System

Abstract

An object of a system according to an embodiment is to automate questions and answers using generated AI and to secure appropriate human resources.SOLUTION: A system includes a question generation unit, an answer analysis unit, and an evaluation generation unit. The question generator asks a question set in advance to the applicant. The answer analyzer analyzes an answer of the applicant to the question asked by the question generator. The evaluation generation part generates an evaluation result based on the answer of the applicant analyzed by the answer analysis part.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, there was a problem in recruiting, where interviewers' personal opinions and hiring standards varied, making it difficult to secure suitable personnel.

[0005] The system of the embodiment aims to automate question and answer sessions using generative AI and secure appropriate personnel. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generation unit, an answer analysis unit, and an evaluation generation unit. The question generation unit asks predetermined questions to applicants. The answer analysis unit analyzes the applicant's answers to the questions asked by the question generation unit. The evaluation generation unit generates an evaluation result based on the applicant's answers analyzed by the answer analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment uses generative AI to automate question and answer sessions and ensure the availability of appropriate personnel. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) The question and answer system according to an embodiment of the present invention uses generative AI to automate questions and answers in recruitment, reducing costs and ensuring consistency in recruitment standards. This eliminates the personal opinions and inconsistencies in judgment standards of interviewers, and automates the process of recruiting suitable personnel.

[0029] A question-and-answer system according to an embodiment includes a question generation unit, an answer analysis unit, and an evaluation generation unit. The question generation unit asks a predetermined question to an applicant. For example, the generation AI generates a question such as, "Tell me about your work experience to date." The question generation unit can also generate follow-up questions based on the applicant's answers. For example, if the applicant answers, "I worked as a project manager," the generation AI adds a question such as, "What specific projects were you in charge of?" The answer analysis unit analyzes the applicant's answers to the questions posed by the question generation unit. For example, the generation AI performs text analysis on the applicant's answers to evaluate their specificity and clarity of skills. The answer analysis unit can also compare the applicant's answers with past successes and failures to predict the likelihood of success. For example, if the applicant's answers are similar to past successes, a high evaluation is given. The evaluation generation unit generates an evaluation result based on the applicant's answers analyzed by the answer analysis unit. For example, the generation AI generates an evaluation result in the form, "This applicant has extensive past work experience and demonstrates team leadership, so we will give him a high evaluation." The evaluation generation unit can also visualize the evaluation results and display them in graphs or charts. For example, it can display the applicant's skill evaluation in a radar chart. This allows the question-and-answer system according to the embodiment to reduce recruitment costs and ensure consistency in recruitment criteria. For example, the generation AI can ask the same questions to all applicants and analyze their answers using the same evaluation criteria, eliminating the interviewer's personal opinions and inconsistencies in judgment criteria. The generation AI can also reduce the interviewer's time and effort, allowing them to efficiently evaluate many applicants.

[0030] The question generation unit generates follow-up questions in real time in response to applicants' answers, allowing for deeper information to be extracted. For example, the question generation unit uses a generation AI to analyze an applicant's answers and instantly generate follow-up questions based on their content. For example, if an applicant answers, "I worked as a project manager," the generation AI adds a question such as, "What specific projects were you in charge of?" This allows for more detailed information to be extracted from the applicant.

[0031] Based on the applicant's answers, the question generation unit can present specific scenarios related to the applicant's past work experience and skills and ask the applicant to respond to them. For example, the question generation unit uses a generation AI to analyze the applicant's answers and present specific scenarios based on the content of the answers. For example, if the applicant answers, "I worked as a project manager," the generation AI will present a scenario such as, "How did you respond when a project was delayed?" This makes it possible to specifically evaluate the applicant's practical abilities.

[0032] Applicants' responses can be recorded using audio or video, and non-verbal communication can also be analyzed. Applicants' responses can be recorded using audio or video, and non-verbal communication can also be analyzed. For example, generative AI can record applicants' responses using audio, and analyze non-verbal communication as well. For example, it can analyze the applicant's tone of voice and speaking style to evaluate the reliability and confidence of their answers. This makes it possible to make a comprehensive evaluation that includes non-verbal information.

[0033] The question generation unit can automatically select question sets specialized for different industries or job types and apply them to applicants. For example, the generation AI automatically selects question sets specialized for different industries or job types and applies them to applicants. For example, technical questions are asked to applicants in the IT industry, and strategic questions are asked to applicants in the marketing industry. This makes it possible to ask questions appropriate to the industry or job type.

[0034] When analyzing the applicant's answers, the answer analysis unit can introduce an algorithm that evaluates the consistency and logic of the answers. For example, the answer analysis unit introduces an algorithm in which the generation AI analyzes the applicant's answers and evaluates the consistency of the answers. For example, it checks whether the applicant's answers are consistent from beginning to end, and gives a high score to answers that are consistent. This makes it possible to evaluate the consistency and logic of the applicant's answers.

[0035] The answer analysis unit can compare the applicant's answers with past successes and failures to predict the likelihood of success. For example, the generation AI can compare the applicant's answers with past successes and predict the likelihood of success. For example, if the applicant's answers are similar to past successes, a high score can be given. This makes it possible to predict the applicant's likelihood of success.

[0036] The answer analysis unit can compare the applicant's answers with those of other applicants and perform a relative evaluation. For example, the generation AI can compare the applicant's answers with those of other applicants and perform a relative evaluation. For example, if the applicant's answers are more specific than those of other applicants, a higher evaluation can be given. This makes it possible to perform a relative evaluation of applicants.

[0037] The response analysis unit translates applicants' responses into different languages ​​and evaluates them from an international perspective. For example, the response analysis unit uses a generation AI to translate applicants' responses into different languages ​​and evaluate them from an international perspective. For example, the responses are translated into English and French, and then evaluated by evaluators with different cultural backgrounds. This allows applicants to be evaluated from an international perspective.

[0038] The evaluation generation unit can automatically generate a detailed analysis report of the applicant's answers. For example, the evaluation generation unit uses a generation AI to analyze the applicant's answers and automatically generate a detailed analysis report. For example, it can describe in detail the strengths and weaknesses of the applicant's skills and experience. This makes it possible to automatically generate a detailed analysis report of the applicant's answers.

[0039] The evaluation generation unit can provide specific feedback to the applicant based on the evaluation results. For example, the generation AI provides specific feedback to the applicant based on the evaluation results. For example, it provides feedback such as "Your leadership skills are highly rated." This makes it possible to provide specific feedback to the applicant.

[0040] The evaluation generation unit can visualize the evaluation results and display them in graphs and charts. For example, the generation AI in the evaluation generation unit visualizes the evaluation results and displays them in graphs and charts. For example, the skill evaluation of an applicant can be displayed in a radar chart. This makes it possible to display the evaluation results in a way that is visually easy to understand.

[0041] The evaluation generation unit can customize the evaluation results according to different departments and positions, and propose appropriate personnel placement. For example, the generation AI can customize the evaluation results according to different departments and positions, and propose appropriate personnel placement. For example, applicants with strong communication skills can be placed in the sales department. This makes it possible to propose appropriate personnel placement.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The Q&A system can also provide a personalized learning plan based on the applicant's answers. For example, if the applicant is assessed as lacking a certain skill, it can suggest online courses or training programs to improve that skill. It can also suggest appropriate career paths and growth opportunities based on the applicant's career goals. This gives the applicant a concrete action plan to address their weaknesses and advance their career.

[0044] Q&A systems can provide real-time feedback based on the applicant's answers. For example, if an applicant provides an insufficient answer to a particular question, the system can provide immediate feedback such as "Please be more specific." It can also provide positive feedback such as "That's a great answer" if the applicant provides a good answer. This gives the applicant an opportunity to improve their answers during the interview.

[0045] The Q&A system can be equipped with an automatic resume generation function based on the applicant's answers. For example, it can automatically organize the work experience and skills that the applicant mentioned during the interview and generate a resume. It can also generate a resume optimized for a specific job when the applicant applies for that job. This allows applicants to create professional resumes without any effort.

[0046] Q&A systems can be equipped with automated recommendations based on applicants' answers. For example, if an applicant has specific skills or experience, the system can recommend suitable jobs based on those skills and experience. It can also suggest future career paths based on the applicant's career goals. This helps applicants find the jobs and career paths that best suit them.

[0047] Q&A systems can be equipped with an automatic scheduling function based on applicants' answers. For example, when an applicant selects a date and time for an interview, the system can automatically check the interviewer's schedule and suggest the most suitable date and time. It can also automatically send interview reminders. This streamlines interview scheduling and makes the system convenient for both applicants and interviewers.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The question generator asks the applicant pre-defined questions. For example, the generator generates a question such as, "Tell me about your work experience to date." The question generator can also generate follow-up questions based on the applicant's answers. For example, if the applicant answers, "I worked as a project manager," the generator adds a question such as, "What specific projects were you in charge of?" Step 2: The answer analysis unit analyzes the applicant's answers to the questions posed by the question generation unit. For example, the generation AI performs text analysis of the applicant's answers to evaluate their specificity and clarity of skills. The answer analysis unit can also compare the applicant's answers with past successes and failures to predict the likelihood of success. For example, if the applicant's answers are similar to past successes, a high score will be given. Step 3: The evaluation generation unit generates evaluation results based on the applicant's answers analyzed by the answer analysis unit. For example, the generation AI may generate evaluation results such as, "This applicant has extensive past work experience and demonstrates team leadership, so we will give him a high evaluation." The evaluation generation unit can also visualize the evaluation results and display them in graphs and charts. For example, it can display the applicant's skill evaluation in a radar chart.

[0050] (Example 2) The question and answer system according to an embodiment of the present invention uses generative AI to automate questions and answers in recruitment, reducing costs and ensuring consistency in recruitment standards. This eliminates the personal opinions and inconsistencies in judgment standards of interviewers, and automates the process of recruiting suitable personnel.

[0051] A question-and-answer system according to an embodiment includes a question generation unit, an answer analysis unit, and an evaluation generation unit. The question generation unit asks a predetermined question to an applicant. For example, the generation AI generates a question such as, "Tell me about your work experience to date." The question generation unit can also generate follow-up questions based on the applicant's answers. For example, if the applicant answers, "I worked as a project manager," the generation AI adds a question such as, "What specific projects were you in charge of?" The answer analysis unit analyzes the applicant's answers to the questions posed by the question generation unit. For example, the generation AI performs text analysis on the applicant's answers to evaluate their specificity and clarity of skills. The answer analysis unit can also compare the applicant's answers with past successes and failures to predict the likelihood of success. For example, if the applicant's answers are similar to past successes, a high evaluation is given. The evaluation generation unit generates an evaluation result based on the applicant's answers analyzed by the answer analysis unit. For example, the generation AI generates an evaluation result in the form, "This applicant has extensive past work experience and demonstrates team leadership, so we will give him a high evaluation." The evaluation generation unit can also visualize the evaluation results and display them in graphs or charts. For example, it can display the applicant's skill evaluation in a radar chart. This allows the question-and-answer system according to the embodiment to reduce recruitment costs and ensure consistency in recruitment criteria. For example, the generation AI can ask the same questions to all applicants and analyze their answers using the same evaluation criteria, eliminating the interviewer's personal opinions and inconsistencies in judgment criteria. The generation AI can also reduce the interviewer's time and effort, allowing them to efficiently evaluate many applicants.

[0052] The question generation unit generates follow-up questions in real time in response to applicants' answers, allowing for deeper information to be extracted. For example, the question generation unit uses a generation AI to analyze an applicant's answers and instantly generate follow-up questions based on their content. For example, if an applicant answers, "I worked as a project manager," the generation AI adds a question such as, "What specific projects were you in charge of?" This allows for more detailed information to be extracted from the applicant.

[0053] Based on the applicant's answers, the question generation unit can present specific scenarios related to the applicant's past work experience and skills and ask the applicant to respond to them. For example, the question generation unit uses a generation AI to analyze the applicant's answers and present specific scenarios based on the content of the answers. For example, if the applicant answers, "I worked as a project manager," the generation AI will present a scenario such as, "How did you respond when a project was delayed?" This makes it possible to specifically evaluate the applicant's practical abilities.

[0054] The question generation unit can use the emotion estimation function to analyze the emotions of applicants when they answer questions and generate questions that will elicit emotionally stable answers. For example, the question generation unit uses a generation AI to analyze the emotions of applicants when they answer questions and generate questions that will elicit emotionally stable answers. For example, if an applicant is nervous, the generation AI will ask a question such as, "Please answer while relaxing. Please tell me about a specific example of a successful project." This makes it possible to ask questions that take the applicant's emotions into consideration.

[0055] Applicants' responses can be recorded using audio or video, and non-verbal communication can also be analyzed. Applicants' responses can be recorded using audio or video, and non-verbal communication can also be analyzed. For example, generative AI can record applicants' responses using audio, and analyze non-verbal communication as well. For example, it can analyze the applicant's tone of voice and speaking style to evaluate the reliability and confidence of their answers. This makes it possible to make a comprehensive evaluation that includes non-verbal information.

[0056] The question generation unit can automatically select question sets specialized for different industries or job types and apply them to applicants. For example, the generation AI automatically selects question sets specialized for different industries or job types and applies them to applicants. For example, technical questions are asked to applicants in the IT industry, and strategic questions are asked to applicants in the marketing industry. This makes it possible to ask questions appropriate to the industry or job type.

[0057] The question generation unit can use the emotion estimation function to generate questions that have a relaxing effect so that the applicant can answer in a relaxed state. The question generation unit, for example, uses the emotion estimation function to generate questions that have a relaxing effect so that the applicant can answer in a relaxed state. For example, the question generation unit asks, "Please answer in a relaxed manner. Tell us about your hobbies and interests so far." This can provide an environment in which the applicant can answer in a relaxed manner.

[0058] When analyzing the applicant's answers, the answer analysis unit can introduce an algorithm that evaluates the consistency and logic of the answers. For example, the answer analysis unit introduces an algorithm in which the generation AI analyzes the applicant's answers and evaluates the consistency of the answers. For example, it checks whether the applicant's answers are consistent from beginning to end, and gives a high score to answers that are consistent. This makes it possible to evaluate the consistency and logic of the applicant's answers.

[0059] The answer analysis unit can compare the applicant's answers with past successes and failures to predict the likelihood of success. For example, the generation AI can compare the applicant's answers with past successes and predict the likelihood of success. For example, if the applicant's answers are similar to past successes, a high score can be given. This makes it possible to predict the applicant's likelihood of success.

[0060] The answer analysis unit can use the emotion estimation function to analyze the emotional nuances included in the applicant's answers and evaluate the applicant's emotional aptitude. The answer analysis unit, for example, uses the emotion estimation function to analyze the emotional nuances included in the applicant's answers and evaluate the applicant's emotional aptitude. For example, if the applicant's answers show positive emotions, a high evaluation is given. This makes it possible to evaluate the applicant's emotional aptitude.

[0061] The answer analysis unit can compare the applicant's answers with those of other applicants and perform a relative evaluation. For example, the generation AI can compare the applicant's answers with those of other applicants and perform a relative evaluation. For example, if the applicant's answers are more specific than those of other applicants, a higher evaluation can be given. This makes it possible to perform a relative evaluation of applicants.

[0062] The response analysis unit translates applicants' responses into different languages ​​and evaluates them from an international perspective. For example, the response analysis unit uses a generation AI to translate applicants' responses into different languages ​​and evaluate them from an international perspective. For example, the responses are translated into English and French, and then evaluated by evaluators with different cultural backgrounds. This allows applicants to be evaluated from an international perspective.

[0063] The answer analysis unit can use the emotion estimation function to analyze the interviewer's emotional response to the applicant's answers and eliminate bias in the evaluation. For example, the answer analysis unit uses the emotion estimation function to analyze the interviewer's emotional response to the applicant's answers and eliminate bias in the evaluation. For example, if the interviewer has positive emotions, this influence is eliminated. This eliminates bias in the evaluation and enables fair evaluation.

[0064] The evaluation generation unit can automatically generate a detailed analysis report of the applicant's answers. For example, the evaluation generation unit uses a generation AI to analyze the applicant's answers and automatically generate a detailed analysis report. For example, it can describe in detail the strengths and weaknesses of the applicant's skills and experience. This makes it possible to automatically generate a detailed analysis report of the applicant's answers.

[0065] The evaluation generation unit can provide specific feedback to the applicant based on the evaluation results. For example, the generation AI provides specific feedback to the applicant based on the evaluation results. For example, it provides feedback such as "Your leadership skills are highly rated." This makes it possible to provide specific feedback to the applicant.

[0066] The evaluation generation unit can use the emotion estimation function to predict the applicant's emotional reaction to the evaluation result and provide appropriate feedback. The evaluation generation unit can, for example, use the emotion estimation function to predict the applicant's emotional reaction to the evaluation result and provide appropriate feedback. For example, it can provide feedback that causes the applicant to have positive emotions. This makes it possible to predict the applicant's emotional reaction and provide appropriate feedback.

[0067] The evaluation generation unit can visualize the evaluation results and display them in graphs and charts. For example, the generation AI in the evaluation generation unit visualizes the evaluation results and displays them in graphs and charts. For example, the skill evaluation of an applicant can be displayed in a radar chart. This makes it possible to display the evaluation results in a way that is visually easy to understand.

[0068] The evaluation generation unit can customize the evaluation results according to different departments and positions, and propose appropriate personnel placement. For example, the generation AI can customize the evaluation results according to different departments and positions, and propose appropriate personnel placement. For example, applicants with strong communication skills can be placed in the sales department. This makes it possible to propose appropriate personnel placement.

[0069] The evaluation generation unit uses the emotion estimation function to analyze the emotional reactions of interviewers and hiring managers to the evaluation results, thereby ensuring the transparency of the evaluation. The evaluation generation unit, for example, uses the emotion estimation function to analyze the emotional reactions of interviewers and hiring managers to the evaluation results, thereby ensuring the transparency of the evaluation. For example, the evaluation is prevented from being influenced by the emotions of the interviewer. This ensures the transparency of the evaluation.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The Q&A system can also provide a personalized learning plan based on the applicant's answers. For example, if the applicant is assessed as lacking a certain skill, it can suggest online courses or training programs to improve that skill. It can also suggest appropriate career paths and growth opportunities based on the applicant's career goals. This gives the applicant a concrete action plan to address their weaknesses and advance their career.

[0072] The Q&A system uses emotion estimation to monitor an applicant's stress level and insert questions that have a relaxing effect if stress levels rise. For example, if an applicant is nervous, the system will ask a relaxing question such as, "Tell us how you spent your recent holiday." After the applicant's stress level has subsided, the system will return to important questions, maximizing the applicant's performance. This allows the system to provide an environment where applicants can relax and perform to the best of their abilities.

[0073] Q&A systems can provide real-time feedback based on the applicant's answers. For example, if an applicant provides an insufficient answer to a particular question, the system can provide immediate feedback such as "Please be more specific." It can also provide positive feedback such as "That's a great answer" if the applicant provides a good answer. This gives the applicant an opportunity to improve their answers during the interview.

[0074] The Q&A system can use emotion estimation to analyze an applicant's motivation level and generate questions to increase motivation. For example, if an applicant is estimated to have low motivation, it can ask questions such as, "What achievement are you most proud of?" to elicit positive emotions. Once motivation increases, it can ask more challenging questions to maximize the applicant's performance. This can increase applicant motivation and elicit better performance.

[0075] The Q&A system can be equipped with an automatic resume generation function based on the applicant's answers. For example, it can automatically organize the work experience and skills that the applicant mentioned during the interview and generate a resume. It can also generate a resume optimized for a specific job when the applicant applies for that job. This allows applicants to create professional resumes without any effort.

[0076] The Q&A system uses emotion estimation to analyze the interviewer's emotional response to the applicant's answers and can eliminate interviewer bias. For example, if the interviewer has positive emotions toward a particular answer, the system can eliminate that influence and provide a fair evaluation. Similarly, if the interviewer has negative emotions, the system can eliminate that influence. This enables a fair evaluation that is not influenced by the interviewer's emotions.

[0077] Q&A systems can be equipped with automated recommendations based on applicants' answers. For example, if an applicant has specific skills or experience, the system can recommend suitable jobs based on those skills and experience. It can also suggest future career paths based on the applicant's career goals. This helps applicants find the jobs and career paths that best suit them.

[0078] The Q&A system can use the emotion estimation function to analyze the emotional nuances contained in the applicant's answers and evaluate their emotional aptitude. For example, if the applicant's answers show positive emotions, the system can give them a high rating. If the applicant's answers show negative emotions, the system can analyze the reason and provide appropriate feedback. This allows the system to evaluate the applicant's emotional aptitude and provide appropriate feedback.

[0079] Q&A systems can be equipped with an automatic scheduling function based on applicants' answers. For example, when an applicant selects a date and time for an interview, the system can automatically check the interviewer's schedule and suggest the most suitable date and time. It can also automatically send interview reminders. This streamlines interview scheduling and makes the system convenient for both applicants and interviewers.

[0080] The Q&A system uses emotion estimation to analyze the interviewer's emotional response to the applicant's answers, ensuring transparency in the evaluation. For example, if the interviewer has positive emotions toward a particular answer, the system can eliminate that influence and provide a fair evaluation. Similarly, if the interviewer has negative emotions, the system can eliminate that influence. This enables a fair evaluation that is not influenced by the interviewer's emotions.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The question generator asks the applicant pre-defined questions. For example, the generator generates a question such as, "Tell me about your work experience to date." The question generator can also generate follow-up questions based on the applicant's answers. For example, if the applicant answers, "I worked as a project manager," the generator adds a question such as, "What specific projects were you in charge of?" Step 2: The answer analysis unit analyzes the applicant's answers to the questions posed by the question generation unit. For example, the generation AI performs text analysis of the applicant's answers to evaluate their specificity and clarity of skills. The answer analysis unit can also compare the applicant's answers with past successes and failures to predict the likelihood of success. For example, if the applicant's answers are similar to past successes, a high score will be given. Step 3: The evaluation generation unit generates evaluation results based on the applicant's answers analyzed by the answer analysis unit. For example, the generation AI may generate evaluation results such as, "This applicant has extensive past work experience and demonstrates team leadership, so we will give him a high evaluation." The evaluation generation unit can also visualize the evaluation results and display them in graphs and charts. For example, it can display the applicant's skill evaluation in a radar chart.

[0083] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0084] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0085] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0088] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0089] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0090] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0091] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0092] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0093] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0094] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0095] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0096] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0097] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0098] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0099] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0100] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0104] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0105] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0106] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0107] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0108] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0109] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0110] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0111] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0112] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0113] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0119] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0120] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0121] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0122] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0123] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0124] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0126] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0127] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0128] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0129] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0132] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0133] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0134] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0135] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0136] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0137] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0138] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0139] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0140] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0141] 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.

[0142] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0143] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0144] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0145] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0146] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0147] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0148] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0149] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question generation unit that asks pre-set questions to applicants; a response analysis unit that analyzes responses of applicants to questions posed by the question generation unit; an evaluation generation unit that generates an evaluation result based on the applicant's answers analyzed by the answer analysis unit; A system characterized by:

2. The question generation unit Generate real-time follow-up questions to the applicant's answers to elicit more in-depth information 2. The system of claim 1.

3. The applicant's responses will be recorded audio and video, and non-verbal communication will also be analyzed.

2. The system of claim 1.

4. The answer analysis unit When analyzing the applicant's responses, an algorithm is introduced to evaluate the consistency and logic of the responses.

2. The system of claim 1.

5. The evaluation generation unit Automatically generate a detailed analysis report of the applicant's responses 2. The system of claim 1.

6. The question generation unit Analyzing the emotions of the applicant when answering questions and generating questions to elicit emotionally stable answers 2. The system of claim 1.

7. The answer analysis unit Analyzing the emotional nuances contained in the applicant's answers to assess emotional suitability 2. The system of claim 1.

8. The evaluation generation unit Predicting the applicant's emotional response to the evaluation results and providing appropriate feedback 2. The system of claim 1.

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