system

Abstract

The system according to this embodiment aims to reduce the workload of interviewers and to conduct fair evaluations. [Solution] The system according to the embodiment comprises an input unit, a document screening unit, an interview unit, and a final interview unit. The input unit inputs the company's recruitment policy. The document screening unit conducts a document screening based on the information input by the input unit. The interview unit conducts a first interview with the candidates selected by the document screening unit. The final interview unit conducts a final interview with the candidates selected by the interview unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that the workload of interviewers in the adoption process is large and it is difficult to conduct a fair review.

[0005] The system according to the embodiment aims to reduce the workload of interviewers and conduct a fair review.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an input unit, a document screening unit, an interview unit, and a final interview unit. The input unit receives the company's recruitment policy as input. The document screening unit conducts a document screening based on the information input by the input unit. The interview unit conducts a first interview with the candidates selected by the document screening unit. The final interview unit conducts a final interview with the candidates selected by the interview unit. [Effects of the Invention]

[0007] The system according to this embodiment can reduce the workload of interviewers and enable fair evaluations. [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 controls 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 recruitment support system according to an embodiment of the present invention is a system that uses AI to perform document screening and first interviews on behalf of interviewers, thereby reducing the workload of interviewers and ensuring fair evaluation. The recruitment support system inputs the company's recruitment policy into a generating AI in advance, conducts document screening using the same criteria, and selects candidates who meet the recruitment criteria. Furthermore, the generating AI conducts first interviews with the same questions and selects candidates who meet the recruitment criteria. Finally, employees conduct interviews with the candidates selected by the generating AI to perform a double-check. This mechanism reduces the workload of interviewers and allows applicants to receive a fair evaluation. First, the company's recruitment policy is input into the generating AI. At this time, information such as the number of people to be hired, required skills, and performance evaluations of past hires is entered. For example, if a company is hiring 10 engineers as new graduates, the required programming skills and evaluation data of past hires are entered into the generating AI. Next, the generating AI conducts document screening using the same criteria. Based on the inputted recruitment policy, the generating AI analyzes the applicants' resumes and work histories and selects candidates who meet the recruitment criteria. For example, the system evaluates applicants' skill sets and work experience to select candidates with the necessary skills. Furthermore, the generating AI conducts a first interview using the same questions. The generating AI conducts online interviews with applicants using pre-set questions. For example, it asks all applicants questions such as, "Please tell me about your strengths and weaknesses," and evaluates their answers. Finally, employees conduct interviews with the candidates selected by the generating AI to perform a double-check. Employees conduct final interviews with the candidates selected by the generating AI and make the final hiring decision. This significantly reduces the workload of interviewers, and applicants receive a fair evaluation. This system allows companies' recruitment departments to reduce the workload involved in hiring new graduates and mid-career professionals, and alleviates the burden on interviewers. In addition, applicants receive a fair evaluation by being screened using the same criteria by the generating AI. For example, because the generating AI asks the same questions to all applicants and evaluates their answers, the influence of the interviewer's subjectivity or compatibility can be eliminated.This allows the recruitment support system to conduct document screening and first interviews based on the company's recruitment policy, followed by a final interview, thereby reducing the workload on interviewers and ensuring a fair evaluation process.

[0029] The recruitment support system according to this embodiment comprises an input unit, a document screening unit, an interview unit, and a final interview unit. The input unit inputs the company's recruitment policy. The input unit inputs information such as the number of people to be hired, required skills, and performance evaluations of past hires. For example, if a company is hiring 10 engineers as new graduates, the input unit inputs the necessary programming skills and evaluation data of past hires into the generating AI. The document screening unit conducts a document screening based on the information input by the input unit. For example, the document screening unit analyzes the applicant's resume and work history and selects candidates who meet the recruitment criteria. For example, the document screening unit evaluates the applicant's skill set and work experience and selects candidates with the necessary skills. Some or all of the above processing in the document screening unit may be performed using the generating AI, or without using the generating AI. For example, the document screening unit inputs the applicant's resume and work history into the generating AI, which analyzes the data and selects candidates who meet the recruitment criteria. The interview department conducts first-round interviews with candidates selected by the document screening department. The interview department conducts first-round interviews with applicants online, for example, using pre-set questions. The interview department asks all applicants questions such as, "Please tell me about your strengths and weaknesses," and evaluates their answers. Some or all of the above processes in the interview department may be performed using, for example, a generative AI, or not. For example, the interview department inputs pre-set questions into a generative AI, which conducts first-round interviews with applicants online and evaluates their answers. The final interview department conducts final interviews with candidates selected by the interview department. For example, the final interview department has employees conduct final interviews with candidates selected by the generative AI and makes a final hiring decision. Some or all of the above processes in the final interview department may be performed using, for example, a generative AI, or not. For example, the final interview department has employees conduct final interviews with candidates selected by the generative AI and makes a final hiring decision.As a result, the recruitment support system according to this embodiment can reduce the workload of interviewers and ensure fair evaluation by conducting document screening and first interviews based on the company's recruitment policy, followed by a final interview.

[0030] The input unit receives input on the company's recruitment policies. Specifically, it inputs detailed information such as the number of hires the company plans to make, required skills, and performance evaluations of past hires. For example, if a company is hiring 10 engineers through new graduate recruitment, it inputs the necessary programming skills and evaluation data of past hires into the generating AI. Based on this information, the generating AI sets criteria for selecting candidates that best fit the company's recruitment policies. The input unit centrally manages information related to the company's recruitment policies and can update it as needed. For example, if a company changes the required skill set or the number of hires when starting a new project, the input unit responds quickly and provides the new information to the generating AI. The input unit can also share data on the company's recruitment policies with other departments to facilitate collaboration and ensure a smooth overall recruitment process. In this way, the input unit accurately reflects the company's recruitment policies and provides a foundation for an efficient and effective recruitment process.

[0031] The document screening department conducts document screening based on information input by the input department. Specifically, it analyzes applicants' resumes and work histories to select candidates who meet the hiring criteria. The document screening department evaluates applicants' skill sets and work experience to select candidates with the necessary skills. For example, applicants' resumes and work histories are input into a generating AI, which analyzes them and selects candidates who meet the hiring criteria. The generating AI uses natural language processing technology to analyze applicants' documents and automatically evaluates their skills and experience. For example, it identifies candidates with specific skill sets that the company requires, such as programming skills or project management experience. Furthermore, the generating AI can learn the characteristics of successful hires based on past hiring data and evaluate candidates based on that. This allows the document screening department to efficiently and fairly select candidates and find the talent best suited to the company's hiring policy. In addition, the document screening department can share the candidate evaluations based on the generating AI's analysis results with human reviewers to help them make final decisions. This allows the document review department to leverage generational AI technology to achieve rapid and accurate document review, significantly streamlining the company's recruitment process.

[0032] The interview department conducts first-round interviews with candidates selected by the document screening department. Specifically, they conduct online first-round interviews with applicants using pre-set questions. For example, they might ask all applicants, "Please tell me about your strengths and weaknesses," and evaluate their answers. The interview department uses a generative AI to input pre-set questions into the AI, which then conducts the online first-round interviews with applicants and evaluates their answers. The generative AI uses speech recognition technology to convert the applicant's answers into text and natural language processing technology to analyze the content of the answers. For example, it evaluates communication skills and problem-solving abilities from the applicant's answers and determines whether they match the skill set required by the company. The generative AI can also analyze non-verbal elements such as the applicant's facial expressions and tone of voice to make a comprehensive evaluation. This allows the interview department to conduct first-round interviews efficiently and fairly, and select the candidates best suited to the company's recruitment policy. Furthermore, the interview department can share the candidate evaluations based on the generative AI's analysis results with human interviewers to help them make final decisions. This allows the interview department to leverage generative AI technology to conduct quick and accurate first-round interviews, significantly streamlining the company's recruitment process.

[0033] The final interview department conducts final interviews with candidates selected by the interview department. Specifically, employees conduct final interviews with candidates selected by the generative AI and make the final hiring decision. Based on the analysis results of the generative AI, the final interview department can share the candidate evaluations with human interviewers and use them as a reference for making the final decision. For example, the final interview questions are set based on the candidate's skill set, work experience, and answers from the first interview, as analyzed by the generative AI. The final interview department evaluates the candidate's suitability and fit to the company culture and makes the final hiring decision. For example, they evaluate the candidate's communication skills, leadership, and teamwork abilities to determine if they match the company's desired candidate profile. The final interview department also evaluates the candidate's past performance and future career vision, enabling them to make hiring decisions from a long-term perspective. In this way, the final interview department can leverage generative AI technology to achieve efficient and fair final interviews and find the talent best suited to the company's hiring policy. Furthermore, based on the results of the final interview, the final interview department can provide feedback to candidates, increasing the transparency of the hiring process. This allows the final interview department to significantly streamline the company's recruitment process and quickly hire the most suitable talent.

[0034] The input unit can input information such as the number of people to be hired, required skills, and past performance evaluations of hired individuals. For example, if a company is hiring 10 engineers as new graduates, the input unit will input the required programming skills and past hiring evaluation data into the generating AI. The input unit will input, for example, the number of people to be hired, the required skills, and past hiring evaluations. By inputting information such as the number of people to be hired, required skills, and past hiring evaluations, more accurate document screening and interviews become possible. Some or all of the above processing in the input unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the input unit inputs the number of people to be hired, required skills, and past hiring evaluations into the generating AI, and the generating AI conducts document screening and interviews based on this information.

[0035] The document screening department can analyze applicants' resumes and work histories to select candidates who meet the hiring criteria. For example, the document screening department can analyze applicants' resumes and work histories to select candidates who meet the hiring criteria. For example, the document screening department can evaluate applicants' skill sets and work experience to select candidates with the necessary skills. For example, the document screening department can analyze applicants' resumes to evaluate their educational background, work history, and skill sets. For example, the document screening department can analyze applicants' work histories to evaluate past projects, roles, and achievements. This allows for efficient document screening by analyzing applicants' resumes and work histories to select candidates who meet the hiring criteria. Some or all of the above processes in the document screening department may be performed using, for example, a generative AI, or not. For example, the document screening department can input applicants' resumes and work histories into a generative AI, which then analyzes them and selects candidates who meet the hiring criteria.

[0036] The interview department can conduct initial online interviews with applicants using pre-set questions. For example, the interview department can conduct initial online interviews with applicants using pre-set questions. The interview department can ask all applicants questions such as, "Please tell me about your strengths and weaknesses," and evaluate their answers. The interview department can pre-set technical and behavioral questions and conduct initial online interviews with applicants. The interview department can evaluate applicants' answers and select candidates who meet the hiring criteria. This ensures fair interviews by conducting initial online interviews using pre-set questions. Some or all of the above processes in the interview department may be performed using, for example, generative AI, or not. For example, the interview department can input pre-set questions into a generative AI, which then conducts initial online interviews with applicants and evaluates their answers.

[0037] The final interview department can conduct final interviews with candidates selected by the generation AI and make final hiring decisions. For example, the final interview department can have employees conduct final interviews with candidates selected by the generation AI and make final hiring decisions. The final interview department can have employees conduct final interviews with candidates selected by the generation AI and make final hiring decisions. This reduces the workload on interviewers and ensures a fair selection process by having employees conduct final interviews with candidates selected by the generation AI. Some or all of the above processes in the final interview department may be performed using the generation AI, for example, or without the generation AI. For example, the final interview department can have employees conduct final interviews with candidates selected by the generation AI and make final hiring decisions.

[0038] The input unit can select the optimal input method by referring to past recruitment data during the input process. For example, the input unit can analyze successful recruitment processes from past recruitment data and propose similar input methods. For example, the input unit can select the optimal input method for a specific skill set based on past recruitment data. For example, the input unit can refer to past recruitment data and propose the most effective input method for a specific job type. This allows for more effective information input by selecting the optimal input method by referring to past recruitment data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input past recruitment data into AI, and the AI ​​can select the optimal input method.

[0039] The input unit can adjust the input content based on the company's current market situation and competitive information during the input process. For example, the input unit may analyze the current market situation and prompt the input of the necessary skill sets. For example, the input unit may refer to the hiring trends of competing companies and adjust the input content. For example, the input unit may update the input information to the latest version by considering market trends. This allows for more appropriate information input by adjusting the input content based on the company's current market situation and competitive information. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit may input the company's current market situation and competitive information into the AI, and the AI ​​may adjust the input content.

[0040] The input unit can customize the input content based on the company's geographical location information during the input process. For example, the input unit may prompt the input of necessary skill sets based on the company's location. For example, the input unit may adjust the information to be input, taking geographical characteristics into consideration. For example, the input unit may suggest the optimal input method based on the company's geographical location information. This allows for more appropriate information input by customizing the input content based on the company's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit may input the company's geographical location information into the AI, and the AI ​​will customize the input content.

[0041] The input unit can analyze a company's social media activities and input relevant information during the input process. For example, the input unit can analyze a company's social media activities and prompt the input of necessary skill sets. For example, the input unit can adjust the information to be input by considering social media trends. For example, the input unit can suggest the optimal input method based on a company's social media activities. This enables more appropriate information input by analyzing a company's social media activities and inputting relevant information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input a company's social media activities into AI, and the AI ​​can input relevant information.

[0042] The document screening department can improve the accuracy of its screening process by thoroughly analyzing applicants' past work histories during the document screening stage. For example, the document screening department can thoroughly analyze applicants' past work histories and evaluate their skill sets. For example, the document screening department can select candidates with specific skills and experience from applicants' work histories. For example, the document screening department can set criteria to improve the accuracy of the screening process based on applicants' work histories. This improves the accuracy of the screening process by thoroughly analyzing applicants' past work histories. Some or all of the above processes in the document screening department may be performed using AI, for example, or not. For example, the document screening department can input applicants' past work histories into an AI, which can then perform a detailed analysis to improve the accuracy of the screening process.

[0043] The document review department can apply different review algorithms during the document review process depending on the format and content of the applicant's submitted documents. For example, the document review department may apply the most suitable review algorithm based on the format of the applicant's submitted documents. For example, the document review department may apply a different review algorithm based on the content of the submitted documents. For example, the document review department may analyze the format and content of the applicant's submitted documents and select the most suitable review algorithm. This allows for more appropriate document review by applying different review algorithms depending on the format and content of the applicant's submitted documents. Some or all of the above processes in the document review department may be performed using AI, or not. For example, the document review department may input the format and content of the applicant's submitted documents into an AI, which then applies the most suitable review algorithm.

[0044] The document review department can adjust the review process based on the applicant's geographical location information during the document review. For example, the document review department adjusts the review process based on the applicant's geographical location information. For example, the document review department sets review criteria considering geographical characteristics. For example, the document review department selects the optimal review method by referring to the applicant's geographical location information. This makes it possible to conduct a more appropriate document review by adjusting the review process based on the applicant's geographical location information. Some or all of the above processes in the document review department may be performed using AI, for example, or not using AI. For example, the document review department inputs the applicant's geographical location information into AI, and the AI ​​adjusts the review process.

[0045] The document screening department can analyze applicants' social media activity during the document screening process and incorporate relevant information into the screening. For example, the document screening department can analyze applicants' social media activity and evaluate their skill sets. For example, the document screening department can adjust the screening process considering social media trends. For example, the document screening department can select the most appropriate screening method based on applicants' social media activity. This allows for a more appropriate document screening by analyzing applicants' social media activity and incorporating relevant information into the screening process. Some or all of the above processes in the document screening department may be performed using AI, for example, or not. For example, the document screening department can input applicants' social media activity into AI, and the AI ​​can incorporate relevant information into the screening process.

[0046] The interview department can select the most appropriate questions during an interview by referring to the applicant's past interview history. For example, the interview department can refer to the applicant's past interview history and select the most appropriate questions. For example, the interview department can ask questions related to specific skills or experience based on the past interview history. For example, the interview department can select the most appropriate questioning method based on the applicant's interview history. This makes it possible to conduct more effective interviews by selecting the most appropriate questions by referring to the applicant's past interview history. Some or all of the above processes in the interview department may be performed using AI, for example, or not using AI. For example, the interview department can input the applicant's past interview history into AI, and the AI ​​can select the most appropriate questions.

[0047] The interview department can apply different question algorithms during interviews depending on the applicant's work experience and skills. For example, the interview department may apply the optimal question algorithm based on the applicant's work experience. For example, the interview department may apply different question algorithms depending on the applicant's skill set. For example, the interview department may analyze the applicant's work experience and skills and select the optimal question algorithm. This allows for more appropriate interviews by applying different question algorithms depending on the applicant's work experience and skills. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department may input the applicant's work experience and skills into an AI, which then applies the optimal question algorithm.

[0048] The interview department can customize the interview content based on the applicant's geographical location information during the interview. For example, the interview department customizes the interview content based on the applicant's geographical location information. For example, the interview department adjusts the interview content considering geographical characteristics. For example, the interview department selects the optimal interview method by referring to the applicant's geographical location information. This makes it possible to conduct more appropriate interviews by customizing the interview content based on the applicant's geographical location information. Some or all of the above processes in the interview department may be performed using AI, for example, or not using AI. For example, the interview department inputs the applicant's geographical location information into AI, and the AI ​​customizes the interview content.

[0049] The interview department can analyze the applicant's social media activity during the interview and ask relevant questions. For example, the interview department can analyze the applicant's social media activity and ask relevant questions. For example, the interview department can adjust the questions considering social media trends. For example, the interview department can select the most appropriate questioning method based on the applicant's social media activity. This allows for more appropriate interviews by analyzing the applicant's social media activity and asking relevant questions. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the applicant's social media activity into AI, and the AI ​​can ask relevant questions.

[0050] The final interview department can select the optimal evaluation method during the final interview by referring to the applicant's past interview results. For example, the final interview department can refer to the applicant's past interview results and select the optimal evaluation method. For example, the final interview department can set evaluation criteria for specific skills and experience based on past interview results. For example, the final interview department can select the optimal evaluation method based on the applicant's interview results. This makes it possible to conduct a more effective final interview by referring to the applicant's past interview results and selecting the optimal evaluation method. Some or all of the above processes in the final interview department may be performed using AI, for example, or not using AI. For example, the final interview department can input the applicant's past interview results into AI, and the AI ​​will select the optimal evaluation method.

[0051] The final interview department can apply different evaluation algorithms to applicants during the final interview, depending on their work experience and skills. For example, the final interview department may apply the optimal evaluation algorithm based on the applicant's work experience. For example, the final interview department may apply different evaluation algorithms depending on the applicant's skill set. For example, the final interview department may analyze the applicant's work experience and skills and select the optimal evaluation algorithm. This allows for more appropriate final interviews by applying different evaluation algorithms according to the applicant's work experience and skills. Some or all of the above processes in the final interview department may be performed using AI, or not. For example, the final interview department may input the applicant's work experience and skills into an AI, which then applies the optimal evaluation algorithm.

[0052] The final interview department can adjust the evaluation criteria based on the applicant's geographical location information during the final interview. For example, the final interview department adjusts the evaluation criteria based on the applicant's geographical location information. For example, the final interview department sets evaluation criteria considering geographical characteristics. For example, the final interview department selects the optimal evaluation method by referring to the applicant's geographical location information. This makes it possible to conduct a more appropriate final interview by adjusting the evaluation criteria based on the applicant's geographical location information. Some or all of the above processes in the final interview department may be performed using AI, for example, or not using AI. For example, the final interview department inputs the applicant's geographical location information into the AI, and the AI ​​adjusts the evaluation criteria.

[0053] The final interview department can analyze the applicant's social media activity during the final interview and incorporate relevant information into the evaluation. For example, the final interview department can analyze the applicant's social media activity and assess their skill set. For example, the final interview department can adjust the evaluation content to take social media trends into consideration. For example, the final interview department can select the optimal evaluation method based on the applicant's social media activity. This allows for a more appropriate final interview by analyzing the applicant's social media activity and incorporating relevant information into the evaluation. Some or all of the above processes in the final interview department may be performed using AI, for example, or not. For example, the final interview department can input the applicant's social media activity into AI, and the AI ​​will incorporate relevant information into the evaluation.

[0054] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0055] The recruitment support system can also include a cultural fit assessment unit that evaluates candidates' cultural suitability. This unit assesses candidates' suitability based on the company's culture and values. For example, it can ask candidates questions about the company's values ​​and evaluate their responses. It can also analyze candidates' past work experience and behavioral patterns to assess their fit with the company culture. Furthermore, it can analyze candidates' social media activity to assess whether it aligns with the company's values. This allows for the selection of candidates who are a good fit for the company's culture and values, thereby creating a better work environment.

[0056] The recruitment support system can also include a stress tolerance assessment unit to evaluate candidates' stress tolerance. This unit asks questions to assess candidates' stress tolerance and analyzes their responses. For example, it might ask about how candidates have coped with past stressful situations and evaluate their answers. It can also collect physiological data from candidates (such as heart rate and skin electrical activity) to assess stress tolerance. Furthermore, it can analyze candidates' past work experience and behavioral patterns to evaluate stress tolerance. This allows for the selection of candidates with high stress tolerance, which is expected to improve workplace performance.

[0057] The recruitment support system can also include a leadership assessment section to evaluate candidates' leadership abilities. This section asks questions to assess candidates' leadership skills and analyzes their responses. For example, it might ask about past leadership experience and leadership style and evaluate the answers. It can also analyze candidates' past work experience and behavioral patterns to assess their leadership abilities. Furthermore, it can analyze candidates' social media activity to evaluate leadership-related behaviors. This allows for the selection of candidates with strong leadership skills, thereby promoting organizational growth.

[0058] The recruitment support system can also include a creativity assessment unit to evaluate candidates' creativity. This unit asks questions to assess a candidate's creativity and analyzes their responses. For example, it might ask about past creative projects or ideas and evaluate their answers. It can also analyze a candidate's past work experience and behavioral patterns to assess their creativity. Furthermore, it can analyze a candidate's social media activity to evaluate their creative activities and ideas. This allows for the selection of highly creative candidates, which is expected to lead to the realization of innovative ideas and projects.

[0059] The recruitment support system can also include a communication assessment unit to evaluate candidates' communication skills. This unit asks questions to assess candidates' communication abilities and analyzes their responses. For example, it might ask about past team projects and interpersonal experience and evaluate their answers. It can also analyze candidates' past work experience and behavioral patterns to assess their communication skills. Furthermore, it can analyze candidates' social media activity to evaluate communication-related behaviors. This allows for the selection of candidates with strong communication skills, which is expected to improve teamwork.

[0060] The following briefly describes the processing flow for example form 1.

[0061] Step 1: The input section receives the company's recruitment policy. For example, it inputs information such as the number of people to be hired, required skills, and performance evaluations of past hires. If a company is hiring 10 engineers as new graduates, it inputs the necessary programming skills and evaluation data of past hires into the generating AI. Step 2: The document screening department conducts a document screening based on the information input by the input department. For example, it analyzes applicants' resumes and work histories and selects candidates who meet the hiring criteria. Some or all of the processing in the document screening department may be performed using generative AI, or it may be performed without using generative AI. Step 3: The interview department conducts first-round interviews with candidates selected by the document screening department. For example, the interview department conducts online first-round interviews with applicants using pre-set questions. Some or all of the processing in the interview department may be performed using generative AI, or it may not be performed using generative AI. Step 4: The final interview department conducts final interviews with candidates selected by the interview department. For example, employees conduct final interviews with candidates selected by the generative AI and make a final hiring decision. Some or all of the processing in the final interview department may be performed using the generative AI, or it may be performed without the generative AI.

[0062] (Example of form 2) The recruitment support system according to an embodiment of the present invention is a system that uses AI to perform document screening and first interviews on behalf of interviewers, thereby reducing the workload of interviewers and ensuring fair evaluation. The recruitment support system inputs the company's recruitment policy into a generating AI in advance, conducts document screening using the same criteria, and selects candidates who meet the recruitment criteria. Furthermore, the generating AI conducts first interviews with the same questions and selects candidates who meet the recruitment criteria. Finally, employees conduct interviews with the candidates selected by the generating AI to perform a double-check. This mechanism reduces the workload of interviewers and allows applicants to receive a fair evaluation. First, the company's recruitment policy is input into the generating AI. At this time, information such as the number of people to be hired, required skills, and performance evaluations of past hires is entered. For example, if a company is hiring 10 engineers as new graduates, the required programming skills and evaluation data of past hires are entered into the generating AI. Next, the generating AI conducts document screening using the same criteria. Based on the inputted recruitment policy, the generating AI analyzes the applicants' resumes and work histories and selects candidates who meet the recruitment criteria. For example, the system evaluates applicants' skill sets and work experience to select candidates with the necessary skills. Furthermore, the generating AI conducts a first interview using the same questions. The generating AI conducts online interviews with applicants using pre-set questions. For example, it asks all applicants questions such as, "Please tell me about your strengths and weaknesses," and evaluates their answers. Finally, employees conduct interviews with the candidates selected by the generating AI to perform a double-check. Employees conduct final interviews with the candidates selected by the generating AI and make the final hiring decision. This significantly reduces the workload of interviewers, and applicants receive a fair evaluation. This system allows companies' recruitment departments to reduce the workload involved in hiring new graduates and mid-career professionals, and alleviates the burden on interviewers. In addition, applicants receive a fair evaluation by being screened using the same criteria by the generating AI. For example, because the generating AI asks the same questions to all applicants and evaluates their answers, the influence of the interviewer's subjectivity or compatibility can be eliminated.This allows the recruitment support system to conduct document screening and first interviews based on the company's recruitment policy, followed by a final interview, thereby reducing the workload on interviewers and ensuring a fair evaluation process.

[0063] The recruitment support system according to this embodiment comprises an input unit, a document screening unit, an interview unit, and a final interview unit. The input unit inputs the company's recruitment policy. The input unit inputs information such as the number of people to be hired, required skills, and performance evaluations of past hires. For example, if a company is hiring 10 engineers as new graduates, the input unit inputs the necessary programming skills and evaluation data of past hires into the generating AI. The document screening unit conducts a document screening based on the information input by the input unit. For example, the document screening unit analyzes the applicant's resume and work history and selects candidates who meet the recruitment criteria. For example, the document screening unit evaluates the applicant's skill set and work experience and selects candidates with the necessary skills. Some or all of the above processing in the document screening unit may be performed using the generating AI, or without using the generating AI. For example, the document screening unit inputs the applicant's resume and work history into the generating AI, which analyzes the data and selects candidates who meet the recruitment criteria. The interview department conducts first-round interviews with candidates selected by the document screening department. The interview department conducts first-round interviews with applicants online, for example, using pre-set questions. The interview department asks all applicants questions such as, "Please tell me about your strengths and weaknesses," and evaluates their answers. Some or all of the above processes in the interview department may be performed using, for example, a generative AI, or not. For example, the interview department inputs pre-set questions into a generative AI, which conducts first-round interviews with applicants online and evaluates their answers. The final interview department conducts final interviews with candidates selected by the interview department. For example, the final interview department has employees conduct final interviews with candidates selected by the generative AI and makes a final hiring decision. Some or all of the above processes in the final interview department may be performed using, for example, a generative AI, or not. For example, the final interview department has employees conduct final interviews with candidates selected by the generative AI and makes a final hiring decision.As a result, the recruitment support system according to this embodiment can reduce the workload of interviewers and ensure fair evaluation by conducting document screening and first interviews based on the company's recruitment policy, followed by a final interview.

[0064] The input unit receives input on the company's recruitment policies. Specifically, it inputs detailed information such as the number of hires the company plans to make, required skills, and performance evaluations of past hires. For example, if a company is hiring 10 engineers through new graduate recruitment, it inputs the necessary programming skills and evaluation data of past hires into the generating AI. Based on this information, the generating AI sets criteria for selecting candidates that best fit the company's recruitment policies. The input unit centrally manages information related to the company's recruitment policies and can update it as needed. For example, if a company changes the required skill set or the number of hires when starting a new project, the input unit responds quickly and provides the new information to the generating AI. The input unit can also share data on the company's recruitment policies with other departments to facilitate collaboration and ensure a smooth overall recruitment process. In this way, the input unit accurately reflects the company's recruitment policies and provides a foundation for an efficient and effective recruitment process.

[0065] The document screening department conducts document screening based on information input by the input department. Specifically, it analyzes applicants' resumes and work histories to select candidates who meet the hiring criteria. The document screening department evaluates applicants' skill sets and work experience to select candidates with the necessary skills. For example, applicants' resumes and work histories are input into a generating AI, which analyzes them and selects candidates who meet the hiring criteria. The generating AI uses natural language processing technology to analyze applicants' documents and automatically evaluates their skills and experience. For example, it identifies candidates with specific skill sets that the company requires, such as programming skills or project management experience. Furthermore, the generating AI can learn the characteristics of successful hires based on past hiring data and evaluate candidates based on that. This allows the document screening department to efficiently and fairly select candidates and find the talent best suited to the company's hiring policy. In addition, the document screening department can share the candidate evaluations based on the generating AI's analysis results with human reviewers to help them make final decisions. This allows the document review department to leverage generational AI technology to achieve rapid and accurate document review, significantly streamlining the company's recruitment process.

[0066] The interview department conducts first-round interviews with candidates selected by the document screening department. Specifically, they conduct online first-round interviews with applicants using pre-set questions. For example, they might ask all applicants, "Please tell me about your strengths and weaknesses," and evaluate their answers. The interview department uses a generative AI to input pre-set questions into the AI, which then conducts the online first-round interviews with applicants and evaluates their answers. The generative AI uses speech recognition technology to convert the applicant's answers into text and natural language processing technology to analyze the content of the answers. For example, it evaluates communication skills and problem-solving abilities from the applicant's answers and determines whether they match the skill set required by the company. The generative AI can also analyze non-verbal elements such as the applicant's facial expressions and tone of voice to make a comprehensive evaluation. This allows the interview department to conduct first-round interviews efficiently and fairly, and select the candidates best suited to the company's recruitment policy. Furthermore, the interview department can share the candidate evaluations based on the generative AI's analysis results with human interviewers to help them make final decisions. This allows the interview department to leverage generative AI technology to conduct quick and accurate first-round interviews, significantly streamlining the company's recruitment process.

[0067] The final interview department conducts final interviews with candidates selected by the interview department. Specifically, employees conduct final interviews with candidates selected by the generative AI and make the final hiring decision. Based on the analysis results of the generative AI, the final interview department can share the candidate evaluations with human interviewers and use them as a reference for making the final decision. For example, the final interview questions are set based on the candidate's skill set, work experience, and answers from the first interview, as analyzed by the generative AI. The final interview department evaluates the candidate's suitability and fit to the company culture and makes the final hiring decision. For example, they evaluate the candidate's communication skills, leadership, and teamwork abilities to determine if they match the company's desired candidate profile. The final interview department also evaluates the candidate's past performance and future career vision, enabling them to make hiring decisions from a long-term perspective. In this way, the final interview department can leverage generative AI technology to achieve efficient and fair final interviews and find the talent best suited to the company's hiring policy. Furthermore, based on the results of the final interview, the final interview department can provide feedback to candidates, increasing the transparency of the hiring process. This allows the final interview department to significantly streamline the company's recruitment process and quickly hire the most suitable talent.

[0068] The input unit can input information such as the number of people to be hired, required skills, and past performance evaluations of hired individuals. For example, if a company is hiring 10 engineers as new graduates, the input unit will input the required programming skills and past hiring evaluation data into the generating AI. The input unit will input, for example, the number of people to be hired, the required skills, and past hiring evaluations. By inputting information such as the number of people to be hired, required skills, and past hiring evaluations, more accurate document screening and interviews become possible. Some or all of the above processing in the input unit may be performed using the generating AI, or it may be performed without using the generating AI. For example, the input unit inputs the number of people to be hired, required skills, and past hiring evaluations into the generating AI, and the generating AI conducts document screening and interviews based on this information.

[0069] The document screening department can analyze applicants' resumes and work histories to select candidates who meet the hiring criteria. For example, the document screening department can analyze applicants' resumes and work histories to select candidates who meet the hiring criteria. For example, the document screening department can evaluate applicants' skill sets and work experience to select candidates with the necessary skills. For example, the document screening department can analyze applicants' resumes to evaluate their educational background, work history, and skill sets. For example, the document screening department can analyze applicants' work histories to evaluate past projects, roles, and achievements. This allows for efficient document screening by analyzing applicants' resumes and work histories to select candidates who meet the hiring criteria. Some or all of the above processes in the document screening department may be performed using, for example, a generative AI, or not. For example, the document screening department can input applicants' resumes and work histories into a generative AI, which then analyzes them and selects candidates who meet the hiring criteria.

[0070] The interview department can conduct initial online interviews with applicants using pre-set questions. For example, the interview department can conduct initial online interviews with applicants using pre-set questions. The interview department can ask all applicants questions such as, "Please tell me about your strengths and weaknesses," and evaluate their answers. The interview department can pre-set technical and behavioral questions and conduct initial online interviews with applicants. The interview department can evaluate applicants' answers and select candidates who meet the hiring criteria. This ensures fair interviews by conducting initial online interviews using pre-set questions. Some or all of the above processes in the interview department may be performed using, for example, generative AI, or not. For example, the interview department can input pre-set questions into a generative AI, which then conducts initial online interviews with applicants and evaluates their answers.

[0071] The final interview department can conduct final interviews with candidates selected by the generation AI and make final hiring decisions. For example, the final interview department can have employees conduct final interviews with candidates selected by the generation AI and make final hiring decisions. The final interview department can have employees conduct final interviews with candidates selected by the generation AI and make final hiring decisions. This reduces the workload on interviewers and ensures a fair selection process by having employees conduct final interviews with candidates selected by the generation AI. Some or all of the above processes in the final interview department may be performed using the generation AI, for example, or without the generation AI. For example, the final interview department can have employees conduct final interviews with candidates selected by the generation AI and make final hiring decisions.

[0072] The input unit can estimate the user's emotions and determine the priority of the information to be input based on the estimated emotions. For example, if the user is stressed, the input unit will prompt the user to input the most important information first. For example, if the user is relaxed, the input unit will prompt the user to input detailed information. For example, if the user is in a hurry, the input unit will prompt the user to input only the minimum necessary information. This allows for more efficient information input by determining the priority of the information to be input based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using a generative AI, or not using a generative AI. For example, the input unit inputs the user's emotion data into a generative AI, which estimates the emotion and determines the priority of the information to be input.

[0073] The input unit can select the optimal input method by referring to past recruitment data during the input process. For example, the input unit can analyze successful recruitment processes from past recruitment data and propose similar input methods. For example, the input unit can select the optimal input method for a specific skill set based on past recruitment data. For example, the input unit can refer to past recruitment data and propose the most effective input method for a specific job type. This allows for more effective information input by selecting the optimal input method by referring to past recruitment data. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input past recruitment data into AI, and the AI ​​can select the optimal input method.

[0074] The input unit can adjust the input content based on the company's current market situation and competitive information during the input process. For example, the input unit may analyze the current market situation and prompt the input of the necessary skill sets. For example, the input unit may refer to the hiring trends of competing companies and adjust the input content. For example, the input unit may update the input information to the latest version by considering market trends. This allows for more appropriate information input by adjusting the input content based on the company's current market situation and competitive information. Some or all of the above processing in the input unit may be performed using AI, or not. For example, the input unit may input the company's current market situation and competitive information into the AI, and the AI ​​may adjust the input content.

[0075] The input unit can estimate the user's emotions and adjust the level of detail of the input information based on the estimated emotions. For example, if the user is nervous, the input unit may prompt for concise and to-the-point information to be input. For example, if the user is relaxed, the input unit may prompt for detailed information to be input. For example, if the user is in a hurry, the input unit may prompt for only minimal information to be input. This allows for more efficient information input by adjusting the level of detail of the input information based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the input unit may be performed using a generative AI, or not using a generative AI. For example, the input unit inputs the user's emotion data into the generative AI, the generative AI estimates the emotion, and adjusts the level of detail of the input information.

[0076] The input unit can customize the input content based on the company's geographical location information during the input process. For example, the input unit may prompt the input of necessary skill sets based on the company's location. For example, the input unit may adjust the information to be input, taking geographical characteristics into consideration. For example, the input unit may suggest the optimal input method based on the company's geographical location information. This allows for more appropriate information input by customizing the input content based on the company's geographical location information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit may input the company's geographical location information into the AI, and the AI ​​will customize the input content.

[0077] The input unit can analyze a company's social media activities and input relevant information during the input process. For example, the input unit can analyze a company's social media activities and prompt the input of necessary skill sets. For example, the input unit can adjust the information to be input by considering social media trends. For example, the input unit can suggest the optimal input method based on a company's social media activities. This enables more appropriate information input by analyzing a company's social media activities and inputting relevant information. Some or all of the above processing in the input unit may be performed using AI, for example, or without AI. For example, the input unit can input a company's social media activities into AI, and the AI ​​can input relevant information.

[0078] The document review department can estimate the user's emotions and adjust the review criteria based on the estimated emotions. For example, if the user is nervous, the review department may relax the review criteria. For example, if the user is relaxed, the review department may tighten the review criteria. For example, if the user is in a hurry, the review department may set criteria for a quick review. This allows for more appropriate document review by adjusting the review criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the document review department may be performed using or without a generative AI. For example, the document review department inputs user emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the review criteria.

[0079] The document screening department can improve the accuracy of its screening process by thoroughly analyzing applicants' past work histories during the document screening stage. For example, the document screening department can thoroughly analyze applicants' past work histories and evaluate their skill sets. For example, the document screening department can select candidates with specific skills and experience from applicants' work histories. For example, the document screening department can set criteria to improve the accuracy of the screening process based on applicants' work histories. This improves the accuracy of the screening process by thoroughly analyzing applicants' past work histories. Some or all of the above processes in the document screening department may be performed using AI, for example, or not. For example, the document screening department can input applicants' past work histories into an AI, which can then perform a detailed analysis to improve the accuracy of the screening process.

[0080] The document review department can apply different review algorithms during the document review process depending on the format and content of the applicant's submitted documents. For example, the document review department may apply the most suitable review algorithm based on the format of the applicant's submitted documents. For example, the document review department may apply a different review algorithm based on the content of the submitted documents. For example, the document review department may analyze the format and content of the applicant's submitted documents and select the most suitable review algorithm. This allows for more appropriate document review by applying different review algorithms depending on the format and content of the applicant's submitted documents. Some or all of the above processes in the document review department may be performed using AI, or not. For example, the document review department may input the format and content of the applicant's submitted documents into an AI, which then applies the most suitable review algorithm.

[0081] The document review unit can estimate the user's emotions and determine the priority of document review based on the estimated emotions. For example, if the user is nervous, the document review unit will prioritize the review. If the user is relaxed, the document review unit will prioritize the review at the normal priority level. If the user is in a hurry, the document review unit will set a priority for a quick review. This allows for more efficient document review by determining the priority of document review based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the document review unit may be performed using a generative AI, or not. For example, the document review unit inputs user emotion data into a generative AI, which estimates the emotions and determines the priority of document review.

[0082] The document review department can adjust the review process based on the applicant's geographical location information during the document review. For example, the document review department adjusts the review process based on the applicant's geographical location information. For example, the document review department sets review criteria considering geographical characteristics. For example, the document review department selects the optimal review method by referring to the applicant's geographical location information. This makes it possible to conduct a more appropriate document review by adjusting the review process based on the applicant's geographical location information. Some or all of the above processes in the document review department may be performed using AI, for example, or not using AI. For example, the document review department inputs the applicant's geographical location information into AI, and the AI ​​adjusts the review process.

[0083] The document screening department can analyze applicants' social media activity during the document screening process and incorporate relevant information into the screening. For example, the document screening department can analyze applicants' social media activity and evaluate their skill sets. For example, the document screening department can adjust the screening process considering social media trends. For example, the document screening department can select the most appropriate screening method based on applicants' social media activity. This allows for a more appropriate document screening by analyzing applicants' social media activity and incorporating relevant information into the screening process. Some or all of the above processes in the document screening department may be performed using AI, for example, or not. For example, the document screening department can input applicants' social media activity into AI, and the AI ​​can incorporate relevant information into the screening process.

[0084] The interviewer can estimate the user's emotions and adjust the interview questions based on those emotions. For example, if the user is nervous, the interviewer can ask questions to help them relax. If the user is relaxed, the interviewer can ask detailed questions. If the user is in a hurry, the interviewer can ask questions that can be answered quickly. By adjusting the interview questions based on the user's emotions, a more appropriate interview can be conducted. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewer may be performed using a generative AI, or not. For example, the interviewer can input user emotion data into a generative AI, which can then estimate the emotions and adjust the interview questions.

[0085] The interview department can select the most appropriate questions during an interview by referring to the applicant's past interview history. For example, the interview department can refer to the applicant's past interview history and select the most appropriate questions. For example, the interview department can ask questions related to specific skills or experience based on the past interview history. For example, the interview department can select the most appropriate questioning method based on the applicant's interview history. This makes it possible to conduct more effective interviews by selecting the most appropriate questions by referring to the applicant's past interview history. Some or all of the above processes in the interview department may be performed using AI, for example, or not using AI. For example, the interview department can input the applicant's past interview history into AI, and the AI ​​can select the most appropriate questions.

[0086] The interview department can apply different question algorithms during interviews depending on the applicant's work experience and skills. For example, the interview department may apply the optimal question algorithm based on the applicant's work experience. For example, the interview department may apply different question algorithms depending on the applicant's skill set. For example, the interview department may analyze the applicant's work experience and skills and select the optimal question algorithm. This allows for more appropriate interviews by applying different question algorithms depending on the applicant's work experience and skills. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department may input the applicant's work experience and skills into an AI, which then applies the optimal question algorithm.

[0087] The interview unit can estimate the user's emotions and adjust the interview order based on the estimated emotions. For example, if the user is nervous, the interview unit will conduct the interview in an order designed to help them relax. If the user is relaxed, the interview unit will conduct the interview in the normal order. If the user is in a hurry, the interview unit will set an order to conduct the interview quickly. By adjusting the interview order based on the user's emotions, a more appropriate interview can be conducted. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interview unit may be performed using a generative AI, or not. For example, the interview unit inputs the user's emotion data into a generative AI, which estimates the emotions and adjusts the interview order.

[0088] The interview department can customize the interview content based on the applicant's geographical location information during the interview. For example, the interview department customizes the interview content based on the applicant's geographical location information. For example, the interview department adjusts the interview content considering geographical characteristics. For example, the interview department selects the optimal interview method by referring to the applicant's geographical location information. This makes it possible to conduct more appropriate interviews by customizing the interview content based on the applicant's geographical location information. Some or all of the above processes in the interview department may be performed using AI, for example, or not using AI. For example, the interview department inputs the applicant's geographical location information into AI, and the AI ​​customizes the interview content.

[0089] The interview department can analyze the applicant's social media activity during the interview and ask relevant questions. For example, the interview department can analyze the applicant's social media activity and ask relevant questions. For example, the interview department can adjust the questions considering social media trends. For example, the interview department can select the most appropriate questioning method based on the applicant's social media activity. This allows for more appropriate interviews by analyzing the applicant's social media activity and asking relevant questions. Some or all of the above processes in the interview department may be performed using AI, or not. For example, the interview department can input the applicant's social media activity into AI, and the AI ​​can ask relevant questions.

[0090] The final interview unit can estimate the user's emotions and adjust the evaluation criteria for the final interview based on the estimated emotions. For example, if the user is nervous, the final interview unit may relax the evaluation criteria. For example, if the user is relaxed, the final interview unit may tighten the evaluation criteria. For example, if the user is in a hurry, the final interview unit may set criteria for a quick evaluation. This allows for a more appropriate final interview by adjusting the evaluation criteria based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the final interview unit may be performed using a generative AI or not. For example, the final interview unit inputs the user's emotion data into a generative AI, the generative AI estimates the emotions, and adjusts the evaluation criteria for the final interview.

[0091] The final interview department can select the optimal evaluation method during the final interview by referring to the applicant's past interview results. For example, the final interview department can refer to the applicant's past interview results and select the optimal evaluation method. For example, the final interview department can set evaluation criteria for specific skills and experience based on past interview results. For example, the final interview department can select the optimal evaluation method based on the applicant's interview results. This makes it possible to conduct a more effective final interview by referring to the applicant's past interview results and selecting the optimal evaluation method. Some or all of the above processes in the final interview department may be performed using AI, for example, or not using AI. For example, the final interview department can input the applicant's past interview results into AI, and the AI ​​will select the optimal evaluation method.

[0092] The final interview department can apply different evaluation algorithms to applicants during the final interview, depending on their work experience and skills. For example, the final interview department may apply the optimal evaluation algorithm based on the applicant's work experience. For example, the final interview department may apply different evaluation algorithms depending on the applicant's skill set. For example, the final interview department may analyze the applicant's work experience and skills and select the optimal evaluation algorithm. This allows for more appropriate final interviews by applying different evaluation algorithms according to the applicant's work experience and skills. Some or all of the above processes in the final interview department may be performed using AI, or not. For example, the final interview department may input the applicant's work experience and skills into an AI, which then applies the optimal evaluation algorithm.

[0093] The final interview unit can estimate the user's emotions and determine the priority of the final interview based on the estimated emotions. For example, if the user is nervous, the final interview unit will prioritize the interview. If the user is relaxed, the final interview unit will conduct the interview with the normal priority. If the user is in a hurry, the final interview unit will set a priority for a quick interview. This allows for more efficient final interviews by determining the priority of the final interview based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the final interview unit may be performed using a generative AI, or not. For example, the final interview unit inputs user emotion data into a generative AI, the generative AI estimates the emotions, and determines the priority of the final interview.

[0094] The final interview department can adjust the evaluation criteria based on the applicant's geographical location information during the final interview. For example, the final interview department adjusts the evaluation criteria based on the applicant's geographical location information. For example, the final interview department sets evaluation criteria considering geographical characteristics. For example, the final interview department selects the optimal evaluation method by referring to the applicant's geographical location information. This makes it possible to conduct a more appropriate final interview by adjusting the evaluation criteria based on the applicant's geographical location information. Some or all of the above processes in the final interview department may be performed using AI, for example, or not using AI. For example, the final interview department inputs the applicant's geographical location information into the AI, and the AI ​​adjusts the evaluation criteria.

[0095] The final interview department can analyze the applicant's social media activity during the final interview and incorporate relevant information into the evaluation. For example, the final interview department can analyze the applicant's social media activity and assess their skill set. For example, the final interview department can adjust the evaluation content to take social media trends into consideration. For example, the final interview department can select the optimal evaluation method based on the applicant's social media activity. This allows for a more appropriate final interview by analyzing the applicant's social media activity and incorporating relevant information into the evaluation. Some or all of the above processes in the final interview department may be performed using AI, for example, or not. For example, the final interview department can input the applicant's social media activity into AI, and the AI ​​will incorporate relevant information into the evaluation.

[0096] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0097] The recruitment support system can also include a cultural fit assessment unit that evaluates candidates' cultural suitability. This unit assesses candidates' suitability based on the company's culture and values. For example, it can ask candidates questions about the company's values ​​and evaluate their responses. It can also analyze candidates' past work experience and behavioral patterns to assess their fit with the company culture. Furthermore, it can analyze candidates' social media activity to assess whether it aligns with the company's values. This allows for the selection of candidates who are a good fit for the company's culture and values, thereby creating a better work environment.

[0098] The recruitment support system can also include a stress tolerance assessment unit to evaluate candidates' stress tolerance. This unit asks questions to assess candidates' stress tolerance and analyzes their responses. For example, it might ask about how candidates have coped with past stressful situations and evaluate their answers. It can also collect physiological data from candidates (such as heart rate and skin electrical activity) to assess stress tolerance. Furthermore, it can analyze candidates' past work experience and behavioral patterns to evaluate stress tolerance. This allows for the selection of candidates with high stress tolerance, which is expected to improve workplace performance.

[0099] The recruitment support system can also include a leadership assessment section to evaluate candidates' leadership abilities. This section asks questions to assess candidates' leadership skills and analyzes their responses. For example, it might ask about past leadership experience and leadership style and evaluate the answers. It can also analyze candidates' past work experience and behavioral patterns to assess their leadership abilities. Furthermore, it can analyze candidates' social media activity to evaluate leadership-related behaviors. This allows for the selection of candidates with strong leadership skills, thereby promoting organizational growth.

[0100] The recruitment support system can also include a creativity assessment unit to evaluate candidates' creativity. This unit asks questions to assess a candidate's creativity and analyzes their responses. For example, it might ask about past creative projects or ideas and evaluate their answers. It can also analyze a candidate's past work experience and behavioral patterns to assess their creativity. Furthermore, it can analyze a candidate's social media activity to evaluate their creative activities and ideas. This allows for the selection of highly creative candidates, which is expected to lead to the realization of innovative ideas and projects.

[0101] The recruitment support system can also include a communication assessment unit to evaluate candidates' communication skills. This unit asks questions to assess candidates' communication abilities and analyzes their responses. For example, it might ask about past team projects and interpersonal experience and evaluate their answers. It can also analyze candidates' past work experience and behavioral patterns to assess their communication skills. Furthermore, it can analyze candidates' social media activity to evaluate communication-related behaviors. This allows for the selection of candidates with strong communication skills, which is expected to improve teamwork.

[0102] The recruitment support system can also include a feedback unit that estimates the candidate's emotions and provides interview feedback based on those estimated emotions. The feedback unit estimates the candidate's emotions during the interview and provides appropriate feedback based on those emotions. For example, if the candidate is nervous, it provides feedback to help them relax. If the candidate is confident, it can also provide feedback to reinforce that confidence. Furthermore, if the candidate is feeling anxious, it can provide feedback to alleviate that anxiety. This allows for improved interview quality by providing appropriate feedback based on the candidate's emotions.

[0103] The recruitment support system may also include a progress adjustment unit that estimates the candidate's emotions and adjusts the interview process based on those emotions. The progress adjustment unit estimates the candidate's emotions during the interview and adjusts the interview process accordingly. For example, if the candidate is nervous, the interview process may be slowed down to help them relax. Conversely, if the candidate is confident, the interview process may be made smoother. Furthermore, if the candidate is feeling anxious, the process may be adjusted to alleviate that anxiety. This allows for an appropriate interview process based on the candidate's emotions, thereby improving the quality of the interview.

[0104] The recruitment support system may also include a question adjustment unit that estimates the candidate's emotions and adjusts the interview questions based on those emotions. The question adjustment unit estimates the candidate's emotions during the interview and adjusts the questions accordingly. For example, if the candidate is nervous, it will ask questions to help them relax. If the candidate is confident, it can ask more detailed questions. Furthermore, if the candidate is feeling anxious, it can ask questions to alleviate that anxiety. This allows for improved interview quality by asking appropriate questions based on the candidate's emotions.

[0105] The recruitment support system may also include an evaluation criteria adjustment unit that estimates the candidate's emotions and adjusts the interview evaluation criteria based on those estimated emotions. The evaluation criteria adjustment unit estimates the candidate's emotions during the interview and adjusts the evaluation criteria accordingly. For example, if the candidate is nervous, the evaluation criteria may be relaxed. Conversely, if the candidate is confident, the evaluation criteria may be made stricter. Furthermore, if the candidate is feeling anxious, evaluation criteria may be set to alleviate that anxiety. This allows for the improvement of interview quality by setting appropriate evaluation criteria based on the candidate's emotions.

[0106] The recruitment support system can also include a feedback unit that estimates the candidate's emotions and provides interview feedback based on those estimated emotions. The feedback unit estimates the candidate's emotions during the interview and provides appropriate feedback based on those emotions. For example, if the candidate is nervous, it provides feedback to help them relax. If the candidate is confident, it can also provide feedback to reinforce that confidence. Furthermore, if the candidate is feeling anxious, it can provide feedback to alleviate that anxiety. This allows for improved interview quality by providing appropriate feedback based on the candidate's emotions.

[0107] The following briefly describes the processing flow for example form 2.

[0108] Step 1: The input section receives the company's recruitment policy. For example, it inputs information such as the number of people to be hired, required skills, and performance evaluations of past hires. If a company is hiring 10 engineers as new graduates, it inputs the necessary programming skills and evaluation data of past hires into the generating AI. Step 2: The document screening department conducts a document screening based on the information input by the input department. For example, it analyzes applicants' resumes and work histories and selects candidates who meet the hiring criteria. Some or all of the processing in the document screening department may be performed using generative AI, or it may be performed without using generative AI. Step 3: The interview department conducts first-round interviews with candidates selected by the document screening department. For example, the interview department conducts online first-round interviews with applicants using pre-set questions. Some or all of the processing in the interview department may be performed using generative AI, or it may not be performed using generative AI. Step 4: The final interview department conducts final interviews with candidates selected by the interview department. For example, employees conduct final interviews with candidates selected by the generative AI and make a final hiring decision. Some or all of the processing in the final interview department may be performed using the generative AI, or it may be performed without the generative AI.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] Each of the multiple elements described above, including the input unit, document screening unit, interview unit, and final interview unit, is implemented by, for example, at least one of the smart device 14 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart device 14 and inputs the company's recruitment policy. The document screening unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the applicant's resume and work history. The interview unit is implemented by, for example, the control unit 46A of the smart device 14 and conducts the first interview online. The final interview unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and has an employee conduct the final interview. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0113] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.).

[0125] 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.

[0126] 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.

[0127] 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.

[0128] Each of the multiple elements described above, including the input unit, document screening unit, interview unit, and final interview unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the smart glasses 214 and inputs the company's recruitment policy. The document screening unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the applicant's resume and work history. The interview unit is implemented by the control unit 46A of the smart glasses 214 and conducts the first interview online. The final interview unit is implemented by the specific processing unit 290 of the data processing unit 12 and has an employee conduct the final interview. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0129] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0134] 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).

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] Each of the multiple elements described above, including the input unit, document screening unit, interview unit, and final interview unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the headset terminal 314 and inputs the company's recruitment policy. The document screening unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the applicant's resume and work history. The interview unit is implemented by, for example, the control unit 46A of the headset terminal 314 and conducts the first interview online. The final interview unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and has an employee conduct the final interview. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0145] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0150] 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).

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.).

[0158] 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.

[0159] 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.

[0160] 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.

[0161] Each of the multiple elements described above, including the input unit, document screening unit, interview unit, and final interview unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the input unit is implemented by the control unit 46A of the robot 414 and inputs the company's recruitment policy. The document screening unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and analyzes the applicant's resume and work history. The interview unit is implemented by, for example, the control unit 46A of the robot 414 and conducts the first interview online. The final interview unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and has an employee conduct the final interview. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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."

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] (Note 1) The input department receives the company's recruitment policies, A document review unit conducts a document review based on the information input by the aforementioned input unit, The interview department conducts the first interview with the candidates selected by the aforementioned document review department, The system comprises a final interview department that conducts final interviews with candidates selected by the aforementioned interview department. A system characterized by the following features. (Note 2) The aforementioned input unit is, Input information such as the number of people to be hired, required skills, and performance evaluations of past hires. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned document review department, We analyze applicants' resumes and work histories to select candidates who meet our hiring criteria. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned interview department, We conduct an online first interview with applicants using pre-set questions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned final interview section, The AI-generated candidates are then interviewed in a final round to make the final hiring decision. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned input unit is, It estimates the user's emotions and determines the priority of input information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned input unit is, During the input process, past recruitment data is referenced to select the most suitable input method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned input unit is, During the input process, the input content is adjusted based on the company's current market situation and competitive information. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned input unit is, It estimates the user's emotions and adjusts the level of detail of the input information based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned input unit is, During the input process, the input content is customized based on the company's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned input unit is, During the input phase, the company's social media activities are analyzed, and relevant information is input. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned document review department, We estimate the user's emotions and adjust the document review criteria based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned document review department, During the document screening process, we conduct a detailed analysis of applicants' past work experience to improve the accuracy of the screening. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned document review department, During the document screening process, different screening algorithms are applied depending on the format and content of the applicant's submitted documents. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned document review department, The system estimates user sentiment and determines the priority of document review based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned document review department, During the document screening process, the screening criteria will be adjusted based on the applicant's geographical location information. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned document review department, During the document screening process, we analyze applicants' social media activity and incorporate relevant information into the evaluation. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned interview department, The system estimates the user's emotions and adjusts the interview questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned interview department, During the interview, we select the most appropriate questions by referring to the applicant's past interview history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned interview department, During interviews, different question algorithms are applied depending on the applicant's work experience and skills. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned interview department, The system estimates the user's emotions and adjusts the interview order based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned interview department, During the interview, the interview content will be customized based on the applicant's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned interview department, During the interview, we analyze the applicant's social media activity and ask related questions. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned final interview section, The system estimates the user's emotions and adjusts the evaluation criteria for the final interview based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned final interview section, During the final interview, the most suitable evaluation method will be selected by referring to the applicant's past interview results. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned final interview section, During the final interview, different evaluation algorithms are applied depending on the applicant's work experience and skills. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned final interview section, The system estimates the user's emotions and determines the priority of the final interview based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned final interview section, During the final interview, the evaluation criteria will be adjusted based on the applicant's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned final interview section, During the final interview, we will analyze the applicant's social media activity and incorporate relevant information into the evaluation. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0181] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The input department receives the company's recruitment policies, A document review unit conducts a document review based on the information input by the aforementioned input unit, The interview department conducts the first interview with the candidates selected by the aforementioned document review department, The system comprises a final interview department that conducts final interviews with candidates selected by the aforementioned interview department. A system characterized by the following features.

2. The aforementioned input unit is, Input information such as the number of people to be hired, required skills, and performance evaluations of past hires. The system according to feature 1.

3. The aforementioned document review department, We analyze applicants' resumes and work histories to select candidates who meet our hiring criteria. The system according to feature 1.

4. The aforementioned interview department, We conduct an online first interview with applicants using pre-set questions. The system according to feature 1.

5. The aforementioned final interview section, The AI-generated candidates are selected for a final interview, after which the final hiring decision is made. The system according to feature 1.

6. The aforementioned input unit is, It estimates the user's emotions and determines the priority of input information based on the estimated user emotions. The system according to feature 1.

7. The aforementioned input unit is, During the input process, past recruitment data is referenced to select the most suitable input method. The system according to feature 1.

8. The aforementioned input unit is, During the input process, the input content is adjusted based on the company's current market situation and competitive information. The system according to feature 1.

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