system

Abstract

The system according to this embodiment aims to streamline the interview process and acquire the most suitable personnel while maintaining consistency in evaluation criteria. [Solution] The system according to the embodiment comprises an interview unit, a question generation unit, and a personality acquisition unit. The interview unit eliminates the need for an application form and conducts the interview using AI. The question generation unit generates follow-up questions in real time during the interview conducted by the interview unit. The personality acquisition unit acquires the personality of the applicant during the interview conducted by the interview unit by utilizing an emotion engine and speech synthesis technology.

Description

Technical Field

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there are problems such as the prolongation of interview schedules and variations in evaluation criteria, making it difficult to acquire the optimal human resources.

[0005] The system according to the embodiment aims to streamline the interview process and acquire the optimal human resources while maintaining the consistency of evaluation criteria.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an interview unit, a question generation unit, and a personality acquisition unit. The interview unit eliminates the need for application forms and conducts interviews using AI. The question generation unit generates follow-up questions in real time during the interview conducted by the interview unit. The personality acquisition unit acquires personality information during the interview conducted by the interview unit using an emotion engine and speech synthesis technology. [Effects of the Invention]

[0007] The system according to this embodiment can streamline the interview process and acquire the most suitable personnel while maintaining consistency in evaluation criteria. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) The AI-powered recruitment interviewer system according to an embodiment of the present invention is a system that uses AI to conduct recruitment interviews. This AI-powered recruitment interviewer system is designed to solve problems in conventional recruitment processes, such as the length of interview schedules and inconsistencies in evaluation criteria. First, application forms are unnecessary, and instead, the AI ​​conducts the interview. In the AI ​​interview, not only are basic items such as educational background, motivation for applying, and self-introduction asked, but in-depth questions are generated on the spot, and questions that test flexibility can also be asked in real time. This improves the quality of the interview and reduces inconsistencies in evaluation criteria. Furthermore, by utilizing an emotion engine and speech synthesis technology, it is possible to accurately acquire the personality of the interviewee, such as their enthusiasm and sociability. This makes it possible to provide an interview experience equivalent to that of a real interviewer. This system will first be introduced on a trial basis within companies, and then the aim is to expand the service mainly to large companies with a large number of applicants. This is expected to revolutionize the job hunting selection process throughout Japan and realize efficiency and fairness in recruitment. As a result, the AI-powered recruitment interviewer system can improve the quality of interviews and reduce inconsistencies in evaluation criteria.

[0029] The AI-powered recruitment interviewer system according to this embodiment comprises an interview unit, a question generation unit, and a personality acquisition unit. The interview unit eliminates the need for application forms and conducts interviews using AI. The interview unit can ask basic questions such as the applicant's educational background, motivation for applying, and self-introduction. The interview unit also works in conjunction with a question generation unit that generates follow-up questions in real time based on the applicant's answers. The question generation unit can generate questions that test the applicant's flexibility based on their answers. The question generation unit uses a generation AI to analyze the applicant's answers and generate appropriate questions. For example, the question generation unit can input a prompt to the generation AI saying, "Generate questions that test the applicant's flexibility based on their answers," and the generation AI will generate appropriate questions. The question generation unit can also use the generation AI to generate the next question in real time based on the applicant's answers. The personality acquisition unit utilizes an emotion engine and speech synthesis technology to acquire the applicant's personality, such as their enthusiasm and sociability. The personality acquisition unit analyzes, for example, the interviewee's facial expressions and tone of voice, and estimates their emotions using an emotion engine. The emotion engine takes the interviewee's facial expressions and tone of voice as input and outputs emotions. For example, the emotion engine estimates emotions such as joy, sadness, and anger from the interviewee's facial expressions. The personality acquisition unit also uses speech synthesis technology to analyze the interviewee's tone of voice and speaking style and evaluates their sociability. The speech synthesis technology takes the interviewee's tone of voice and speaking style as input and outputs sociability. For example, the speech synthesis technology evaluates the level of sociability from the interviewee's tone of voice. As a result, the AI ​​recruitment interviewer system according to this embodiment can improve the quality of interviews and reduce variability in evaluation criteria.

[0030] The interview department will eliminate application forms and conduct interviews using AI. The interview department can ask basic questions such as the applicant's educational background, motivation for applying, and self-introduction. Specifically, when an applicant participates in an interview, the interview department will first ask basic questions to gather background information. This includes educational background, work experience, motivation for applying, and self-introduction. These questions are based on pre-set templates, but may be flexibly modified based on the applicant's answers. For example, if the applicant has been involved in a specific project, detailed questions about that project may be added. The interview department analyzes the applicant's answers in real time and provides data to generate the next questions. This allows the interview department to ask questions that lead to a deeper understanding based on the applicant's responses. Furthermore, the interview department records the applicant's answers and stores them in a database for later analysis. This data is used for interview evaluation and feedback. Furthermore, the interview department records the interviewee's responses and stores them in a database for later analysis. This data is used for interview evaluation and feedback.

[0031] The question generation unit can, for example, generate questions that test the interviewee's flexibility based on their answers. The question generation unit uses a generation AI to analyze the interviewee's answers and generate appropriate questions. Specifically, the question generation unit analyzes the interviewee's answers in real time and generates the next question based on that content. For example, if the interviewee answers, "I have experience managing projects in a team," the question generation unit will generate specific questions such as, "What was the biggest challenge you faced in that project?" or "What did you do to improve communication with team members?" The generation AI uses natural language processing technology to analyze the interviewee's answers. This allows it to understand the context and intent of the interviewee's answers and generate appropriate questions. For example, the generation AI can extract keywords and phrases included in the interviewee's answers and generate related questions based on them. The generation AI can also analyze the tone and emotion of the interviewee's answers and generate questions accordingly. For example, if the interviewee answers confidently, the generation AI can generate more challenging questions to test the interviewee's flexibility and problem-solving abilities. This allows the question generation unit to generate questions based on the interviewee's answers to gain a deeper understanding, thereby improving the quality of the interview.

[0032] The personality acquisition unit utilizes an emotion engine and speech synthesis technology to acquire personality traits such as enthusiasm and sociability from the interviewee. Specifically, the personality acquisition unit analyzes the interviewee's facial expressions and voice tone and estimates emotions using the emotion engine. The emotion engine takes the interviewee's facial expressions and voice tone as input and outputs emotions. For example, the emotion engine estimates emotions such as joy, sadness, and anger from the interviewee's facial expressions. The personality acquisition unit also uses speech synthesis technology to analyze the interviewee's voice tone and speaking style and evaluate sociability. The speech synthesis technology takes the interviewee's voice tone and speaking style as input and outputs sociability. For example, the speech synthesis technology evaluates the level of sociability from the interviewee's voice tone. As a result, the personality acquisition unit can analyze the interviewee's emotions and sociability in real time and use this information to evaluate the interview. Furthermore, the personality acquisition unit can also analyze the interviewee's nonverbal communication. For example, the personality assessment unit can analyze the interviewee's posture and gestures to evaluate their level of nervousness and confidence. This allows the unit to comprehensively evaluate the interviewee's overall personality and improve the quality of the interview.

[0033] The recording unit can record the interview results. For example, the recording unit can record the interviewee's responses during the interview as text data. The recording unit can also record the interviewee's responses using technology that converts audio data into text data. For example, the recording unit can use speech recognition technology to convert the interviewee's responses into text data in real time and record them. The recording unit can also record the interviewee's facial expressions and tone of voice during the interview. For example, the recording unit can use a camera and microphone to record the interviewee's facial expressions and tone of voice and analyze them later. This makes it easier to evaluate and analyze the interview results afterward. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the interviewee's responses into a generating AI, and the generating AI can generate text data.

[0034] The evaluation unit can perform evaluations based on interview results. For example, the evaluation unit can perform evaluations based on the interviewee's responses, facial expressions, and tone of voice. The evaluation unit can evaluate the interviewee's performance according to evaluation criteria. For example, the evaluation unit can analyze the interviewee's responses and assign a score based on evaluation criteria. The evaluation unit can also analyze the interviewee's facial expressions and tone of voice, estimate emotions using an emotion engine, and reflect the results in the evaluation. For example, the evaluation unit can use an emotion engine to estimate emotions from the interviewee's facial expressions and reflect the results in the evaluation. This ensures consistency in evaluation by performing evaluations based on interview results. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the interviewee's responses into a generating AI, and the generating AI can perform the evaluation.

[0035] The service provider can provide interview results to HR personnel. For example, the service provider can provide interview results to HR personnel as text data. The service provider can also provide interview results via email or web application. For example, the service provider can send interview results to HR personnel via email. Alternatively, the service provider can display interview results to HR personnel via a web application. This streamlines the recruitment process by providing interview results to HR personnel. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input interview results into a generating AI, which can then provide them to HR personnel in an appropriate format.

[0036] The question generation unit can generate questions that test the interviewee's flexibility based on their answers. For example, the question generation unit analyzes the interviewee's answers and generates questions that test their flexibility. The question generation unit uses a generation AI to generate appropriate questions based on the interviewee's answers. For example, the question generation unit inputs the prompt "Generate questions that test the interviewee's flexibility based on their answers" to the generation AI, and the generation AI generates appropriate questions. The question generation unit can also use the generation AI to generate the next question in real time based on the interviewee's answers. This allows for a deeper evaluation by generating questions that test the interviewee's flexibility. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's answers into the generation AI, and the generation AI can generate appropriate questions.

[0037] The personality acquisition unit can acquire the interviewee's enthusiasm and sociability. For example, the personality acquisition unit analyzes the interviewee's facial expressions and tone of voice and estimates emotions using an emotion engine. The emotion engine takes the interviewee's facial expressions and tone of voice as input and outputs emotions. For example, the emotion engine estimates emotions such as joy, sadness, and anger from the interviewee's facial expressions. The personality acquisition unit also uses speech synthesis technology to analyze the interviewee's tone of voice and speaking style and evaluates sociability. The speech synthesis technology takes the interviewee's tone of voice and speaking style as input and outputs sociability. For example, the speech synthesis technology evaluates the level of sociability from the interviewee's tone of voice. By acquiring the interviewee's enthusiasm and sociability, a more accurate personality evaluation becomes possible. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit inputs the interviewee's facial expressions and tone of voice into a generating AI, which can then estimate their emotions.

[0038] The interview department can analyze the candidate's past interview history and select the most appropriate interview method. For example, the interview department can select the most suitable question format based on the candidate's evaluation of past interviews. The interview department can identify the candidate's strengths in question formats from their past interview history and conduct the interview accordingly. Furthermore, the interview department can analyze the candidate's past interview history and adjust the interview to avoid question formats they struggle with. This allows for the selection of a more appropriate interview method by analyzing the candidate's past interview history. Some or all of the above processes in the interview department may be performed using AI or not. For example, the interview department can input the candidate's past interview history into a generating AI, which can then select the most appropriate interview method.

[0039] The interview department can customize questions during the interview based on the interviewee's current work experience and skill set. For example, the interview department can ask questions about specific job duties based on the interviewee's work experience. The interview department can customize technical questions based on the interviewee's skill set. The interview department can also combine the interviewee's work experience and skill set to generate practical questions. This allows for a more specific evaluation by customizing questions based on the interviewee's work experience and skill set. Some or all of the above processes in the interview department may be performed using AI or not. For example, the interview department can input the interviewee's work experience and skill set into a generating AI, which can then generate appropriate questions.

[0040] The interviewer can prioritize asking relevant questions during the interview, taking into account the interviewee's geographical location. For example, the interviewer can ask questions about companies and industries related to the interviewee's place of residence. Based on the interviewee's geographical location, the interviewer can ask questions about commute time and work location. Furthermore, the interviewer can also ask questions about region-specific issues and challenges, taking the interviewee's geographical location into consideration. This allows for more relevant questions to be asked by considering the interviewee's geographical location. Some or all of the above processes in the interviewer may be performed using AI, or not. For example, the interviewer can input the interviewee's geographical location into a generating AI, which can then generate relevant questions.

[0041] The interviewer can analyze the interviewee's social media activity during the interview and generate relevant questions. For example, the interviewer can ask questions about the interviewee's interests and areas of concern based on their social media activity. The interviewer can also ask questions about the interviewee's personality based on their social media posts and comments. Furthermore, the interviewer can analyze the interviewee's social media activity and ask questions about their past experiences and achievements. This allows for more personalized questions by analyzing the interviewee's social media activity. Some or all of the above processes in the interviewer may be performed using AI or not. For example, the interviewer can input the interviewee's social media activity into a generating AI, which can then generate relevant questions.

[0042] The question generation unit can generate subsequent questions based on the level of detail in the interviewee's answers. For example, if the interviewee's answers are detailed, the question generation unit can generate questions that delve deeper. If the interviewee's answers are concise, the question generation unit can generate questions that request more details. The question generation unit can also generate related questions based on the content of the interviewee's answers. This allows for a deeper evaluation by generating subsequent questions based on the level of detail in the interviewee's answers. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's answers into a generation AI, which can then generate the next question.

[0043] The question generation unit can apply different question algorithms depending on the interviewee's work experience when generating questions. For example, if the interviewee has extensive work experience, the question generation unit can apply an algorithm that generates specialized questions. If the interviewee has limited work experience, the question generation unit can apply an algorithm that generates basic questions. The question generation unit can also select an appropriate question algorithm based on the interviewee's work experience and generate questions accordingly. This allows for more appropriate questions to be asked by applying different question algorithms depending on the interviewee's work experience. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's work experience into a generating AI, which can then apply an appropriate question algorithm to generate questions.

[0044] The question generation unit can determine the priority of questions based on when the interviewee submits their answers. For example, if the interviewee submits their answers early, the question generation unit will prioritize generating the next question. If the interviewee submits their answers late, the question generation unit can prioritize generating important questions. The question generation unit can also adjust the priority of questions based on when the interviewee submits their answers. This allows for more efficient interviews by determining the priority of questions based on when the interviewee submits their answers. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's response submission timing into a generation AI, which can then determine the priority of questions.

[0045] The question generation unit can adjust the order of questions based on the relevance of the interviewee during question generation. For example, the question generation unit can prioritize generating highly relevant questions based on the interviewee's answers. The question generation unit can also generate highly relevant questions in a specific order based on the interviewee's work experience and skill set. Furthermore, the question generation unit can analyze the relevance of the interviewee's answers and determine the optimal question order. This allows for more effective interviews by adjusting the order of questions based on the interviewee's relevance. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's answers into a generating AI, which can then adjust the order of the questions.

[0046] The personality acquisition unit can analyze the interviewee's past behavioral history to select the optimal acquisition method when acquiring personality. For example, the personality acquisition unit can select the optimal question format based on the interviewee's past behavioral history. The personality acquisition unit can identify the interviewee's preferred question format from their past behavioral history and acquire personality based on that. Furthermore, the personality acquisition unit can analyze the interviewee's past behavioral history and adjust the personality acquisition method to avoid question formats they are not good at. This allows for the selection of a more appropriate personality acquisition method by analyzing the interviewee's past behavioral history. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit can input the interviewee's past behavioral history into a generating AI, which can then select the optimal acquisition method.

[0047] The personality acquisition unit can customize the acquisition method based on the interviewee's current living situation when acquiring personality information. For example, the personality acquisition unit can acquire personality information by asking appropriate questions based on the interviewee's current work situation. The personality acquisition unit can acquire personality information by asking relevant questions based on the interviewee's home environment. Furthermore, the personality acquisition unit can acquire personality information by asking optimal questions based on the interviewee's lifestyle habits. By customizing the acquisition method based on the interviewee's current living situation, a more accurate personality assessment becomes possible. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit can input the interviewee's current living situation into a generating AI, which can then customize the optimal acquisition method.

[0048] The personality acquisition unit can select the optimal acquisition method when acquiring personality information, taking into account the interviewee's geographical location. For example, the personality acquisition unit can acquire personality information by asking questions related to the interviewee's place of residence. Based on the interviewee's geographical location, the personality acquisition unit can acquire personality information by asking questions about commuting time and workplace. Furthermore, the personality acquisition unit can acquire personality information by asking questions about region-specific problems and issues, taking into account the interviewee's geographical location. This allows for the selection of a more appropriate personality acquisition method by considering the interviewee's geographical location. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit can input the interviewee's geographical location information into a generating AI, which can then select the optimal acquisition method.

[0049] The personality acquisition unit can analyze the interviewee's social media activity and propose methods for acquiring personality. For example, the personality acquisition unit can acquire personality by asking questions about the interviewee's interests and areas of concern based on their social media activity. The personality acquisition unit can also acquire personality by asking questions based on the interviewee's statements and posts on social media. Furthermore, the personality acquisition unit can analyze the interviewee's social media activity and acquire personality by asking questions about their past experiences and achievements. By analyzing the interviewee's social media activity, it is possible to propose more appropriate methods for acquiring personality. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit can input the interviewee's social media activity into a generating AI, which can then propose the optimal acquisition method.

[0050] The recording unit can select the optimal recording method by referring to the interviewee's past interview history during recording. For example, the recording unit can select the optimal recording format based on the interviewee's past interview history. The recording unit can identify the interviewee's preferred question formats from their past interview history and record accordingly. The recording unit can also analyze the interviewee's past interview history and adjust the recording method to avoid question formats they are not comfortable with. This allows for the selection of a more appropriate recording method by referring to the interviewee's past interview history. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the interviewee's past interview history into a generating AI, which can then select the optimal recording method.

[0051] The recording unit can select the optimal recording method while considering the interviewee's geographical location information. For example, the recording unit can include questions related to the interviewee's place of residence in the recording. Based on the interviewee's geographical location information, the recording unit can include questions about commuting time and workplace in the recording. Furthermore, the recording unit can also include questions about region-specific problems and issues in the recording, taking the interviewee's geographical location information into consideration. This allows for the selection of a more appropriate recording method by considering the interviewee's geographical location information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the interviewee's geographical location information into a generating AI, which can then select the optimal recording method.

[0052] The evaluation unit can select the optimal evaluation method by referring to the interviewee's past interview history during the evaluation process. For example, the evaluation unit can select the optimal evaluation criteria based on the interviewee's past interview history. The evaluation unit can identify the interviewee's preferred question formats from their past interview history and conduct the evaluation based on those. The evaluation unit can also analyze the interviewee's past interview history and adjust the evaluation method to avoid question formats they are not good at. This allows for the selection of a more appropriate evaluation method by referring to the interviewee's past interview history. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the interviewee's past interview history into a generating AI, which can then select the optimal evaluation method.

[0053] The evaluation unit can select the most appropriate evaluation method during the evaluation process, taking into account the interviewee's geographical location information. For example, the evaluation unit can include answers to questions related to the interviewee's place of residence in the evaluation. Based on the interviewee's geographical location information, the evaluation unit can also include answers to questions about commuting time and work location in the evaluation. Furthermore, the evaluation unit can also include answers to questions about region-specific problems and challenges in the evaluation, taking into account the interviewee's geographical location information. This allows for the selection of a more appropriate evaluation method by considering the interviewee's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the interviewee's geographical location information into a generating AI, which can then select the most appropriate evaluation method.

[0054] The information delivery unit can select the optimal delivery method by referring to the interviewee's past interview history at the time of delivery. For example, the information delivery unit can select the optimal information delivery format based on the interviewee's past interview history. The information delivery unit can identify the interviewee's preferred information delivery format from their past interview history and provide information based on that. The information delivery unit can also analyze the interviewee's past interview history and adjust the delivery method to avoid information delivery formats they are not good at. This allows for the selection of a more appropriate delivery method by referring to the interviewee's past interview history. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the interviewee's past interview history into a generating AI, which can then select the optimal delivery method.

[0055] The information delivery unit can select the most appropriate delivery method at the time of delivery, taking into account the interviewee's geographical location information. For example, the information delivery unit can provide information related to the interviewee's place of residence. Based on the interviewee's geographical location information, the information delivery unit can provide information on commuting time and workplace. Furthermore, the information delivery unit can also provide information on region-specific problems and issues, taking into account the interviewee's geographical location information. This allows for the selection of a more appropriate delivery method by considering the interviewee's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the interviewee's geographical location information into a generating AI, which can then select the most appropriate delivery method.

[0056] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0057] The interviewing department can generate scenario-based questions related to specific tasks based on the interviewee's past work experience. For example, if the interviewee has project management experience, the department can present specific scenarios related to project progress management and risk management and ask how they would handle them. If the interviewee has a technical background, they can present technical problem-solving scenarios and ask how they would resolve them. Furthermore, if the interviewee has sales experience, they can present scenarios related to customer service and sales strategies and ask how they would handle them. This makes it possible to evaluate the interviewee's practical abilities in a more concrete way.

[0058] The recording unit can record the interviewee's nonverbal communication during the interview. For example, it can record the interviewee's gestures and posture with a camera and analyze that data. It can also track the interviewee's eye movements to evaluate their level of concentration and interest during the interview. Furthermore, it can record the interviewee's hand movements and changes in facial expressions to analyze changes in their emotions in detail. In this way, recording the interviewee's nonverbal communication allows for a more multifaceted evaluation.

[0059] The evaluation department can assess specific skill sets based on the interviewee's responses. For example, to assess leadership skills, questions can be asked about team management experience and methods of demonstrating leadership. Similarly, questions can be asked about past problem-solving experiences and approaches to assess problem-solving abilities. Furthermore, questions can be asked about presentation and negotiation experience to assess communication skills. This allows for a detailed assessment of specific skill sets of the interviewee.

[0060] The service provider can offer a dashboard that visually displays interview results. For example, it can display the interviewee's evaluation score and emotional changes in graphs and charts. It can also centrally display data on the interviewee's responses and nonverbal communication. Furthermore, it can update the interview progress and evaluation details in real time, enabling HR personnel to make quick decisions. This allows for a more intuitive understanding of interview results by visually displaying them.

[0061] The question generation unit can adjust the difficulty level of subsequent questions based on the interviewee's answers. For example, if the interviewee provides a detailed answer, it can generate follow-up questions. Conversely, if the interviewee provides a concise answer, it can generate questions requesting more details. Furthermore, it can also generate questions on related topics based on the interviewee's answers. By adjusting the difficulty level of questions based on the interviewee's responses, a more appropriate evaluation becomes possible.

[0062] The following briefly describes the processing flow for example form 1.

[0063] Step 1: The interview department will eliminate application forms and conduct interviews using AI. The interview department will be able to ask basic questions such as the applicant's educational background, motivation for applying, and self-introduction. The interview department will also work in conjunction with a question generation department that generates follow-up questions in real time based on the applicant's answers. Step 2: The question generation unit can generate questions that test flexibility based on the interviewee's answers. The question generation unit uses a generation AI to analyze the interviewee's answers and generate appropriate questions. For example, the question generation unit can input a prompt to the generation AI such as, "Generate questions that test flexibility based on the interviewee's answers," and the generation AI will generate appropriate questions. The question generation unit can also use the generation AI to generate the next question in real time based on the interviewee's answers. Step 3: The personality acquisition unit uses an emotion engine and speech synthesis technology to acquire personality traits such as enthusiasm and sociability from the interviewee. The personality acquisition unit analyzes the interviewee's facial expressions and tone of voice and estimates emotions using the emotion engine. The emotion engine takes the interviewee's facial expressions and tone of voice as input and outputs emotions. For example, the emotion engine estimates emotions such as joy, sadness, and anger from the interviewee's facial expressions. The personality acquisition unit also uses speech synthesis technology to analyze the interviewee's tone of voice and speaking style and evaluates sociability. The speech synthesis technology takes the interviewee's tone of voice and speaking style as input and outputs sociability. For example, the speech synthesis technology evaluates the level of sociability from the interviewee's tone of voice.

[0064] (Example of form 2) The AI-powered recruitment interviewer system according to an embodiment of the present invention is a system that uses AI to conduct recruitment interviews. This AI-powered recruitment interviewer system is designed to solve problems in conventional recruitment processes, such as the length of interview schedules and inconsistencies in evaluation criteria. First, application forms are unnecessary, and instead, the AI ​​conducts the interview. In the AI ​​interview, not only are basic items such as educational background, motivation for applying, and self-introduction asked, but in-depth questions are generated on the spot, and questions that test flexibility can also be asked in real time. This improves the quality of the interview and reduces inconsistencies in evaluation criteria. Furthermore, by utilizing an emotion engine and speech synthesis technology, it is possible to accurately acquire the personality of the interviewee, such as their enthusiasm and sociability. This makes it possible to provide an interview experience equivalent to that of a real interviewer. This system will first be introduced on a trial basis within companies, and then the aim is to expand the service mainly to large companies with a large number of applicants. This is expected to revolutionize the job hunting selection process throughout Japan and realize efficiency and fairness in recruitment. As a result, the AI-powered recruitment interviewer system can improve the quality of interviews and reduce inconsistencies in evaluation criteria.

[0065] The AI-powered recruitment interviewer system according to this embodiment comprises an interview unit, a question generation unit, and a personality acquisition unit. The interview unit eliminates the need for application forms and conducts interviews using AI. The interview unit can ask basic questions such as the applicant's educational background, motivation for applying, and self-introduction. The interview unit also works in conjunction with a question generation unit that generates follow-up questions in real time based on the applicant's answers. The question generation unit can generate questions that test the applicant's flexibility based on their answers. The question generation unit uses a generation AI to analyze the applicant's answers and generate appropriate questions. For example, the question generation unit can input a prompt to the generation AI saying, "Generate questions that test the applicant's flexibility based on their answers," and the generation AI will generate appropriate questions. The question generation unit can also use the generation AI to generate the next question in real time based on the applicant's answers. The personality acquisition unit utilizes an emotion engine and speech synthesis technology to acquire the applicant's personality, such as their enthusiasm and sociability. The personality acquisition unit analyzes, for example, the interviewee's facial expressions and tone of voice, and estimates their emotions using an emotion engine. The emotion engine takes the interviewee's facial expressions and tone of voice as input and outputs emotions. For example, the emotion engine estimates emotions such as joy, sadness, and anger from the interviewee's facial expressions. The personality acquisition unit also uses speech synthesis technology to analyze the interviewee's tone of voice and speaking style and evaluates their sociability. The speech synthesis technology takes the interviewee's tone of voice and speaking style as input and outputs sociability. For example, the speech synthesis technology evaluates the level of sociability from the interviewee's tone of voice. As a result, the AI ​​recruitment interviewer system according to this embodiment can improve the quality of interviews and reduce variability in evaluation criteria.

[0066] The interview department will eliminate application forms and conduct interviews using AI. The interview department can ask basic questions such as the applicant's educational background, motivation for applying, and self-introduction. Specifically, when an applicant participates in an interview, the interview department will first ask basic questions to gather background information. This includes educational background, work experience, motivation for applying, and self-introduction. These questions are based on pre-set templates, but may be flexibly modified based on the applicant's answers. For example, if the applicant has been involved in a specific project, detailed questions about that project may be added. The interview department analyzes the applicant's answers in real time and provides data to generate the next questions. This allows the interview department to ask questions that lead to a deeper understanding based on the applicant's responses. Furthermore, the interview department records the applicant's answers and stores them in a database for later analysis. This data is used for interview evaluation and feedback. Furthermore, the interview department records the interviewee's responses and stores them in a database for later analysis. This data is used for interview evaluation and feedback.

[0067] The question generation unit can, for example, generate questions that test the interviewee's flexibility based on their answers. The question generation unit uses a generation AI to analyze the interviewee's answers and generate appropriate questions. Specifically, the question generation unit analyzes the interviewee's answers in real time and generates the next question based on that content. For example, if the interviewee answers, "I have experience managing projects in a team," the question generation unit will generate specific questions such as, "What was the biggest challenge you faced in that project?" or "What did you do to improve communication with team members?" The generation AI uses natural language processing technology to analyze the interviewee's answers. This allows it to understand the context and intent of the interviewee's answers and generate appropriate questions. For example, the generation AI can extract keywords and phrases included in the interviewee's answers and generate related questions based on them. The generation AI can also analyze the tone and emotion of the interviewee's answers and generate questions accordingly. For example, if the interviewee answers confidently, the generation AI can generate more challenging questions to test the interviewee's flexibility and problem-solving abilities. This allows the question generation unit to generate questions based on the interviewee's answers to gain a deeper understanding, thereby improving the quality of the interview.

[0068] The personality acquisition unit utilizes an emotion engine and speech synthesis technology to acquire personality traits such as enthusiasm and sociability from the interviewee. Specifically, the personality acquisition unit analyzes the interviewee's facial expressions and voice tone and estimates emotions using the emotion engine. The emotion engine takes the interviewee's facial expressions and voice tone as input and outputs emotions. For example, the emotion engine estimates emotions such as joy, sadness, and anger from the interviewee's facial expressions. The personality acquisition unit also uses speech synthesis technology to analyze the interviewee's voice tone and speaking style and evaluate sociability. The speech synthesis technology takes the interviewee's voice tone and speaking style as input and outputs sociability. For example, the speech synthesis technology evaluates the level of sociability from the interviewee's voice tone. As a result, the personality acquisition unit can analyze the interviewee's emotions and sociability in real time and use this information to evaluate the interview. Furthermore, the personality acquisition unit can also analyze the interviewee's nonverbal communication. For example, the personality assessment unit can analyze the interviewee's posture and gestures to evaluate their level of nervousness and confidence. This allows the unit to comprehensively evaluate the interviewee's overall personality and improve the quality of the interview.

[0069] The recording unit can record the interview results. For example, the recording unit can record the interviewee's responses during the interview as text data. The recording unit can also record the interviewee's responses using technology that converts audio data into text data. For example, the recording unit can use speech recognition technology to convert the interviewee's responses into text data in real time and record them. The recording unit can also record the interviewee's facial expressions and tone of voice during the interview. For example, the recording unit can use a camera and microphone to record the interviewee's facial expressions and tone of voice and analyze them later. This makes it easier to evaluate and analyze the interview results afterward. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the interviewee's responses into a generating AI, and the generating AI can generate text data.

[0070] The evaluation unit can perform evaluations based on interview results. For example, the evaluation unit can perform evaluations based on the interviewee's responses, facial expressions, and tone of voice. The evaluation unit can evaluate the interviewee's performance according to evaluation criteria. For example, the evaluation unit can analyze the interviewee's responses and assign a score based on evaluation criteria. The evaluation unit can also analyze the interviewee's facial expressions and tone of voice, estimate emotions using an emotion engine, and reflect the results in the evaluation. For example, the evaluation unit can use an emotion engine to estimate emotions from the interviewee's facial expressions and reflect the results in the evaluation. This ensures consistency in evaluation by performing evaluations based on interview results. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the interviewee's responses into a generating AI, and the generating AI can perform the evaluation.

[0071] The service provider can provide interview results to HR personnel. For example, the service provider can provide interview results to HR personnel as text data. The service provider can also provide interview results via email or web application. For example, the service provider can send interview results to HR personnel via email. Alternatively, the service provider can display interview results to HR personnel via a web application. This streamlines the recruitment process by providing interview results to HR personnel. Some or all of the above processing in the service provider may be performed using AI or not. For example, the service provider can input interview results into a generating AI, which can then provide them to HR personnel in an appropriate format.

[0072] The question generation unit can generate questions that test the interviewee's flexibility based on their answers. For example, the question generation unit analyzes the interviewee's answers and generates questions that test their flexibility. The question generation unit uses a generation AI to generate appropriate questions based on the interviewee's answers. For example, the question generation unit inputs the prompt "Generate questions that test the interviewee's flexibility based on their answers" to the generation AI, and the generation AI generates appropriate questions. The question generation unit can also use the generation AI to generate the next question in real time based on the interviewee's answers. This allows for a deeper evaluation by generating questions that test the interviewee's flexibility. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's answers into the generation AI, and the generation AI can generate appropriate questions.

[0073] The personality acquisition unit can acquire the interviewee's enthusiasm and sociability. For example, the personality acquisition unit analyzes the interviewee's facial expressions and tone of voice and estimates emotions using an emotion engine. The emotion engine takes the interviewee's facial expressions and tone of voice as input and outputs emotions. For example, the emotion engine estimates emotions such as joy, sadness, and anger from the interviewee's facial expressions. The personality acquisition unit also uses speech synthesis technology to analyze the interviewee's tone of voice and speaking style and evaluates sociability. The speech synthesis technology takes the interviewee's tone of voice and speaking style as input and outputs sociability. For example, the speech synthesis technology evaluates the level of sociability from the interviewee's tone of voice. By acquiring the interviewee's enthusiasm and sociability, a more accurate personality evaluation becomes possible. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit inputs the interviewee's facial expressions and tone of voice into a generating AI, which can then estimate their emotions.

[0074] The interviewer can estimate the interviewee's emotions and adjust the pace of the interview based on the estimated emotions. For example, if the interviewee is nervous, the interviewer can slow down the pace of the interview and intersperse questions to help them relax. If the interviewee is relaxed, the interviewer can speed up the pace of the interview and proceed with questions efficiently. Furthermore, if the interviewer is anxious, the interviewer can adjust the pace and ask questions to help them calm down. In this way, by adjusting the pace of the interview according to the interviewee's emotions, a more relaxed interview environment can be provided. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewer may be performed using AI or not. For example, the interviewer can input the interviewee's facial expressions and tone of voice into the generative AI, which can estimate emotions and adjust the pace of the interview based on the results.

[0075] The interview department can analyze the candidate's past interview history and select the most appropriate interview method. For example, the interview department can select the most suitable question format based on the candidate's evaluation of past interviews. The interview department can identify the candidate's strengths in question formats from their past interview history and conduct the interview accordingly. Furthermore, the interview department can analyze the candidate's past interview history and adjust the interview to avoid question formats they struggle with. This allows for the selection of a more appropriate interview method by analyzing the candidate's past interview history. Some or all of the above processes in the interview department may be performed using AI or not. For example, the interview department can input the candidate's past interview history into a generating AI, which can then select the most appropriate interview method.

[0076] The interview department can customize questions during the interview based on the interviewee's current work experience and skill set. For example, the interview department can ask questions about specific job duties based on the interviewee's work experience. The interview department can customize technical questions based on the interviewee's skill set. The interview department can also combine the interviewee's work experience and skill set to generate practical questions. This allows for a more specific evaluation by customizing questions based on the interviewee's work experience and skill set. Some or all of the above processes in the interview department may be performed using AI or not. For example, the interview department can input the interviewee's work experience and skill set into a generating AI, which can then generate appropriate questions.

[0077] The interviewer can estimate the interviewee's emotions and adjust the order of interview questions based on the estimated emotions. For example, if the interviewer is nervous, the interviewer can start with simple questions to help them relax. If the interviewer is relaxed, the interviewer can ask important questions earlier. Also, if the interviewer is anxious, the interviewer can start with questions to help them calm down. By adjusting the order of questions according to the interviewee's emotions, a more effective interview becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the interviewer may be performed using AI or not. For example, the interviewer can input the interviewee's facial expressions and tone of voice into a generative AI, which can estimate emotions and adjust the order of questions based on the results.

[0078] The interviewer can prioritize asking relevant questions during the interview, taking into account the interviewee's geographical location. For example, the interviewer can ask questions about companies and industries related to the interviewee's place of residence. Based on the interviewee's geographical location, the interviewer can ask questions about commute time and work location. Furthermore, the interviewer can also ask questions about region-specific issues and challenges, taking the interviewee's geographical location into consideration. This allows for more relevant questions to be asked by considering the interviewee's geographical location. Some or all of the above processes in the interviewer may be performed using AI, or not. For example, the interviewer can input the interviewee's geographical location into a generating AI, which can then generate relevant questions.

[0079] The interviewer can analyze the interviewee's social media activity during the interview and generate relevant questions. For example, the interviewer can ask questions about the interviewee's interests and areas of concern based on their social media activity. The interviewer can also ask questions about the interviewee's personality based on their social media posts and comments. Furthermore, the interviewer can analyze the interviewee's social media activity and ask questions about their past experiences and achievements. This allows for more personalized questions by analyzing the interviewee's social media activity. Some or all of the above processes in the interviewer may be performed using AI or not. For example, the interviewer can input the interviewee's social media activity into a generating AI, which can then generate relevant questions.

[0080] The question generation unit can estimate the interviewee's emotions and adjust the difficulty of the questions based on the estimated emotions. For example, if the interviewee is nervous, the question generation unit can start with easy questions and gradually increase the difficulty. If the interviewee is relaxed, the question generation unit can ask difficult questions earlier. Also, if the interviewee is anxious, the question generation unit can proceed with the questions while adjusting the difficulty. In this way, by adjusting the difficulty of the questions according to the interviewee's emotions, more appropriate questions can be asked. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's facial expressions and tone of voice into the generative AI, which can estimate emotions and adjust the difficulty of the questions based on the results.

[0081] The question generation unit can generate subsequent questions based on the level of detail in the interviewee's answers. For example, if the interviewee's answers are detailed, the question generation unit can generate questions that delve deeper. If the interviewee's answers are concise, the question generation unit can generate questions that request more details. The question generation unit can also generate related questions based on the content of the interviewee's answers. This allows for a deeper evaluation by generating subsequent questions based on the level of detail in the interviewee's answers. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's answers into a generation AI, which can then generate the next question.

[0082] The question generation unit can apply different question algorithms depending on the interviewee's work experience when generating questions. For example, if the interviewee has extensive work experience, the question generation unit can apply an algorithm that generates specialized questions. If the interviewee has limited work experience, the question generation unit can apply an algorithm that generates basic questions. The question generation unit can also select an appropriate question algorithm based on the interviewee's work experience and generate questions accordingly. This allows for more appropriate questions to be asked by applying different question algorithms depending on the interviewee's work experience. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's work experience into a generating AI, which can then apply an appropriate question algorithm to generate questions.

[0083] The question generation unit can estimate the interviewee's emotions and adjust the length of the questions based on the estimated emotions. For example, if the interviewee is nervous, the question generation unit can generate short, concise questions. If the interviewee is relaxed, the question generation unit can generate longer questions that require more detailed explanations. Furthermore, if the interviewee is anxious, the question generation unit can generate short, to-the-point questions. By adjusting the length of questions according to the interviewee's emotions, more appropriate questions can be asked. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's facial expressions and tone of voice into the generative AI, which can estimate emotions and adjust the length of the questions based on the results.

[0084] The question generation unit can determine the priority of questions based on when the interviewee submits their answers. For example, if the interviewee submits their answers early, the question generation unit will prioritize generating the next question. If the interviewee submits their answers late, the question generation unit can prioritize generating important questions. The question generation unit can also adjust the priority of questions based on when the interviewee submits their answers. This allows for more efficient interviews by determining the priority of questions based on when the interviewee submits their answers. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's response submission timing into a generation AI, which can then determine the priority of questions.

[0085] The question generation unit can adjust the order of questions based on the relevance of the interviewee during question generation. For example, the question generation unit can prioritize generating highly relevant questions based on the interviewee's answers. The question generation unit can also generate highly relevant questions in a specific order based on the interviewee's work experience and skill set. Furthermore, the question generation unit can analyze the relevance of the interviewee's answers and determine the optimal question order. This allows for more effective interviews by adjusting the order of questions based on the interviewee's relevance. Some or all of the above processing in the question generation unit may be performed using AI or not. For example, the question generation unit can input the interviewee's answers into a generating AI, which can then adjust the order of the questions.

[0086] The personality acquisition unit can estimate the interviewee's emotions and adjust the personality acquisition method based on the estimated emotions. For example, if the interviewee is nervous, the personality acquisition unit can ask questions to help them relax and acquire their personality. If the interviewee is relaxed, the personality acquisition unit can ask detailed questions to delve deeper into their personality. Furthermore, if the interviewee is anxious, the personality acquisition unit can ask questions to help them calm down and acquire their personality. By adjusting the personality acquisition method according to the interviewee's emotions, a more accurate personality assessment becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit inputs the interviewee's facial expressions and voice tone into a generating AI, which estimates their emotions and adjusts the personality acquisition method based on the results.

[0087] The personality acquisition unit can analyze the interviewee's past behavioral history to select the optimal acquisition method when acquiring personality. For example, the personality acquisition unit can select the optimal question format based on the interviewee's past behavioral history. The personality acquisition unit can identify the interviewee's preferred question format from their past behavioral history and acquire personality based on that. Furthermore, the personality acquisition unit can analyze the interviewee's past behavioral history and adjust the personality acquisition method to avoid question formats they are not good at. This allows for the selection of a more appropriate personality acquisition method by analyzing the interviewee's past behavioral history. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit can input the interviewee's past behavioral history into a generating AI, which can then select the optimal acquisition method.

[0088] The personality acquisition unit can customize the acquisition method based on the interviewee's current living situation when acquiring personality information. For example, the personality acquisition unit can acquire personality information by asking appropriate questions based on the interviewee's current work situation. The personality acquisition unit can acquire personality information by asking relevant questions based on the interviewee's home environment. Furthermore, the personality acquisition unit can acquire personality information by asking optimal questions based on the interviewee's lifestyle habits. By customizing the acquisition method based on the interviewee's current living situation, a more accurate personality assessment becomes possible. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit can input the interviewee's current living situation into a generating AI, which can then customize the optimal acquisition method.

[0089] The personality acquisition unit can estimate the interviewee's emotions and determine the priority of personality based on the estimated emotions. For example, if the interviewee is nervous, the personality acquisition unit will prioritize questions to help them relax. If the interviewee is relaxed, the personality acquisition unit will prioritize detailed questions. Furthermore, if the interviewee is anxious, the personality acquisition unit can also prioritize questions to help them calm down. By determining the priority of personality according to the interviewee's emotions, a more effective personality assessment becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit inputs the interviewee's facial expressions and tone of voice into a generating AI, which estimates their emotions and determines the priority of personality traits based on the results.

[0090] The personality acquisition unit can select the optimal acquisition method when acquiring personality information, taking into account the interviewee's geographical location. For example, the personality acquisition unit can acquire personality information by asking questions related to the interviewee's place of residence. Based on the interviewee's geographical location, the personality acquisition unit can acquire personality information by asking questions about commuting time and workplace. Furthermore, the personality acquisition unit can acquire personality information by asking questions about region-specific problems and issues, taking into account the interviewee's geographical location. This allows for the selection of a more appropriate personality acquisition method by considering the interviewee's geographical location. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit can input the interviewee's geographical location information into a generating AI, which can then select the optimal acquisition method.

[0091] The personality acquisition unit can analyze the interviewee's social media activity and propose methods for acquiring personality. For example, the personality acquisition unit can acquire personality by asking questions about the interviewee's interests and areas of concern based on their social media activity. The personality acquisition unit can also acquire personality by asking questions based on the interviewee's statements and posts on social media. Furthermore, the personality acquisition unit can analyze the interviewee's social media activity and acquire personality by asking questions about their past experiences and achievements. By analyzing the interviewee's social media activity, it is possible to propose more appropriate methods for acquiring personality. Some or all of the above processing in the personality acquisition unit may be performed using AI or not. For example, the personality acquisition unit can input the interviewee's social media activity into a generating AI, which can then propose the optimal acquisition method.

[0092] The recording unit can estimate the interviewee's emotions and adjust the recording method based on the estimated emotions. For example, if the interviewee is nervous, the recording unit can include questions to help them relax. If the interviewee is relaxed, the recording unit can include detailed answers. Also, if the interviewee is anxious, the recording unit can include questions to help them calm down. This allows for more accurate recording by adjusting the recording method according to the interviewee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the interviewee's facial expressions and tone of voice into the generative AI, which can estimate emotions and adjust the recording method based on the results.

[0093] The recording unit can select the optimal recording method by referring to the interviewee's past interview history during recording. For example, the recording unit can select the optimal recording format based on the interviewee's past interview history. The recording unit can identify the interviewee's preferred question formats from their past interview history and record accordingly. The recording unit can also analyze the interviewee's past interview history and adjust the recording method to avoid question formats they are not comfortable with. This allows for the selection of a more appropriate recording method by referring to the interviewee's past interview history. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the interviewee's past interview history into a generating AI, which can then select the optimal recording method.

[0094] The recording unit can estimate the interviewee's emotions and determine the priority of recording based on the estimated emotions. For example, if the interviewee is nervous, the recording unit can prioritize recording questions to help them relax. If the interviewee is relaxed, the recording unit can prioritize recording detailed answers. Also, if the interviewee is anxious, the recording unit can prioritize recording questions to help them calm down. This allows for more effective recording by determining the priority of recording according to the interviewee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the interviewee's facial expressions and tone of voice into the generative AI, which can estimate emotions and determine the priority of recording based on the results.

[0095] The recording unit can select the optimal recording method while considering the interviewee's geographical location information. For example, the recording unit can include questions related to the interviewee's place of residence in the recording. Based on the interviewee's geographical location information, the recording unit can include questions about commuting time and workplace in the recording. Furthermore, the recording unit can also include questions about region-specific problems and issues in the recording, taking the interviewee's geographical location information into consideration. This allows for the selection of a more appropriate recording method by considering the interviewee's geographical location information. Some or all of the above processing in the recording unit may be performed using AI or not. For example, the recording unit can input the interviewee's geographical location information into a generating AI, which can then select the optimal recording method.

[0096] The evaluation unit can estimate the interviewee's emotions and adjust the evaluation criteria based on the estimated emotions. For example, if the interviewee is nervous, the evaluation unit may emphasize answers to questions designed to help them relax. If the interviewee is relaxed, the evaluation unit may emphasize detailed answers. Furthermore, if the interviewee is anxious, the evaluation unit may emphasize answers to questions designed to calm them down. By adjusting the evaluation criteria according to the interviewee's emotions, a more accurate evaluation becomes possible. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit may input the interviewee's facial expressions and tone of voice into the generative AI, which will estimate the emotions and adjust the evaluation criteria based on the results.

[0097] The evaluation unit can select the optimal evaluation method by referring to the interviewee's past interview history during the evaluation process. For example, the evaluation unit can select the optimal evaluation criteria based on the interviewee's past interview history. The evaluation unit can identify the interviewee's preferred question formats from their past interview history and conduct the evaluation based on those. The evaluation unit can also analyze the interviewee's past interview history and adjust the evaluation method to avoid question formats they are not good at. This allows for the selection of a more appropriate evaluation method by referring to the interviewee's past interview history. Some or all of the above processes in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the interviewee's past interview history into a generating AI, which can then select the optimal evaluation method.

[0098] The evaluation unit can estimate the interviewee's emotions and determine evaluation priorities based on the estimated emotions. For example, if the interviewee is nervous, the evaluation unit can prioritize evaluating answers to questions designed to help them relax. If the interviewee is relaxed, the evaluation unit can prioritize evaluating detailed answers. Furthermore, if the interviewee is anxious, the evaluation unit can prioritize evaluating answers to questions designed to calm them down. This allows for a more effective evaluation by determining evaluation priorities according to the interviewee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the interviewee's facial expressions and tone of voice into a generative AI, which can estimate emotions and determine evaluation priorities based on the results.

[0099] The evaluation unit can select the most appropriate evaluation method during the evaluation process, taking into account the interviewee's geographical location information. For example, the evaluation unit can include answers to questions related to the interviewee's place of residence in the evaluation. Based on the interviewee's geographical location information, the evaluation unit can also include answers to questions about commuting time and work location in the evaluation. Furthermore, the evaluation unit can also include answers to questions about region-specific problems and challenges in the evaluation, taking into account the interviewee's geographical location information. This allows for the selection of a more appropriate evaluation method by considering the interviewee's geographical location information. Some or all of the above processing in the evaluation unit may be performed using AI or not. For example, the evaluation unit can input the interviewee's geographical location information into a generating AI, which can then select the most appropriate evaluation method.

[0100] The information provider can estimate the interviewee's emotions and adjust the method of providing information based on the estimated emotions. For example, if the interviewee is nervous, the provider can provide information to help them relax. If the interviewee is relaxed, the provider can provide detailed information. Furthermore, if the interviewee is anxious, the provider can provide information to help them calm down. This allows for more appropriate information provision by adjusting the method of providing information according to the interviewee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the information provider may be performed using AI or not. For example, the information provider can input the interviewee's facial expressions and tone of voice into the generative AI, which will estimate the emotions and adjust the method of providing information based on the results.

[0101] The information delivery unit can select the optimal delivery method by referring to the interviewee's past interview history at the time of delivery. For example, the information delivery unit can select the optimal information delivery format based on the interviewee's past interview history. The information delivery unit can identify the interviewee's preferred information delivery format from their past interview history and provide information based on that. The information delivery unit can also analyze the interviewee's past interview history and adjust the delivery method to avoid information delivery formats they are not good at. This allows for the selection of a more appropriate delivery method by referring to the interviewee's past interview history. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the interviewee's past interview history into a generating AI, which can then select the optimal delivery method.

[0102] The information provider can estimate the interviewee's emotions and prioritize the information to be provided based on the estimated emotions. For example, if the interviewee is nervous, the information provider can prioritize providing information to help them relax. If the interviewee is relaxed, the information provider can prioritize providing detailed information. Furthermore, if the interviewee is anxious, the information provider can prioritize providing information to help them calm down. This allows for more effective information provision by prioritizing the information to be provided according to the interviewee's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the information provider may be performed using AI or not. For example, the information provider can input the interviewee's facial expressions and tone of voice into the generative AI, which will estimate the emotions and determine the priority of the information to be provided based on the results.

[0103] The information delivery unit can select the most appropriate delivery method at the time of delivery, taking into account the interviewee's geographical location information. For example, the information delivery unit can provide information related to the interviewee's place of residence. Based on the interviewee's geographical location information, the information delivery unit can provide information on commuting time and workplace. Furthermore, the information delivery unit can also provide information on region-specific problems and issues, taking into account the interviewee's geographical location information. This allows for the selection of a more appropriate delivery method by considering the interviewee's geographical location information. Some or all of the above processing in the information delivery unit may be performed using AI or not. For example, the information delivery unit can input the interviewee's geographical location information into a generating AI, which can then select the most appropriate delivery method.

[0104] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0105] The interviewing department can generate scenario-based questions related to specific tasks based on the interviewee's past work experience. For example, if the interviewee has project management experience, the department can present specific scenarios related to project progress management and risk management and ask how they would handle them. If the interviewee has a technical background, they can present technical problem-solving scenarios and ask how they would resolve them. Furthermore, if the interviewee has sales experience, they can present scenarios related to customer service and sales strategies and ask how they would handle them. This makes it possible to evaluate the interviewee's practical abilities in a more concrete way.

[0106] The recording unit can record the interviewee's nonverbal communication during the interview. For example, it can record the interviewee's gestures and posture with a camera and analyze that data. It can also track the interviewee's eye movements to evaluate their level of concentration and interest during the interview. Furthermore, it can record the interviewee's hand movements and changes in facial expressions to analyze changes in their emotions in detail. In this way, recording the interviewee's nonverbal communication allows for a more multifaceted evaluation.

[0107] The evaluation department can assess specific skill sets based on the interviewee's responses. For example, to assess leadership skills, questions can be asked about team management experience and methods of demonstrating leadership. Similarly, questions can be asked about past problem-solving experiences and approaches to assess problem-solving abilities. Furthermore, questions can be asked about presentation and negotiation experience to assess communication skills. This allows for a detailed assessment of specific skill sets of the interviewee.

[0108] The service provider can offer a dashboard that visually displays interview results. For example, it can display the interviewee's evaluation score and emotional changes in graphs and charts. It can also centrally display data on the interviewee's responses and nonverbal communication. Furthermore, it can update the interview progress and evaluation details in real time, enabling HR personnel to make quick decisions. This allows for a more intuitive understanding of interview results by visually displaying them.

[0109] The question generation unit can adjust the difficulty level of subsequent questions based on the interviewee's answers. For example, if the interviewee provides a detailed answer, it can generate follow-up questions. Conversely, if the interviewee provides a concise answer, it can generate questions requesting more details. Furthermore, it can also generate questions on related topics based on the interviewee's answers. By adjusting the difficulty level of questions based on the interviewee's responses, a more appropriate evaluation becomes possible.

[0110] The interviewer can estimate the interviewee's emotions and adjust the pace of the interview based on those estimates. For example, if the interviewee is nervous, the interviewer can slow down the pace and include questions to help them relax. If the interviewee is relaxed, the interviewer can speed up the pace and ask questions more efficiently. If the interviewee is anxious, the interviewer can also adjust the pace and ask questions to help them calm down. By adjusting the pace of the interview according to the interviewee's emotions, a more relaxed interview environment can be provided.

[0111] The question generation unit can estimate the interviewee's emotions and adjust the difficulty of the questions based on those emotions. For example, if the interviewee is nervous, the system can start with easy questions and gradually increase their difficulty. If the interviewee is relaxed, it can ask more difficult questions sooner. Furthermore, if the interviewee is anxious, the system can adjust the difficulty of the questions as it progresses. This allows for more appropriate questioning by adjusting the difficulty of the questions according to the interviewee's emotions.

[0112] The personality assessment unit can estimate the interviewee's emotions and adjust the personality assessment method based on those estimates. For example, if the interviewee is nervous, it can ask questions to help them relax and then assess their personality. If the interviewee is relaxed, it can ask more detailed questions to delve deeper into their personality. Similarly, if the interviewee is anxious, it can ask questions to help them calm down and then assess their personality. By adjusting the personality assessment method according to the interviewee's emotions, a more accurate personality evaluation becomes possible.

[0113] The recording unit can estimate the interviewee's emotions and adjust the recording method based on the estimated emotions. For example, if the interviewee is nervous, questions to help them relax can be included in the recording. If the interviewee is relaxed, detailed answers can be included in the recording. Also, if the interviewee is anxious, questions to help them calm down can be included in the recording. In this way, by adjusting the recording method according to the interviewee's emotions, more accurate recording becomes possible.

[0114] The evaluation unit can estimate the interviewee's emotions and adjust the evaluation criteria based on those estimates. For example, if the interviewee is nervous, emphasis can be placed on answers to questions designed to help them relax. If the interviewee is relaxed, emphasis can be placed on detailed answers. Similarly, if the interviewee is anxious, emphasis can be placed on answers to questions designed to calm them down. By adjusting the evaluation criteria according to the interviewee's emotions, a more accurate evaluation becomes possible.

[0115] The following briefly describes the processing flow for example form 2.

[0116] Step 1: The interview department will eliminate application forms and conduct interviews using AI. The interview department will be able to ask basic questions such as the applicant's educational background, motivation for applying, and self-introduction. The interview department will also work in conjunction with a question generation department that generates follow-up questions in real time based on the applicant's answers. Step 2: The question generation unit can generate questions that test flexibility based on the interviewee's answers. The question generation unit uses a generation AI to analyze the interviewee's answers and generate appropriate questions. For example, the question generation unit can input a prompt to the generation AI such as, "Generate questions that test flexibility based on the interviewee's answers," and the generation AI will generate appropriate questions. The question generation unit can also use the generation AI to generate the next question in real time based on the interviewee's answers. Step 3: The personality acquisition unit uses an emotion engine and speech synthesis technology to acquire personality traits such as enthusiasm and sociability from the interviewee. The personality acquisition unit analyzes the interviewee's facial expressions and tone of voice and estimates emotions using the emotion engine. The emotion engine takes the interviewee's facial expressions and tone of voice as input and outputs emotions. For example, the emotion engine estimates emotions such as joy, sadness, and anger from the interviewee's facial expressions. The personality acquisition unit also uses speech synthesis technology to analyze the interviewee's tone of voice and speaking style and evaluates sociability. The speech synthesis technology takes the interviewee's tone of voice and speaking style as input and outputs sociability. For example, the speech synthesis technology evaluates the level of sociability from the interviewee's tone of voice.

[0117] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0118] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0119] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0120] Each of the multiple elements described above, including the interview unit, question generation unit, personality acquisition unit, recording unit, evaluation unit, and provision unit, is implemented by at least one of the smart device 14 and the data processing unit 12. For example, the interview unit is implemented by the control unit 46A of the smart device 14 and can ask basic items such as the interviewee's educational background and motivation for applying. The question generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates follow-up questions in real time based on the interviewee's answers. The personality acquisition unit analyzes the interviewee's facial expressions and tone of voice using the camera 42 and microphone 38B of the smart device 14 and estimates emotions using an emotion engine. The recording unit records the interviewee's answers using the camera 42 and microphone 38B of the smart device 14 and converts them into text data using the identification processing unit 290 of the data processing unit 12. The evaluation unit evaluates the interviewee's performance using the identification processing unit 290 of the data processing unit 12. The data processing unit provides the interview results to the human resources personnel via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0121] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0122] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0123] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0124] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0125] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0126] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0127] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0128] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0129] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0130] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0131] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0132] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0133] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0134] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0135] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0136] Each of the multiple elements described above, including the interview unit, question generation unit, personality acquisition unit, recording unit, evaluation unit, and provision unit, is implemented by at least one of the smart glasses 214 and the data processing unit 12. For example, the interview unit is implemented by the control unit 46A of the smart glasses 214 and can ask basic items such as the interviewee's educational background and motivation for applying. The question generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates follow-up questions in real time based on the interviewee's answers. The personality acquisition unit analyzes the interviewee's facial expressions and tone of voice using the camera 42 and microphone 238 of the smart glasses 214 and estimates emotions using an emotion engine. The recording unit records the interviewee's answers using the camera 42 and microphone 238 of the smart glasses 214 and converts them into text data using the identification processing unit 290 of the data processing unit 12. The evaluation unit evaluates the interviewee's performance using the identification processing unit 290 of the data processing unit 12. The data processing unit provides the interview results to the human resources personnel via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0137] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0138] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0139] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0140] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0141] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0142] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0143] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0144] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0145] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0146] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0147] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0148] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0149] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0150] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0151] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0152] Each of the multiple elements described above, including the interview unit, question generation unit, personality acquisition unit, recording unit, evaluation unit, and provision unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the interview unit is implemented by the control unit 46A of the headset terminal 314 and can ask basic items such as the interviewee's educational background and motivation for applying. The question generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates follow-up questions in real time based on the interviewee's answers. The personality acquisition unit analyzes the interviewee's facial expressions and tone of voice using the camera 42 and microphone 238 of the headset terminal 314 and estimates emotions using an emotion engine. The recording unit records the interviewee's answers using the camera 42 and microphone 238 of the headset terminal 314 and converts them into text data using the identification processing unit 290 of the data processing unit 12. The evaluation unit evaluates the interviewee's performance using the identification processing unit 290 of the data processing unit 12. The data processing unit provides the interview results to the human resources personnel via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0153] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0154] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0155] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0156] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0157] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0158] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0159] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0160] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0161] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0162] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0163] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0164] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0165] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0166] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0167] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0168] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0169] Each of the multiple elements described above, including the interview unit, question generation unit, personality acquisition unit, recording unit, evaluation unit, and provision unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the interview unit is implemented by the control unit 46A of the robot 414 and can ask basic items such as the interviewee's educational background and motivation for applying. The question generation unit is implemented by the identification processing unit 290 of the data processing unit 12 and generates follow-up questions in real time based on the interviewee's answers. The personality acquisition unit analyzes the interviewee's facial expressions and tone of voice using the camera 42 and microphone 238 of the robot 414 and estimates emotions using an emotion engine. The recording unit records the interviewee's answers using the camera 42 and microphone 238 of the robot 414 and converts them into text data using the identification processing unit 290 of the data processing unit 12. The evaluation unit evaluates the interviewee's performance using the identification processing unit 290 of the data processing unit 12. The data processing unit provides the interview results to the human resources personnel via the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0170] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0171] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0172] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0173] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0174] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0175] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0176] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0177] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0178] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0179] 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.

[0180] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0181] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0182] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0183] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0184] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0185] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0186] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0187] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0188] (Note 1) They abolished the application form and created an interview department where AI conducts interviews. A question generation unit that generates in-depth questions in real time during an interview conducted by the aforementioned interview unit, The system includes a personality acquisition unit that acquires personality information during an interview conducted by the aforementioned interview unit, utilizing an emotion engine and speech synthesis technology. A system characterized by the following features. (Note 2) It is equipped with a recording unit for recording interview results. The system described in Appendix 1, characterized by the features described herein. (Note 3) The company has an evaluation department that conducts evaluations based on interview results. The system described in Appendix 1, characterized by the features described herein. (Note 4) The facility includes a department that provides interview results to HR personnel. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned question generation unit, Generate questions that test flexibility based on the interviewee's answers. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned personality acquisition unit is To acquire the interviewee's enthusiasm and social skills. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned interview department, The interviewee's emotions are estimated, and the pace of the interview is adjusted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned interview department, Analyze the interviewee's past interview history and select the most suitable interview method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned interview department, During the interview, customize the questions based on the interviewee's current work experience and skill set. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned interview department, The interviewee's emotions are estimated, and the order of interview questions is adjusted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned interview department, During the interview, we prioritize asking questions that are highly relevant to the interviewee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned interview department, During the interview, we analyze the interviewee's social media activity and generate relevant questions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned question generation unit, The interviewee's emotions are estimated, and the difficulty of the questions is adjusted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned question generation unit, When generating questions, the next question is generated based on the level of detail in the interviewee's answers. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned question generation unit, When generating questions, different question algorithms are applied depending on the interviewee's work experience. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question generation unit, The interviewee's emotions are estimated, and the length of the questions is adjusted based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned question generation unit, When generating questions, the priority of questions is determined based on when the interviewee submitted their answers. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned question generation unit, When generating questions, adjust the order of questions based on the relevance of the interviewee. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned personality acquisition unit is The interviewee's emotions are estimated, and the method of acquiring personality traits is adjusted based on these estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned personality acquisition unit is When acquiring personality traits, the interviewee's past behavioral history is analyzed to select the most suitable acquisition method. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned personality acquisition unit is When acquiring personality traits, the acquisition method is customized based on the interviewee's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned personality acquisition unit is The system estimates the interviewee's emotions and prioritizes personality traits based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned personality acquisition unit is When acquiring personality data, the most suitable acquisition method is selected considering the interviewee's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned personality acquisition unit is When acquiring personality traits, we analyze the interviewee's social media activity and propose methods for obtaining those traits. The system described in Appendix 1, characterized by the features described herein. (Note 25) The recording unit is, Estimate the interviewee's emotions and adjust the recording method based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 26) The recording unit is, During recording, the most suitable recording method is selected by referring to the interviewee's past interview history. The system described in Appendix 2, characterized by the features described herein. (Note 27) The recording unit is, The interviewee's emotions are estimated, and the priority of the recording is determined based on the estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 28) The recording unit is, When recording, the most suitable recording method is selected, taking into account the interviewee's geographical location. The system described in Appendix 2, characterized by the features described herein. (Note 29) The evaluation unit described above, The interviewee's emotions are estimated, and the evaluation criteria are adjusted based on these estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 30) The evaluation unit described above, During the evaluation process, the most suitable evaluation method is selected by referring to the interviewee's past interview history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The evaluation unit described above, The system estimates the interviewee's emotions and determines evaluation priorities based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The evaluation unit described above, During the evaluation process, the most appropriate evaluation method will be selected, taking into account the interviewee's geographical location. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned supply unit is, We estimate the interviewee's emotions and adjust the method of providing information based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned supply unit is, When providing the information, the most suitable method of delivery is selected by referring to the interviewee's past interview history. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned supply unit is, The system estimates the interviewee's emotions and prioritizes the information to be provided based on those estimated emotions. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned supply unit is, When providing the information, the most suitable method of delivery will be selected, taking into account the geographical location of the interviewee. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0189] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. We have abolished the application form and created an interview department where AI conducts interviews. A question generation unit that generates in-depth questions in real time during an interview conducted by the aforementioned interview unit, The system includes a personality acquisition unit that acquires personality information during an interview conducted by the aforementioned interview unit, utilizing an emotion engine and speech synthesis technology. A system characterized by the following features.

2. It is equipped with a recording unit for recording interview results. The system according to feature 1.

3. The company has an evaluation department that conducts evaluations based on interview results. The system according to feature 1.

4. The facility includes a department that provides interview results to HR personnel. The system according to feature 1.

5. The aforementioned question generation unit, Generate questions that test flexibility based on the interviewee's answers. The system according to feature 1.

6. The aforementioned personality acquisition unit is To acquire the interviewee's enthusiasm and social skills. The system according to feature 1.

7. The aforementioned interview department, The interviewee's emotions are estimated, and the pace of the interview is adjusted based on those estimated emotions. The system according to feature 1.

8. The aforementioned interview department, Analyze the interviewee's past interview history and select the most suitable interview method. The system according to feature 1.

9. The aforementioned interview department, During the interview, customize the questions based on the interviewee's current work experience and skill set. The system according to feature 1.

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