specific cultures and customs by taking into account the candidate's geographical location information. Furthermore, the evaluation unit can conduct evaluations of local companies and industries based on the candidate's geographical location information. For example, the evaluation unit can obtain the candidate's geographical location information from a database and conduct region-specific evaluations using generative AI. This enables the evaluation unit to conduct region-specific evaluations by taking into account the candidate's geographical location information. Some or all of the above-described processes in the evaluation unit may be performed using generative AI, for example, or without using generative AI.

[0053] The evaluation department can analyze candidates' social media activity and reflect it in the evaluation results. For example, the evaluation department can analyze candidates' social media activity and reflect job-related posts in the evaluation results. For example, the evaluation department can also reflect information about candidates' areas of interest and hobbies in the evaluation results based on candidates' social media activity. Furthermore, the evaluation department can analyze candidates' social media activity and reflect job-related skills and experience in the evaluation results. For example, the evaluation department can input candidates' social media activity into a generative AI and reflect the relevant information in the evaluation results. This allows the evaluation department to conduct a more multifaceted evaluation by analyzing candidates' social media activity. Some or all of the above processing in the evaluation department may be performed using, for example, a generative AI, or without using a generative AI.

[0054] The proposal unit can evaluate the reliability of the evaluation results and make proposals based on highly reliable results. For example, the proposal unit can evaluate the source of the evaluation results and make proposals based on highly reliable results. For example, the proposal unit can evaluate the consistency of the evaluation results and make proposals based on consistent results. Furthermore, the proposal unit can evaluate the timeliness of the evaluation results and make proposals based on the latest results. For example, the proposal unit can input the evaluation results into a generating AI and have the generating AI perform a reliability evaluation. This allows the proposal unit to make highly reliable proposals by evaluating the reliability of the evaluation results. Some or all of the above processing in the proposal unit may be performed using a generating AI, for example, or without using a generating AI.

[0055] The proposal department can customize the focus of its proposals based on the candidate's work history and skill set. For example, it can suggest relevant roles and assignments based on the candidate's work history. For example, it can suggest specific technical roles based on the candidate's skill set. It can also combine the candidate's work history and skill set to suggest the most suitable roles and assignments. For example, the proposal department can retrieve the candidate's work history from a database and customize the focus of its proposals using generative AI. This allows the proposal department to make more appropriate proposals by customizing the focus of its proposals based on the candidate's work history and skill set. Some or all of the above processes in the proposal department may be performed using generative AI, or not.

[0056] The proposal department can make region-specific proposals by taking into account the candidate's geographical location information. For example, the proposal department can make proposals regarding local companies and industries based on the candidate's geographical location information. For example, the proposal department can also propose roles that are suitable for the region's unique culture and customs, taking into account the candidate's geographical location information. Furthermore, the proposal department can make proposals regarding the local labor market based on the candidate's geographical location information. For example, the proposal department can obtain the candidate's geographical location information from a database and make region-specific proposals using generative AI. This enables the proposal department to make region-specific proposals by taking into account the candidate's geographical location information. Some or all of the above processing in the proposal department may be performed using generative AI, for example, or without using generative AI.

[0057] The proposal department can analyze candidates' social media activity and reflect it in their proposals. For example, the proposal department can analyze candidates' social media activity and reflect job-related posts in their proposals. For example, the proposal department can also reflect information about candidates' areas of interest and hobbies based on their social media activity in their proposals. Furthermore, the proposal department can analyze candidates' social media activity and reflect job-related skills and experience in their proposals. For example, the proposal department can input candidates' social media activity into a generative AI and reflect the relevant information in their proposals. This allows the proposal department to make more multifaceted proposals by analyzing candidates' social media activity. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI.

[0058] The recording unit can evaluate the reliability of the recorded information and prioritize recording reliable information. For example, the recording unit can evaluate the source of the recorded information and prioritize recording reliable information. For example, the recording unit can evaluate the consistency of the information and prioritize recording consistent information. Furthermore, the recording unit can evaluate the timeliness of the information and prioritize recording the latest information. For example, the recording unit can input the recorded information into a generating AI and have the generating AI perform the reliability evaluation. This enables the recording unit to perform reliable recording by evaluating the reliability of the recorded information. Some or all of the above processing in the recording unit may be performed using a generating AI, for example, or without using a generating AI.

[0059] The recording unit can customize the focus of recording based on the candidate's work history and skill set. For example, the recording unit can record relevant skills and experience based on the candidate's work history. For example, the recording unit can record specific technical skills based on the candidate's skill set. The recording unit can also combine the candidate's work history and skill set to select the optimal recording method. For example, the recording unit can retrieve the candidate's work history from a database and customize the focus of recording using generative AI. This allows the recording unit to perform more appropriate recordings by customizing the focus of recording based on the candidate's work history and skill set. Some or all of the above processing in the recording unit may be performed using generative AI, for example, or without generative AI.

[0060] The recording unit can record region-specific information, taking into account the candidate's geographical location. For example, the recording unit can record information about the local labor market based on the candidate's geographical location. For example, the recording unit can also record information about region-specific culture and customs, taking into account the candidate's geographical location. Furthermore, the recording unit can record information about local companies and industries based on the candidate's geographical location. For example, the recording unit can obtain the candidate's geographical location from a database and record region-specific information using generative AI. This enables the recording unit to record region-specific information by taking the candidate's geographical location into account. Some or all of the above-described processes in the recording unit may be performed using generative AI, for example, or without using generative AI.

[0061] The recording unit can analyze a candidate's social media activity and reflect it in the record. For example, the recording unit can analyze a candidate's social media activity and reflect job-related posts in the record. For example, the recording unit can also reflect information about a candidate's areas of interest and hobbies in the record based on their social media activity. Furthermore, the recording unit can analyze a candidate's social media activity and reflect job-related skills and experience in the record. For example, the recording unit can input a candidate's social media activity into a generative AI and reflect the relevant information in the record. This allows the recording unit to create more multifaceted records by analyzing a candidate's social media activity. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without using a generative AI.

[0062] The comparison unit can evaluate the reliability of recorded information and perform comparisons based on reliable information. For example, the comparison unit can evaluate the source of recorded information and perform comparisons based on reliable information. For example, the comparison unit can evaluate the consistency of information and perform comparisons based on consistent information. Furthermore, the comparison unit can evaluate the timeliness of information and perform comparisons based on the latest information. For example, the comparison unit can input recorded information into a generating AI and have the generating AI perform the reliability evaluation. This allows the comparison unit to perform highly reliable comparisons by evaluating the reliability of recorded information. Some or all of the above-described processes in the comparison unit may be performed using a generating AI, for example, or without using a generating AI.

[0063] The comparison unit can customize the focus of the comparison based on the candidate's work history and skill set. For example, the comparison unit can compare relevant skills and experience based on the candidate's work history. For example, the comparison unit can also compare specific technical skills based on the candidate's skill set. Furthermore, the comparison unit can combine the candidate's work history and skill set to select the optimal comparison method. For example, the comparison unit can retrieve the candidate's work history from a database and customize the focus of the comparison using generative AI. This allows the comparison unit to perform more appropriate comparisons by customizing the focus of the comparison based on the candidate's work history and skill set. Some or all of the above processing in the comparison unit may be performed using generative AI, for example, or without generative AI.

[0064] The comparison unit can perform region-specific comparisons by taking into account the candidate's geographical location information. For example, the comparison unit can perform comparisons regarding regional labor markets based on the candidate's geographical location information. The comparison unit can also perform comparisons regarding region-specific cultures and customs by taking into account the candidate's geographical location information. Furthermore, the comparison unit can perform comparisons regarding regional companies and industries based on the candidate's geographical location information. For example, the comparison unit can obtain the candidate's geographical location information from a database and perform region-specific comparisons using generative AI. This enables the comparison unit to perform region-specific comparisons by taking into account the candidate's geographical location information. Some or all of the above processing in the comparison unit may be performed using generative AI, for example, or without using generative AI.

[0065] The comparison unit can analyze candidates' social media activity and reflect it in the comparison results. For example, the comparison unit can analyze candidates' social media activity and reflect job-related posts in the comparison results. For example, the comparison unit can also reflect information about candidates' areas of interest and hobbies based on their social media activity in the comparison results. Furthermore, the comparison unit can analyze candidates' social media activity and reflect job-related skills and experience in the comparison results. For example, the comparison unit can input candidates' social media activity into a generative AI and reflect the relevant information in the comparison results. This allows the comparison unit to perform a more multifaceted comparison by analyzing candidates' social media activity. Some or all of the above processing in the comparison unit may be performed using, for example, a generative AI, or without using a generative AI.

[0066] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0067] The IntelliInterview System automatically collects information on a candidate's past projects and achievements, and utilizes this information during the interview. For example, it can collect details of projects a candidate has been involved in in the past and ask questions related to those projects. It can also ask questions about specific achievements and challenges based on the candidate's performance. Furthermore, it can evaluate the relevance of a candidate's past projects to their current position and generate appropriate questions. This makes the interview more specific and meaningful.

[0068] The IntelliInterview system can take into account the candidate's geographical location and add region-specific questions. For example, if a candidate lives in a particular region, questions about the local labor market and culture can be asked. Similarly, if a candidate is applying from a different region, questions about the characteristics and customs of that region can be added. Furthermore, based on the candidate's geographical location, it's possible to ask questions about region-specific challenges and opportunities. This makes the interview more personalized and allows for questions tailored to the candidate's background.

[0069] The IntelliInterview System analyzes a candidate's social media activity and utilizes that information during the interview. For example, it can ask questions about projects and achievements the candidate has shared on social media. It can also ask relevant questions based on the candidate's interests and hobbies. Furthermore, it can use the candidate's social media activity to ask questions about job-related skills and experience. This makes the interview more personalized and allows for questions tailored to the candidate's background.

[0070] The IntelliInterview System can customize interview questions based on a candidate's work history and skill set. For example, it can ask questions about relevant skills and experience based on the candidate's past work history. It can also add specific technical questions based on the candidate's skill set. Furthermore, it can combine the candidate's work history and skill set to generate optimal questions. This makes the interview more specific and meaningful.

[0071] The IntelliInterview system can customize interview content by taking into account the candidate's geographical location. For example, if a candidate lives in a specific region, questions about the local labor market and culture can be asked. If a candidate is applying from a different region, additional questions about the characteristics and customs of that region can be added. Furthermore, based on the candidate's geographical location, it's possible to ask questions about region-specific challenges and opportunities. This makes the interview more personalized and allows for questions tailored to the candidate's background.

[0072] The following briefly describes the processing flow for example form 1.

[0073] Step 1: The collection unit collects information about the candidates. For example, the collection unit can collect information such as thinking style, personality, facial expressions, and tone of voice through conversations with the candidates. The collection unit can also collect information such as the candidates' work history and skill sets through interviews. Furthermore, the collection unit can collect information about the candidates' personality traits using questionnaires. In addition, the collection unit can retrieve candidate information from databases. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, use data mining techniques to analyze the candidate's strengths and characteristics. The analysis unit can also, for example, use statistical analysis to evaluate the candidate's skill set. Furthermore, the analysis unit can use machine learning algorithms to evaluate the candidate's suitability. Step 3: The evaluation unit assesses the candidate's suitability based on the analysis results obtained by the analysis unit. The evaluation unit can, for example, use a scoring system to assess the candidate's suitability. The evaluation unit can also, for example, use an evaluation model to assess the candidate's job suitability. Furthermore, the evaluation unit can also assess team suitability. Step 4: The proposal department proposes suitable roles and assignments for candidates based on the evaluation results obtained by the evaluation department. For example, the proposal department may propose specific roles based on the candidate's skill set. For example, the proposal department may also propose appropriate assignments based on the candidate's work history. Furthermore, the proposal department may propose assignments that take into account the candidate's personality traits and team suitability. Step 5: The recording unit records the conversation with the candidate. The recording unit can record the conversation with the candidate using, for example, audio recording. The recording unit can also record the conversation with the candidate using, for example, text recording. In addition, the recording unit can record the conversation with the candidate using video recording. Step 6: The comparison unit performs re-evaluation and comparison based on the information recorded by the recording unit. The comparison unit can, for example, compare with past evaluation results. The comparison unit can also, for example, compare with other candidates. Furthermore, the comparison unit can re-evaluate the candidate based on the recorded information.

[0074] (Example of form 2) The intelligent interview system according to an embodiment of the present invention is an innovative recruitment process utilizing generative AI. This intelligent interview system eliminates the lack of objectivity and consistency that has been problematic in conventional recruitment processes, thereby achieving efficient and fair recruitment. Through dialogue between the AI ​​and the candidate, the intelligent interview system collects information such as thinking style, personality, facial expressions, and tone of voice, and objectively and accurately evaluates the candidate's strengths and characteristics. This process supports the selection of suitable roles and assignments for candidates, enabling effective recruitment decisions. Furthermore, the intelligent interview system automatically records and analyzes dialogue with candidates, allowing for later re-evaluation and comparison. In addition, consistent evaluation by AI realizes a fair and impartial recruitment process, significantly reducing recruitment errors and job suitability mistakes for companies. The introduction of the intelligent interview system can be expected to improve the efficiency and reduce costs of the recruitment process. By using AI automation and a data-driven evaluation system, it is possible to reduce the recruitment cost per person and shorten the recruitment time. Moreover, by objectively evaluating the suitability of candidates without relying on conventional subjective judgments, fair recruitment is achieved. This system offers multilingual support from a global perspective, facilitating seamless communication with candidates from different regions. This enhances corporate diversity and inclusion, contributing to improved overall organizational performance and enhanced competitiveness. The IntelliInterview system is a new approach to revolutionizing corporate recruitment processes, enabling optimal talent selection and placement. It provides an efficient and fair recruitment experience, powerfully supporting organizational growth and success. The IntelliInterview system allows for efficient collection, analysis, evaluation, proposal, recording, and comparison of candidate information.

[0075] The intelligent interview system according to this embodiment comprises a collection unit, an analysis unit, an evaluation unit, a proposal unit, a recording unit, and a comparison unit. The collection unit collects information about candidates. For example, the collection unit can collect information such as thinking style, personality, facial expressions, and tone of voice through dialogue with candidates. The collection unit can also collect the candidate's work history and skill set through interviews. Furthermore, the collection unit can collect the candidate's personality traits using questionnaires. In addition, the collection unit can obtain candidate information from a database. The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can analyze the candidate's strengths and characteristics using data mining techniques. For example, the analysis unit can evaluate the candidate's skill set using statistical analysis. Furthermore, the analysis unit can evaluate the candidate's suitability using machine learning algorithms. The evaluation unit evaluates the candidate's suitability based on the analysis results obtained by the analysis unit. For example, the evaluation unit can evaluate the candidate's suitability using a scoring system. For example, the evaluation unit can evaluate the candidate's job suitability using an evaluation model. The evaluation unit can also assess team suitability. The proposal unit proposes suitable roles and assignments for candidates based on the evaluation results obtained by the evaluation unit. For example, the proposal unit can propose specific roles based on the candidate's skill set. For example, the proposal unit can propose appropriate assignments based on the candidate's work history. The proposal unit can also propose assignments that take team suitability into account based on the candidate's personality traits. The recording unit records conversations with candidates. For example, the recording unit can record conversations with candidates using audio recordings. For example, the recording unit can record conversations with candidates using text recordings. The recording unit can also record conversations with candidates using video recordings. The comparison unit performs re-evaluations and comparisons based on the information recorded by the recording unit. For example, the comparison unit can compare with past evaluation results. For example, the comparison unit can compare with other candidates. The comparison unit can also re-evaluate candidates based on the recorded information.This enables the intelligent interview system according to the embodiment to efficiently collect, analyze, evaluate, propose, record, and compare candidate information.

[0076] The data collection unit collects information about candidates. For example, it can collect information such as thinking style, personality, facial expressions, and tone of voice through conversations with candidates. Specifically, the data collection unit uses high-precision microphones and cameras during interviews to capture changes in the candidate's tone of voice and facial expressions in real time. This allows for a detailed understanding of the candidate's emotions and reactions. The data collection unit can also collect the candidate's work history and skill set through interviews. For example, it can obtain detailed information about the projects the candidate has been involved in in the past and the technologies and tools they have used. Furthermore, the data collection unit can collect the candidate's personality traits using questionnaires. The questionnaires include psychological questions and questions about situational judgment, and are designed to reveal the candidate's personality and values. This allows for a deeper understanding of the candidate's inner characteristics. In addition, the data collection unit can retrieve candidate information from databases. For example, it can automatically collect and integrate information such as resumes, letters of recommendation, and online profiles that the candidate has previously submitted. This allows the data collection unit to efficiently collect multifaceted information about candidates and improve the quality of interviews.

[0077] The analysis unit analyzes the information collected by the collection unit. For example, the analysis unit can use data mining techniques to analyze a candidate's strengths and characteristics. Specifically, it statistically analyzes the collected data to extract patterns in the candidate's skills and experience. For example, it can use text analysis techniques to extract keywords from a candidate's responses and resume and evaluate their frequency and relevance. The analysis unit can also use statistical analysis to evaluate a candidate's skill set. For example, it can quantify the level of a candidate's skills and how frequently those skills are used, and compare them with other candidates. Furthermore, the analysis unit can use machine learning algorithms to evaluate a candidate's suitability. For example, it can use a model trained on past interview results and work history data to predict a candidate's future performance. This allows the analysis unit to analyze the collected data from multiple perspectives and clearly identify a candidate's strengths and weaknesses. In addition, the analysis unit can use sentiment analysis techniques to detect changes in emotion from a candidate's facial expressions and tone of voice, and evaluate their stress level and confidence level during the interview. This allows the analysis unit to more accurately grasp the candidate's inner characteristics and emotional state, enabling a comprehensive evaluation.

[0078] The evaluation department assesses a candidate's suitability based on the analysis results obtained by the analysis department. For example, the evaluation department can use a scoring system to assess a candidate's suitability. Specifically, it quantifies the candidate's skills, experience, and personality traits and calculates an overall score. The evaluation department can also use an evaluation model to assess a candidate's job suitability. For example, it can use a model based on the skills and experience required for a specific job to evaluate how well-suited a candidate is for that job. Furthermore, the evaluation department can assess team suitability. For example, it can evaluate compatibility with existing team members based on a candidate's personality traits and communication style to determine whether they will contribute to improving the overall team performance. This allows the evaluation department to comprehensively evaluate a candidate's suitability and provide information for selecting the most suitable talent. Additionally, the evaluation department can relatively evaluate a candidate's evaluation results by comparing them to past evaluation data and industry standards. This allows the evaluation department to evaluate a candidate's suitability more objectively and fairly, supporting the selection of the most suitable talent.

[0079] The Proposal Department proposes suitable roles and assignments for candidates based on the evaluation results obtained by the Evaluation Department. For example, the Proposal Department can propose specific roles based on a candidate's skill set. Specifically, it proposes the most suitable duties and projects based on the candidate's skills and experience. The Proposal Department can also propose appropriate assignments based on a candidate's work history. For example, it can identify departments or teams where a candidate can contribute most effectively based on their past work experience and project results. Furthermore, the Proposal Department can propose assignments that consider team suitability based on the candidate's personality traits. For example, it can evaluate compatibility with existing team members based on the candidate's communication style and values ​​and propose the optimal team composition. In this way, the Proposal Department can propose assignments that maximize the candidate's strengths and contribute to improving the overall performance of the organization. In addition, the Proposal Department can propose assignments from a long-term perspective, taking into account the candidate's career path and growth potential. In this way, the Proposal Department can make optimal proposals that balance the growth of the candidate with the development of the organization.

[0080] The recording unit records the conversation with the candidate. For example, the recording unit can record the conversation using voice recording. Specifically, it uses a high-quality microphone to clearly record all audio from the interview. The recording unit can also record the conversation with the candidate using text recording. For example, it can use speech recognition technology to transcribe the interview content in real time, making it easier to search and analyze later. Furthermore, the recording unit can record the conversation with the candidate using video recording. For example, it can use a high-resolution camera to record the candidate's facial expressions and gestures in detail, which can then be used for analysis and evaluation. This allows the recording unit to record all aspects of the interview in detail, making re-evaluation and comparison easier later. In addition, the recording unit is equipped with a data management system to securely store the recorded data and make it accessible as needed. This allows the recording unit to efficiently and securely manage all interview information, improving the overall reliability of the system.

[0081] The comparison unit performs re-evaluation and comparison based on the information recorded by the recording unit. For example, the comparison unit can compare current evaluation results with past evaluation results. Specifically, it relatively evaluates the current candidate's evaluation results based on past interview results and evaluation data. This allows for understanding the candidate's growth and changes, and enables more accurate evaluation. The comparison unit can also compare candidates with other candidates. For example, it can compare the evaluation results of multiple candidates who applied for the same position and select the most suitable candidate. Furthermore, the comparison unit can re-evaluate candidates based on the recorded information. For example, it can re-analyze changes in interview content, facial expressions, and tone of voice to confirm and revise the initial evaluation results. This allows the comparison unit to improve the accuracy of evaluations and support the selection of the most suitable personnel. In addition, the comparison unit can visualize the evaluation results and display them clearly using graphs and charts. This makes it easier for evaluators to grasp the characteristics and aptitudes of candidates at a glance, enabling more effective decision-making.

[0082] The data collection unit can collect information such as thinking style, personality, facial expressions, and voice tone through dialogue with candidates. For example, the data collection unit can capture the facial expressions a candidate displays during an interview with a camera and analyze them using facial recognition technology. For example, the data collection unit can analyze the candidate's voice tone using voice analysis technology and estimate their emotions. The data collection unit can also analyze the candidate's thinking style using text analysis technology. For example, the data collection unit can record what the candidate says during an interview as text data and analyze it using natural language processing technology. This allows the data collection unit to collect diverse information about the candidate, enabling a more accurate evaluation. Some or all of the above processing in the data collection unit may be performed using, for example, generative AI, or without generative AI. For example, the data collection unit can input the candidate's facial expression data into a generative AI and have the generative AI perform facial expression analysis.

[0083] The analysis unit can analyze the collected information and evaluate the strengths and characteristics of candidates. For example, the analysis unit can analyze a candidate's strengths using data mining techniques. The analysis unit can also evaluate a candidate's characteristics using statistical analysis. Furthermore, the analysis unit can evaluate a candidate's suitability using machine learning algorithms. For example, the analysis unit can input a candidate's skill set into a machine learning algorithm to evaluate their suitability. This allows the analysis unit to accurately evaluate a candidate's strengths and characteristics and propose appropriate roles and assignments. Some or all of the above-described processes in the analysis unit may be performed using, for example, generative AI, or without generative AI. For example, the analysis unit can input the collected candidate data into a generative AI and have the generative AI perform the evaluation of strengths and characteristics.

[0084] The evaluation unit can assess a candidate's suitability based on the analysis results. The evaluation unit can, for example, use a scoring system to assess a candidate's suitability. The evaluation unit can also, for example, use an evaluation model to assess a candidate's job suitability. Furthermore, the evaluation unit can assess team suitability. For example, the evaluation unit can input the candidate's analysis results into a scoring system and evaluate their suitability. This allows the evaluation unit to perform a more accurate assessment by evaluating the candidate's suitability based on the analysis results. Some or all of the above-described processes in the evaluation unit may be performed using, for example, a generative AI, or without a generative AI. For example, the evaluation unit can input the analysis results into a generative AI and have the generative AI perform the suitability evaluation.

[0085] The proposal department can suggest suitable roles and assignments for candidates based on evaluation results. For example, the proposal department can suggest specific roles based on a candidate's skill set. For example, the proposal department can suggest appropriate assignments based on a candidate's work history. Furthermore, the proposal department can suggest assignments that take team suitability into account based on a candidate's personality traits. For example, the proposal department can use generative AI to suggest the optimal roles and assignments based on evaluation results. This allows the proposal department to maximize the potential of candidates by suggesting appropriate roles and assignments based on evaluation results. Some or all of the above-described processes in the proposal department may be performed using generative AI, or not. For example, the proposal department can input evaluation results into generative AI and have the generative AI propose roles and assignments.

[0086] The recording unit can record conversations with candidates so that they can be re-evaluated and compared later. The recording unit can record conversations with candidates using, for example, audio recording. The recording unit can also record conversations with candidates using, for example, text recording. Furthermore, the recording unit can record conversations with candidates using video recording. For example, the recording unit can automatically record conversations with candidates using generative AI. This allows the recording unit to record conversations with candidates so that they can be re-evaluated and compared later. Some or all of the above processing in the recording unit may be performed using, for example, generative AI, or not using generative AI. For example, the recording unit can input the audio data of the conversation into the generative AI and have the generative AI perform the transcription of the audio into text.

[0087] The comparison unit can re-evaluate and compare candidates based on the information recorded by the recording unit. The comparison unit can, for example, compare candidates with past evaluation results. The comparison unit can also, for example, compare candidates with other candidates. Furthermore, the comparison unit can re-evaluate candidates based on the recorded information. For example, the comparison unit can input the recorded information into a generating AI and have the generating AI perform the re-evaluation and comparison. This allows the comparison unit to perform a more accurate evaluation by re-evaluating and comparing based on the recorded information. Some or all of the above processing in the comparison unit may be performed using a generating AI, for example, or without using a generating AI.

[0088] The data collection unit can estimate the candidate's emotions and adjust the pace of the conversation based on the estimated emotions. For example, if the candidate is nervous, the data collection unit can slow down the pace of the conversation to help them relax. For example, if the candidate is relaxed, the data collection unit can maintain a normal pace of conversation. Also, if the candidate is in a hurry, the data collection unit can speed up the pace of the conversation to efficiently collect information. For example, the data collection unit can capture the candidate's facial expressions with a camera and estimate their emotions using facial recognition technology. For example, the data collection unit can analyze the candidate's tone of voice using speech analysis technology and estimate their emotions. This allows the data collection unit to adjust the pace of the conversation according to the candidate's emotions, enabling a more relaxed conversation. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the data collection unit may be performed using, for example, a generative AI, or without a generative AI.

[0089] The data collection unit can automatically collect a candidate's past work history and skill set, and customize the content of the conversation. For example, the data collection unit can automatically generate relevant questions based on the candidate's past work history. The data collection unit can also add specific technical questions based on the candidate's skill set. Furthermore, the data collection unit can combine the candidate's work history and skill set to construct the optimal conversation scenario. For example, the data collection unit can retrieve the candidate's work history from a database and generate relevant questions using generative AI. This allows the data collection unit to ask more appropriate questions by customizing the conversation content based on the candidate's past work history and skill set. Some or all of the above processing in the data collection unit may be performed using generative AI, or not.

[0090] The data collection unit can prioritize the information to be collected based on the candidate's current job status and areas of interest. For example, the data collection unit can prioritize the collection of relevant information based on the candidate's current job status. For example, the data collection unit can prioritize the collection of information on specific topics based on the candidate's areas of interest. The data collection unit can also combine the candidate's job status and areas of interest to set an optimal information collection strategy. For example, the data collection unit can obtain the candidate's job status from a database and prioritize the information using generative AI. This enables efficient information collection by prioritizing information based on the candidate's current job status and areas of interest. Some or all of the above processing in the data collection unit may be performed using generative AI, for example, or without generative AI.

[0091] The data collection unit can estimate the candidate's emotions and adjust the depth of information collected based on the estimated emotions. For example, if the candidate is nervous, the data collection unit can collect only basic information and avoid detailed questions. If the candidate is relaxed, the data collection unit can collect detailed information and ask in-depth questions. If the candidate is excited, the data collection unit can also collect detailed information on topics that interest them. For example, the data collection unit can capture the candidate's facial expressions with a camera and estimate their emotions using facial recognition technology. The data collection unit can also analyze the candidate's tone of voice using speech analysis technology and estimate their emotions. This allows the data collection unit to collect more appropriate information by adjusting the depth of information collected according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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, for example, generative AI, or without generative AI.

[0092] The data collection unit can add region-specific questions, taking into account the candidate's geographical location. For example, the data collection unit can add questions about the local labor market based on the candidate's geographical location. The data collection unit can also ask questions about local culture and customs, taking into account the candidate's geographical location. Furthermore, the data collection unit can add questions about local companies and industries based on the candidate's geographical location. For example, the data collection unit can obtain the candidate's geographical location from a database and generate region-specific questions using generative AI. This enables the data collection unit to ask region-specific questions by taking the candidate's geographical location into account. Some or all of the above processing in the data collection unit may be performed using generative AI, for example, or without using generative AI.

[0093] The data collection unit can analyze a candidate's social media activity and collect relevant information. For example, the data collection unit can analyze a candidate's social media activity and collect posts related to the job. For example, the data collection unit can also collect information about a candidate's areas of interest and hobbies based on their social media activity. Furthermore, the data collection unit can analyze a candidate's social media activity and collect skills and experience related to the job. For example, the data collection unit can input a candidate's social media activity into a generative AI and collect relevant information. This allows the data collection unit to collect information from a more multifaceted perspective by analyzing a candidate's social media activity. Some or all of the above-described processes in the data collection unit may be performed using, for example, a generative AI, or without using a generative AI.

[0094] The analysis unit can estimate the candidate's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the candidate is nervous, the analysis unit can adjust the analysis algorithm to account for emotional fluctuations. For example, if the candidate is relaxed, the analysis unit can use the normal analysis algorithm. Furthermore, if the candidate is excited, the analysis unit can adjust the analysis algorithm to account for emotional peaks. For example, the analysis unit can capture the candidate's facial expressions with a camera and estimate their emotions using facial recognition technology. For example, the analysis unit can analyze the candidate's voice tone using speech analysis technology and estimate their emotions. This allows the analysis unit to perform more accurate analysis by adjusting the analysis algorithm according to the candidate'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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the analysis unit may be performed using a generative AI, for example, or without a generative AI.

[0095] The analysis unit can evaluate the reliability of the collected information and prioritize the analysis of highly reliable information. For example, the analysis unit can evaluate the source of the collected information and prioritize the analysis of highly reliable information. For example, the analysis unit can evaluate the consistency of the information and prioritize the analysis of consistent information. Furthermore, the analysis unit can evaluate the timeliness of the information and prioritize the analysis of the latest information. For example, the analysis unit can input the collected information into a generating AI and have the generating AI perform the reliability evaluation. This enables the analysis unit to perform highly reliable analysis by evaluating the reliability of the collected information. Some or all of the above-described processes in the analysis unit may be performed using a generating AI, for example, or without using a generating AI.

[0096] The analysis unit can customize the focus of its analysis based on the candidate's work history and skill set. For example, the analysis unit can analyze relevant skills and experience based on the candidate's work history. For example, the analysis unit can analyze specific technical skills based on the candidate's skill set. The analysis unit can also combine the candidate's work history and skill set to select the optimal analysis method. For example, the analysis unit can retrieve the candidate's work history from a database and customize the focus of its analysis using generative AI. This allows the analysis unit to perform more appropriate analysis by customizing the focus of its analysis based on the candidate's work history and skill set. Some or all of the above-described processes in the analysis unit may be performed using generative AI, for example, or without using generative AI.

[0097] The analysis unit can estimate the candidate's emotions and adjust the display method of the analysis results based on the estimated candidate's emotions. For example, if the candidate is nervous, the analysis unit can provide a simple and highly visible display method. For example, if the candidate is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the candidate is excited, the analysis unit can provide a visually stimulating display method. For example, the analysis unit can capture the candidate's facial expressions with a camera and estimate their emotions using facial recognition technology. The analysis unit can also analyze the candidate's voice tone using speech analysis technology and estimate their emotions. This allows the analysis unit to adjust the display method of the analysis results according to the candidate's emotions, enabling a more visually appealing display. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using a generative AI, for example, or without a generative AI.

[0098] The analysis unit can perform region-specific analyses by taking into account the candidate's geographical location information. For example, the analysis unit can perform analyses of the regional labor market based on the candidate's geographical location information. The analysis unit can also perform analyses of region-specific cultures and customs by taking into account the candidate's geographical location information. Furthermore, the analysis unit can perform analyses of regional companies and industries based on the candidate's geographical location information. For example, the analysis unit can obtain the candidate's geographical location information from a database and perform region-specific analyses using generative AI. This enables the analysis unit to perform region-specific analyses by taking into account the candidate's geographical location information. Some or all of the above-described processes in the analysis unit may be performed using generative AI, for example, or without using generative AI.

[0099] The analysis unit can analyze a candidate's social media activity and reflect it in the analysis results. For example, the analysis unit can analyze a candidate's social media activity and reflect job-related posts in the analysis results. For example, the analysis unit can also reflect information about a candidate's areas of interest and hobbies in the analysis results based on a candidate's social media activity. Furthermore, the analysis unit can analyze a candidate's social media activity and reflect job-related skills and experience in the analysis results. For example, the analysis unit can input a candidate's social media activity into a generating AI and reflect the relevant information in the analysis results. This allows the analysis unit to perform a more multifaceted analysis by analyzing a candidate's social media activity. Some or all of the above-described processes in the analysis unit may be performed using, for example, a generating AI, or without using a generating AI.

[0100] The evaluation unit can estimate a candidate's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if a candidate is nervous, the evaluation unit can adjust the evaluation criteria to account for emotional fluctuations. For example, if a candidate is relaxed, the evaluation unit can use the normal evaluation criteria. Furthermore, if a candidate is excited, the evaluation unit can adjust the evaluation criteria to account for emotional peaks. For example, the evaluation unit can capture a candidate's facial expression with a camera and estimate their emotions using facial recognition technology. For example, the evaluation unit can analyze a candidate's voice tone using speech analysis technology and estimate their emotions. This allows the evaluation unit to make more accurate evaluations by adjusting the evaluation criteria according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the evaluation unit may be performed using, for example, generative AI, or without generative AI.

[0101] The evaluation unit can assess the reliability of the analysis results and prioritize evaluation of highly reliable results. For example, the evaluation unit can assess the source of the analysis results and prioritize evaluation of highly reliable results. For example, the evaluation unit can assess the consistency of the analysis results and prioritize evaluation of consistent results. Furthermore, the evaluation unit can assess the timeliness of the analysis results and prioritize evaluation of the latest results. For example, the evaluation unit can input the analysis results into a generating AI and have the generating AI perform the reliability assessment. This allows the evaluation unit to perform a highly reliable assessment by evaluating the reliability of the analysis results. Some or all of the above-described processes in the evaluation unit may be performed using a generating AI, for example, or without using a generating AI.

[0102] The evaluation unit can customize the focus of its evaluation based on the candidate's work history and skill set. For example, the evaluation unit can assess relevant skills and experience based on the candidate's work history. For example, the evaluation unit can assess specific technical skills based on the candidate's skill set. The evaluation unit can also combine the candidate's work history and skill set to select the optimal evaluation method. For example, the evaluation unit can retrieve the candidate's work history from a database and customize the focus of its evaluation using generative AI. This allows the evaluation unit to perform more appropriate evaluations by customizing the focus of its evaluation based on the candidate's work history and skill set. Some or all of the above processes in the evaluation unit may be performed using generative AI, for example, or without generative AI.

[0103] The evaluation unit can estimate the candidate's emotions and adjust the display method of the evaluation results based on the estimated candidate's emotions. For example, if the candidate is nervous, the evaluation unit can provide a simple and highly visible display method. For example, if the candidate is relaxed, the evaluation unit can provide a display method that includes detailed information. Furthermore, if the candidate is excited, the evaluation unit can provide a visually stimulating display method. For example, the evaluation unit can capture the candidate's facial expressions with a camera and estimate their emotions using facial recognition technology. For example, the evaluation unit can analyze the candidate's tone of voice using speech analysis technology and estimate their emotions. This allows the evaluation unit to adjust the display method of the evaluation results according to the candidate's emotions, enabling a more visually appealing display. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the evaluation unit may be performed using, for example, generative AI, or without generative AI.

[0104] The evaluation unit can conduct region-specific evaluations by taking into account the candidate's geographical location information. For example, the evaluation unit can conduct evaluations of the local labor market based on the candidate's geographical location information. The evaluation unit can also conduct evaluations of region-specific cultures and customs by taking into account the candidate's geographical location information. Furthermore, the evaluation unit can conduct evaluations of local companies and industries based on the candidate's geographical location information. For example, the evaluation unit can obtain the candidate's geographical location information from a database and conduct region-specific evaluations using generative AI. This enables the evaluation unit to conduct region-specific evaluations by taking into account the candidate's geographical location information. Some or all of the above-described processes in the evaluation unit may be performed using generative AI, for example, or without using generative AI.

[0105] The evaluation department can analyze candidates' social media activity and reflect it in the evaluation results. For example, the evaluation department can analyze candidates' social media activity and reflect job-related posts in the evaluation results. For example, the evaluation department can also reflect information about candidates' areas of interest and hobbies in the evaluation results based on candidates' social media activity. Furthermore, the evaluation department can analyze candidates' social media activity and reflect job-related skills and experience in the evaluation results. For example, the evaluation department can input candidates' social media activity into a generative AI and reflect the relevant information in the evaluation results. This allows the evaluation department to conduct a more multifaceted evaluation by analyzing candidates' social media activity. Some or all of the above processing in the evaluation department may be performed using, for example, a generative AI, or without using a generative AI.

[0106] The proposal unit can estimate the candidate's emotions and adjust the way the proposal is presented based on the estimated emotions. For example, if the candidate is nervous, the proposal unit can present the proposal in a calm manner. If the candidate is relaxed, the proposal unit can present the proposal in a normal manner. Furthermore, if the candidate is excited, the proposal unit can present the proposal in a visually stimulating manner. For example, the proposal unit can capture the candidate's facial expression with a camera and estimate their emotions using facial recognition technology. The proposal unit can also analyze the candidate's tone of voice using speech analysis technology and estimate their emotions. This allows the proposal unit to make more appropriate proposals by adjusting the way the proposal is presented according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the proposal unit may be performed using, for example, a generative AI, or without a generative AI.

[0107] The proposal unit can evaluate the reliability of the evaluation results and make proposals based on highly reliable results. For example, the proposal unit can evaluate the source of the evaluation results and make proposals based on highly reliable results. For example, the proposal unit can evaluate the consistency of the evaluation results and make proposals based on consistent results. Furthermore, the proposal unit can evaluate the timeliness of the evaluation results and make proposals based on the latest results. For example, the proposal unit can input the evaluation results into a generating AI and have the generating AI perform a reliability evaluation. This allows the proposal unit to make highly reliable proposals by evaluating the reliability of the evaluation results. Some or all of the above processing in the proposal unit may be performed using a generating AI, for example, or without using a generating AI.

[0108] The proposal department can customize the focus of its proposals based on the candidate's work history and skill set. For example, it can suggest relevant roles and assignments based on the candidate's work history. For example, it can suggest specific technical roles based on the candidate's skill set. It can also combine the candidate's work history and skill set to suggest the most suitable roles and assignments. For example, the proposal department can retrieve the candidate's work history from a database and customize the focus of its proposals using generative AI. This allows the proposal department to make more appropriate proposals by customizing the focus of its proposals based on the candidate's work history and skill set. Some or all of the above processes in the proposal department may be performed using generative AI, or not.

[0109] The proposal unit can estimate a candidate's emotions and determine the priority of proposals based on the estimated emotions. For example, if a candidate is nervous, the proposal unit can prioritize roles that help them relax. If a candidate is relaxed, the proposal unit can also prioritize proposals in the usual order of priority. Furthermore, if a candidate is excited, the proposal unit can prioritize challenging roles. For example, the proposal unit can capture a candidate's facial expression with a camera and estimate their emotions using facial recognition technology. The proposal unit can also analyze a candidate's voice tone using speech analysis technology and estimate their emotions. This allows the proposal unit to make more appropriate proposals by prioritizing proposals according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processes in the proposal unit may be performed using, for example, generative AI, or without generative AI.

[0110] The proposal department can make region-specific proposals by taking into account the candidate's geographical location information. For example, the proposal department can make proposals regarding local companies and industries based on the candidate's geographical location information. For example, the proposal department can also propose roles that are suitable for the region's unique culture and customs, taking into account the candidate's geographical location information. Furthermore, the proposal department can make proposals regarding the local labor market based on the candidate's geographical location information. For example, the proposal department can obtain the candidate's geographical location information from a database and make region-specific proposals using generative AI. This enables the proposal department to make region-specific proposals by taking into account the candidate's geographical location information. Some or all of the above processing in the proposal department may be performed using generative AI, for example, or without using generative AI.

[0111] The proposal department can analyze candidates' social media activity and reflect it in their proposals. For example, the proposal department can analyze candidates' social media activity and reflect job-related posts in their proposals. For example, the proposal department can also reflect information about candidates' areas of interest and hobbies based on their social media activity in their proposals. Furthermore, the proposal department can analyze candidates' social media activity and reflect job-related skills and experience in their proposals. For example, the proposal department can input candidates' social media activity into a generative AI and reflect the relevant information in their proposals. This allows the proposal department to make more multifaceted proposals by analyzing candidates' social media activity. Some or all of the above processing in the proposal department may be performed using, for example, a generative AI, or without using a generative AI.

[0112] The recording unit can estimate the candidate's emotions and adjust the recording method based on the estimated emotions. For example, if the candidate is nervous, the recording unit can adjust the recording method of the conversation to help them relax. For example, if the candidate is relaxed, the recording unit can use the normal recording method. Also, if the candidate is excited, the recording unit can adjust the recording method to take into account emotional fluctuations. For example, the recording unit can capture the candidate's facial expressions with a camera and estimate their emotions using facial recognition technology. For example, the recording unit can analyze the candidate's tone of voice using speech analysis technology and estimate their emotions. This allows the recording unit to make more appropriate recordings by adjusting the recording method according to the candidate'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 recording unit may be performed using, for example, a generative AI, or without a generative AI.

[0113] The recording unit can evaluate the reliability of the recorded information and prioritize recording reliable information. For example, the recording unit can evaluate the source of the recorded information and prioritize recording reliable information. For example, the recording unit can evaluate the consistency of the information and prioritize recording consistent information. Furthermore, the recording unit can evaluate the timeliness of the information and prioritize recording the latest information. For example, the recording unit can input the recorded information into a generating AI and have the generating AI perform the reliability evaluation. This enables the recording unit to perform reliable recording by evaluating the reliability of the recorded information. Some or all of the above processing in the recording unit may be performed using a generating AI, for example, or without using a generating AI.

[0114] The recording unit can customize the focus of recording based on the candidate's work history and skill set. For example, the recording unit can record relevant skills and experience based on the candidate's work history. For example, the recording unit can record specific technical skills based on the candidate's skill set. The recording unit can also combine the candidate's work history and skill set to select the optimal recording method. For example, the recording unit can retrieve the candidate's work history from a database and customize the focus of recording using generative AI. This allows the recording unit to perform more appropriate recordings by customizing the focus of recording based on the candidate's work history and skill set. Some or all of the above processing in the recording unit may be performed using generative AI, for example, or without generative AI.

[0115] The recording unit can estimate the candidate's emotions and determine the recording priority based on the estimated emotions. For example, if the candidate is nervous, the recording unit can prioritize recording conversations to help them relax. If the candidate is relaxed, the recording unit can record according to the normal priority. Furthermore, if the candidate is excited, the recording unit can determine the recording priority considering emotional fluctuations. For example, the recording unit can capture the candidate's facial expressions with a camera and estimate their emotions using facial recognition technology. The recording unit can also analyze the candidate's tone of voice using speech analysis technology and estimate their emotions. This allows the recording unit to determine the recording priority according to the candidate's emotions, enabling more appropriate recording. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the recording unit may be performed using, for example, generative AI, or without generative AI.

[0116] The recording unit can record region-specific information, taking into account the candidate's geographical location. For example, the recording unit can record information about the local labor market based on the candidate's geographical location. For example, the recording unit can also record information about region-specific culture and customs, taking into account the candidate's geographical location. Furthermore, the recording unit can record information about local companies and industries based on the candidate's geographical location. For example, the recording unit can obtain the candidate's geographical location from a database and record region-specific information using generative AI. This enables the recording unit to record region-specific information by taking the candidate's geographical location into account. Some or all of the above-described processes in the recording unit may be performed using generative AI, for example, or without using generative AI.

[0117] The recording unit can analyze a candidate's social media activity and reflect it in the record. For example, the recording unit can analyze a candidate's social media activity and reflect job-related posts in the record. For example, the recording unit can also reflect information about a candidate's areas of interest and hobbies in the record based on their social media activity. Furthermore, the recording unit can analyze a candidate's social media activity and reflect job-related skills and experience in the record. For example, the recording unit can input a candidate's social media activity into a generative AI and reflect the relevant information in the record. This allows the recording unit to create more multifaceted records by analyzing a candidate's social media activity. Some or all of the above processing in the recording unit may be performed using, for example, a generative AI, or without using a generative AI.

[0118] The comparison unit can estimate the candidate's emotions and adjust the comparison criteria based on the estimated candidate's emotions. For example, if the candidate is nervous, the comparison unit can adjust the comparison criteria to account for emotional fluctuations. For example, if the candidate is relaxed, the comparison unit can use the normal comparison criteria. Furthermore, if the candidate is excited, the comparison unit can adjust the comparison criteria to account for emotional peaks. For example, the comparison unit can capture the candidate's facial expression with a camera and estimate their emotions using facial recognition technology. For example, the comparison unit can analyze the candidate's tone of voice using speech analysis technology and estimate their emotions. This allows the comparison unit to make more accurate comparisons by adjusting the comparison criteria according to the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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-described processing in the comparison unit may be performed using, for example, generative AI, or without generative AI.

[0119] The comparison unit can evaluate the reliability of recorded information and perform comparisons based on reliable information. For example, the comparison unit can evaluate the source of recorded information and perform comparisons based on reliable information. For example, the comparison unit can evaluate the consistency of information and perform comparisons based on consistent information. Furthermore, the comparison unit can evaluate the timeliness of information and perform comparisons based on the latest information. For example, the comparison unit can input recorded information into a generating AI and have the generating AI perform the reliability evaluation. This allows the comparison unit to perform highly reliable comparisons by evaluating the reliability of recorded information. Some or all of the above-described processes in the comparison unit may be performed using a generating AI, for example, or without using a generating AI.

[0120] The comparison unit can customize the focus of the comparison based on the candidate's work history and skill set. For example, the comparison unit can compare relevant skills and experience based on the candidate's work history. For example, the comparison unit can also compare specific technical skills based on the candidate's skill set. Furthermore, the comparison unit can combine the candidate's work history and skill set to select the optimal comparison method. For example, the comparison unit can retrieve the candidate's work history from a database and customize the focus of the comparison using generative AI. This allows the comparison unit to perform more appropriate comparisons by customizing the focus of the comparison based on the candidate's work history and skill set. Some or all of the above processing in the comparison unit may be performed using generative AI, for example, or without generative AI.

[0121] The comparison unit can estimate the candidate's emotions and adjust the display method of the comparison results based on the estimated candidate's emotions. For example, if the candidate is nervous, the comparison unit can provide a simple and highly visible display method. For example, if the candidate is relaxed, the comparison unit can provide a display method that includes detailed information. Furthermore, if the candidate is excited, the comparison unit can provide a visually stimulating display method. For example, the comparison unit can capture the candidate's facial expression with a camera and estimate their emotions using facial recognition technology. For example, the comparison unit can analyze the candidate's tone of voice using speech analysis technology and estimate their emotions. This allows the comparison unit to adjust the display method of the comparison results according to the candidate's emotions, enabling a more visually appealing display. 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, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processing in the comparison unit may be performed using a generative AI, for example, or without a generative AI.

[0122] The comparison unit can perform region-specific comparisons by taking into account the candidate's geographical location information. For example, the comparison unit can perform comparisons regarding regional labor markets based on the candidate's geographical location information. The comparison unit can also perform comparisons regarding region-specific cultures and customs by taking into account the candidate's geographical location information. Furthermore, the comparison unit can perform comparisons regarding regional companies and industries based on the candidate's geographical location information. For example, the comparison unit can obtain the candidate's geographical location information from a database and perform region-specific comparisons using generative AI. This enables the comparison unit to perform region-specific comparisons by taking into account the candidate's geographical location information. Some or all of the above processing in the comparison unit may be performed using generative AI, for example, or without using generative AI.

[0123] The comparison unit can analyze candidates' social media activity and reflect it in the comparison results. For example, the comparison unit can analyze candidates' social media activity and reflect job-related posts in the comparison results. For example, the comparison unit can also reflect information about candidates' areas of interest and hobbies based on their social media activity in the comparison results. Furthermore, the comparison unit can analyze candidates' social media activity and reflect job-related skills and experience in the comparison results. For example, the comparison unit can input candidates' social media activity into a generative AI and reflect the relevant information in the comparison results. This allows the comparison unit to perform a more multifaceted comparison by analyzing candidates' social media activity. Some or all of the above processing in the comparison unit may be performed using, for example, a generative AI, or without using a generative AI.

[0124] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0125] The IntelliInterview System can estimate a candidate's emotions and dynamically adjust the interview questions based on those estimates. For example, if a candidate is nervous, the interviewer can start with lighter questions to help them relax. If the candidate is relaxed, the interviewer can move on to deeper questions. Furthermore, if a candidate is excited, the interviewer can leverage that excitement to ask challenging questions. This improves the quality of the interview and helps to bring out the candidate's true potential.

[0126] The IntelliInterview System automatically collects information on a candidate's past projects and achievements, and utilizes this information during the interview. For example, it can collect details of projects a candidate has been involved in in the past and ask questions related to those projects. It can also ask questions about specific achievements and challenges based on the candidate's performance. Furthermore, it can evaluate the relevance of a candidate's past projects to their current position and generate appropriate questions. This makes the interview more specific and meaningful.

[0127] The IntelliInterview System can estimate a candidate's emotions and adjust the interview process based on those estimates. For example, if a candidate is nervous, the interview can be slowed down to allow time for relaxation. If the candidate is relaxed, the interview can proceed at a normal pace. Furthermore, if a candidate is excited, the system can leverage that excitement to guide the interview. This enables flexible interviews that respond to the candidate's emotions.

[0128] The IntelliInterview system can take into account the candidate's geographical location and add region-specific questions. For example, if a candidate lives in a particular region, questions about the local labor market and culture can be asked. Similarly, if a candidate is applying from a different region, questions about the characteristics and customs of that region can be added. Furthermore, based on the candidate's geographical location, it's possible to ask questions about region-specific challenges and opportunities. This makes the interview more personalized and allows for questions tailored to the candidate's background.

[0129] The IntelliInterview System can estimate a candidate's emotions and provide interview feedback based on those estimates. For example, if a candidate is nervous, it can offer advice on how to relax. If a candidate is relaxed, it can provide feedback to help them maintain that state. Furthermore, if a candidate is excited, it can provide feedback to help them leverage that excitement to move forward to the next step. This makes it possible to support candidates in achieving better performance.

[0130] The IntelliInterview System analyzes a candidate's social media activity and utilizes that information during the interview. For example, it can ask questions about projects and achievements the candidate has shared on social media. It can also ask relevant questions based on the candidate's interests and hobbies. Furthermore, it can use the candidate's social media activity to ask questions about job-related skills and experience. This makes the interview more personalized and allows for questions tailored to the candidate's background.

[0131] The IntelliInterview System can estimate a candidate's emotions and adjust the interview evaluation criteria based on those estimates. For example, if a candidate is nervous, the evaluation criteria can be relaxed to account for that nervousness. Conversely, if a candidate is relaxed, the normal evaluation criteria can be applied. Furthermore, if a candidate is excited, the evaluation criteria can be adjusted to take advantage of that excitement. This allows for flexible evaluation that responds to the candidate's emotions.

[0132] The IntelliInterview System can customize interview questions based on a candidate's work history and skill set. For example, it can ask questions about relevant skills and experience based on the candidate's past work history. It can also add specific technical questions based on the candidate's skill set. Furthermore, it can combine the candidate's work history and skill set to generate optimal questions. This makes the interview more specific and meaningful.

[0133] The IntelliInterview System can estimate a candidate's emotions and adjust the interview pace based on those estimates. For example, if a candidate is nervous, the interview pace can be slowed to help them relax. If the candidate is relaxed, the interview can proceed at a normal pace. Furthermore, if a candidate is excited, the interview pace can be accelerated to capitalize on that excitement. This allows for flexible interviews that respond to the candidate's emotions.

[0134] The IntelliInterview system can customize interview content by taking into account the candidate's geographical location. For example, if a candidate lives in a specific region, questions about the local labor market and culture can be asked. If a candidate is applying from a different region, additional questions about the characteristics and customs of that region can be added. Furthermore, based on the candidate's geographical location, it's possible to ask questions about region-specific challenges and opportunities. This makes the interview more personalized and allows for questions tailored to the candidate's background.

[0135] The following briefly describes the processing flow for example form 2.

[0136] Step 1: The collection unit collects information about the candidates. For example, the collection unit can collect information such as thinking style, personality, facial expressions, and tone of voice through conversations with the candidates. The collection unit can also collect information such as the candidates' work history and skill sets through interviews. Furthermore, the collection unit can collect information about the candidates' personality traits using questionnaires. In addition, the collection unit can retrieve candidate information from databases. Step 2: The analysis unit analyzes the information collected by the collection unit. The analysis unit can, for example, use data mining techniques to analyze the candidate's strengths and characteristics. The analysis unit can also, for example, use statistical analysis to evaluate the candidate's skill set. Furthermore, the analysis unit can use machine learning algorithms to evaluate the candidate's suitability. Step 3: The evaluation unit assesses the candidate's suitability based on the analysis results obtained by the analysis unit. The evaluation unit can, for example, use a scoring system to assess the candidate's suitability. The evaluation unit can also, for example, use an evaluation model to assess the candidate's job suitability. Furthermore, the evaluation unit can also assess team suitability. Step 4: The proposal department proposes suitable roles and assignments for candidates based on the evaluation results obtained by the evaluation department. For example, the proposal department may propose specific roles based on the candidate's skill set. For example, the proposal department may also propose appropriate assignments based on the candidate's work history. Furthermore, the proposal department may propose assignments that take into account the candidate's personality traits and team suitability. Step 5: The recording unit records the conversation with the candidate. The recording unit can record the conversation with the candidate using, for example, audio recording. The recording unit can also record the conversation with the candidate using, for example, text recording. In addition, the recording unit can record the conversation with the candidate using video recording. Step 6: The comparison unit performs re-evaluation and comparison based on the information recorded by the recording unit. The comparison unit can, for example, compare with past evaluation results. The comparison unit can also, for example, compare with other candidates. Furthermore, the comparison unit can re-evaluate the candidate based on the recorded information.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, proposal unit, recording unit, and comparison unit, is implemented, for example, by at least one of the smart device 14 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 38B of the smart device 14 to collect the candidate's facial expressions and tone of voice, and the control unit 46A collects the candidate's information. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected information. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and evaluates the candidate's suitability based on the analysis results. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes a suitable role or assignment to the candidate based on the evaluation results. The recording unit is implemented, for example, by the control unit 46A of the smart device 14, and records the conversation with the candidate. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and performs re-evaluation and comparison based on the recorded information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0141] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.).

[0153] 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.

[0154] 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.

[0155] 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.

[0156] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, proposal unit, recording unit, and comparison 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 uses the camera 42 and microphone 238 of the smart glasses 214 to collect the candidate's facial expressions and tone of voice, and the control unit 46A collects the candidate's information. The analysis unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and analyzes the collected information. The evaluation unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and evaluates the candidate's suitability based on the analysis results. The proposal unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and proposes a suitable role or assignment to the candidate based on the evaluation results. The recording unit is implemented, for example, in the control unit 46A of the smart glasses 214, and records the conversation with the candidate. The comparison unit is implemented, for example, in the identification processing unit 290 of the data processing unit 12, and performs re-evaluation and comparison based on the recorded information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0157] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.).

[0169] 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.

[0170] 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.

[0171] 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.

[0172] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, proposal unit, recording unit, and comparison unit, is implemented, for example, by at least one of the headset terminal 314 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the headset terminal 314 to collect the candidate's facial expressions and tone of voice, and the control unit 46A collects the candidate's information. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected information. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and evaluates the candidate's suitability based on the analysis results. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes a suitable role or assignment to the candidate based on the evaluation results. The recording unit is implemented, for example, by the control unit 46A of the headset terminal 314, and records the conversation with the candidate. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and performs re-evaluation and comparison based on the recorded information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0178] 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).

[0179] 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.

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.).

[0186] 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.

[0187] 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.

[0188] 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.

[0189] Each of the multiple elements described above, including the collection unit, analysis unit, evaluation unit, proposal unit, recording unit, and comparison unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the collection unit uses the camera 42 and microphone 238 of the robot 414 to collect the candidate's facial expressions and tone of voice, and the control unit 46A collects the candidate's information. The analysis unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and analyzes the collected information. The evaluation unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and evaluates the candidate's suitability based on the analysis results. The proposal unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and proposes a suitable role or assignment to the candidate based on the evaluation results. The recording unit is implemented, for example, by the control unit 46A of the robot 414, and records the conversation with the candidate. The comparison unit is implemented, for example, by the identification processing unit 290 of the data processing unit 12, and performs re-evaluation and comparison based on the recorded information. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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.

[0195] 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."

[0196] 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.

[0197] 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.

[0198] 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.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] 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.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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.

[0208] (Note 1) A collection department that collects information on candidates, An analysis unit analyzes the information collected by the aforementioned collection unit, An evaluation unit that evaluates the suitability of candidates based on the analysis results obtained by the analysis unit, Based on the evaluation results obtained by the aforementioned evaluation unit, the proposal unit proposes suitable roles and assignments for candidates. A recording unit for recording conversations with the aforementioned candidates, The system includes a comparison unit that performs re-evaluation and comparison based on the information recorded by the recording unit. A system characterized by the following features. (Note 2) The aforementioned collection unit is Through dialogue with candidates, we collect information such as their thinking style, personality, facial expressions, and tone of voice. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, The collected information is analyzed to evaluate the candidates' strengths and characteristics. The system described in Appendix 1, characterized by the features described herein. (Note 4) The evaluation unit, The suitability of candidates is evaluated based on the analysis results. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned proposal section is, Based on the evaluation results, we propose suitable roles and assignments for the candidates. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned recording unit is Record conversations with candidates so that they can be re-evaluated and compared later. The system described in Appendix 1, characterized by the features described herein. (Note 7) The comparison unit is, Based on the information recorded by the records department, candidates will be re-evaluated and compared. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned collection unit is The system estimates the candidate's emotions and adjusts the pace of the conversation based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned collection unit is Automatically collects candidates' past work history and skill sets to customize the content of the conversation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned collection unit is Prioritize the information to collect based on the candidate's current job status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned collection unit is We estimate the candidate's sentiment and adjust the depth of information we collect based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned collection unit is Consider the candidate's geographical location and add region-specific questions. 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 candidate's emotions and adjusts the analysis algorithm based on the estimated candidate's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, The reliability of the collected information is evaluated, and the most reliable information is prioritized for analysis. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, Customize the focus of the analysis based on the candidate's work history and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, The system estimates the candidate's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, We will perform region-specific analysis, taking into account the candidates' geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned analysis unit, Analyze candidates' social media activity and reflect the results in the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 20) The evaluation unit, Estimate the candidate's emotions and adjust the evaluation criteria based on the estimated candidate's emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The evaluation unit, Evaluate the reliability of the analysis results and prioritize evaluating the results with high reliability. The system described in Appendix 1, characterized by the features described herein. (Note 22) The evaluation unit, Customize the focus of the evaluation based on the candidate's work history and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 23) The evaluation unit, The system estimates the candidate's emotions and adjusts how the evaluation results are displayed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The evaluation unit, Considering the candidate's geographical location, a region-specific evaluation will be conducted. The system described in Appendix 1, characterized by the features described herein. (Note 25) The evaluation unit, Analyze candidates' social media activity and reflect it in the evaluation results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned proposal section is, The system estimates the candidate's sentiments and adjusts the way the proposal is presented based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned proposal section is, We evaluate the reliability of the evaluation results and make proposals based on highly reliable results. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned proposal section is, Customize the focus of your proposal based on the candidate's work history and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned proposal section is, The system estimates the candidates' sentiments and determines the priority of proposals based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned proposal section is, Proposals tailored to the region will be made, taking into account the candidate's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned proposal section is, Analyze candidates' social media activity and incorporate it into the proposal. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned recording unit is Estimate the candidates' emotions and adjust the recording methods based on the estimated emotions of the candidates. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned recording unit is Evaluate the reliability of recorded information and prioritize recording only the most reliable information. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned recording unit is Customize the focus of the record based on the candidate's work history and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned recording unit is The system estimates the candidates' emotions and prioritizes the recordings based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned recording unit is Record region-specific information, taking into account the candidate's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 37) The aforementioned recording unit is Analyze candidates' social media activity and reflect it in the records. The system described in Appendix 1, characterized by the features described herein. (Note 38) The comparison unit is, Estimate the candidates' sentiments and adjust the comparison criteria based on the estimated candidates' sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 39) The comparison unit is, Evaluate the reliability of the recorded information and make comparisons based on the most reliable information. The system described in Appendix 1, characterized by the features described herein. (Note 40) The comparison unit is, Customize the focus of the comparison based on the candidate's work history and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 41) The comparison unit is, The system estimates the candidates' sentiments and adjusts how comparison results are displayed based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 42) The comparison unit is, Take into account the candidates' geographical location to conduct region-specific comparisons. The system described in Appendix 1, characterized by the features described herein. (Note 43) The comparison unit is, Analyze candidates' social media activity and reflect it in the comparison results. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0209] 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 information on candidates, An analysis unit analyzes the information collected by the aforementioned collection unit, An evaluation unit that evaluates the suitability of candidates based on the analysis results obtained by the analysis unit, Based on the evaluation results obtained by the aforementioned evaluation unit, the proposal unit proposes suitable roles and assignments for candidates. A recording unit for recording conversations with the aforementioned candidates, The system includes a comparison unit that performs re-evaluation and comparison based on the information recorded by the recording unit. A system characterized by the following features.

2. The aforementioned collection unit is Through dialogue with candidates, we collect information such as their thinking style, personality, facial expressions, and tone of voice. The system according to feature 1.

3. The aforementioned analysis unit, The collected information is analyzed to evaluate the candidates' strengths and characteristics. The system according to feature 1.

4. The evaluation unit, The suitability of candidates is evaluated based on the analysis results. The system according to feature 1.

5. The aforementioned proposal section is, Based on the evaluation results, we propose suitable roles and assignments for the candidates. The system according to feature 1.

6. The aforementioned recording unit is Record conversations with candidates so that they can be re-evaluated and compared later. The system according to feature 1.

7. The comparison unit is, Based on the information recorded by the aforementioned recording unit, candidates are re-evaluated and compared. The system according to feature 1.

8. The aforementioned collection unit is The system estimates the candidate's emotions and adjusts the pace of the conversation based on those estimated emotions. The system according to feature 1.

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