system

Abstract

The system according to this embodiment aims to efficiently collect and evaluate candidate information, transcending language and cultural barriers. [Solution] The system according to the embodiment comprises a reception unit, a question generation unit, a translation unit, an answer presentation unit, and a cultural consideration unit. The reception unit receives self-introductions and skill introductions from candidates. The question generation unit generates questions based on the information received by the reception unit. The translation unit translates the questions generated by the question generation unit in real time. The answer presentation unit presents appropriate answers to the questions translated by the translation unit. The cultural consideration unit makes minor adjustments to the questions based on the answers presented by the answer presentation unit.

Description

Technical Field

[0004] ,

[0006] , , , , , ,

[0005] , , , , ,

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

Background Art

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

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there are problems that there are language and cultural barriers during interviews with overseas candidates, and it is difficult to generate appropriate questions and answers.

[0005] The system according to the embodiment aims to efficiently collect and evaluate candidate information across language and cultural barriers.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a question generation unit, a translation unit, an answer presentation unit, and a cultural consideration unit. The reception unit receives self-introductions and skill introductions from candidates. The question generation unit generates questions based on the information received by the reception unit. The translation unit translates the questions generated by the question generation unit in real time. The answer presentation unit presents appropriate answers to the questions translated by the translation unit. The cultural consideration unit makes minor adjustments to the questions based on the answers presented by the answer presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can efficiently collect and evaluate candidate information, transcending language and cultural barriers. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example of form 1) An interview support system according to an embodiment of the present invention is a system that uses AI to generate questions on demand during an interview and translates them between two languages ​​in real time. When a candidate introduces themselves or their skills, the AI ​​immediately generates appropriate questions based on that information. The generated questions are translated between two languages ​​in real time, enabling smooth communication between the interviewer and the candidate. Furthermore, the system also provides appropriate answers to each question and makes minor adjustments to the questions to show respect for the cultural background of each country. This allows the interviewer to efficiently obtain information that transcends not only language but also cultural barriers, enabling a deeper evaluation. For example, a candidate introduces themselves or their skills. At this time, the candidate talks about their background and skills. For example, "I have 5 years of experience in software development, and I have a particular strength in AI technology." This information is input into the AI. Next, the AI ​​analyzes the input information and generates appropriate questions. For example, a question such as "Please tell me more about the projects you have worked on in the past" is generated. Since this question is generated based on the candidate's skills and experience, the interviewer can immediately obtain effective questions. The generated questions are translated between two languages ​​in real time. For example, questions generated in Japanese are translated into English. This translation is done by AI, making it fast and accurate. This allows for smooth communication between the interviewer and the candidate. Furthermore, appropriate answers to each question are also provided. For example, in response to the question, "Please tell me more about the projects you have worked on in the past," an answer such as, "I developed an image recognition system using AI" is provided. This makes it easier for the interviewer to evaluate the candidate's answers. In addition, questions are slightly modified to show respect for the cultural background of each country. For example, direct questions are preferred for American candidates, but more polite expressions are required for Japanese candidates. Such cultural considerations allow interviewers to evaluate candidates while taking their cultural background into account. As a result, interviewers can efficiently obtain information that transcends not only language barriers but also cultural barriers, enabling a deeper evaluation.For example, it's possible to evaluate not only a candidate's skills and experience, but also their cultural background and communication style. This enables more accurate character assessment, dramatically improving the quality and efficiency of recruitment. As a result, the interview support system can generate appropriate questions based on the candidate's self-introduction and skills presentation, translate them in real time, and ask questions that take cultural background into account.

[0029] The interview support system according to this embodiment comprises a reception unit, a question generation unit, a translation unit, an answer presentation unit, and a cultural consideration unit. The reception unit receives the candidate's self-introduction and skill introduction. The candidate's self-introduction and skill introduction may include, but are not limited to, text format, audio format, or video format. For example, the reception unit accepts the candidate entering a self-introduction in text format. The reception unit may also accept the candidate recording a self-introduction in audio format. Furthermore, the reception unit may accept the candidate recording a self-introduction in video format. For example, the reception unit provides an input form when the candidate enters a self-introduction in text format. When recording a self-introduction in audio format, it provides a recording function. When recording a self-introduction in video format, it provides a recording function. The question generation unit generates questions based on the information received by the reception unit. For example, the question generation unit generates questions based on the candidate's skills and experience. For example, the question generation unit generates questions such as, "Please tell me more about the projects the candidate has worked on in the past." The question generation unit may also generate questions based on the candidate's career history. For example, the question generation unit generates questions based on the candidate's background, such as "Please tell me more about your past work experience." The question generation unit can also generate questions based on the candidate's skills. For example, the question generation unit can generate questions based on the candidate's skills, such as "Please tell me more about this specific technology." The translation unit translates the questions generated by the question generation unit in real time. For example, the translation unit translates the generated questions from Japanese to English. For example, when translating the generated questions from Japanese to English, the translation unit uses AI to translate quickly and accurately. The translation unit can also translate the generated questions from English to Japanese. For example, when translating the generated questions from English to Japanese, the translation unit uses AI to translate quickly and accurately. Furthermore, the translation unit can translate the generated questions into other languages. For example, the translation unit can translate the generated questions into French or German. The answer presentation unit presents appropriate answers to the questions translated by the translation unit.The answer-providing unit, for example, provides appropriate answers to each question. For instance, in response to the question, "Please tell me more about the projects you have worked on in the past," the answer-providing unit might provide an answer such as, "I developed an image recognition system using AI." It could also provide an answer such as, "I have five years of experience in software development," in response to the question, "Please tell me more about your past work experience." Furthermore, it could provide an answer such as, "I have a strong background in AI technology," in response to the question, "Please tell me more about a specific technology." The cultural considerations unit then fine-tunes the questions based on the answers provided by the answer-providing unit. For example, the cultural considerations unit might revise questions to show respect for the cultural background of each country. For instance, it might suggest that direct questions are preferred for American candidates, while more polite language is required for Japanese candidates. This cultural consideration allows interviewers to evaluate candidates while taking their cultural background into account. As a result, the interview support system according to this embodiment can generate appropriate questions based on the candidate's self-introduction and skills presentation, translate them in real time, and ask questions that take cultural background into account.

[0030] The reception desk accepts self-introductions and skill descriptions from candidates. These self-introductions and skill descriptions may include, but are not limited to, text, audio, and video formats. For example, the reception desk may accept self-introductions entered in text format. It may also accept self-introductions recorded in audio format. Furthermore, it may accept self-introductions recorded in video format. For example, the reception desk provides an input form when a candidate enters a self-introduction in text format. When a candidate records a self-introduction in audio format, it provides a recording function. When a candidate records a self-introduction in video format, it provides a recording function. This allows the reception desk to choose the means by which candidates can most effectively communicate their strengths and experience. In addition, the reception desk has the ability to automatically categorize the information submitted by candidates and save it in the appropriate format. For example, text-based self-introductions are saved directly to the database, and audio-based self-introductions are saved as audio files. Video-based self-introductions are not only saved as video files, but may also be converted to text using speech recognition technology as needed. This allows interviewers to easily search for candidate information and quickly obtain the necessary details. Furthermore, the reception department assists candidates in providing appropriate information by offering guidelines and samples when submitting their self-introductions. For example, they provide advice on points to emphasize and expressions to avoid during the self-introduction. This allows candidates to showcase their strengths to the fullest, and interviewers to accurately assess the candidates' suitability.

[0031] The question generation unit generates questions based on the information received by the reception unit. For example, the question generation unit generates questions based on the candidate's skills and experience. For instance, it might generate a question such as, "Please tell me more about the projects the candidate has worked on in the past." The question generation unit can also generate questions based on the candidate's background. For example, it might generate a question such as, "Please tell me more about your past work experience." The question generation unit can also generate questions based on the candidate's skills. For example, it might generate a question such as, "Please tell me more about this specific technology." The question generation unit uses AI to analyze candidate information and generate optimal questions. The AI ​​uses natural language processing technology to analyze the candidate's self-introduction and skills description, extracting relevant keywords and phrases. This allows the question generation unit to generate specific questions based on the candidate's strengths and experience. Furthermore, the question generation unit can learn from past interview data and success stories to generate effective question patterns. For example, it can analyze what questions were effective for candidates with a specific skill set and generate new questions based on the results. This allows the question generation unit to consistently provide effective questions based on the latest information. Furthermore, the unit can receive feedback from interviewers and continuously improve the accuracy and effectiveness of its questions. For example, it can record how interviewers respond to specific questions and adjust the content and format of the questions based on that data. This enables the question generation unit to improve the quality of interviews and help accurately assess candidates' suitability.

[0032] The translation unit translates questions generated by the question generation unit in real time. For example, the translation unit translates generated questions from Japanese to English. For example, when translating generated questions from Japanese to English, the translation unit uses AI to perform the translation quickly and accurately. The translation unit can also translate generated questions from English to Japanese. For example, when translating generated questions from English to Japanese, the translation unit uses AI to perform the translation quickly and accurately. Furthermore, the translation unit can translate generated questions into other languages. For example, the translation unit can translate generated questions into French or German. The translation unit uses AI and natural language processing technology to perform translations that accurately capture the context and nuances. This enables smooth communication even if the candidate and interviewer speak different languages. In addition, the translation unit utilizes specialized dictionaries and glossaries to handle technical terms and industry-specific expressions. For example, in interviews for technical positions, there are many questions about specific programming languages ​​and technologies, so it is necessary to accurately translate these technical terms. The translation unit appropriately translates these technical terms to prevent misunderstandings between the candidate and the interviewer. Furthermore, the translation department continuously improves its translation accuracy by learning from past translation data and refining its translation algorithms. For example, it analyzes questions used in past interviews and their translation results to evaluate which translations were most effective. This allows the translation department to always provide highly accurate translations based on the latest information. In addition, the translation department utilizes servers and cloud services with high processing power to provide translation results in real time. This enables the delivery of fast and accurate translations without disrupting the progress of the interview.

[0033] The answer-providing unit presents appropriate answers to questions translated by the translation unit. For example, the answer-providing unit provides appropriate answers to each question. For instance, in response to the question, "Please tell me more about the projects you have worked on in the past," the answer-providing unit might provide an answer such as, "I developed an image recognition system using AI." It could also respond to the question, "Please tell me more about your past work experience," with an answer such as, "I have five years of experience in software development." Furthermore, it could respond to the question, "Please tell me more about a specific technology," with an answer such as, "I have a strong advantage in AI technology." The answer-providing unit uses AI to analyze candidate information and past interview data to generate optimal answers. The AI ​​uses natural language generation technology to generate specific answers based on the candidate's skills and experience. This allows candidates to effectively highlight their strengths, and interviewers to accurately assess the candidate's suitability. Additionally, the answer-providing unit evaluates the candidate's answers in real time and can generate additional or follow-up questions as needed. For example, if a candidate explains a specific project in detail, the system can generate additional questions related to that project to further explore the candidate's understanding and experience. The answer generation system can also analyze the candidate's responses and provide interviewers with evaluation points and areas to pay attention to. This allows interviewers to effectively evaluate the candidate's responses and provide appropriate feedback. Furthermore, the answer generation system can learn from past interview data and continuously improve the quality of its responses. For instance, it can analyze response patterns that received high marks in past interviews and generate new responses based on those results. This ensures that the answer generation system always provides high-quality responses based on the latest information.

[0034] The Cultural Considerations Department makes minor adjustments to questions based on the answers provided by the Answer Presentation Department. For example, the Cultural Considerations Department makes minor adjustments to questions to show respect for the cultural background of each country. For instance, the Cultural Considerations Department might determine that direct questions are preferred for American candidates, while more polite language is required for Japanese candidates. This cultural consideration allows interviewers to evaluate candidates while taking their cultural background into account. The Cultural Considerations Department uses AI to learn the cultural backgrounds and communication styles of each country and generate the most appropriate question format. For example, it generates specific and direct questions for American candidates and polite and indirect questions for Japanese candidates. This allows candidates to answer questions that are appropriate to their cultural background in a relaxed manner. The Cultural Considerations Department also provides interviewers with cultural background-based advice. For example, it explains what expressions and attitudes are preferred in certain cultures, helping interviewers to respond appropriately to candidates. Furthermore, the Cultural Considerations Department analyzes past interview data to learn patterns of questions and answers based on cultural background. This allows the Cultural Considerations Department to always provide the most appropriate question format based on the latest information. Furthermore, the Cultural Considerations Department can provide real-time advice based on cultural background during the interview. For example, it can advise interviewers in real time on how to respond to specific questions, improving the quality of the interview. This allows the Cultural Considerations Department to facilitate smooth communication between candidates and interviewers, increasing the success rate of the interview.

[0035] The question generation unit can generate questions based on the candidate's skills and experience. For example, based on the candidate's skills and experience, it can generate questions such as, "Please tell me more about the projects you have worked on in the past." The question generation unit can also generate questions such as, "Please tell me more about your past work experience," based on the candidate's background. Furthermore, the question generation unit can generate questions such as, "Please tell me more about a specific technology," based on the candidate's skills. This allows for the generation of appropriate questions based on the candidate's skills and experience. Some or all of the above processing in the question generation unit may be performed using AI, for example, or not using AI. For example, the question generation unit can input information about the candidate's skills and experience into an AI, which can then analyze that information to generate appropriate questions.

[0036] The translation unit can translate generated questions between two languages ​​in real time. For example, the translation unit can translate generated questions from Japanese to English. When translating generated questions from Japanese to English, for example, the translation unit uses AI to perform the translation quickly and accurately. The translation unit can also translate generated questions from English to Japanese. For example, when translating generated questions from English to Japanese, the translation unit uses AI to perform the translation quickly and accurately. Furthermore, the translation unit can also translate generated questions into other languages. For example, the translation unit can translate generated questions into French or German. This enables real-time translation between two languages ​​of generated questions. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input a generated question into AI, which can then translate the question quickly and accurately.

[0037] The answer-presenting unit can provide appropriate answers to each question. For example, in response to the question, "Please tell me more about the projects you have worked on in the past," the answer-presenting unit might provide the answer, "I developed an image recognition system using AI." For example, in response to the question, "Please tell me more about your past work experience," the answer-presenting unit might provide the answer, "I have five years of experience in software development." Furthermore, in response to the question, "Please tell me more about a specific technology," the answer-presenting unit might provide the answer, "I have a strong background in AI technology." In this way, it can provide appropriate answers to each question. Some or all of the above processing in the answer-presenting unit may be performed using AI, for example, or not using AI. For example, the answer-presenting unit can input appropriate answers to each question into an AI, which can then analyze the answers and provide appropriate responses.

[0038] The Cultural Considerations Department can make minor adjustments to questions to show respect for each country's cultural background. For example, the Cultural Considerations Department might prefer direct questions for American candidates, while more polite language is required for Japanese candidates. Such cultural considerations allow interviewers to evaluate candidates while taking their cultural background into account. This enables the department to make minor adjustments to questions to show respect for each country's cultural background. Some or all of the above processing by the Cultural Considerations Department may be performed using AI, for example, or not. For example, the Cultural Considerations Department can input information about a candidate's cultural background into an AI, which can then analyze that information and make appropriate minor adjustments to the questions.

[0039] The reception department can analyze a candidate's past interview history and select the most suitable reception method. For example, if a candidate has previously preferred online interviews, the reception department will prioritize online reception. If a candidate has previously preferred in-person interviews, the reception department can also prioritize in-person reception. Furthermore, if a candidate has previously had interviews at a specific time slot, the reception department can schedule the reception accordingly. This allows the reception department to select the most suitable reception method based on the candidate's past interview history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the candidate's past interview history data into an AI, which can then analyze the data to select the most suitable reception method.

[0040] The reception desk can filter candidates based on their current work situation and areas of interest when receiving self-introductions and skill introductions. For example, when a candidate enters their current work situation, the reception desk can prioritize receiving skill introductions related to that job. The reception desk can also prioritize receiving self-introductions related to an area of ​​interest when a candidate enters an area of ​​interest. Furthermore, if a candidate expresses interest in a particular industry, the reception desk can prioritize receiving information related to that industry. This allows for filtering based on the candidate's current work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input information about the candidate's work situation and areas of interest into an AI, which can then analyze and filter that information.

[0041] The reception desk can prioritize receiving highly relevant information when receiving self-introductions and skill introductions, taking into account the candidate's geographical location. For example, if a candidate lives in a specific region, the reception desk can prioritize receiving information related to that region. If a candidate lives in a specific city, the reception desk can also prioritize receiving information related to that city. Furthermore, if a candidate lives in a specific country, the reception desk can prioritize receiving information related to that country. This allows for the priority of receiving highly relevant information based on the candidate's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the candidate's geographical location information into an AI, which can then analyze that information and prioritize receiving highly relevant information.

[0042] The reception desk can analyze a candidate's social media activity and receive relevant information when they submit self-introductions and skill introductions. For example, the reception desk can receive skill introductions based on projects the candidate has shared on social media. The reception desk can also receive self-introductions based on areas the candidate has shown interest in on social media. Furthermore, the reception desk can receive information based on the industries the candidate follows on social media. This allows the reception desk to receive relevant information based on the candidate's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the candidate's social media activity data into an AI, which can then analyze the data and receive relevant information.

[0043] The question generation unit can adjust the level of detail of questions based on the importance of the candidate's skills and experience when generating questions. For example, if the candidate has high skills, the question generation unit will generate detailed questions. If the candidate has extensive experience, the question generation unit can also generate questions about specific projects. Furthermore, if the candidate has limited skills and experience, the question generation unit can generate basic questions. This allows for adjustment of the level of detail of questions based on the importance of the candidate's skills and experience. Some or all of the above processing in the question generation unit may be performed using AI, for example, or without AI. For example, the question generation unit can input information about the candidate's skills and experience into the AI, which can then analyze that information to adjust the level of detail of the questions.

[0044] The question generation unit can apply different question generation algorithms depending on the candidate's job category when generating questions. For example, the question generation unit can apply an algorithm that generates technical questions to candidates for technical positions. For example, the question generation unit can also apply an algorithm that generates leadership-related questions to candidates for management positions. Furthermore, the question generation unit can apply an algorithm that generates creativity-related questions to candidates for creative positions. This allows for the application of different question generation algorithms depending on the candidate's job category. Some or all of the above processing in the question generation unit may be performed using AI, for example, or without AI. For example, the question generation unit can input information about the candidate's job category into the AI, which can then analyze that information and apply an appropriate question generation algorithm.

[0045] The question generation unit can prioritize questions based on when the candidate submitted their self-introduction and skills introduction. For example, if the candidate recently submitted a self-introduction, the question generation unit will prioritize questions based on its content. For example, if the candidate has previously submitted a skills introduction, the question generation unit can also prioritize questions based on its content. Furthermore, the question generation unit can prioritize questions based on information submitted by the candidate at a specific time. This allows the question generation unit to determine the priority of questions based on when the candidate submitted their self-introduction and skills introduction. Some or all of the above processing in the question generation unit may be performed using AI, for example, or not. For example, the question generation unit can input information about the candidate's submission timing into the AI, which can then analyze that information to determine the priority of questions.

[0046] The question generation unit can adjust the order of questions based on the candidate's relevance during question generation. For example, the question generation unit may generate questions related to the candidate's skills first. It may also generate questions related to the candidate's experience next. Furthermore, it may generate questions related to the candidate's interests last. This allows the order of questions to be adjusted based on the candidate's relevance. Some or all of the above processing in the question generation unit may be performed using AI, for example, or not using AI. For example, the question generation unit can input information about the candidate's relevance into the AI, which can then analyze that information to adjust the order of questions.

[0047] The translation unit can improve the accuracy of its translations by considering the relationships between questions during the translation process. For example, the translation unit can perform contextually appropriate translations by considering the context of the questions. For example, the translation unit can also select appropriate terminology by considering the relevance of the questions. Furthermore, the translation unit can analyze the content of the questions to produce a consistent translation. This allows for improved translation accuracy by considering the relationships between questions. Some or all of the above processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input information about the relationships between questions into the AI, which can then analyze that information to improve translation accuracy.

[0048] The translation department can perform translations while considering the candidate's attribute information. For example, the translation department can translate using appropriate language according to the candidate's age. For example, the translation department can also translate specialized terminology appropriately according to the candidate's job duties. Furthermore, the translation department can perform culturally appropriate translations according to the candidate's cultural background. This allows for appropriate translations based on the candidate's attribute information. Some or all of the above processes in the translation department may be performed using AI, for example, or not. For example, the translation department can input the candidate's attribute information into AI, which can then analyze that information and perform an appropriate translation.

[0049] The translation unit can consider the geographical distribution of questions when translating. For example, if a question relates to a specific region, the translation unit will consider the language and dialect of that region when translating. If a question is international, the translation unit can also translate it into a standard language. Furthermore, if a question relates to a specific city, the translation unit can consider the culture and language of that city when translating. This allows for appropriate translations that take into account the geographical distribution of questions. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input information about the geographical distribution of questions into an AI, which can then analyze that information to produce an appropriate translation.

[0050] The translation department can improve the accuracy of its translations by referring to relevant literature related to the question during the translation process. For example, the translation department can refer to academic papers related to the question to appropriately translate technical terms. The translation department can also refer to industry reports related to the question to appropriately translate industry terminology. Furthermore, the translation department can refer to books related to the question to provide contextually appropriate translations. This allows for improved translation accuracy by referring to relevant literature related to the question. Some or all of the above processes in the translation department may be performed using AI, for example, or not. For example, the translation department can input information about relevant literature related to the question into an AI, which can then analyze that information to improve translation accuracy.

[0051] The response presentation unit can analyze the candidate's past response history to present the most suitable response. For example, the response presentation unit can present the most suitable response based on the candidate's past responses. For example, the response presentation unit can also present relevant responses from the candidate's past response history. Furthermore, the response presentation unit can analyze the candidate's past response history to present a consistent response. This allows the response presentation unit to present the most suitable response based on the candidate's past response history. Some or all of the above processing in the response presentation unit may be performed using AI, for example, or without AI. For example, the response presentation unit can input the candidate's past response history data into AI, which can then analyze the data to present the most suitable response.

[0052] The response presentation unit can customize the means of providing responses based on the candidate's current job situation when providing responses. For example, when a candidate inputs their current job situation, the response presentation unit can provide responses related to that job. For example, the response presentation unit can also provide responses regarding relevant projects or tasks based on the candidate's current job situation. Furthermore, the response presentation unit can provide responses regarding appropriate skills and experience based on the candidate's current job situation. This allows for the provision of appropriate responses based on the candidate's current job situation. Some or all of the above processing in the response presentation unit may be performed using AI, for example, or not using AI. For example, the response presentation unit can input information about the candidate's job situation into AI, and the AI ​​can analyze that information to customize the means of providing responses.

[0053] The response presentation unit can present the most suitable response by considering the candidate's geographical location information when presenting a response. For example, if the candidate lives in a specific region, the response presentation unit can present a response related to that region. For example, if the candidate lives in a specific city, the response presentation unit can also present a response related to that city. Furthermore, if the candidate lives in a specific country, the response presentation unit can also present a response related to that country. This allows the response presentation unit to present the most suitable response based on the candidate's geographical location information. Some or all of the above processing in the response presentation unit may be performed using AI, for example, or without AI. For example, the response presentation unit can input the candidate's geographical location information into AI, which can then analyze that information and present the most suitable response.

[0054] The response generation unit can analyze the candidate's social media activity and suggest a response method when generating a response. For example, the response generation unit can generate a response based on projects the candidate has shared on social media. The response generation unit can also generate a response based on areas the candidate has shown interest in on social media. Furthermore, the response generation unit can generate a response based on industries the candidate follows on social media. This allows for the generation of appropriate responses based on the candidate's social media activity. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the candidate's social media activity data into an AI, which can then analyze the data and suggest a response method.

[0055] The Cultural Consideration Department can analyze a candidate's past cultural background and select the most appropriate consideration method during the cultural consideration process. For example, the Cultural Consideration Department can select an appropriate consideration method based on the candidate's past cultural experiences. The Cultural Consideration Department can also provide relevant cultural considerations based on the candidate's past cultural background. Furthermore, the Cultural Consideration Department can analyze the candidate's past cultural background and provide consistent cultural considerations. This allows for the selection of the most appropriate consideration method based on the candidate's past cultural background. Some or all of the above processes in the Cultural Consideration Department may be performed using AI, for example, or without AI. For example, the Cultural Consideration Department can input information about the candidate's past cultural background into an AI, which can then analyze that information and select the most appropriate consideration method.

[0056] The Cultural Consideration Department can customize the means of cultural consideration based on the candidate's current living situation. For example, when a candidate inputs their current living situation, the Cultural Consideration Department will provide cultural considerations relevant to that situation. For example, the Cultural Consideration Department can also provide considerations that take into account the relevant cultural background based on the candidate's current living situation. Furthermore, the Cultural Consideration Department can provide appropriate cultural considerations based on the candidate's current living situation. This ensures that appropriate cultural considerations are provided based on the candidate's current living situation. Some or all of the above processing in the Cultural Consideration Department may be performed using AI, for example, or without AI. For example, the Cultural Consideration Department can input information about the candidate's living situation into AI, and the AI ​​can analyze that information to customize the means of cultural consideration.

[0057] The Cultural Consideration Department can select the most appropriate cultural consideration method when providing cultural considerations, taking into account the candidate's geographical location. For example, if the candidate lives in a specific region, the Cultural Consideration Department can provide considerations based on the culture of that region. If the candidate lives in a specific city, the Cultural Consideration Department can also provide considerations based on the culture of that city. Furthermore, if the candidate lives in a specific country, the Cultural Consideration Department can provide considerations based on the culture of that country. This allows for the provision of the most appropriate cultural considerations based on the candidate's geographical location. Some or all of the above processing in the Cultural Consideration Department may be performed using AI, for example, or without AI. For example, the Cultural Consideration Department can input the candidate's geographical location information into an AI, which can then analyze that information to select the most appropriate cultural consideration method.

[0058] The Cultural Consideration Department can analyze a candidate's social media activity and propose means of cultural consideration during the cultural consideration process. For example, the Cultural Consideration Department may consider the candidate's cultural background based on the cultural background shared on social media. The Cultural Consideration Department may also consider the cultures the candidate has shown interest in on social media. Furthermore, the Cultural Consideration Department may consider the cultures the candidate follows on social media. This allows for appropriate cultural consideration based on the candidate's social media activity. Some or all of the above processing in the Cultural Consideration Department may be performed using AI, for example, or without AI. For example, the Cultural Consideration Department can input the candidate's social media activity data into an AI, which can then analyze the data and propose means of cultural consideration.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The interview support system can analyze a candidate's past interview history and generate optimal questions. For example, if a candidate has previously given strong answers to technical questions, the question generator can generate detailed questions related to that field. Similarly, if a candidate has previously emphasized experience in a particular area, the system can prioritize generating questions related to that area. Furthermore, it can analyze patterns of questions the candidate has struggled with in the past and either avoid such questions or ask them again in an improved form. This allows for the generation of more effective questions based on the candidate's past interview history.

[0061] The interview support system can generate questions based on the candidate's current work situation and areas of interest. For example, when a candidate enters their current work situation, it will prioritize generating questions related to that job. When a candidate enters their areas of interest, it can also generate questions related to those areas. Furthermore, if a candidate expresses interest in a particular industry, it can generate questions related to that industry. This allows for the generation of more relevant questions based on the candidate's current work situation and areas of interest.

[0062] The interview support system can generate questions that take into account the candidate's geographical location. For example, if a candidate lives in a specific region, it can generate questions related to that region. If a candidate lives in a specific city, it can generate questions related to that city. It can also generate questions related to a specific country if a candidate lives in that country. This allows for the generation of more relevant questions based on the candidate's geographical location.

[0063] The interview support system can analyze a candidate's social media activity and generate relevant questions. For example, it can generate questions based on projects the candidate has shared on social media. It can also generate questions based on areas the candidate has shown interest in on social media. Furthermore, it can generate questions based on the industries the candidate follows on social media. This allows for the generation of more relevant questions based on the candidate's social media activity.

[0064] The interview support system can analyze a candidate's past response history and suggest the most appropriate answers. For example, it can suggest the best answers based on the candidate's past responses. It can also suggest relevant answers based on the candidate's past response history. Furthermore, it can analyze the candidate's past response history and suggest consistent answers. This allows for the suggestion of more effective answers based on the candidate's past response history.

[0065] The following briefly describes the processing flow for example form 1.

[0066] Step 1: The reception desk receives self-introductions and skill introductions from candidates. These introductions can be in text, audio, or video formats. For example, the reception desk provides an input form for candidates to enter their self-introductions in text format, a recording function for those who want to record their self-introductions in audio format, and a recording function for those who want to record their self-introductions in video format. Step 2: The question generation unit generates questions based on the information received by the reception unit. For example, based on the candidate's skills and experience, it generates questions such as "Please tell us more about past projects, work experience, and specific technologies." Step 3: The translation unit translates the questions generated by the question generation unit in real time. For example, when translating generated questions from Japanese to English, or from English to Japanese, AI is used to perform the translation quickly and accurately. Furthermore, it can also translate into other languages ​​(such as French or German). Step 4: The answer presentation section provides appropriate answers to the questions translated by the translation section. For example, in response to a question about past projects, it provides specific project details, and in response to a question about work experience, it provides specific years of experience and content. Step 5: The Cultural Considerations Department makes minor adjustments to the questions based on the answers provided by the Answer Presentation Department. For example, to show respect for each country's cultural background, they might ask American candidates more direct questions and Japanese candidates more polite questions.

[0067] (Example of form 2) An interview support system according to an embodiment of the present invention is a system that uses AI to generate questions on demand during an interview and translates them between two languages ​​in real time. When a candidate introduces themselves or their skills, the AI ​​immediately generates appropriate questions based on that information. The generated questions are translated between two languages ​​in real time, enabling smooth communication between the interviewer and the candidate. Furthermore, the system also provides appropriate answers to each question and makes minor adjustments to the questions to show respect for the cultural background of each country. This allows the interviewer to efficiently obtain information that transcends not only language but also cultural barriers, enabling a deeper evaluation. For example, a candidate introduces themselves or their skills. At this time, the candidate talks about their background and skills. For example, "I have 5 years of experience in software development, and I have a particular strength in AI technology." This information is input into the AI. Next, the AI ​​analyzes the input information and generates appropriate questions. For example, a question such as "Please tell me more about the projects you have worked on in the past" is generated. Since this question is generated based on the candidate's skills and experience, the interviewer can immediately obtain effective questions. The generated questions are translated between two languages ​​in real time. For example, questions generated in Japanese are translated into English. This translation is done by AI, making it fast and accurate. This allows for smooth communication between the interviewer and the candidate. Furthermore, appropriate answers to each question are also provided. For example, in response to the question, "Please tell me more about the projects you have worked on in the past," an answer such as, "I developed an image recognition system using AI" is provided. This makes it easier for the interviewer to evaluate the candidate's answers. In addition, questions are slightly modified to show respect for the cultural background of each country. For example, direct questions are preferred for American candidates, but more polite expressions are required for Japanese candidates. Such cultural considerations allow interviewers to evaluate candidates while taking their cultural background into account. As a result, interviewers can efficiently obtain information that transcends not only language barriers but also cultural barriers, enabling a deeper evaluation.For example, it's possible to evaluate not only a candidate's skills and experience, but also their cultural background and communication style. This enables more accurate character assessment, dramatically improving the quality and efficiency of recruitment. As a result, the interview support system can generate appropriate questions based on the candidate's self-introduction and skills presentation, translate them in real time, and ask questions that take cultural background into account.

[0068] The interview support system according to this embodiment comprises a reception unit, a question generation unit, a translation unit, an answer presentation unit, and a cultural consideration unit. The reception unit receives the candidate's self-introduction and skill introduction. The candidate's self-introduction and skill introduction may include, but are not limited to, text format, audio format, or video format. For example, the reception unit accepts the candidate entering a self-introduction in text format. The reception unit may also accept the candidate recording a self-introduction in audio format. Furthermore, the reception unit may accept the candidate recording a self-introduction in video format. For example, the reception unit provides an input form when the candidate enters a self-introduction in text format. When recording a self-introduction in audio format, it provides a recording function. When recording a self-introduction in video format, it provides a recording function. The question generation unit generates questions based on the information received by the reception unit. For example, the question generation unit generates questions based on the candidate's skills and experience. For example, the question generation unit generates questions such as, "Please tell me more about the projects the candidate has worked on in the past." The question generation unit may also generate questions based on the candidate's career history. For example, the question generation unit generates questions based on the candidate's background, such as "Please tell me more about your past work experience." The question generation unit can also generate questions based on the candidate's skills. For example, the question generation unit can generate questions based on the candidate's skills, such as "Please tell me more about this specific technology." The translation unit translates the questions generated by the question generation unit in real time. For example, the translation unit translates the generated questions from Japanese to English. For example, when translating the generated questions from Japanese to English, the translation unit uses AI to translate quickly and accurately. The translation unit can also translate the generated questions from English to Japanese. For example, when translating the generated questions from English to Japanese, the translation unit uses AI to translate quickly and accurately. Furthermore, the translation unit can translate the generated questions into other languages. For example, the translation unit can translate the generated questions into French or German. The answer presentation unit presents appropriate answers to the questions translated by the translation unit.The answer-providing unit, for example, provides appropriate answers to each question. For instance, in response to the question, "Please tell me more about the projects you have worked on in the past," the answer-providing unit might provide an answer such as, "I developed an image recognition system using AI." It could also provide an answer such as, "I have five years of experience in software development," in response to the question, "Please tell me more about your past work experience." Furthermore, it could provide an answer such as, "I have a strong background in AI technology," in response to the question, "Please tell me more about a specific technology." The cultural considerations unit then fine-tunes the questions based on the answers provided by the answer-providing unit. For example, the cultural considerations unit might revise questions to show respect for the cultural background of each country. For instance, it might suggest that direct questions are preferred for American candidates, while more polite language is required for Japanese candidates. This cultural consideration allows interviewers to evaluate candidates while taking their cultural background into account. As a result, the interview support system according to this embodiment can generate appropriate questions based on the candidate's self-introduction and skills presentation, translate them in real time, and ask questions that take cultural background into account.

[0069] The reception desk accepts self-introductions and skill descriptions from candidates. These self-introductions and skill descriptions may include, but are not limited to, text, audio, and video formats. For example, the reception desk may accept self-introductions entered in text format. It may also accept self-introductions recorded in audio format. Furthermore, it may accept self-introductions recorded in video format. For example, the reception desk provides an input form when a candidate enters a self-introduction in text format. When a candidate records a self-introduction in audio format, it provides a recording function. When a candidate records a self-introduction in video format, it provides a recording function. This allows the reception desk to choose the means by which candidates can most effectively communicate their strengths and experience. In addition, the reception desk has the ability to automatically categorize the information submitted by candidates and save it in the appropriate format. For example, text-based self-introductions are saved directly to the database, and audio-based self-introductions are saved as audio files. Video-based self-introductions are not only saved as video files, but may also be converted to text using speech recognition technology as needed. This allows interviewers to easily search for candidate information and quickly obtain the necessary details. Furthermore, the reception department assists candidates in providing appropriate information by offering guidelines and samples when submitting their self-introductions. For example, they provide advice on points to emphasize and expressions to avoid during the self-introduction. This allows candidates to showcase their strengths to the fullest, and interviewers to accurately assess the candidates' suitability.

[0070] The question generation unit generates questions based on the information received by the reception unit. For example, the question generation unit generates questions based on the candidate's skills and experience. For instance, it might generate a question such as, "Please tell me more about the projects the candidate has worked on in the past." The question generation unit can also generate questions based on the candidate's background. For example, it might generate a question such as, "Please tell me more about your past work experience." The question generation unit can also generate questions based on the candidate's skills. For example, it might generate a question such as, "Please tell me more about this specific technology." The question generation unit uses AI to analyze candidate information and generate optimal questions. The AI ​​uses natural language processing technology to analyze the candidate's self-introduction and skills description, extracting relevant keywords and phrases. This allows the question generation unit to generate specific questions based on the candidate's strengths and experience. Furthermore, the question generation unit can learn from past interview data and success stories to generate effective question patterns. For example, it can analyze what questions were effective for candidates with a specific skill set and generate new questions based on the results. This allows the question generation unit to consistently provide effective questions based on the latest information. Furthermore, the unit can receive feedback from interviewers and continuously improve the accuracy and effectiveness of its questions. For example, it can record how interviewers respond to specific questions and adjust the content and format of the questions based on that data. This enables the question generation unit to improve the quality of interviews and help accurately assess candidates' suitability.

[0071] The translation unit translates questions generated by the question generation unit in real time. For example, the translation unit translates generated questions from Japanese to English. For example, when translating generated questions from Japanese to English, the translation unit uses AI to perform the translation quickly and accurately. The translation unit can also translate generated questions from English to Japanese. For example, when translating generated questions from English to Japanese, the translation unit uses AI to perform the translation quickly and accurately. Furthermore, the translation unit can translate generated questions into other languages. For example, the translation unit can translate generated questions into French or German. The translation unit uses AI and natural language processing technology to perform translations that accurately capture the context and nuances. This enables smooth communication even if the candidate and interviewer speak different languages. In addition, the translation unit utilizes specialized dictionaries and glossaries to handle technical terms and industry-specific expressions. For example, in interviews for technical positions, there are many questions about specific programming languages ​​and technologies, so it is necessary to accurately translate these technical terms. The translation unit appropriately translates these technical terms to prevent misunderstandings between the candidate and the interviewer. Furthermore, the translation department continuously improves its translation accuracy by learning from past translation data and refining its translation algorithms. For example, it analyzes questions used in past interviews and their translation results to evaluate which translations were most effective. This allows the translation department to always provide highly accurate translations based on the latest information. In addition, the translation department utilizes servers and cloud services with high processing power to provide translation results in real time. This enables the delivery of fast and accurate translations without disrupting the progress of the interview.

[0072] The answer-providing unit presents appropriate answers to questions translated by the translation unit. For example, the answer-providing unit provides appropriate answers to each question. For instance, in response to the question, "Please tell me more about the projects you have worked on in the past," the answer-providing unit might provide an answer such as, "I developed an image recognition system using AI." It could also respond to the question, "Please tell me more about your past work experience," with an answer such as, "I have five years of experience in software development." Furthermore, it could respond to the question, "Please tell me more about a specific technology," with an answer such as, "I have a strong advantage in AI technology." The answer-providing unit uses AI to analyze candidate information and past interview data to generate optimal answers. The AI ​​uses natural language generation technology to generate specific answers based on the candidate's skills and experience. This allows candidates to effectively highlight their strengths, and interviewers to accurately assess the candidate's suitability. Additionally, the answer-providing unit evaluates the candidate's answers in real time and can generate additional or follow-up questions as needed. For example, if a candidate explains a specific project in detail, the system can generate additional questions related to that project to further explore the candidate's understanding and experience. The answer generation system can also analyze the candidate's responses and provide interviewers with evaluation points and areas to pay attention to. This allows interviewers to effectively evaluate the candidate's responses and provide appropriate feedback. Furthermore, the answer generation system can learn from past interview data and continuously improve the quality of its responses. For instance, it can analyze response patterns that received high marks in past interviews and generate new responses based on those results. This ensures that the answer generation system always provides high-quality responses based on the latest information.

[0073] The Cultural Considerations Department makes minor adjustments to questions based on the answers provided by the Answer Presentation Department. For example, the Cultural Considerations Department makes minor adjustments to questions to show respect for the cultural background of each country. For instance, the Cultural Considerations Department might determine that direct questions are preferred for American candidates, while more polite language is required for Japanese candidates. This cultural consideration allows interviewers to evaluate candidates while taking their cultural background into account. The Cultural Considerations Department uses AI to learn the cultural backgrounds and communication styles of each country and generate the most appropriate question format. For example, it generates specific and direct questions for American candidates and polite and indirect questions for Japanese candidates. This allows candidates to answer questions that are appropriate to their cultural background in a relaxed manner. The Cultural Considerations Department also provides interviewers with cultural background-based advice. For example, it explains what expressions and attitudes are preferred in certain cultures, helping interviewers to respond appropriately to candidates. Furthermore, the Cultural Considerations Department analyzes past interview data to learn patterns of questions and answers based on cultural background. This allows the Cultural Considerations Department to always provide the most appropriate question format based on the latest information. Furthermore, the Cultural Considerations Department can provide real-time advice based on cultural background during the interview. For example, it can advise interviewers in real time on how to respond to specific questions, improving the quality of the interview. This allows the Cultural Considerations Department to facilitate smooth communication between candidates and interviewers, increasing the success rate of the interview.

[0074] The question generation unit can generate questions based on the candidate's skills and experience. For example, based on the candidate's skills and experience, it can generate questions such as, "Please tell me more about the projects you have worked on in the past." The question generation unit can also generate questions such as, "Please tell me more about your past work experience," based on the candidate's background. Furthermore, the question generation unit can generate questions such as, "Please tell me more about a specific technology," based on the candidate's skills. This allows for the generation of appropriate questions based on the candidate's skills and experience. Some or all of the above processing in the question generation unit may be performed using AI, for example, or not using AI. For example, the question generation unit can input information about the candidate's skills and experience into an AI, which can then analyze that information to generate appropriate questions.

[0075] The translation unit can translate generated questions between two languages ​​in real time. For example, the translation unit can translate generated questions from Japanese to English. When translating generated questions from Japanese to English, for example, the translation unit uses AI to perform the translation quickly and accurately. The translation unit can also translate generated questions from English to Japanese. For example, when translating generated questions from English to Japanese, the translation unit uses AI to perform the translation quickly and accurately. Furthermore, the translation unit can also translate generated questions into other languages. For example, the translation unit can translate generated questions into French or German. This enables real-time translation between two languages ​​of generated questions. Some or all of the above processing in the translation unit may be performed using AI, for example, or without AI. For example, the translation unit can input a generated question into AI, which can then translate the question quickly and accurately.

[0076] The answer-presenting unit can provide appropriate answers to each question. For example, in response to the question, "Please tell me more about the projects you have worked on in the past," the answer-presenting unit might provide the answer, "I developed an image recognition system using AI." For example, in response to the question, "Please tell me more about your past work experience," the answer-presenting unit might provide the answer, "I have five years of experience in software development." Furthermore, in response to the question, "Please tell me more about a specific technology," the answer-presenting unit might provide the answer, "I have a strong background in AI technology." In this way, it can provide appropriate answers to each question. Some or all of the above processing in the answer-presenting unit may be performed using AI, for example, or not using AI. For example, the answer-presenting unit can input appropriate answers to each question into an AI, which can then analyze the answers and provide appropriate responses.

[0077] The Cultural Considerations Department can make minor adjustments to questions to show respect for each country's cultural background. For example, the Cultural Considerations Department might prefer direct questions for American candidates, while more polite language is required for Japanese candidates. Such cultural considerations allow interviewers to evaluate candidates while taking their cultural background into account. This enables the department to make minor adjustments to questions to show respect for each country's cultural background. Some or all of the above processing by the Cultural Considerations Department may be performed using AI, for example, or not. For example, the Cultural Considerations Department can input information about a candidate's cultural background into an AI, which can then analyze that information and make appropriate minor adjustments to the questions.

[0078] The reception desk can estimate the candidate's emotions and adjust the timing of self-introductions and skill presentations based on the estimated emotions. For example, if a candidate is nervous, the reception desk may prompt them to introduce themselves after giving them some time to relax. If a candidate is relaxed, the reception desk may prompt them to start their self-introduction immediately. The reception desk may also adjust the timing to start the self-introduction quickly if the candidate is in a hurry. This allows the reception desk to adjust the timing of self-introductions and skill presentations based on the candidate'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 reception desk may be performed using AI or not. For example, the reception desk can input the candidate's facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the reception timing.

[0079] The reception department can analyze a candidate's past interview history and select the most suitable reception method. For example, if a candidate has previously preferred online interviews, the reception department will prioritize online reception. If a candidate has previously preferred in-person interviews, the reception department can also prioritize in-person reception. Furthermore, if a candidate has previously had interviews at a specific time slot, the reception department can schedule the reception accordingly. This allows the reception department to select the most suitable reception method based on the candidate's past interview history. Some or all of the above processes in the reception department may be performed using AI, for example, or not. For example, the reception department can input the candidate's past interview history data into an AI, which can then analyze the data to select the most suitable reception method.

[0080] The reception desk can filter candidates based on their current work situation and areas of interest when receiving self-introductions and skill introductions. For example, when a candidate enters their current work situation, the reception desk can prioritize receiving skill introductions related to that job. The reception desk can also prioritize receiving self-introductions related to an area of ​​interest when a candidate enters an area of ​​interest. Furthermore, if a candidate expresses interest in a particular industry, the reception desk can prioritize receiving information related to that industry. This allows for filtering based on the candidate's current work situation and areas of interest. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input information about the candidate's work situation and areas of interest into an AI, which can then analyze and filter that information.

[0081] The reception desk can estimate the candidate's emotions and prioritize the information to be received based on the estimated emotions. For example, if the candidate is nervous, the reception desk may start with simple questions to help them relax. If the candidate is relaxed, the reception desk may prioritize receiving more detailed information. Also, if the candidate is in a hurry, the reception desk may prioritize receiving important information. This allows the reception desk to prioritize the information to be received based on the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input the candidate's facial expression data into a generative AI, which can analyze the data to estimate emotions and determine the priority of information.

[0082] The reception desk can prioritize receiving highly relevant information when receiving self-introductions and skill introductions, taking into account the candidate's geographical location. For example, if a candidate lives in a specific region, the reception desk can prioritize receiving information related to that region. If a candidate lives in a specific city, the reception desk can also prioritize receiving information related to that city. Furthermore, if a candidate lives in a specific country, the reception desk can prioritize receiving information related to that country. This allows for the priority of receiving highly relevant information based on the candidate's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the candidate's geographical location information into an AI, which can then analyze that information and prioritize receiving highly relevant information.

[0083] The reception desk can analyze a candidate's social media activity and receive relevant information when they submit self-introductions and skill introductions. For example, the reception desk can receive skill introductions based on projects the candidate has shared on social media. The reception desk can also receive self-introductions based on areas the candidate has shown interest in on social media. Furthermore, the reception desk can receive information based on the industries the candidate follows on social media. This allows the reception desk to receive relevant information based on the candidate's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the candidate's social media activity data into an AI, which can then analyze the data and receive relevant information.

[0084] The question generation unit can estimate the candidate's emotions and adjust the wording of the questions based on the estimated emotions. For example, if the candidate is nervous, the question generation unit will ask questions in a gentle tone. If the candidate is relaxed, the question generation unit may ask detailed questions. Also, if the candidate is in a hurry, the question generation unit may ask concise questions. In this way, the wording of the questions can be adjusted based on the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using 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, for example, or not using AI. For example, the question generation unit can input the candidate's facial expression data into the generative AI, which can analyze the data to estimate emotions and adjust the wording of the questions.

[0085] The question generation unit can adjust the level of detail of questions based on the importance of the candidate's skills and experience when generating questions. For example, if the candidate has high skills, the question generation unit will generate detailed questions. If the candidate has extensive experience, the question generation unit can also generate questions about specific projects. Furthermore, if the candidate has limited skills and experience, the question generation unit can generate basic questions. This allows for adjustment of the level of detail of questions based on the importance of the candidate's skills and experience. Some or all of the above processing in the question generation unit may be performed using AI, for example, or without AI. For example, the question generation unit can input information about the candidate's skills and experience into the AI, which can then analyze that information to adjust the level of detail of the questions.

[0086] The question generation unit can apply different question generation algorithms depending on the candidate's job category when generating questions. For example, the question generation unit can apply an algorithm that generates technical questions to candidates for technical positions. For example, the question generation unit can also apply an algorithm that generates leadership-related questions to candidates for management positions. Furthermore, the question generation unit can apply an algorithm that generates creativity-related questions to candidates for creative positions. This allows for the application of different question generation algorithms depending on the candidate's job category. Some or all of the above processing in the question generation unit may be performed using AI, for example, or without AI. For example, the question generation unit can input information about the candidate's job category into the AI, which can then analyze that information and apply an appropriate question generation algorithm.

[0087] The question generation unit can estimate the candidate's emotions and adjust the length of the question based on the estimated emotions. For example, if the candidate is nervous, the question generation unit can generate a short question. For example, if the candidate is relaxed, the question generation unit can also generate a longer question. Furthermore, if the candidate is in a hurry, the question generation unit can generate a concise question. This allows the length of the question to be adjusted based on the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 using AI. For example, the question generation unit can input the candidate's facial expression data into the generative AI, which can analyze the data to estimate emotions and adjust the length of the question.

[0088] The question generation unit can prioritize questions based on when the candidate submitted their self-introduction and skills introduction. For example, if the candidate recently submitted a self-introduction, the question generation unit will prioritize questions based on its content. For example, if the candidate has previously submitted a skills introduction, the question generation unit can also prioritize questions based on its content. Furthermore, the question generation unit can prioritize questions based on information submitted by the candidate at a specific time. This allows the question generation unit to determine the priority of questions based on when the candidate submitted their self-introduction and skills introduction. Some or all of the above processing in the question generation unit may be performed using AI, for example, or not. For example, the question generation unit can input information about the candidate's submission timing into the AI, which can then analyze that information to determine the priority of questions.

[0089] The question generation unit can adjust the order of questions based on the candidate's relevance during question generation. For example, the question generation unit may generate questions related to the candidate's skills first. It may also generate questions related to the candidate's experience next. Furthermore, it may generate questions related to the candidate's interests last. This allows the order of questions to be adjusted based on the candidate's relevance. Some or all of the above processing in the question generation unit may be performed using AI, for example, or not using AI. For example, the question generation unit can input information about the candidate's relevance into the AI, which can then analyze that information to adjust the order of questions.

[0090] The translation unit can estimate the candidate's emotions and adjust the translation criteria based on the estimated emotions. For example, if the candidate is nervous, the translation unit will produce a simple and easy-to-understand translation. If the candidate is relaxed, the translation unit may also produce a detailed translation. Furthermore, if the candidate is in a hurry, the translation unit may produce a rapid translation. This allows the translation criteria to be adjusted based on the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the translation unit may be performed using AI or not. For example, the translation unit can input candidate facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the translation criteria.

[0091] The translation unit can improve the accuracy of its translations by considering the relationships between questions during the translation process. For example, the translation unit can perform contextually appropriate translations by considering the context of the questions. For example, the translation unit can also select appropriate terminology by considering the relevance of the questions. Furthermore, the translation unit can analyze the content of the questions to produce a consistent translation. This allows for improved translation accuracy by considering the relationships between questions. Some or all of the above processes in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input information about the relationships between questions into the AI, which can then analyze that information to improve translation accuracy.

[0092] The translation department can perform translations while considering the candidate's attribute information. For example, the translation department can translate using appropriate language according to the candidate's age. For example, the translation department can also translate specialized terminology appropriately according to the candidate's job duties. Furthermore, the translation department can perform culturally appropriate translations according to the candidate's cultural background. This allows for appropriate translations based on the candidate's attribute information. Some or all of the above processes in the translation department may be performed using AI, for example, or not. For example, the translation department can input the candidate's attribute information into AI, which can then analyze that information and perform an appropriate translation.

[0093] The translation unit can estimate the candidate's emotions and adjust the order in which the translation results are displayed based on the estimated emotions. For example, if the candidate is nervous, the translation unit may display important information first. If the candidate is relaxed, the translation unit may also display detailed information first. Furthermore, if the candidate is in a hurry, the translation unit may also display concise information first. This allows the order in which the translation results are displayed to be adjusted based on the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 translation unit may be performed using AI or not using AI. For example, the translation unit can input candidate facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the order in which the translation results are displayed.

[0094] The translation unit can consider the geographical distribution of questions when translating. For example, if a question relates to a specific region, the translation unit will consider the language and dialect of that region when translating. If a question is international, the translation unit can also translate it into a standard language. Furthermore, if a question relates to a specific city, the translation unit can consider the culture and language of that city when translating. This allows for appropriate translations that take into account the geographical distribution of questions. Some or all of the above processing in the translation unit may be performed using AI, for example, or not. For example, the translation unit can input information about the geographical distribution of questions into an AI, which can then analyze that information to produce an appropriate translation.

[0095] The translation department can improve the accuracy of its translations by referring to relevant literature related to the question during the translation process. For example, the translation department can refer to academic papers related to the question to appropriately translate technical terms. The translation department can also refer to industry reports related to the question to appropriately translate industry terminology. Furthermore, the translation department can refer to books related to the question to provide contextually appropriate translations. This allows for improved translation accuracy by referring to relevant literature related to the question. Some or all of the above processes in the translation department may be performed using AI, for example, or not. For example, the translation department can input information about relevant literature related to the question into an AI, which can then analyze that information to improve translation accuracy.

[0096] The response presentation unit can estimate the candidate's emotions and adjust the method of presenting the answers based on the estimated emotions. For example, if the candidate is nervous, the response presentation unit may present a simple and easy-to-understand answer. If the candidate is relaxed, the response presentation unit may also present a detailed answer. Furthermore, if the candidate is in a hurry, the response presentation unit may also present a quick answer. This allows the method of presenting answers to be adjusted based on the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 response presentation unit may be performed using AI or not using AI. For example, the response presentation unit can input the candidate's facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust the method of presenting the answers.

[0097] The response presentation unit can analyze the candidate's past response history to present the most suitable response. For example, the response presentation unit can present the most suitable response based on the candidate's past responses. For example, the response presentation unit can also present relevant responses from the candidate's past response history. Furthermore, the response presentation unit can analyze the candidate's past response history to present a consistent response. This allows the response presentation unit to present the most suitable response based on the candidate's past response history. Some or all of the above processing in the response presentation unit may be performed using AI, for example, or without AI. For example, the response presentation unit can input the candidate's past response history data into AI, which can then analyze the data to present the most suitable response.

[0098] The response presentation unit can customize the means of providing responses based on the candidate's current job situation when providing responses. For example, when a candidate inputs their current job situation, the response presentation unit can provide responses related to that job. For example, the response presentation unit can also provide responses regarding relevant projects or tasks based on the candidate's current job situation. Furthermore, the response presentation unit can provide responses regarding appropriate skills and experience based on the candidate's current job situation. This allows for the provision of appropriate responses based on the candidate's current job situation. Some or all of the above processing in the response presentation unit may be performed using AI, for example, or not using AI. For example, the response presentation unit can input information about the candidate's job situation into AI, and the AI ​​can analyze that information to customize the means of providing responses.

[0099] The response presentation unit can estimate the candidate's emotions and determine the priority of responses based on the estimated emotions. For example, if the candidate is nervous, the response presentation unit may prioritize simple and easy-to-understand responses. For example, if the candidate is relaxed, the response presentation unit may prioritize detailed responses. Also, if the candidate is in a hurry, the response presentation unit may prioritize quick responses. This allows the system to determine the priority of responses based on the candidate's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with 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 response presentation unit may be performed using AI or not using AI. For example, the response presentation unit can input candidate facial expression data into a generative AI, which can analyze the data to estimate emotions and determine the priority of responses.

[0100] The response presentation unit can present the most suitable response by considering the candidate's geographical location information when presenting a response. For example, if the candidate lives in a specific region, the response presentation unit can present a response related to that region. For example, if the candidate lives in a specific city, the response presentation unit can also present a response related to that city. Furthermore, if the candidate lives in a specific country, the response presentation unit can also present a response related to that country. This allows the response presentation unit to present the most suitable response based on the candidate's geographical location information. Some or all of the above processing in the response presentation unit may be performed using AI, for example, or without AI. For example, the response presentation unit can input the candidate's geographical location information into AI, which can then analyze that information and present the most suitable response.

[0101] The response generation unit can analyze the candidate's social media activity and suggest a response method when generating a response. For example, the response generation unit can generate a response based on projects the candidate has shared on social media. The response generation unit can also generate a response based on areas the candidate has shown interest in on social media. Furthermore, the response generation unit can generate a response based on industries the candidate follows on social media. This allows for the generation of appropriate responses based on the candidate's social media activity. Some or all of the above processing in the response generation unit may be performed using AI, for example, or without AI. For example, the response generation unit can input the candidate's social media activity data into an AI, which can then analyze the data and suggest a response method.

[0102] The cultural considerations unit can estimate the candidate's emotions and adjust its cultural considerations based on those estimates. For example, if the candidate is nervous, the cultural considerations unit can use culturally appropriate language to help them relax. If the candidate is relaxed, the cultural considerations unit can also ask questions that take into account their detailed cultural background. Furthermore, if the candidate is in a hurry, the cultural considerations unit can ask concise and culturally appropriate questions. This allows the cultural considerations unit to adjust its cultural considerations based on the candidate'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 cultural considerations unit may be performed using AI or not. For example, the cultural considerations unit can input the candidate's facial expression data into a generative AI, which can analyze the data to estimate emotions and adjust its cultural considerations accordingly.

[0103] The Cultural Consideration Department can analyze a candidate's past cultural background and select the most appropriate consideration method during the cultural consideration process. For example, the Cultural Consideration Department can select an appropriate consideration method based on the candidate's past cultural experiences. The Cultural Consideration Department can also provide relevant cultural considerations based on the candidate's past cultural background. Furthermore, the Cultural Consideration Department can analyze the candidate's past cultural background and provide consistent cultural considerations. This allows for the selection of the most appropriate consideration method based on the candidate's past cultural background. Some or all of the above processes in the Cultural Consideration Department may be performed using AI, for example, or without AI. For example, the Cultural Consideration Department can input information about the candidate's past cultural background into an AI, which can then analyze that information and select the most appropriate consideration method.

[0104] The Cultural Consideration Department can customize the means of cultural consideration based on the candidate's current living situation. For example, when a candidate inputs their current living situation, the Cultural Consideration Department will provide cultural considerations relevant to that situation. For example, the Cultural Consideration Department can also provide considerations that take into account the relevant cultural background based on the candidate's current living situation. Furthermore, the Cultural Consideration Department can provide appropriate cultural considerations based on the candidate's current living situation. This ensures that appropriate cultural considerations are provided based on the candidate's current living situation. Some or all of the above processing in the Cultural Consideration Department may be performed using AI, for example, or without AI. For example, the Cultural Consideration Department can input information about the candidate's living situation into AI, and the AI ​​can analyze that information to customize the means of cultural consideration.

[0105] The cultural considerations unit can estimate the candidate's emotions and determine the priority of cultural considerations based on the estimated emotions. For example, if the candidate is nervous, the cultural considerations unit will prioritize using culturally appropriate expressions to help them relax. If the candidate is relaxed, the cultural considerations unit may also prioritize asking questions that take into account their detailed cultural background. Furthermore, if the candidate is in a hurry, the cultural considerations unit may prioritize asking concise and culturally appropriate questions. This allows for the prioritization of cultural considerations based on the candidate'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 cultural considerations unit may be performed using AI or not. For example, the cultural considerations unit can input candidate facial expression data into a generative AI, which can analyze the data to estimate emotions and determine the priority of cultural considerations.

[0106] The Cultural Consideration Department can select the most appropriate cultural consideration method when providing cultural considerations, taking into account the candidate's geographical location. For example, if the candidate lives in a specific region, the Cultural Consideration Department can provide considerations based on the culture of that region. If the candidate lives in a specific city, the Cultural Consideration Department can also provide considerations based on the culture of that city. Furthermore, if the candidate lives in a specific country, the Cultural Consideration Department can provide considerations based on the culture of that country. This allows for the provision of the most appropriate cultural considerations based on the candidate's geographical location. Some or all of the above processing in the Cultural Consideration Department may be performed using AI, for example, or without AI. For example, the Cultural Consideration Department can input the candidate's geographical location information into an AI, which can then analyze that information to select the most appropriate cultural consideration method.

[0107] The Cultural Consideration Department can analyze a candidate's social media activity and propose means of cultural consideration during the cultural consideration process. For example, the Cultural Consideration Department may consider the candidate's cultural background based on the cultural background shared on social media. The Cultural Consideration Department may also consider the cultures the candidate has shown interest in on social media. Furthermore, the Cultural Consideration Department may consider the cultures the candidate follows on social media. This allows for appropriate cultural consideration based on the candidate's social media activity. Some or all of the above processing in the Cultural Consideration Department may be performed using AI, for example, or without AI. For example, the Cultural Consideration Department can input the candidate's social media activity data into an AI, which can then analyze the data and propose means of cultural consideration.

[0108] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0109] The interview support system can analyze a candidate's past interview history and generate optimal questions. For example, if a candidate has previously given strong answers to technical questions, the question generator can generate detailed questions related to that field. Similarly, if a candidate has previously emphasized experience in a particular area, the system can prioritize generating questions related to that area. Furthermore, it can analyze patterns of questions the candidate has struggled with in the past and either avoid such questions or ask them again in an improved form. This allows for the generation of more effective questions based on the candidate's past interview history.

[0110] The interview support system can estimate the candidate's emotions and adjust the interview process based on those estimates. For example, if the candidate is nervous, the question generator can start with simple questions to help them relax. If the candidate is relaxed, more detailed and in-depth questions can be asked. Furthermore, if the candidate is in a hurry, important questions can be prioritized. This allows the interview process to be adjusted based on the candidate's emotions, resulting in a more effective interview.

[0111] The interview support system can generate questions based on the candidate's current work situation and areas of interest. For example, when a candidate enters their current work situation, it will prioritize generating questions related to that job. When a candidate enters their areas of interest, it can also generate questions related to those areas. Furthermore, if a candidate expresses interest in a particular industry, it can generate questions related to that industry. This allows for the generation of more relevant questions based on the candidate's current work situation and areas of interest.

[0112] The interview support system can estimate the candidate's emotions and adjust the accuracy of the translation based on those emotions. For example, if the candidate is nervous, the translation unit can produce a simple and easy-to-understand translation. If the candidate is relaxed, a more detailed translation is possible. Furthermore, if the candidate is in a hurry, a rapid translation can be provided. This allows for more effective communication by adjusting the accuracy of the translation based on the candidate's emotions.

[0113] The interview support system can generate questions that take into account the candidate's geographical location. For example, if a candidate lives in a specific region, it can generate questions related to that region. If a candidate lives in a specific city, it can generate questions related to that city. It can also generate questions related to a specific country if a candidate lives in that country. This allows for the generation of more relevant questions based on the candidate's geographical location.

[0114] The interview support system can estimate a candidate's emotions and adjust how answers are presented based on those estimates. For example, if a candidate is nervous, it can provide simple and easy-to-understand answers. If the candidate is relaxed, it can provide more detailed answers. It can also provide quick answers if the candidate is in a hurry. This allows for more effective interviews by adjusting the answer presentation based on the candidate's emotions.

[0115] The interview support system can analyze a candidate's social media activity and generate relevant questions. For example, it can generate questions based on projects the candidate has shared on social media. It can also generate questions based on areas the candidate has shown interest in on social media. Furthermore, it can generate questions based on the industries the candidate follows on social media. This allows for the generation of more relevant questions based on the candidate's social media activity.

[0116] The interview support system can estimate a candidate's emotions and adjust the wording of questions based on those emotions. For example, if a candidate is nervous, questions can be asked in a gentle tone. If a candidate is relaxed, more detailed questions can be asked. Furthermore, if a candidate is in a hurry, concise questions can be asked. This allows for more effective interviews by adjusting the wording of questions based on the candidate's emotions.

[0117] The interview support system can analyze a candidate's past response history and suggest the most appropriate answers. For example, it can suggest the best answers based on the candidate's past responses. It can also suggest relevant answers based on the candidate's past response history. Furthermore, it can analyze the candidate's past response history and suggest consistent answers. This allows for the suggestion of more effective answers based on the candidate's past response history.

[0118] The interview support system can estimate the candidate's emotions and adjust the length of questions based on those estimates. For example, if the candidate is nervous, it can generate shorter questions. If the candidate is relaxed, it can generate longer questions. It can also generate concise questions if the candidate is in a hurry. This allows for more effective interviews by adjusting question length based on the candidate's emotions.

[0119] The following briefly describes the processing flow for example form 2.

[0120] Step 1: The reception desk receives self-introductions and skill introductions from candidates. These introductions can be in text, audio, or video formats. For example, the reception desk provides an input form for candidates to enter their self-introductions in text format, a recording function for those who want to record their self-introductions in audio format, and a recording function for those who want to record their self-introductions in video format. Step 2: The question generation unit generates questions based on the information received by the reception unit. For example, based on the candidate's skills and experience, it generates questions such as "Please tell us more about past projects, work experience, and specific technologies." Step 3: The translation unit translates the questions generated by the question generation unit in real time. For example, when translating generated questions from Japanese to English, or from English to Japanese, AI is used to perform the translation quickly and accurately. Furthermore, it can also translate into other languages ​​(such as French or German). Step 4: The answer presentation section provides appropriate answers to the questions translated by the translation section. For example, in response to a question about past projects, it provides specific project details, and in response to a question about work experience, it provides specific years of experience and content. Step 5: The Cultural Considerations Department makes minor adjustments to the questions based on the answers provided by the Answer Presentation Department. For example, to show respect for each country's cultural background, they might ask American candidates more direct questions and Japanese candidates more polite questions.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] Each of the multiple elements described above, including the reception unit, question generation unit, translation unit, answer presentation unit, and cultural consideration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and receives the candidate's self-introduction and skill introduction. The question generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates questions based on the information received by the reception unit. The translation unit is implemented by the specific processing unit 290 of the data processing unit 12 and translates the generated questions in real time. The answer presentation unit is implemented by the specific processing unit 290 of the data processing unit 12 and presents appropriate answers to the translated questions. The cultural consideration unit is implemented by the specific processing unit 290 of the data processing unit 12 and makes minor adjustments to the questions to show respect for the cultural background of each country. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0125] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] Each of the multiple elements described above, including the reception unit, question generation unit, translation unit, answer presentation unit, and cultural consideration unit, is implemented, for example, in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives the candidate's self-introduction and skill introduction. The question generation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and generates questions based on the information received by the reception unit. The translation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and translates the generated questions in real time. The answer presentation unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and presents appropriate answers to the translated questions. The cultural consideration unit is implemented, for example, by the specific processing unit 290 of the data processing unit 12 and makes minor adjustments to the questions to show respect for the cultural background of each country. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0141] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0142] 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.

[0143] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0144] The 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.

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

[0146] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0147] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0148] Figure 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.

[0149] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0150] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0151] In the 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.

[0152] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0153] The specific processing unit 290 transmits the result of the specific processing to the 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.

[0154] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0155] The data processing system 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.

[0156] Each of the multiple elements described above, including the reception unit, question generation unit, translation unit, answer presentation unit, and cultural consideration unit, is implemented by, for example, at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and receives the candidate's self-introduction and skill description. The question generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates questions based on the information received by the reception unit. The translation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and translates the generated questions in real time. The answer presentation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents appropriate answers to the translated questions. The cultural consideration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes minor adjustments to the questions to show respect for the cultural background of each country. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0157] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0160] The 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.

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

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS 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).

[0163] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.).

[0170] 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.

[0171] 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.

[0172] 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.

[0173] Each of the multiple elements described above, including the reception unit, question generation unit, translation unit, answer presentation unit, and cultural consideration unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives the candidate's self-introduction and skill description. The question generation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and generates questions based on the information received by the reception unit. The translation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and translates the generated questions in real time. The answer presentation unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and presents appropriate answers to the translated questions. The cultural consideration unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12 and makes minor adjustments to the questions to show respect for the cultural background of each country. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] (Note 1) The reception area accepts self-introductions and skill descriptions from candidates, A question generation unit generates questions based on the information received by the reception unit, A translation unit that translates the questions generated by the aforementioned question generation unit in real time, An answer presentation unit that presents appropriate answers to questions translated by the aforementioned translation unit, The system includes a cultural consideration unit that makes minor adjustments to the question based on the answer provided by the answer presentation unit. A system characterized by the following features. (Note 2) The aforementioned question generation unit, Generate questions based on the candidate's skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned translation department, The generated questions are translated between two languages ​​in real time. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned response presentation section is, Provide the perfect answer to each question. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned Cultural Considerations Department, We will make minor adjustments to the questions to show respect for the cultural backgrounds of each country. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is The system estimates the candidate's emotions and adjusts the timing of self-introductions and skill presentations based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is Analyze the candidate's past interview history and select the most suitable application method. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is When receiving self-introductions and skill presentations, candidates are filtered based on their current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is The system estimates the candidate's sentiments and prioritizes the information to be received based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When accepting self-introductions and skill descriptions, the system prioritizes receiving information that is highly relevant, taking into account the candidate's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving self-introductions and skill descriptions, the system analyzes the candidate's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned question generation unit, The system estimates the candidate's emotions and adjusts the wording of the questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned question generation unit, When generating questions, adjust the level of detail based on the importance of the candidate's skills and experience. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned question generation unit, When generating questions, different question generation algorithms are applied depending on the candidate's job category. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned question generation unit, The system estimates the candidate's emotions and adjusts the length of the questions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned question generation unit, When generating questions, the priority of questions is determined based on when candidates submitted their self-introductions and skill profiles. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned question generation unit, When generating questions, adjust the order of questions based on the relevance of the candidate. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned translation department, Estimate the candidate's sentiment and adjust the translation criteria based on the estimated candidate's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned translation department, When translating, consider the relationships between questions to improve translation accuracy. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned translation department, When translating, the candidate's attribute information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned translation department, It estimates the candidate's sentiment and adjusts the order in which the translation results are displayed based on the estimated candidate's sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned translation department, When translating, take into account the geographical distribution of the questions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned translation department, When translating, refer to relevant literature related to the question to improve the accuracy of the translation. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned response presentation section is, The system estimates the candidate's emotions and adjusts how responses are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned response presentation section is, When presenting answers, the system analyzes the candidate's past response history to suggest the most suitable answer. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned response presentation section is, When presenting responses, customize the response method based on the candidate's current job situation. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned response presentation section is, The system estimates the candidates' emotions and prioritizes responses based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned response presentation section is, When presenting answers, the system will consider the candidate's geographical location to provide the most suitable response. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned response presentation section is, When presenting responses, we analyze candidates' social media activity and suggest ways to respond. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned Cultural Considerations Department, Estimate the candidate's sentiments and adjust the methods of cultural consideration based on the estimated candidate's sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned Cultural Considerations Department, When considering cultural considerations, we analyze the candidate's past cultural background to select the most appropriate method of consideration. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned Cultural Considerations Department, When considering cultural considerations, customize the means of cultural consideration based on the candidate's current living situation. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned Cultural Considerations Department, Estimate the candidate's sentiments and determine the priority of cultural considerations based on those estimated sentiments. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned Cultural Considerations Department, When considering cultural considerations, the most appropriate method of cultural consideration will be selected, taking into account the candidate's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned Cultural Considerations Department, When considering cultural considerations, we analyze candidates' social media activity and propose ways to address cultural considerations. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The reception area accepts self-introductions and skill descriptions from candidates, A question generation unit generates questions based on the information received by the reception unit, A translation unit that translates the questions generated by the aforementioned question generation unit in real time, An answer presentation unit that presents appropriate answers to questions translated by the aforementioned translation unit, The system includes a cultural consideration unit that makes minor adjustments to the question based on the answer provided by the answer presentation unit. A system characterized by the following features.

2. The aforementioned question generation unit, Generate questions based on the candidate's skills and experience. The system according to feature 1.

3. The aforementioned translation department, The generated questions are translated between two languages ​​in real time. The system according to feature 1.

4. The aforementioned response presentation section is, Provide the perfect answer to each question. The system according to feature 1.

5. The aforementioned Cultural Considerations Department, We will make minor adjustments to the questions to show respect for the cultural backgrounds of each country. The system according to feature 1.

6. The aforementioned reception unit is The system estimates the candidate's emotions and adjusts the timing of self-introductions and skill presentations based on those estimated emotions. The system according to feature 1.

7. The aforementioned reception unit is Analyze the candidate's past interview history and select the most suitable application method. The system according to feature 1.

8. The aforementioned reception unit is When receiving self-introductions and skill presentations, candidates are filtered based on their current work situation and areas of interest. The system according to feature 1.

9. The aforementioned reception unit is The system estimates the candidate's sentiments and prioritizes the information to be received based on those estimated sentiments. The system according to feature 1.

10. The aforementioned reception unit is When accepting self-introductions and skill descriptions, the system prioritizes receiving information that is highly relevant, taking into account the candidate's geographical location. The system according to feature 1.

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