System

Abstract

The system according to the embodiment aims to streamline and optimize the interview process.SOLUTION: A system according to an embodiment includes an interviewer generating unit, an interview experience optimizing unit, and an interview result analyzing unit. The interviewer generating unit generates an interviewer based on the requirements of the company. The interview experience optimization unit analyzes in real time the progress of the interview and the answers of the interviewers generated by the interviewer generation unit, and optimizes the interview experience. The interview result analyzer analyzes the answers of the candidates after the interview experience optimized by the interview experience optimizer and generates a report.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Existing technologies do not adequately streamline or optimize the interview process, leaving room for improvement.

[0005] The system according to the embodiment aims to streamline and optimize the interview process. [Means for solving the problem]

[0006] The system according to the embodiment includes an interviewer generation unit, an interview experience optimization unit, and an interview result analysis unit. The interviewer generation unit generates interviewers based on the requirements of the company. The interview experience optimization unit optimizes the interview experience by having the interviewers generated by the interviewer generation unit analyze the progress of the interview and the candidates' responses in real time. The interview result analysis unit analyzes the candidates' responses after the interview experience optimized by the interview experience optimization unit and generates a report. [Effects of the Invention]

[0007] The system according to the embodiment can streamline and optimize the interview process. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The Interactive Recruiter according to an embodiment of the present invention is a system that automatically generates interviewers for companies and optimizes the interview experience, thereby enabling companies to evolve their recruitment interview process in an innovative and efficient manner.

[0029] An interactive recruiter according to an embodiment includes an interviewer generation unit, an interview experience optimization unit, and an interview result analysis unit. The interviewer generation unit generates interviewers based on a company's requirements. For example, by inputting the skills and experience desired by a company as prompts into the generation AI, an interviewer matching those requirements is generated. The interviewer generation unit can also generate interviewers who ask questions based on the company's culture and values. The interview experience optimization unit optimizes the interview experience by having the interviewer generated by the interviewer generation unit analyze the interview progress and the candidate's answers in real time. For example, if a candidate has difficulty answering a particular question, the generation AI analyzes the situation and generates an appropriate follow-up question. The interview experience optimization unit can also automatically suggest the next appropriate question as the interview progresses. After the interview experience optimized by the interview experience optimization unit, the interview result analysis unit analyzes the candidate's answers and generates a report. For example, the generation AI performs text analysis of the candidate's answers and evaluates them for specific skills and experience. This report can be used as a reference for companies when evaluating candidates. As a result, the interactive recruiter according to the embodiment can generate interviewers based on the requirements of the company, optimize the interview experience, and analyze the interview results, thereby realizing an efficient recruitment process.

[0030] The interviewer generation unit customizes the personality and speaking style of the interviewer, allowing it to provide optimal communication to the candidate. The interviewer generation unit, for example, uses generation AI to customize the personality of the interviewer. For example, it generates an interviewer with a friendly personality so that the candidate can relax. The interviewer generation unit also customizes the speaking style of the interviewer to provide optimal communication to the candidate. For example, when asking technical questions, it avoids using technical jargon and explains in easy-to-understand terms. The interviewer generation unit also uses generation AI to customize the personality and speaking style of the interviewer to ease the candidate's tension. For example, it generates an interviewer who speaks with a sense of humor. In this way, by customizing the personality and speaking style of the interviewer, it is possible to provide optimal communication to the candidate.

[0031] The interviewer generation unit can learn from past interview data and incorporate the characteristics of successful interviewers. The interviewer generation unit, for example, learns from past interview data and incorporates the characteristics of successful interviewers. For example, it reproduces the questioning patterns of interviewers who received high evaluations from candidates. The generation AI also analyzes past interview data and incorporates the characteristics of successful interviewers. For example, it learns how to provide appropriate feedback to candidates' answers. The interviewer generation unit also incorporates the characteristics of successful interviewers based on past interview data. For example, it reproduces the facial expressions and gestures of interviewers to give candidates a friendly impression. In this way, by learning from past interview data and incorporating the characteristics of successful interviewers, more effective interviewers can be generated.

[0032] The interviewer generation unit generates interviewers who are competent in different languages ​​and cultures, thereby supporting global recruitment activities. The interviewer generation unit, for example, uses generation AI to generate interviewers who are competent in different languages. For example, interviews are conducted in multiple languages, such as English, French, and Chinese. It also generates interviewers who are competent in different cultures, thereby supporting global recruitment activities. For example, it generates interviewers who ask questions that take cultural backgrounds into consideration. It also uses generation AI to generate interviewers who are competent in different languages ​​and cultures, thereby supporting global recruitment activities. For example, it generates interviewers who understand international manners and etiquette. This makes it possible to support global recruitment activities by generating interviewers who are competent in different languages ​​and cultures.

[0033] The interviewer generation unit can generate interviewers with specialized knowledge specialized in a specific industry or job type. The interviewer generation unit, for example, uses generation AI to generate interviewers with specialized knowledge specialized in a specific industry. For example, it generates interviewers who can answer technical questions in the IT industry. It also generates interviewers with specialized knowledge specialized in a specific job type to evaluate the skills of candidates. For example, it generates interviewers who ask appropriate questions to candidates for marketing jobs. It also uses generation AI to generate interviewers with specialized knowledge specialized in a specific industry or job type. For example, it generates interviewers with specialized knowledge in the medical industry to evaluate candidates for medical jobs. In this way, by generating interviewers with specialized knowledge specialized in a specific industry or job type, more appropriate skill evaluation becomes possible.

[0034] The interview experience optimization unit can monitor the candidate's stress level according to the progress of the interview and suggest breaks at appropriate times. The interview experience optimization unit, for example, uses a generative AI to monitor the candidate's stress level according to the progress of the interview. For example, if the candidate seems nervous, it suggests a break. The interview experience optimization unit also analyzes the candidate's stress level in real time according to the progress of the interview and suggests breaks at appropriate times. For example, if the candidate seems tired, it suggests a short break. The generative AI also monitors the candidate's stress level according to the progress of the interview and suggests breaks at appropriate times. For example, if the candidate is losing concentration, it suggests a break to refresh themselves. In this way, the burden on the candidate can be reduced by monitoring the candidate's stress level according to the progress of the interview and suggesting breaks at appropriate times.

[0035] The interview experience optimization unit can analyze the candidate's past interview data and generate an optimal question sequence. The interview experience optimization unit, for example, analyzes the candidate's past interview data and generates an optimal question sequence. For example, based on past answer patterns, questions in areas in which the candidate is strong are prioritized. In addition, to optimize the interview experience, the candidate's past interview data is analyzed and an optimal question sequence is generated. For example, questions in areas in which the candidate is weak are postponed. In addition, based on the candidate's past interview data, an optimal question sequence is generated to improve the interview experience. For example, questions about successful experiences are asked first to help the candidate gain confidence. In this way, the interview experience can be improved by analyzing the candidate's past interview data and generating an optimal question sequence.

[0036] The interview experience optimization unit can analyze a candidate's non-verbal communication and respond appropriately. The interview experience optimization unit, for example, uses generative AI to analyze a candidate's non-verbal communication and respond appropriately. For example, if the candidate seems nervous, it will say something to relax them. It also analyzes the candidate's facial expressions and gestures in real time and responds appropriately based on the results. For example, if the candidate seems confused, it will explain the question in an easy-to-understand manner. It also uses generative AI to analyze a candidate's non-verbal communication and respond appropriately. For example, it will say a compliment to the candidate to help them feel more confident. In this way, it is possible to improve the interview experience by analyzing a candidate's non-verbal communication and responding appropriately.

[0037] The interview experience optimization unit can collect background information about candidates in advance and generate questions that are individually customized. The interview experience optimization unit, for example, uses a generation AI to collect background information about candidates in advance and generate questions that are individually customized. For example, questions are asked based on the candidate's educational background and work history. The interview experience optimization unit also analyzes the candidate's background information and generates questions that are individually customized based on that information. For example, questions are asked about the candidate's expertise. The interview experience optimization unit also uses a generation AI to collect background information about candidates in advance and generate questions that are individually customized. For example, questions are asked based on the candidate's past project experience. In this way, the interview experience can be improved by collecting background information about candidates in advance and generating questions that are individually customized.

[0038] The interview experience optimization unit can analyze the consistency of a candidate's answers in real time and point out inconsistencies. The interview experience optimization unit, for example, uses a generative AI to analyze the consistency of a candidate's answers in real time and point out inconsistencies. For example, if a candidate gives a different answer, the inconsistency is pointed out. The interview experience optimization unit also analyzes the candidate's answers in real time and checks for consistency. For example, if a candidate makes a statement that contradicts a previous answer, this is pointed out. The generative AI also analyzes the consistency of a candidate's answers in real time and points out inconsistencies. For example, if a candidate says something different from what they said in their self-introduction, this inconsistency is pointed out. In this way, by analyzing the consistency of a candidate's answers in real time and pointing out inconsistencies, more accurate evaluation is possible.

[0039] The interview experience optimization unit can evaluate the candidate's answers according to the progress of the interview and provide instant feedback. In the interview experience optimization unit, for example, the generation AI evaluates the candidate's answers according to the progress of the interview and provides instant feedback. For example, if the candidate gives an appropriate answer, it will praise them. The candidate's answers are also evaluated in real time and feedback is provided based on the results. For example, if the candidate is pointed out an area for improvement, specific advice is provided. The generation AI also evaluates the candidate's answers and provides instant feedback according to the progress of the interview. For example, if the candidate gives an appropriate answer to a question, it will suggest the next question. In this way, by evaluating the candidate's answers according to the progress of the interview and providing instant feedback, it is possible to promote the candidate's growth.

[0040] The interview experience optimization unit can compare a candidate's answers with other candidates according to the progress of the interview and perform a relative evaluation. The interview experience optimization unit, for example, uses a generation AI to compare a candidate's answers with other candidates according to the progress of the interview and perform a relative evaluation. For example, it compares them with the answers of other candidates to the same question. It also analyzes the candidate's answers in real time and compares them with other candidates. For example, it compares answers regarding specific skills and performs a relative evaluation. It also uses a generation AI to compare a candidate's answers with other candidates according to the progress of the interview and perform a relative evaluation. For example, it compares them with past candidate answer data. This enables a fairer evaluation by comparing a candidate's answers with other candidates according to the progress of the interview and performing a relative evaluation.

[0041] The interview experience optimization unit can analyze the candidate's answers according to the progress of the interview and automatically generate the next question. In the interview experience optimization unit, for example, the generation AI analyzes the candidate's answers according to the progress of the interview and automatically generates the next question. For example, it generates related follow-up questions based on the candidate's answers. The interview experience optimization unit also analyzes the candidate's answers in real time and automatically generates the next question based on the results. For example, it generates questions related to skills emphasized by the candidate. The generation AI also analyzes the candidate's answers according to the progress of the interview and automatically generates the next question. For example, it generates questions related to areas in which the candidate has difficulty. In this way, by analyzing the candidate's answers according to the progress of the interview and automatically generating the next question, it is possible to provide a smooth interview experience.

[0042] The interview result analysis unit can analyze a company's past recruitment data and generate profiles of optimal interviewers. The interview result analysis unit, for example, uses generation AI to analyze a company's past recruitment data and generate profiles of optimal interviewers. For example, it generates interviewers based on data on recruitment processes that have been successful in the past. It also analyzes a company's past recruitment data and generates profiles of optimal interviewers. For example, it generates interviewers for evaluating candidates with a specific skill set. It also uses generation AI to analyze a company's past recruitment data and generate profiles of optimal interviewers. For example, it generates interviewers who fit the company's culture and values. In this way, by analyzing a company's past recruitment data and generating profiles of optimal interviewers, it is possible to provide more effective interviewers.

[0043] The interview result analysis unit can customize the appearance and clothing of interviewers based on the requirements of the company, and generate interviewers who fit the corporate culture. The interview result analysis unit, for example, customizes the appearance of interviewers based on the requirements of the company. For example, it generates interviewers dressed in accordance with the company's dress code. It also customizes the interviewer's clothing to match the corporate culture, giving a consistent impression to candidates. For example, it generates interviewers dressed in casual clothing. It also customizes the appearance and clothing of interviewers based on the requirements of the company, and generates interviewers who fit the corporate culture. For example, it generates interviewers dressed in a way that incorporates the company's brand colors. In this way, it is possible to customize the appearance and clothing of interviewers based on the requirements of the company, and generate interviewers who fit the corporate culture, giving a consistent impression to candidates.

[0044] The interview result analysis unit can automatically generate a training program for interviewers that meets the requirements of a company. The interview result analysis unit, for example, uses a generation AI to automatically generate a training program for interviewers that meets the requirements of a company. For example, it generates a training program specialized for evaluating a specific skill. It also automatically generates a training program for interviewers based on the requirements of a company to improve the skills of interviewers. For example, it generates a training program to strengthen communication skills. It also uses a generation AI to automatically generate a training program for interviewers that meets the requirements of a company. For example, it generates a training program based on the culture and values ​​of a company. In this way, it is possible to improve the skills of interviewers by automatically generating a training program for interviewers that meets the requirements of a company.

[0045] The interviewer generation unit can automatically adjust candidate schedules, manage the progress of interviews, and provide feedback on interview results. The interviewer generation unit, for example, automatically adjusts candidate schedules. For example, it uses a calendar system to adjust the schedules of candidates and interviewers. It also automatically manages the progress of interviews. For example, it performs timekeeping and progress monitoring. It also automatically provides feedback on interview results. For example, it performs real-time evaluations and provides feedback to candidates. In this way, by automatically adjusting candidate schedules, managing the progress of interviews, and providing feedback on interview results, it is possible to reduce the burden on human resources personnel at companies and achieve an efficient interview process.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] Interactive Recruiter can also automatically analyze a candidate's past work experience and skill set, allowing the interviewer generator to generate customized questions based on that information. For example, if a candidate has experience in leadership roles on a specific project, it can generate specific questions about that experience. Also, if a candidate is familiar with a particular technology, it can generate in-depth questions about that technology. Furthermore, based on the candidate's past work experience, the interviewer generator can generate questions related to the candidate's career path and evaluate the candidate's future career vision. This allows for deeper insights by generating customized questions based on the candidate's past work experience and skill set.

[0048] The interviewer generation unit can also generate interviewers that match the candidate's learning style. For example, if the candidate has a visual learning style, it can generate an interviewer who provides explanations using visual materials. Alternatively, if the candidate has an auditory learning style, it can generate an interviewer who provides detailed oral explanations. Furthermore, if the candidate has a hands-on learning style, it can generate an interviewer who asks questions using actual work scenarios. In this way, by generating interviewers that match the candidate's learning style, it is possible to deepen the candidate's understanding and provide a more effective interview experience.

[0049] The interviewer generation unit can also generate interviewers that match the cultural background of the candidate. For example, if a candidate has a different cultural background, it generates an interviewer that asks questions that take that culture into consideration. It can also generate interviewers with an appropriate communication style based on the candidate's cultural background. Furthermore, by generating interviewers that take the candidate's cultural background into consideration and provide feedback that is appropriate for that culture, the candidate can feel relaxed during the interview. This makes it possible to provide a more effective interview experience by generating interviewers that are customized to match the candidate's cultural background.

[0050] The interviewer generation unit can also generate interviewers according to the candidate's career goals. For example, if a candidate is aiming for a specific career path, it can generate interviewers who ask questions related to that career path. It can also generate interviewers who provide appropriate advice based on the candidate's career goals. Furthermore, by generating interviewers who take the candidate's career goals into consideration and provide feedback toward those goals, the candidate can approach the interview with confidence. This makes it possible to provide a more effective interview experience by generating interviewers customized according to the candidate's career goals.

[0051] The interviewer generation unit can also analyze a candidate's learning history and customize the interviewer's questions based on the results. For example, if a candidate has completed a specific online course, it can generate an interviewer who asks questions related to that course. It can also generate an interviewer who asks appropriate follow-up questions based on the candidate's learning history. Furthermore, by analyzing a candidate's learning history in real time and adjusting the interviewer's questions based on the results, it can gain deeper insights. This makes it possible to provide a more effective interview experience by generating a customized interviewer based on the candidate's learning history.

[0052] The interviewer generation unit can also analyze the candidate's past interview data and customize the interviewer's questions based on the results. For example, if a candidate found a particular question difficult in a past interview, it can generate an interviewer who avoids that question. It can also generate an interviewer who asks appropriate follow-up questions based on the candidate's past interview data. Furthermore, by analyzing the candidate's past interview data in real time and adjusting the interviewer's questions based on the results, it is possible to achieve more natural communication. This makes it possible to provide a more effective interview experience by generating customized interviewers based on the candidate's past interview data.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The interviewer generation unit generates interviewers based on the company's requirements. For example, by inputting the skills and experience a company is looking for as prompts into the generation AI, interviewers that match those requirements are generated. The interviewer generation unit can also generate interviewers who ask questions based on the company's culture and values. Step 2: The interview experience optimization unit optimizes the interview experience by having the interviewer generated by the interviewer generation unit analyze the interview progress and the candidate's answers in real time. For example, if the candidate has difficulty with a particular question, the generation AI analyzes the situation and generates an appropriate follow-up question. The interview experience optimization unit can also automatically suggest the next most appropriate question as the interview progresses. Step 3: After the interview experience optimized by the interview experience optimization unit, the interview result analysis unit analyzes the candidate's answers and generates a report. For example, the generation AI analyzes the text of the candidate's answers and evaluates them based on specific skills and experience. This report can be used as a reference for companies when evaluating candidates.

[0055] (Example 2) The Interactive Recruiter according to an embodiment of the present invention is a system that automatically generates interviewers for companies and optimizes the interview experience, thereby enabling companies to evolve their recruitment interview process in an innovative and efficient manner.

[0056] An interactive recruiter according to an embodiment includes an interviewer generation unit, an interview experience optimization unit, and an interview result analysis unit. The interviewer generation unit generates interviewers based on a company's requirements. For example, by inputting the skills and experience desired by a company as prompts into the generation AI, an interviewer matching those requirements is generated. The interviewer generation unit can also generate interviewers who ask questions based on the company's culture and values. The interview experience optimization unit optimizes the interview experience by having the interviewer generated by the interviewer generation unit analyze the interview progress and the candidate's answers in real time. For example, if a candidate has difficulty answering a particular question, the generation AI analyzes the situation and generates an appropriate follow-up question. The interview experience optimization unit can also automatically suggest the next appropriate question as the interview progresses. After the interview experience optimized by the interview experience optimization unit, the interview result analysis unit analyzes the candidate's answers and generates a report. For example, the generation AI performs text analysis of the candidate's answers and evaluates them for specific skills and experience. This report can be used as a reference for companies when evaluating candidates. As a result, the interactive recruiter according to the embodiment can generate interviewers based on the requirements of the company, optimize the interview experience, and analyze the interview results, thereby realizing an efficient recruitment process.

[0057] The interviewer generation unit customizes the personality and speaking style of the interviewer, allowing it to provide optimal communication to the candidate. The interviewer generation unit, for example, uses generation AI to customize the personality of the interviewer. For example, it generates an interviewer with a friendly personality so that the candidate can relax. The interviewer generation unit also customizes the speaking style of the interviewer to provide optimal communication to the candidate. For example, when asking technical questions, it avoids using technical jargon and explains in easy-to-understand terms. The interviewer generation unit also uses generation AI to customize the personality and speaking style of the interviewer to ease the candidate's tension. For example, it generates an interviewer who speaks with a sense of humor. In this way, by customizing the personality and speaking style of the interviewer, it is possible to provide optimal communication to the candidate.

[0058] The interviewer generation unit can learn from past interview data and incorporate the characteristics of successful interviewers. The interviewer generation unit, for example, learns from past interview data and incorporates the characteristics of successful interviewers. For example, it reproduces the questioning patterns of interviewers who received high evaluations from candidates. The generation AI also analyzes past interview data and incorporates the characteristics of successful interviewers. For example, it learns how to provide appropriate feedback to candidates' answers. The interviewer generation unit also incorporates the characteristics of successful interviewers based on past interview data. For example, it reproduces the facial expressions and gestures of interviewers to give candidates a friendly impression. In this way, by learning from past interview data and incorporating the characteristics of successful interviewers, more effective interviewers can be generated.

[0059] The interviewer generation unit uses the emotion estimation function to generate an interviewer response that corresponds to the candidate's emotions, thereby providing a more natural interview experience. The interviewer generation unit, for example, uses the emotion estimation function to generate an interviewer response that corresponds to the candidate's emotions. For example, if the candidate is nervous, the interviewer generation unit speaks words that relax the candidate. The interviewer generation unit also analyzes the candidate's emotions in real time to generate an interviewer response that corresponds to the candidate's emotions. For example, if the candidate is confused, the interviewer generation unit explains the question in an easy-to-understand manner. The interviewer generation unit also uses the emotion estimation function to generate an interviewer response that corresponds to the candidate's emotions, thereby providing a more natural interview experience. For example, if the candidate is confident, the interviewer generation unit gives a compliment. In this way, the interviewer generation unit uses the emotion estimation function to generate an interviewer response that corresponds to the candidate's emotions, thereby providing a more natural interview experience.

[0060] The interviewer generation unit generates interviewers who are competent in different languages ​​and cultures, thereby supporting global recruitment activities. The interviewer generation unit, for example, uses generation AI to generate interviewers who are competent in different languages. For example, interviews are conducted in multiple languages, such as English, French, and Chinese. It also generates interviewers who are competent in different cultures, thereby supporting global recruitment activities. For example, it generates interviewers who ask questions that take cultural backgrounds into consideration. It also uses generation AI to generate interviewers who are competent in different languages ​​and cultures, thereby supporting global recruitment activities. For example, it generates interviewers who understand international manners and etiquette. This makes it possible to support global recruitment activities by generating interviewers who are competent in different languages ​​and cultures.

[0061] The interviewer generation unit can generate interviewers with specialized knowledge specialized in a specific industry or job type. The interviewer generation unit, for example, uses generation AI to generate interviewers with specialized knowledge specialized in a specific industry. For example, it generates interviewers who can answer technical questions in the IT industry. It also generates interviewers with specialized knowledge specialized in a specific job type to evaluate the skills of candidates. For example, it generates interviewers who ask appropriate questions to candidates for marketing jobs. It also uses generation AI to generate interviewers with specialized knowledge specialized in a specific industry or job type. For example, it generates interviewers with specialized knowledge in the medical industry to evaluate candidates for medical jobs. In this way, by generating interviewers with specialized knowledge specialized in a specific industry or job type, more appropriate skill evaluation becomes possible.

[0062] The interviewer generation unit can use the emotion estimation function to generate an interviewer who takes the candidate's emotions into consideration and puts them at ease. The interviewer generation unit, for example, uses the emotion estimation function to generate an interviewer who takes the candidate's emotions into consideration and puts them at ease. For example, if the candidate is nervous, an interviewer who speaks calmly is generated. The interviewer generation unit also analyzes the candidate's emotions in real time and generates an interviewer according to that emotion. For example, if the candidate is feeling anxious, an interviewer who speaks to reassure the candidate is generated. The emotion estimation function also generates an interviewer who takes the candidate's emotions into consideration and puts the candidate at ease. For example, an interviewer who speaks with humor to put the candidate at ease is generated to help the candidate relax. In this way, by using the emotion estimation function to generate an interviewer who takes the candidate's emotions into consideration and puts the candidate at ease, the candidate's tension can be alleviated.

[0063] The interview experience optimization unit can monitor the candidate's stress level according to the progress of the interview and suggest breaks at appropriate times. The interview experience optimization unit, for example, uses a generative AI to monitor the candidate's stress level according to the progress of the interview. For example, if the candidate seems nervous, it suggests a break. The interview experience optimization unit also analyzes the candidate's stress level in real time according to the progress of the interview and suggests breaks at appropriate times. For example, if the candidate seems tired, it suggests a short break. The generative AI also monitors the candidate's stress level according to the progress of the interview and suggests breaks at appropriate times. For example, if the candidate is losing concentration, it suggests a break to refresh themselves. In this way, the burden on the candidate can be reduced by monitoring the candidate's stress level according to the progress of the interview and suggesting breaks at appropriate times.

[0064] The interview experience optimization unit can analyze the candidate's past interview data and generate an optimal question sequence. The interview experience optimization unit, for example, analyzes the candidate's past interview data and generates an optimal question sequence. For example, based on past answer patterns, questions in areas in which the candidate is strong are prioritized. In addition, to optimize the interview experience, the candidate's past interview data is analyzed and an optimal question sequence is generated. For example, questions in areas in which the candidate is weak are postponed. In addition, based on the candidate's past interview data, an optimal question sequence is generated to improve the interview experience. For example, questions about successful experiences are asked first to help the candidate gain confidence. In this way, the interview experience can be improved by analyzing the candidate's past interview data and generating an optimal question sequence.

[0065] The interview experience optimization unit can use the emotion estimation function to provide feedback in real time according to the candidate's emotions. The interview experience optimization unit, for example, uses the emotion estimation function to provide feedback in real time according to the candidate's emotions. For example, if the candidate is nervous, the interview experience optimization unit provides words to relax the candidate. The interview experience optimization unit also analyzes the candidate's emotions in real time and provides feedback in accordance with those emotions. For example, the interview experience optimization unit provides compliments to the candidate to help them feel more confident. The interview experience optimization unit also uses the emotion estimation function to provide feedback in real time according to the candidate's emotions, thereby improving the interview experience. For example, if the candidate is confused, the interview experience optimization unit provides a clearer explanation of the question. In this way, the interview experience can be improved by using the emotion estimation function to provide feedback in real time according to the candidate's emotions.

[0066] The interview experience optimization unit can analyze a candidate's non-verbal communication and respond appropriately. The interview experience optimization unit, for example, uses generative AI to analyze a candidate's non-verbal communication and respond appropriately. For example, if the candidate seems nervous, it will say something to relax them. It also analyzes the candidate's facial expressions and gestures in real time and responds appropriately based on the results. For example, if the candidate seems confused, it will explain the question in an easy-to-understand manner. It also uses generative AI to analyze a candidate's non-verbal communication and respond appropriately. For example, it will say a compliment to the candidate to help them feel more confident. In this way, it is possible to improve the interview experience by analyzing a candidate's non-verbal communication and responding appropriately.

[0067] The interview experience optimization unit can collect background information about candidates in advance and generate questions that are individually customized. The interview experience optimization unit, for example, uses a generation AI to collect background information about candidates in advance and generate questions that are individually customized. For example, questions are asked based on the candidate's educational background and work history. The interview experience optimization unit also analyzes the candidate's background information and generates questions that are individually customized based on that information. For example, questions are asked about the candidate's expertise. The interview experience optimization unit also uses a generation AI to collect background information about candidates in advance and generate questions that are individually customized. For example, questions are asked based on the candidate's past project experience. In this way, the interview experience can be improved by collecting background information about candidates in advance and generating questions that are individually customized.

[0068] The interview experience optimization unit can use the emotion estimation function to suggest relaxation methods based on the candidate's emotions. The interview experience optimization unit, for example, uses the emotion estimation function to suggest relaxation methods based on the candidate's emotions. For example, if the candidate is nervous, it encourages the candidate to take deep breaths. It also analyzes the candidate's emotions in real time and suggests relaxation methods based on those emotions. For example, if the candidate is feeling anxious, it suggests relaxing music. It also uses the emotion estimation function to suggest relaxation methods based on the candidate's emotions. For example, it suggests light stretching to help the candidate relax. In this way, by using the emotion estimation function to suggest relaxation methods based on the candidate's emotions, it is possible to relieve the candidate's tension.

[0069] The interview experience optimization unit can analyze the consistency of a candidate's answers in real time and point out inconsistencies. The interview experience optimization unit, for example, uses a generative AI to analyze the consistency of a candidate's answers in real time and point out inconsistencies. For example, if a candidate gives a different answer, the inconsistency is pointed out. The interview experience optimization unit also analyzes the candidate's answers in real time and checks for consistency. For example, if a candidate makes a statement that contradicts a previous answer, this is pointed out. The generative AI also analyzes the consistency of a candidate's answers in real time and points out inconsistencies. For example, if a candidate says something different from what they said in their self-introduction, this inconsistency is pointed out. In this way, by analyzing the consistency of a candidate's answers in real time and pointing out inconsistencies, more accurate evaluation is possible.

[0070] The interview experience optimization unit can evaluate the candidate's answers according to the progress of the interview and provide instant feedback. In the interview experience optimization unit, for example, the generation AI evaluates the candidate's answers according to the progress of the interview and provides instant feedback. For example, if the candidate gives an appropriate answer, it will praise them. The candidate's answers are also evaluated in real time and feedback is provided based on the results. For example, if the candidate is pointed out an area for improvement, specific advice is provided. The generation AI also evaluates the candidate's answers and provides instant feedback according to the progress of the interview. For example, if the candidate gives an appropriate answer to a question, it will suggest the next question. In this way, by evaluating the candidate's answers according to the progress of the interview and providing instant feedback, it is possible to promote the candidate's growth.

[0071] The interview experience optimization unit can use the emotion estimation function to perform real-time analysis based on the candidate's emotions and suggest an appropriate response to the interviewer. The interview experience optimization unit, for example, uses the emotion estimation function to perform real-time analysis based on the candidate's emotions and suggest an appropriate response to the interviewer. For example, if the candidate is nervous, the interview experience optimization unit can use words to relax the candidate. The interview experience optimization unit can also analyze the candidate's emotions in real time and suggest a response based on those emotions. For example, if the candidate is confused, the interview experience optimization unit can explain the question in an easy-to-understand manner. The interview experience optimization unit can also perform real-time analysis based on the candidate's emotions and suggest an appropriate response to the interviewer. For example, the interview experience optimization unit can use the emotion estimation function to ...

[0072] The interview experience optimization unit can compare a candidate's answers with other candidates according to the progress of the interview and perform a relative evaluation. The interview experience optimization unit, for example, uses a generation AI to compare a candidate's answers with other candidates according to the progress of the interview and perform a relative evaluation. For example, it compares them with the answers of other candidates to the same question. It also analyzes the candidate's answers in real time and compares them with other candidates. For example, it compares answers regarding specific skills and performs a relative evaluation. It also uses a generation AI to compare a candidate's answers with other candidates according to the progress of the interview and perform a relative evaluation. For example, it compares them with past candidate answer data. This enables a fairer evaluation by comparing a candidate's answers with other candidates according to the progress of the interview and performing a relative evaluation.

[0073] The interview experience optimization unit can analyze the candidate's answers according to the progress of the interview and automatically generate the next question. In the interview experience optimization unit, for example, the generation AI analyzes the candidate's answers according to the progress of the interview and automatically generates the next question. For example, it generates related follow-up questions based on the candidate's answers. The interview experience optimization unit also analyzes the candidate's answers in real time and automatically generates the next question based on the results. For example, it generates questions related to skills emphasized by the candidate. The generation AI also analyzes the candidate's answers according to the progress of the interview and automatically generates the next question. For example, it generates questions related to areas in which the candidate has difficulty. In this way, by analyzing the candidate's answers according to the progress of the interview and automatically generating the next question, it is possible to provide a smooth interview experience.

[0074] The interview experience optimization unit can use the emotion estimation function to perform real-time analysis based on the candidate's emotions and notify the interviewer of the candidate's emotional state. The interview experience optimization unit, for example, uses the emotion estimation function to perform real-time analysis based on the candidate's emotions and notify the interviewer of the candidate's emotional state. For example, if the candidate is nervous, the interviewer is notified of that information. The interview experience optimization unit also analyzes the candidate's emotions in real time and notifies the interviewer of the candidate's emotional state. For example, if the candidate is confused, the interviewer is notified of that information. The emotion estimation function also performs real-time analysis based on the candidate's emotions and notifies the interviewer of the candidate's emotional state. For example, if the candidate is confident, the interviewer is notified of that information. In this way, by using the emotion estimation function to perform real-time analysis based on the candidate's emotions and notifying the interviewer of the candidate's emotional state, the interviewer can take appropriate action.

[0075] The interview result analysis unit can analyze a company's past recruitment data and generate profiles of optimal interviewers. The interview result analysis unit, for example, uses generation AI to analyze a company's past recruitment data and generate profiles of optimal interviewers. For example, it generates interviewers based on data on recruitment processes that have been successful in the past. It also analyzes a company's past recruitment data and generates profiles of optimal interviewers. For example, it generates interviewers for evaluating candidates with a specific skill set. It also uses generation AI to analyze a company's past recruitment data and generate profiles of optimal interviewers. For example, it generates interviewers who fit the company's culture and values. In this way, by analyzing a company's past recruitment data and generating profiles of optimal interviewers, it is possible to provide more effective interviewers.

[0076] The interview result analysis unit can use the emotion estimation function to estimate the company's expectations for the candidate's answers and generate questions based on that. The interview result analysis unit, for example, uses the emotion estimation function to estimate the company's expectations for the candidate's answers and generate questions based on that. For example, it generates questions related to skills in which the candidate is confident. It also analyzes the candidate's answers in real time to estimate the company's expectations. For example, it generates follow-up questions based on experience emphasized by the candidate. It also uses the emotion estimation function to estimate the company's expectations for the candidate's answers and generate questions based on that. For example, it generates questions related to areas in which the candidate finds difficulty. In this way, by using the emotion estimation function to estimate the company's expectations for the candidate's answers and generating questions based on that, more appropriate questions can be asked.

[0077] The interview result analysis unit can customize the appearance and clothing of interviewers based on the requirements of the company, and generate interviewers who fit the corporate culture. The interview result analysis unit, for example, customizes the appearance of interviewers based on the requirements of the company. For example, it generates interviewers dressed in accordance with the company's dress code. It also customizes the interviewer's clothing to match the corporate culture, giving a consistent impression to candidates. For example, it generates interviewers dressed in casual clothing. It also customizes the appearance and clothing of interviewers based on the requirements of the company, and generates interviewers who fit the corporate culture. For example, it generates interviewers dressed in a way that incorporates the company's brand colors. In this way, it is possible to customize the appearance and clothing of interviewers based on the requirements of the company, and generate interviewers who fit the corporate culture, giving a consistent impression to candidates.

[0078] The interview result analysis unit can automatically generate a training program for interviewers that meets the requirements of a company. The interview result analysis unit, for example, uses a generation AI to automatically generate a training program for interviewers that meets the requirements of a company. For example, it generates a training program specialized for evaluating a specific skill. It also automatically generates a training program for interviewers based on the requirements of a company to improve the skills of interviewers. For example, it generates a training program to strengthen communication skills. It also uses a generation AI to automatically generate a training program for interviewers that meets the requirements of a company. For example, it generates a training program based on the culture and values ​​of a company. In this way, it is possible to improve the skills of interviewers by automatically generating a training program for interviewers that meets the requirements of a company.

[0079] The interview result analysis unit can use the emotion estimation function to take the candidate's emotions into consideration when generating interviewers based on the company's requirements, thereby generating interviewers who leave a positive impression. The interview result analysis unit, for example, uses the emotion estimation function to take the candidate's emotions into consideration when generating interviewers based on the company's requirements. For example, an interviewer who speaks calmly is generated so that the candidate can relax. The interview result analysis unit also analyzes the candidate's emotions in real time to generate interviewers who leave a positive impression. For example, an interviewer who gives compliments so that the candidate can gain confidence is generated. The emotion estimation function also takes the candidate's emotions into consideration when generating interviewers based on the company's requirements, thereby generating interviewers who leave a positive impression. For example, an interviewer who speaks in a friendly manner is generated so that the candidate can feel at ease. In this way, the emotion estimation function can take the candidate's emotions into consideration when generating interviewers based on the company's requirements, thereby generating interviewers who leave a positive impression, thereby leaving a good impression on the candidate.

[0080] The interviewer generation unit can automatically adjust candidate schedules, manage the progress of interviews, and provide feedback on interview results. The interviewer generation unit, for example, automatically adjusts candidate schedules. For example, it uses a calendar system to adjust the schedules of candidates and interviewers. It also automatically manages the progress of interviews. For example, it performs timekeeping and progress monitoring. It also automatically provides feedback on interview results. For example, it performs real-time evaluations and provides feedback to candidates. In this way, by automatically adjusting candidate schedules, managing the progress of interviews, and providing feedback on interview results, it is possible to reduce the burden on human resources personnel at companies and achieve an efficient interview process.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] Interactive Recruiter can also automatically analyze a candidate's past work experience and skill set, allowing the interviewer generator to generate customized questions based on that information. For example, if a candidate has experience in leadership roles on a specific project, it can generate specific questions about that experience. Also, if a candidate is familiar with a particular technology, it can generate in-depth questions about that technology. Furthermore, based on the candidate's past work experience, the interviewer generator can generate questions related to the candidate's career path and evaluate the candidate's future career vision. This allows for deeper insights by generating customized questions based on the candidate's past work experience and skill set.

[0083] The interviewer generation unit can also analyze the candidate's non-verbal communication and customize the interviewer's responses based on the results. For example, if the candidate is nervous, it can generate an interviewer with gestures and facial expressions that will relax the candidate. Also, if the candidate is confident, it can generate an interviewer who gives positive feedback that will bring out that confidence. Furthermore, by analyzing the candidate's non-verbal communication in real time and adjusting the interviewer's responses based on the results, it is possible to achieve more natural communication. This makes it possible to provide a more effective interview experience by generating a customized interviewer based on the candidate's non-verbal communication.

[0084] The interviewer generation unit can also generate interviewers that match the candidate's learning style. For example, if the candidate has a visual learning style, it can generate an interviewer who provides explanations using visual materials. Alternatively, if the candidate has an auditory learning style, it can generate an interviewer who provides detailed oral explanations. Furthermore, if the candidate has a hands-on learning style, it can generate an interviewer who asks questions using actual work scenarios. In this way, by generating interviewers that match the candidate's learning style, it is possible to deepen the candidate's understanding and provide a more effective interview experience.

[0085] The interviewer generation unit can also monitor the candidate's stress level and adjust the interviewer's responses based on the results. For example, if the candidate shows a high stress level, it can generate an interviewer who uses words to put the candidate at ease. On the other hand, if the candidate's stress level is low, it can generate an interviewer who asks more challenging questions. Furthermore, by analyzing the candidate's stress level in real time and adjusting the interviewer's responses based on the results, it is possible to achieve more natural communication. This makes it possible to provide a more effective interview experience by generating a customized interviewer based on the candidate's stress level.

[0086] The interviewer generation unit can also generate interviewers that match the cultural background of the candidate. For example, if a candidate has a different cultural background, it generates an interviewer that asks questions that take that culture into consideration. It can also generate interviewers with an appropriate communication style based on the candidate's cultural background. Furthermore, by generating interviewers that take the candidate's cultural background into consideration and provide feedback that is appropriate for that culture, the candidate can feel relaxed during the interview. This makes it possible to provide a more effective interview experience by generating interviewers that are customized to match the candidate's cultural background.

[0087] The interviewer generation unit can also generate interviewers according to the candidate's career goals. For example, if a candidate is aiming for a specific career path, it can generate interviewers who ask questions related to that career path. It can also generate interviewers who provide appropriate advice based on the candidate's career goals. Furthermore, by generating interviewers who take the candidate's career goals into consideration and provide feedback toward those goals, the candidate can approach the interview with confidence. This makes it possible to provide a more effective interview experience by generating interviewers customized according to the candidate's career goals.

[0088] The interviewer generation unit can also analyze a candidate's learning history and customize the interviewer's questions based on the results. For example, if a candidate has completed a specific online course, it can generate an interviewer who asks questions related to that course. It can also generate an interviewer who asks appropriate follow-up questions based on the candidate's learning history. Furthermore, by analyzing a candidate's learning history in real time and adjusting the interviewer's questions based on the results, it can gain deeper insights. This makes it possible to provide a more effective interview experience by generating a customized interviewer based on the candidate's learning history.

[0089] The interviewer generation unit can also use the candidate emotion estimation function to provide feedback based on the candidate's emotions in real time. For example, if a candidate is nervous, it can generate an interviewer who will say something to relax the candidate. It can also analyze the candidate's emotions in real time and generate an interviewer who will provide feedback based on those emotions. Furthermore, providing feedback based on the candidate's emotions allows the candidate to approach the interview with confidence. As a result, a more effective interview experience can be provided by using the emotion estimation function to provide feedback based on the candidate's emotions in real time.

[0090] The interviewer generation unit can also analyze the candidate's past interview data and customize the interviewer's questions based on the results. For example, if a candidate found a particular question difficult in a past interview, it can generate an interviewer who avoids that question. It can also generate an interviewer who asks appropriate follow-up questions based on the candidate's past interview data. Furthermore, by analyzing the candidate's past interview data in real time and adjusting the interviewer's questions based on the results, it is possible to achieve more natural communication. This makes it possible to provide a more effective interview experience by generating customized interviewers based on the candidate's past interview data.

[0091] The interviewer generation unit can also use the candidate's emotion estimation function to suggest relaxation methods based on the candidate's emotions. For example, if a candidate is nervous, it can generate an interviewer who encourages the candidate to take deep breaths. It can also analyze the candidate's emotions in real time and generate an interviewer who suggests relaxation methods based on those emotions. Furthermore, by suggesting relaxation methods based on the candidate's emotions, the candidate can approach the interview in a relaxed state. In this way, by using the emotion estimation function to suggest relaxation methods based on the candidate's emotions, it is possible to provide a more effective interview experience.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The interviewer generation unit generates interviewers based on the company's requirements. For example, by inputting the skills and experience a company is looking for as prompts into the generation AI, interviewers that match those requirements are generated. The interviewer generation unit can also generate interviewers who ask questions based on the company's culture and values. Step 2: The interview experience optimization unit optimizes the interview experience by having the interviewer generated by the interviewer generation unit analyze the interview progress and the candidate's answers in real time. For example, if the candidate has difficulty with a particular question, the generation AI analyzes the situation and generates an appropriate follow-up question. The interview experience optimization unit can also automatically suggest the next most appropriate question as the interview progresses. Step 3: After the interview experience optimized by the interview experience optimization unit, the interview result analysis unit analyzes the candidate's answers and generates a report. For example, the generation AI analyzes the text of the candidate's answers and evaluates them based on specific skills and experience. This report can be used as a reference for companies when evaluating candidates.

[0094] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0095] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0096] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0097] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0098] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0099] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0100] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0101] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0102] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0103] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0104] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0105] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0106] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0107] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0108] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0109] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0110] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0111] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0112] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0113] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0114] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0115] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0116] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0117] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0118] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0119] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0120] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0121] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0122] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0123] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0124] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0125] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0126] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0127] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0128] 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.

[0129] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0130] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0131] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0132] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0133] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0134] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0135] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0136] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0137] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0138] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0139] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0140] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0141] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0142] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0143] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0144] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0145] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0146] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0147] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0148] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0149] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0150] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0151] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0152] 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.

[0153] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0154] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0155] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0156] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0157] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0158] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0159] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0160] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0161] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. an interviewer generation unit that generates interviewers based on the requirements of the company; an interview experience optimization unit that analyzes the interview progress and the candidate's answers in real time using the interviewer generated by the interviewer generation unit to optimize the interview experience; an interview result analysis unit that analyzes the candidate's answers and generates a report after the interview experience optimized by the interview experience optimization unit. A system characterized by:

2. The interviewer generation unit Customize the personality and speaking style of the interviewer to provide optimal communication with the candidate 2. The system of claim 1.

3. The interviewer generation unit Learn from past interview data and incorporate the characteristics of successful interviewers 2. The system of claim 1.

4. The interviewer generation unit Generate interviewer responses that correspond to the candidate's emotions, providing a more natural interview experience 2. The system of claim 1.

5. The interviewer generation unit Support global recruitment activities by generating interviewers who can handle different languages ​​and cultures.

2. The system of claim 1.

6. The interviewer generation unit Generate interviewers with specialized expertise in specific industries or roles 2. The system of claim 1.

7. The interviewer generation unit Consider the candidate's feelings and create an interviewer who will put them at ease 2. The system of claim 1.

8. The interview experience optimization unit: Monitor the candidate's stress level as the interview progresses and suggest breaks at appropriate times 2. The system of claim 1.

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