System

Abstract

An object of the system according to the embodiment is to visualize personality diagnosis and future potential based on information input in advance and to use the visualized information as complementary information for an interviewer.SOLUTION: A system according to an embodiment includes a diagnosis unit, a visualization unit, and a complementation unit. The diagnosis part performs personality diagnosis based on information inputted in advance. The visualization unit specifically visualizes the future based on the information obtained by the diagnosis unit. The complementing unit uses the information visualized by the visualizing unit as the complementing information of the interviewer.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, it was difficult to identify talented individuals or individuals who were a good fit for the company based on resumes and interviews alone, and there was a problem in that suitability had to be determined after the individual was hired.

[0005] The system of the embodiment aims to diagnose personality and visualize future potential based on information input in advance, and to use this information as supplementary information for interviewers. [Means for solving the problem]

[0006] The system according to the embodiment includes a diagnosis unit, a visualization unit, and a complement unit. The diagnosis unit performs a personality diagnosis based on information input in advance. The visualization unit specifically visualizes future potential based on the information obtained by the diagnosis unit. The complement unit uses the information visualized by the visualization unit as complement information for the interviewer. [Effects of the Invention]

[0007] The system according to the embodiment performs personality diagnosis and visualizes future potential based on information input in advance, and can be used as supplementary information for interviewers. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A human resource evaluation system according to an embodiment of the present invention performs a personality assessment based on pre-input information, visualizes future potential, and utilizes this information as supplementary information for interviewers. The human resource evaluation system performs a personality assessment based on pre-input information, visualizes future potential, and utilizes this information as supplementary information for interviewers to identify better candidates. For example, the human resource evaluation system inputs information such as an applicant's past work experience, educational background, and hobbies. For example, the human resource evaluation system uses AI to perform a personality assessment on the applicant and visualizes future potential. The visualized information is then reviewed by the interviewer and used as a reference during the interview. For example, the human resource evaluation system allows the interviewer to review the applicant's personality assessment results and future potential assessment and use this information as a reference during the interview. Furthermore, the human resource evaluation system provides functions such as "ranking," "human resource assessment," "industry matching," and "compatibility check." For example, the human resource evaluation system displays applicants' aptitudes in a ranking format, allowing the most suitable candidates to be found. The human resource evaluation system can also match applicants with industries and job types based on the results of the applicant's personality assessment. Furthermore, the human resource evaluation system can check the compatibility between applicants and interviewers, making it possible to facilitate smooth communication during interviews. This allows the human resource evaluation system to find talented individuals who are a good fit for your company and who cannot be identified solely from resumes or interviews on the day. This allows the human resource evaluation system to find talented individuals who are a good fit for your company and who cannot be identified solely from resumes or interviews on the day. For example, it can find individuals with promising futures based on the results of an applicant's personality assessment. It can also determine whether an applicant is suitable for your company by matching them with industry and job type. Furthermore, checking compatibility can facilitate smooth communication during interviews and bring out the applicant's true abilities.

[0029] The human resource evaluation system according to the embodiment includes a diagnosis unit, a visualization unit, and an interpolation unit. The diagnosis unit performs a personality diagnosis based on information input in advance. The information input in advance includes, for example, the applicant's resume information and questionnaire results, but is not limited to these examples. The diagnosis unit, for example, uses a psychological test to diagnose the applicant's personality. The diagnosis unit can also diagnose the applicant's personality using behavioral analysis. The diagnosis unit can also diagnose the applicant's personality using AI. For example, the diagnosis unit inputs the applicant's resume information into AI, which then performs a personality diagnosis. The visualization unit visualizes the applicant's future potential based on the information obtained by the diagnosis unit. For example, graphs, charts, simulation results, etc. are used to visualize the future potential, but are not limited to these examples. For example, the visualization unit displays the applicant's future potential in a graph based on the information obtained by the diagnosis unit. The visualization unit can also visualize the applicant's future potential using simulation results. The visualization unit can also visualize the applicant's future potential using AI. For example, the visualization unit inputs the information obtained by the diagnosis unit into AI, which then visualizes the future potential. The complementing unit utilizes the information visualized by the visualization unit as complementary information for the interviewer. Methods of providing complementary information include, but are not limited to, reports to the interviewer and dashboard displays. For example, the complementing unit provides the interviewer with the information visualized by the visualization unit in the form of a report. The complementing unit can also provide the interviewer with information using a dashboard display. The complementing unit can also provide the interviewer with information using AI. For example, the complementing unit inputs the information visualized by the visualization unit into AI, which then provides the information to the interviewer. As a result, the human resource evaluation system according to the embodiment can identify better human resources by performing a personality diagnosis based on information input in advance, visualizing future potential, and utilizing the visualized information as complementary information for the interviewer.

[0030] The human resource evaluation system includes a ranking unit that displays rankings. The ranking unit displays the aptitudes of applicants in a ranking format. Methods for displaying rankings include, but are not limited to, scoring criteria and rank display formats. For example, the ranking unit displays the aptitudes of applicants in a ranking format based on the scoring criteria. The ranking unit can also display the aptitudes of applicants using a rank display format. The ranking unit can also display the aptitudes of applicants in a ranking format using AI. For example, the ranking unit inputs aptitude information of applicants into AI, which then creates a ranking. As a result, by displaying the rankings, the aptitudes of applicants can be displayed in a ranking format, making it possible to find the most suitable talent.

[0031] The human resource evaluation system includes a matching unit that performs industry matching. The matching unit matches the applicant with an industry or job type based on the results of the applicant's personality assessment. Methods for industry matching include, but are not limited to, industry classification standards and matching algorithms. For example, the matching unit evaluates the applicant's aptitude based on industry classification standards and performs industry matching. The matching unit can also evaluate the applicant's aptitude using a matching algorithm and perform industry matching. The matching unit can also perform industry matching using AI. For example, the matching unit inputs the applicant's personality assessment results into AI, which then performs industry matching. By performing industry matching, it is possible to determine whether the applicant is suitable for the company.

[0032] The human resource evaluation system includes a compatibility section that performs a compatibility check. The compatibility section checks the compatibility between the applicant and the interviewer. Methods for checking compatibility include, but are not limited to, psychological tests and behavioral analysis. For example, the compatibility section checks the compatibility between the applicant and the interviewer using a psychological test. The compatibility section can also check the compatibility between the applicant and the interviewer using behavioral analysis. The compatibility section can also check the compatibility between the applicant and the interviewer using AI. For example, the compatibility section inputs information about the applicant and the interviewer into AI, which then checks the compatibility. By performing a compatibility check in this way, communication during the interview can be smoothed and the applicant's true abilities can be brought out.

[0033] The diagnosis unit can perform a personality diagnosis based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The diagnosis unit performs a personality diagnosis based on, for example, the applicant's past work experience. For example, the diagnosis unit analyzes the applicant's work history and evaluates personality traits. The diagnosis unit can also perform a personality diagnosis based on the applicant's educational background. For example, the diagnosis unit analyzes the applicant's academic performance and field of major and evaluates personality traits. The diagnosis unit can also perform a personality diagnosis based on the applicant's hobbies and special skills. For example, the diagnosis unit analyzes the applicant's hobbies and special skills and evaluates personality traits. This enables a more accurate personality diagnosis by performing a personality diagnosis based on information such as the applicant's past work experience, educational background, and hobbies.

[0034] The visualization unit can visualize the applicant's future prospects based on the information obtained by the diagnosis unit. The visualization unit, for example, displays the applicant's future prospects in a graph based on the information obtained by the diagnosis unit. For example, the visualization unit displays the applicant's future prospects in a line graph. The visualization unit can also display the applicant's future prospects in a chart based on the information obtained by the diagnosis unit. For example, the visualization unit displays the applicant's future prospects in a pie chart. The visualization unit can also display the applicant's future prospects as simulation results based on the information obtained by the diagnosis unit. For example, the visualization unit displays the applicant's future prospects as simulation results. In this way, by visualizing the applicant's future prospects, the applicant's future possibilities can be understood.

[0035] The complementing unit allows the interviewer to check the information visualized by the visualization unit and use it as a reference during the interview. The complementing unit, for example, provides the information visualized by the visualization unit to the interviewer in the form of a report. For example, the complementing unit provides the interviewer with the results of a personality assessment and an evaluation of future potential of the applicant in the form of a report. The complementing unit can also provide the interviewer with the information visualized by the visualization unit in a dashboard display. For example, the complementing unit provides the interviewer with the results of a personality assessment and an evaluation of future potential of the applicant in a dashboard display. This allows the interviewer to check the visualized information and more accurately judge the applicant's suitability during the interview.

[0036] The diagnosis unit can collect additional data to improve the accuracy of the personality diagnosis based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The diagnosis unit, for example, analyzes the applicant's past work experience and collects data to evaluate related personality traits. For example, the diagnosis unit analyzes the applicant's work history and collects data to evaluate personality traits. The diagnosis unit can also collect data to evaluate personality traits related to the applicant's academic performance and field of major based on the applicant's educational background. For example, the diagnosis unit analyzes the applicant's academic performance and field of major and collects data to evaluate personality traits. The diagnosis unit can also collect data to evaluate personality traits based on the applicant's hobbies and interests. For example, the diagnosis unit analyzes the applicant's hobbies and interests and collects data to evaluate personality traits. By collecting additional data, the accuracy of the personality diagnosis can be improved.

[0037] The diagnostic unit can analyze the applicant's response patterns during the personality diagnosis and evaluate the consistency of the responses. For example, the diagnostic unit analyzes the applicant's response patterns over time and evaluates consistent responses. For example, the diagnostic unit analyzes the applicant's response patterns over time and evaluates consistent responses. The diagnostic unit can also analyze the applicant's response patterns for each question and evaluate consistent answers. For example, the diagnostic unit analyzes the applicant's response patterns for each question and evaluates consistent answers. The diagnostic unit can also compare the applicant's response patterns with other applicants and evaluate whether they match general response trends. For example, the diagnostic unit compares the applicant's response patterns with other applicants and evaluates whether they match general response trends. In this way, the reliability of the personality diagnosis is improved by evaluating the consistency of the responses.

[0038] The diagnostic unit can evaluate the stress level of an applicant during a personality diagnosis and reflect the applicant's stress tolerance in the diagnosis results. The diagnostic unit, for example, analyzes the applicant's response speed and evaluates the stress level. For example, the diagnostic unit analyzes the applicant's response speed and evaluates the stress level. The diagnostic unit can also analyze the content of the applicant's responses and extract and evaluate keywords related to stress. For example, the diagnostic unit analyzes the content of the applicant's responses and extract and evaluate keywords related to stress. The diagnostic unit can also collect the applicant's biometric information (heart rate, galvanic skin response, etc.) and evaluate the stress level. For example, the diagnostic unit collects the applicant's biometric information and evaluates the stress level. In this way, the stress tolerance of the applicant can be evaluated by reflecting the stress tolerance in the diagnosis results.

[0039] The diagnosis unit can adjust the diagnosis results by specifically considering the geographical and cultural backgrounds of the applicant during the personality diagnosis. For example, the diagnosis unit adds questions that take into account the cultural background based on the applicant's place of origin. For example, the diagnosis unit adds questions that take into account the cultural background based on the applicant's place of origin. The diagnosis unit can also evaluate personality traits specific to a region based on the applicant's place of residence. For example, the diagnosis unit evaluates personality traits specific to a region based on the applicant's place of residence. The diagnosis unit can also provide diagnosis results that take into account the cultural background based on the applicant's nationality. For example, the diagnosis unit provides diagnosis results that take into account the cultural background based on the applicant's nationality. This makes it possible to provide more accurate diagnosis results by taking into account the geographical and cultural backgrounds.

[0040] The diagnostic unit can analyze the applicant's social media activity during the personality diagnosis and reflect it in the diagnosis results. The diagnostic unit, for example, analyzes the content of the applicant's social media posts to evaluate personality traits. For example, the diagnostic unit analyzes the content of the applicant's social media posts to evaluate personality traits. The diagnostic unit can also analyze the applicant's friendships on social media to evaluate sociability. For example, the diagnostic unit can analyze the applicant's friendships on social media to evaluate sociability. The diagnostic unit can also analyze the frequency of the applicant's social media activity to evaluate proactiveness. For example, the diagnostic unit can analyze the frequency of the applicant's social media activity to evaluate proactiveness. In this way, by analyzing social media activity, more accurate diagnostic results can be provided.

[0041] The diagnosis unit can customize the diagnosis algorithm by reflecting the applicant's past feedback when conducting a personality diagnosis. The diagnosis unit adjusts the personality diagnosis algorithm based on, for example, the applicant's past job evaluations. For example, the diagnosis unit adjusts the personality diagnosis algorithm based on the applicant's past job evaluations. The diagnosis unit can also customize the personality diagnosis algorithm based on the applicant's past interview feedback. For example, the diagnosis unit customizes the personality diagnosis algorithm based on the applicant's past interview feedback. The diagnosis unit can also adjust the personality diagnosis algorithm based on feedback from the applicant's past colleagues. For example, the diagnosis unit adjusts the personality diagnosis algorithm based on feedback from the applicant's past colleagues. In this way, the accuracy of the diagnosis algorithm is improved by reflecting past feedback.

[0042] During visualization, the visualization unit can improve the accuracy of predicting future potential based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The visualization unit, for example, analyzes the applicant's past work experience to improve the accuracy of predicting future potential. For example, the visualization unit analyzes the applicant's past work experience to improve the accuracy of predicting future potential. The visualization unit can also improve the accuracy of predicting future potential based on the applicant's educational background. For example, the visualization unit can improve the accuracy of predicting future potential based on the applicant's educational background. The visualization unit can also improve the accuracy of predicting future potential based on the applicant's hobbies and interests. For example, the visualization unit analyzes the applicant's hobbies and interests to improve the accuracy of predicting future potential. As a result, the accuracy of predicting future potential is improved by using information such as the applicant's past work experience, educational background, and hobbies as a basis.

[0043] During visualization, the visualization unit can simulate the applicant's career path and present future career possibilities. The visualization unit, for example, simulates the applicant's future career path based on the applicant's past work experience. For example, the visualization unit simulates the applicant's future career path based on the applicant's past work experience. The visualization unit can also simulate the future career path based on the applicant's educational background. For example, the visualization unit simulates the applicant's future career path based on the applicant's educational background. The visualization unit can also simulate the future career path based on the applicant's hobbies and interests. For example, the visualization unit simulates the applicant's future career path based on the applicant's hobbies and interests. In this way, by simulating the career path, future career possibilities can be presented.

[0044] During visualization, the visualization unit can predict the evolution of the applicant's skill set and evaluate the applicant's ability to meet future skill demands. The visualization unit, for example, predicts the evolution of the skill set based on the applicant's past work experience. For example, the visualization unit predicts the evolution of the skill set based on the applicant's past work experience. The visualization unit can also predict the evolution of the skill set based on the applicant's educational background. For example, the visualization unit predicts the evolution of the skill set based on the applicant's educational background. The visualization unit can also predict the evolution of the skill set based on the applicant's hobbies and interests. For example, the visualization unit predicts the evolution of the skill set based on the applicant's hobbies and interests. In this way, by predicting the evolution of the skill set, it is possible to evaluate the applicant's ability to meet future skill demands.

[0045] During visualization, the visualization unit can evaluate future potential by specifically considering the applicant's geographical background and cultural background. For example, the visualization unit evaluates future potential taking into account the cultural background based on the applicant's place of origin. For example, the visualization unit evaluates future potential taking into account the cultural background based on the applicant's place of origin. The visualization unit can also evaluate region-specific future potential based on the applicant's place of residence. For example, the visualization unit evaluates region-specific future potential based on the applicant's place of residence. The visualization unit can also evaluate future potential taking into account the cultural background based on the applicant's nationality. For example, the visualization unit evaluates future potential taking into account the cultural background based on the applicant's nationality. This makes it possible to evaluate future potential more accurately by taking into account the geographical background and cultural background.

[0046] The visualization unit can analyze the applicant's social media activity during visualization and reflect it in the evaluation of future potential. The visualization unit, for example, analyzes the content of the applicant's posts on social media and evaluates future potential. For example, the visualization unit analyzes the content of the applicant's posts on social media and evaluates future potential. The visualization unit can also analyze the applicant's friendships on social media and evaluate their sociability. For example, the visualization unit can analyze the applicant's friendships on social media and evaluate their sociability. The visualization unit can also analyze the frequency of the applicant's social media activity and evaluate their proactiveness. For example, the visualization unit can analyze the frequency of the applicant's social media activity and evaluate their proactiveness. In this way, analyzing social media activity enables a more accurate evaluation of future potential.

[0047] The visualization unit can customize the visualization algorithm by reflecting the applicant's past feedback during visualization. The visualization unit adjusts the visualization algorithm based on, for example, the applicant's past job evaluations. For example, the visualization unit adjusts the visualization algorithm based on the applicant's past job evaluations. The visualization unit can also customize the visualization algorithm based on the applicant's past interview feedback. For example, the visualization unit customizes the visualization algorithm based on the applicant's past interview feedback. The visualization unit can also adjust the visualization algorithm based on feedback from the applicant's past colleagues. For example, the visualization unit adjusts the visualization algorithm based on feedback from the applicant's past colleagues. In this way, the accuracy of the visualization algorithm is improved by reflecting the past feedback.

[0048] When providing the complementary information, the complementing unit can improve the accuracy of the complementary information based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The complementing unit improves the accuracy of the complementary information, for example, based on the applicant's past work experience. For example, the complementing unit improves the accuracy of the complementary information based on the applicant's past work experience. The complementing unit can also improve the accuracy of the complementary information based on the applicant's educational background. For example, the complementing unit improves the accuracy of the complementary information based on the applicant's educational background. The complementing unit can also improve the accuracy of the complementary information based on the applicant's hobbies and interests. For example, the complementing unit improves the accuracy of the complementary information based on the applicant's hobbies and interests. In this way, the accuracy of the complementary information is improved by using information such as the applicant's past work experience, educational background, and hobbies as the basis.

[0049] When providing the supplementary information, the completion unit can analyze the applicant's response patterns and suggest questions to the interviewer. The completion unit, for example, analyzes the applicant's response patterns and suggests specific questions to the interviewer. For example, the completion unit analyzes the applicant's response patterns and suggests specific questions to the interviewer. The completion unit can also analyze the applicant's response patterns and suggest follow-up questions to the interviewer. For example, the completion unit analyzes the applicant's response patterns and suggests follow-up questions to the interviewer. The completion unit can also analyze the applicant's response patterns and suggest probing questions to the interviewer. For example, the completion unit analyzes the applicant's response patterns and suggests probing questions to the interviewer. In this way, appropriate questions to the interviewer can be suggested by analyzing the response patterns.

[0050] When providing the supplementary information, the supplementary unit can evaluate the stress level of the applicant and provide advice to the interviewer. For example, the supplementary unit evaluates the stress level of the applicant and provides advice to the interviewer to help them relax. For example, the supplementary unit evaluates the stress level of the applicant and provides advice to the interviewer to help them relax. The supplementary unit can also evaluate the stress level of the applicant and suggest questions to the interviewer to reduce stress. For example, the supplementary unit evaluates the stress level of the applicant and suggests questions to the interviewer to reduce stress. The supplementary unit can also evaluate the stress level of the applicant and advise the interviewer to set up an appropriate interview environment. For example, the supplementary unit evaluates the stress level of the applicant and advises the interviewer to set up an appropriate interview environment. In this way, by evaluating the stress level, appropriate advice can be provided to the interviewer.

[0051] When providing the complementary information, the complementary unit can provide the complementary information by specifically taking into consideration the geographical background and cultural background of the applicant. For example, the complementary unit provides complementary information that takes into consideration the cultural background based on the applicant's place of origin. For example, the complementary unit provides complementary information that takes into consideration the cultural background based on the applicant's place of origin. The complementary unit can also provide region-specific complementary information based on the applicant's place of residence. For example, the complementary unit provides region-specific complementary information based on the applicant's place of residence. The complementary unit can also provide complementary information that takes into consideration the cultural background based on the applicant's nationality. For example, the complementary unit provides complementary information that takes into consideration the cultural background based on the applicant's nationality. This makes it possible to provide more accurate complementary information by taking into consideration the geographical background and cultural background.

[0052] When providing the supplemental information, the supplemental unit can analyze the applicant's social media activity and reflect it in the supplemental information. For example, the supplemental unit analyzes the content of the applicant's posts on social media and reflects it in the supplemental information. For example, the supplemental unit analyzes the content of the applicant's posts on social media and reflects it in the supplemental information. The supplemental unit can also analyze the applicant's friendships on social media and provide supplemental information regarding sociability. For example, the supplemental unit can analyze the applicant's friendships on social media and provide supplemental information regarding sociability. The supplemental unit can also analyze the frequency of the applicant's social media activity and provide supplemental information regarding proactivity. For example, the supplemental unit can analyze the frequency of the applicant's social media activity and provide supplemental information regarding proactivity. In this way, more accurate supplemental information can be provided by analyzing social media activity.

[0053] When providing the complementary information, the complementing unit can customize the method of providing the complementary information by reflecting the applicant's past feedback. The complementing unit adjusts the method of providing the complementary information based on, for example, the applicant's past job evaluations. For example, the complementing unit adjusts the method of providing the complementary information based on the applicant's past job evaluations. The complementing unit can also customize the method of providing the complementary information based on the applicant's past interview feedback. For example, the complementing unit customizes the method of providing the complementary information based on the applicant's past interview feedback. The complementing unit can also adjust the method of providing the complementary information based on feedback from the applicant's past colleagues. For example, the complementing unit adjusts the method of providing the complementary information based on feedback from the applicant's past colleagues. In this way, by reflecting past feedback, the accuracy of the method of providing the complementary information is improved.

[0054] When creating rankings, the ranking unit can improve the accuracy of the rankings based on specific information such as the applicants' past work experience, educational background, hobbies, and special skills. The ranking unit, for example, improves the accuracy of the rankings based on the applicants' past work experience. For example, the ranking unit improves the accuracy of the rankings based on the applicants' past work experience. The ranking unit can also improve the accuracy of the rankings based on the applicants' educational background. For example, the ranking unit improves the accuracy of the rankings based on the applicants' educational background. The ranking unit can also improve the accuracy of the rankings based on the applicants' hobbies and interests. For example, the ranking unit improves the accuracy of the rankings based on the applicants' hobbies and interests. In this way, the accuracy of the rankings is improved by using information such as the applicants' past work experience, educational background, and hobbies.

[0055] When creating rankings, the ranking unit can analyze applicants' response patterns and evaluate the reliability of the rankings. The ranking unit, for example, analyzes applicants' response patterns and creates highly reliable rankings. For example, the ranking unit analyzes applicants' response patterns and creates highly reliable rankings. The ranking unit can also compare applicants' response patterns with other applicants and create highly reliable rankings. For example, the ranking unit compares applicants' response patterns with other applicants and create highly reliable rankings. The ranking unit can also analyze applicants' response patterns over time and create highly reliable rankings. For example, the ranking unit analyzes applicants' response patterns over time and creates highly reliable rankings. In this way, highly reliable rankings can be created by analyzing response patterns.

[0056] When creating rankings, the ranking unit can evaluate the stress levels of applicants and reflect them in the rankings. For example, the ranking unit evaluates the stress levels of applicants and reflects them in the rankings. For example, the ranking unit evaluates the stress levels of applicants and reflects them in the rankings. The ranking unit can also compare the stress levels of applicants with other applicants and reflect them in the rankings. For example, the ranking unit can compare the stress levels of applicants with other applicants and reflect them in the rankings. The ranking unit can also analyze the stress levels of applicants over time and reflect them in the rankings. For example, the ranking unit can analyze the stress levels of applicants over time and reflect them in the rankings. In this way, by evaluating stress levels, more accurate rankings can be created.

[0057] When creating rankings, the ranking unit can create rankings that specifically take into consideration the geographical and cultural backgrounds of applicants. For example, the ranking unit creates rankings that take into consideration cultural backgrounds based on the applicants' hometowns. For example, the ranking unit creates rankings that take into consideration cultural backgrounds based on the applicants' hometowns. The ranking unit can also create region-specific rankings based on the applicants' places of residence. For example, the ranking unit creates region-specific rankings based on the applicants' places of residence. The ranking unit can also create rankings that take into consideration cultural backgrounds based on the applicants' nationalities. For example, the ranking unit creates rankings that take into consideration cultural backgrounds based on the applicants' nationalities. In this way, by taking into consideration geographical and cultural backgrounds, more accurate rankings can be created.

[0058] When creating rankings, the ranking unit can analyze the social media activities of applicants and reflect them in the rankings. For example, the ranking unit analyzes the content of applicants' posts on social media and reflects them in the rankings. For example, the ranking unit analyzes the content of applicants' posts on social media and reflects them in the rankings. The ranking unit can also analyze the applicants' friendships on social media and create a ranking related to sociability. For example, the ranking unit can analyze the applicants' friendships on social media and create a ranking related to sociability. The ranking unit can also analyze the frequency of the applicants' social media activities and create a ranking related to proactivity. For example, the ranking unit can analyze the applicants' social media activities and create a ranking related to proactivity. In this way, by analyzing social media activities, more accurate rankings can be created.

[0059] When creating rankings, the ranking unit can customize the ranking algorithm by reflecting past feedback from applicants. The ranking unit, for example, adjusts the ranking algorithm based on past job evaluations of applicants. For example, the ranking unit adjusts the ranking algorithm based on past job evaluations of applicants. The ranking unit can also customize the ranking algorithm based on past interview feedback of applicants. For example, the ranking unit customizes the ranking algorithm based on past interview feedback of applicants. The ranking unit can also adjust the ranking algorithm based on feedback from past colleagues of applicants. For example, the ranking unit adjusts the ranking algorithm based on feedback from past colleagues of applicants. In this way, the accuracy of the ranking algorithm is improved by reflecting past feedback.

[0060] During matching, the matching unit can improve the accuracy of industry matching based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The matching unit improves the accuracy of industry matching based on, for example, the applicant's past work experience. For example, the matching unit improves the accuracy of industry matching based on the applicant's past work experience. The matching unit can also improve the accuracy of industry matching based on the applicant's educational background. For example, the matching unit improves the accuracy of industry matching based on the applicant's educational background. The matching unit can also improve the accuracy of industry matching based on the applicant's hobbies and interests. For example, the matching unit improves the accuracy of industry matching based on the applicant's hobbies and interests. In this way, the accuracy of industry matching is improved by using information such as the applicant's past work experience, educational background, and hobbies.

[0061] The matching unit can analyze the applicant's response patterns during matching and evaluate the reliability of industry matching. The matching unit, for example, analyzes the applicant's response patterns and performs highly reliable industry matching. For example, the matching unit analyzes the applicant's response patterns and performs highly reliable industry matching. The matching unit can also compare the applicant's response patterns with other applicants and perform highly reliable industry matching. For example, the matching unit can compare the applicant's response patterns with other applicants and perform highly reliable industry matching. The matching unit can also analyze the applicant's response patterns on a time axis and perform highly reliable industry matching. For example, the matching unit analyzes the applicant's response patterns on a time axis and performs highly reliable industry matching. In this way, highly reliable industry matching can be performed by analyzing the response patterns.

[0062] The matching unit can evaluate the stress level of an applicant during matching and reflect it in industry matching. The matching unit, for example, evaluates the stress level of an applicant and reflects it in industry matching. For example, the matching unit evaluates the stress level of an applicant and reflects it in industry matching. The matching unit can also compare the stress level of an applicant with other applicants and reflect it in industry matching. For example, the matching unit can compare the stress level of an applicant with other applicants and reflect it in industry matching. The matching unit can also analyze the stress level of an applicant over time and reflect it in industry matching. For example, the matching unit can analyze the stress level of an applicant over time and reflect it in industry matching. In this way, by evaluating the stress level, more accurate industry matching can be performed.

[0063] When matching, the matching unit can perform industry matching by specifically considering the geographical background and cultural background of the applicant. For example, the matching unit performs industry matching that takes cultural background into consideration based on the applicant's place of origin. For example, the matching unit performs industry matching that takes cultural background into consideration based on the applicant's place of origin. The matching unit can also perform region-specific industry matching based on the applicant's place of residence. For example, the matching unit performs region-specific industry matching based on the applicant's place of residence. The matching unit can also perform industry matching that takes cultural background into consideration based on the applicant's nationality. For example, the matching unit performs industry matching that takes cultural background into consideration based on the applicant's nationality. In this way, more accurate industry matching can be performed by taking geographical background and cultural background into consideration.

[0064] The matching unit can analyze the applicant's social media activity during matching and reflect it in industry matching. The matching unit, for example, analyzes the content of the applicant's posts on social media and reflects it in industry matching. For example, the matching unit analyzes the content of the applicant's posts on social media and reflects it in industry matching. The matching unit can also analyze the applicant's friendships on social media and perform industry matching related to sociability. For example, the matching unit can analyze the applicant's friendships on social media and perform industry matching related to sociability. The matching unit can also analyze the frequency of the applicant's social media activity and perform industry matching related to proactiveness. For example, the matching unit can analyze the applicant's frequency of social media activity and perform industry matching related to proactiveness. In this way, more accurate industry matching can be performed by analyzing social media activity.

[0065] The matching unit can customize the industry matching algorithm by reflecting the applicant's past feedback when matching. The matching unit adjusts the industry matching algorithm based on, for example, the applicant's past job evaluations. For example, the matching unit adjusts the industry matching algorithm based on the applicant's past job evaluations. The matching unit can also customize the industry matching algorithm based on the applicant's past interview feedback. For example, the matching unit customizes the industry matching algorithm based on the applicant's past interview feedback. The matching unit can also adjust the industry matching algorithm based on feedback from the applicant's past colleagues. For example, the matching unit adjusts the industry matching algorithm based on feedback from the applicant's past colleagues. In this way, the accuracy of the industry matching algorithm is improved by reflecting past feedback.

[0066] When checking compatibility, the compatibility section can improve the accuracy of the compatibility check based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The compatibility section, for example, improves the accuracy of the compatibility check based on the applicant's past work experience. For example, the compatibility section improves the accuracy of the compatibility check based on the applicant's past work experience. The compatibility section can also improve the accuracy of the compatibility check based on the applicant's educational background. For example, the compatibility section improves the accuracy of the compatibility check based on the applicant's educational background. The compatibility section can also improve the accuracy of the compatibility check based on the applicant's hobbies and interests. For example, the compatibility section improves the accuracy of the compatibility check based on the applicant's hobbies and interests. In this way, the accuracy of the compatibility check is improved by using information such as the applicant's past work experience, educational background, and hobbies.

[0067] The compatibility section can analyze the applicant's response patterns during a compatibility check and evaluate the reliability of the compatibility check. The compatibility section, for example, analyzes the applicant's response patterns and performs a highly reliable compatibility check. For example, the compatibility section analyzes the applicant's response patterns and performs a highly reliable compatibility check. The compatibility section can also compare the applicant's response patterns with other applicants and perform a highly reliable compatibility check. For example, the compatibility section compares the applicant's response patterns with other applicants and performs a highly reliable compatibility check. The compatibility section can also analyze the applicant's response patterns over time and perform a highly reliable compatibility check. For example, the compatibility section analyzes the applicant's response patterns over time and performs a highly reliable compatibility check. In this way, a highly reliable compatibility check can be performed by analyzing the response patterns.

[0068] The compatibility section can evaluate the stress level of the applicant during the compatibility check and reflect it in the compatibility check. The compatibility section, for example, evaluates the stress level of the applicant and reflects it in the compatibility check. For example, the compatibility section evaluates the stress level of the applicant and reflects it in the compatibility check. The compatibility section can also compare the stress level of the applicant with other applicants and reflect it in the compatibility check. For example, the compatibility section can compare the stress level of the applicant with other applicants and reflect it in the compatibility check. The compatibility section can also analyze the stress level of the applicant over time and reflect it in the compatibility check. For example, the compatibility section can analyze the stress level of the applicant over time and reflect it in the compatibility check. In this way, by evaluating the stress level, a more accurate compatibility check can be performed.

[0069] When performing a compatibility check, the compatibility department can perform a compatibility check that specifically takes into account the applicant's geographical background and cultural background. For example, the compatibility department performs a compatibility check that takes into account the applicant's cultural background based on the applicant's place of origin. For example, the compatibility department performs a compatibility check that takes into account the applicant's cultural background based on the applicant's place of origin. The compatibility department can also perform a region-specific compatibility check based on the applicant's place of residence. For example, the compatibility department can perform a region-specific compatibility check based on the applicant's place of residence. The compatibility department can also perform a compatibility check that takes into account the applicant's cultural background based on the applicant's nationality. For example, the compatibility department can perform a compatibility check that takes into account the applicant's cultural background based on the applicant's nationality. This allows for a more accurate compatibility check by taking into account the geographical background and cultural background.

[0070] The compatibility section can analyze the applicant's social media activity during the compatibility check and reflect the results in the compatibility check. The compatibility section, for example, analyzes the applicant's social media posts and reflects the results in the compatibility check. For example, the compatibility section analyzes the applicant's social media posts and reflects the results in the compatibility check. The compatibility section can also analyze the applicant's social media friendships and perform a compatibility check regarding sociability. For example, the compatibility section can analyze the applicant's social media friendships and perform a compatibility check regarding sociability. The compatibility section can also analyze the frequency of the applicant's social media activity and perform a compatibility check regarding proactiveness. For example, the compatibility section can analyze the applicant's social media activity and perform a compatibility check regarding proactiveness. In this way, a more accurate compatibility check can be performed by analyzing social media activity.

[0071] The compatibility unit can customize the compatibility check algorithm by reflecting the applicant's past feedback when checking compatibility. The compatibility unit, for example, adjusts the compatibility check algorithm based on the applicant's past job evaluations. For example, the compatibility unit adjusts the compatibility check algorithm based on the applicant's past job evaluations. The compatibility unit can also customize the compatibility check algorithm based on the applicant's past interview feedback. For example, the compatibility unit customizes the compatibility check algorithm based on the applicant's past interview feedback. The compatibility unit can also adjust the compatibility check algorithm based on feedback from the applicant's past colleagues. For example, the compatibility unit adjusts the compatibility check algorithm based on feedback from the applicant's past colleagues. In this way, the accuracy of the compatibility check algorithm is improved by reflecting past feedback.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The diagnostic unit can evaluate an applicant's stress tolerance based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. For example, the diagnostic unit can analyze the applicant's past work experience to evaluate stress tolerance. The diagnostic unit can also evaluate stress tolerance related to academic performance and field of major based on the applicant's educational background. Furthermore, the diagnostic unit can evaluate stress tolerance based on the applicant's hobbies and special skills. This allows for a more accurate personality diagnosis by evaluating the applicant's stress tolerance.

[0074] The visualization unit can visualize the applicant's leadership ability based on the information obtained by the diagnosis unit. For example, the visualization unit can display the applicant's leadership ability in a graph based on the applicant's past work experience. The visualization unit can also display the applicant's leadership ability in a chart based on the applicant's educational background and hobbies. Furthermore, the visualization unit can visualize the applicant's leadership ability using simulation results. In this way, by visualizing the applicant's leadership ability, the applicant's future potential can be understood.

[0075] The complementing unit can evaluate the applicant's communication skills based on the information visualized by the visualization unit. For example, the complementing unit can provide the interviewer with a report on the applicant's communication skills based on the applicant's personality assessment results and future potential assessment. The complementing unit can also evaluate the applicant's communication skills using a dashboard display. Furthermore, the complementing unit can evaluate the applicant's communication skills using AI. This allows the interviewer to check the applicant's communication skills and more accurately determine the applicant's suitability during the interview.

[0076] The visualization unit can visualize the applicant's creativity based on the information obtained by the diagnosis unit. For example, the visualization unit can display the applicant's creativity in a graph based on the applicant's past work experience. The visualization unit can also display the applicant's creativity in a chart based on the applicant's educational background and hobbies. Furthermore, the visualization unit can visualize the applicant's creativity using simulation results. In this way, by visualizing the applicant's creativity, the applicant's future potential can be understood.

[0077] The complementing unit can evaluate the applicant's problem-solving ability based on the information visualized by the visualization unit. For example, the complementing unit can provide the interviewer with a report on the applicant's problem-solving ability based on the applicant's personality assessment results and future potential assessment. The complementing unit can also evaluate the applicant's problem-solving ability using a dashboard display. Furthermore, the complementing unit can evaluate the applicant's problem-solving ability using AI. This allows the interviewer to check the applicant's problem-solving ability and more accurately determine the applicant's suitability during the interview.

[0078] The visualization unit can visualize the teamwork ability of the applicant based on the information obtained by the diagnosis unit. For example, the visualization unit can display the teamwork ability in a graph based on the applicant's past work experience. The visualization unit can also display the teamwork ability in a chart based on the applicant's educational background and hobbies. Furthermore, the visualization unit can visualize the teamwork ability of the applicant using simulation results. In this way, by visualizing the applicant's teamwork ability, the applicant's future potential can be understood.

[0079] The supplementation unit can evaluate the applicant's risk management ability based on the information visualized by the visualization unit. For example, the supplementation unit can provide the interviewer with a report on the applicant's risk management ability based on the applicant's personality assessment results and future potential assessment. The supplementation unit can also evaluate the applicant's risk management ability using a dashboard display. Furthermore, the supplementation unit can evaluate the applicant's risk management ability using AI. This allows the interviewer to check the applicant's risk management ability and more accurately determine the applicant's suitability during the interview.

[0080] The processing flow of the first embodiment will be briefly explained below.

[0081] Step 1: The diagnostic department performs a personality diagnosis based on pre-input information. The pre-input information includes the applicant's resume information and questionnaire results. The diagnostic department diagnoses the applicant's personality using psychological tests, behavioral analysis, or AI. For example, the applicant's resume information is input into the AI, which then performs a personality diagnosis. Step 2: The visualization unit visualizes future potential based on the information obtained by the diagnosis unit. Graphs, charts, simulation results, etc. are used to visualize future potential. Based on the information obtained by the diagnosis unit, the visualization unit displays the applicant's future potential in graphs or visualizes it using simulation results. It is also possible to visualize the applicant's future potential using AI. Step 3: The complementing unit uses the information visualized by the visualization unit as complementary information for the interviewer. Methods of providing complementary information include reports to the interviewer and displaying it on a dashboard. The complementing unit provides the information visualized by the visualization unit to the interviewer in the form of a report, or displays the information on a dashboard. It can also provide information to the interviewer using AI.

[0082] (Example 2) A human resource evaluation system according to an embodiment of the present invention performs a personality assessment based on pre-input information, visualizes future potential, and utilizes this information as supplementary information for interviewers. The human resource evaluation system performs a personality assessment based on pre-input information, visualizes future potential, and utilizes this information as supplementary information for interviewers to identify better candidates. For example, the human resource evaluation system inputs information such as an applicant's past work experience, educational background, and hobbies. For example, the human resource evaluation system uses AI to perform a personality assessment on the applicant and visualizes future potential. The visualized information is then reviewed by the interviewer and used as a reference during the interview. For example, the human resource evaluation system allows the interviewer to review the applicant's personality assessment results and future potential assessment and use this information as a reference during the interview. Furthermore, the human resource evaluation system provides functions such as "ranking," "human resource assessment," "industry matching," and "compatibility check." For example, the human resource evaluation system displays applicants' aptitudes in a ranking format, allowing the most suitable candidates to be found. The human resource evaluation system can also match applicants with industries and job types based on the results of the applicant's personality assessment. Furthermore, the human resource evaluation system can check the compatibility between applicants and interviewers, making it possible to facilitate smooth communication during interviews. This allows the human resource evaluation system to find talented individuals who are a good fit for your company and who cannot be identified solely from resumes or interviews on the day. This allows the human resource evaluation system to find talented individuals who are a good fit for your company and who cannot be identified solely from resumes or interviews on the day. For example, it can find individuals with promising futures based on the results of an applicant's personality assessment. It can also determine whether an applicant is suitable for your company by matching them with industry and job type. Furthermore, checking compatibility can facilitate smooth communication during interviews and bring out the applicant's true abilities.

[0083] The human resource evaluation system according to the embodiment includes a diagnosis unit, a visualization unit, and an interpolation unit. The diagnosis unit performs a personality diagnosis based on information input in advance. The information input in advance includes, for example, the applicant's resume information and questionnaire results, but is not limited to these examples. The diagnosis unit, for example, uses a psychological test to diagnose the applicant's personality. The diagnosis unit can also diagnose the applicant's personality using behavioral analysis. The diagnosis unit can also diagnose the applicant's personality using AI. For example, the diagnosis unit inputs the applicant's resume information into AI, which then performs a personality diagnosis. The visualization unit visualizes the applicant's future potential based on the information obtained by the diagnosis unit. For example, graphs, charts, simulation results, etc. are used to visualize the future potential, but are not limited to these examples. For example, the visualization unit displays the applicant's future potential in a graph based on the information obtained by the diagnosis unit. The visualization unit can also visualize the applicant's future potential using simulation results. The visualization unit can also visualize the applicant's future potential using AI. For example, the visualization unit inputs the information obtained by the diagnosis unit into AI, which then visualizes the future potential. The complementing unit utilizes the information visualized by the visualization unit as complementary information for the interviewer. Methods of providing complementary information include, but are not limited to, reports to the interviewer and dashboard displays. For example, the complementing unit provides the interviewer with the information visualized by the visualization unit in the form of a report. The complementing unit can also provide the interviewer with information using a dashboard display. The complementing unit can also provide the interviewer with information using AI. For example, the complementing unit inputs the information visualized by the visualization unit into AI, which then provides the information to the interviewer. As a result, the human resource evaluation system according to the embodiment can identify better human resources by performing a personality diagnosis based on information input in advance, visualizing future potential, and utilizing the visualized information as complementary information for the interviewer.

[0084] The human resource evaluation system includes a ranking unit that displays rankings. The ranking unit displays the aptitudes of applicants in a ranking format. Methods for displaying rankings include, but are not limited to, scoring criteria and rank display formats. For example, the ranking unit displays the aptitudes of applicants in a ranking format based on the scoring criteria. The ranking unit can also display the aptitudes of applicants using a rank display format. The ranking unit can also display the aptitudes of applicants in a ranking format using AI. For example, the ranking unit inputs aptitude information of applicants into AI, which then creates a ranking. As a result, by displaying the rankings, the aptitudes of applicants can be displayed in a ranking format, making it possible to find the most suitable talent.

[0085] The human resource evaluation system includes a matching unit that performs industry matching. The matching unit matches the applicant with an industry or job type based on the results of the applicant's personality assessment. Methods for industry matching include, but are not limited to, industry classification standards and matching algorithms. For example, the matching unit evaluates the applicant's aptitude based on industry classification standards and performs industry matching. The matching unit can also evaluate the applicant's aptitude using a matching algorithm and perform industry matching. The matching unit can also perform industry matching using AI. For example, the matching unit inputs the applicant's personality assessment results into AI, which then performs industry matching. By performing industry matching, it is possible to determine whether the applicant is suitable for the company.

[0086] The human resource evaluation system includes a compatibility section that performs a compatibility check. The compatibility section checks the compatibility between the applicant and the interviewer. Methods for checking compatibility include, but are not limited to, psychological tests and behavioral analysis. For example, the compatibility section checks the compatibility between the applicant and the interviewer using a psychological test. The compatibility section can also check the compatibility between the applicant and the interviewer using behavioral analysis. The compatibility section can also check the compatibility between the applicant and the interviewer using AI. For example, the compatibility section inputs information about the applicant and the interviewer into AI, which then checks the compatibility. By performing a compatibility check in this way, communication during the interview can be smoothed and the applicant's true abilities can be brought out.

[0087] The diagnosis unit can perform a personality diagnosis based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The diagnosis unit performs a personality diagnosis based on, for example, the applicant's past work experience. For example, the diagnosis unit analyzes the applicant's work history and evaluates personality traits. The diagnosis unit can also perform a personality diagnosis based on the applicant's educational background. For example, the diagnosis unit analyzes the applicant's academic performance and field of major and evaluates personality traits. The diagnosis unit can also perform a personality diagnosis based on the applicant's hobbies and special skills. For example, the diagnosis unit analyzes the applicant's hobbies and special skills and evaluates personality traits. This enables a more accurate personality diagnosis by performing a personality diagnosis based on information such as the applicant's past work experience, educational background, and hobbies.

[0088] The visualization unit can visualize the applicant's future prospects based on the information obtained by the diagnosis unit. The visualization unit, for example, displays the applicant's future prospects in a graph based on the information obtained by the diagnosis unit. For example, the visualization unit displays the applicant's future prospects in a line graph. The visualization unit can also display the applicant's future prospects in a chart based on the information obtained by the diagnosis unit. For example, the visualization unit displays the applicant's future prospects in a pie chart. The visualization unit can also display the applicant's future prospects as simulation results based on the information obtained by the diagnosis unit. For example, the visualization unit displays the applicant's future prospects as simulation results. In this way, by visualizing the applicant's future prospects, the applicant's future possibilities can be understood.

[0089] The complementing unit allows the interviewer to check the information visualized by the visualization unit and use it as a reference during the interview. The complementing unit, for example, provides the information visualized by the visualization unit to the interviewer in the form of a report. For example, the complementing unit provides the interviewer with the results of a personality assessment and an evaluation of future potential of the applicant in the form of a report. The complementing unit can also provide the interviewer with the information visualized by the visualization unit in a dashboard display. For example, the complementing unit provides the interviewer with the results of a personality assessment and an evaluation of future potential of the applicant in a dashboard display. This allows the interviewer to check the visualized information and more accurately judge the applicant's suitability during the interview.

[0090] The diagnosis unit can estimate the applicant's emotions and adjust the results of the personality diagnosis based on the estimated emotions of the applicant. For example, if the applicant is nervous, the diagnosis unit adds questions to relax the applicant and adjusts the results of the personality diagnosis. For example, if the applicant is nervous, the diagnosis unit adds questions to relax the applicant and adjusts the results of the personality diagnosis. Furthermore, if the applicant is relaxed, the diagnosis unit can add more detailed questions to refine the results of the personality diagnosis. For example, if the applicant is relaxed, the diagnosis unit can add more detailed questions to refine the results of the personality diagnosis. Furthermore, if the applicant is stressed, the diagnosis unit can add questions to reduce stress and adjust the results of the personality diagnosis. For example, if the applicant is stressed, the diagnosis unit can add questions to reduce stress and adjust the results of the personality diagnosis. This allows for adjusting the results of the personality diagnosis based on the applicant's emotions, enabling a more accurate personality diagnosis. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0091] The diagnosis unit can collect additional data to improve the accuracy of the personality diagnosis based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The diagnosis unit, for example, analyzes the applicant's past work experience and collects data to evaluate related personality traits. For example, the diagnosis unit analyzes the applicant's work history and collects data to evaluate personality traits. The diagnosis unit can also collect data to evaluate personality traits related to the applicant's academic performance and field of major based on the applicant's educational background. For example, the diagnosis unit analyzes the applicant's academic performance and field of major and collects data to evaluate personality traits. The diagnosis unit can also collect data to evaluate personality traits based on the applicant's hobbies and interests. For example, the diagnosis unit analyzes the applicant's hobbies and interests and collects data to evaluate personality traits. By collecting additional data, the accuracy of the personality diagnosis can be improved.

[0092] The diagnostic unit can analyze the applicant's response patterns during the personality diagnosis and evaluate the consistency of the responses. For example, the diagnostic unit analyzes the applicant's response patterns over time and evaluates consistent responses. For example, the diagnostic unit analyzes the applicant's response patterns over time and evaluates consistent responses. The diagnostic unit can also analyze the applicant's response patterns for each question and evaluate consistent answers. For example, the diagnostic unit analyzes the applicant's response patterns for each question and evaluates consistent answers. The diagnostic unit can also compare the applicant's response patterns with other applicants and evaluate whether they match general response trends. For example, the diagnostic unit compares the applicant's response patterns with other applicants and evaluates whether they match general response trends. In this way, the reliability of the personality diagnosis is improved by evaluating the consistency of the responses.

[0093] The diagnostic unit can evaluate the stress level of an applicant during a personality diagnosis and reflect the applicant's stress tolerance in the diagnosis results. The diagnostic unit, for example, analyzes the applicant's response speed and evaluates the stress level. For example, the diagnostic unit analyzes the applicant's response speed and evaluates the stress level. The diagnostic unit can also analyze the content of the applicant's responses and extract and evaluate keywords related to stress. For example, the diagnostic unit analyzes the content of the applicant's responses and extract and evaluate keywords related to stress. The diagnostic unit can also collect the applicant's biometric information (heart rate, galvanic skin response, etc.) and evaluate the stress level. For example, the diagnostic unit collects the applicant's biometric information and evaluates the stress level. In this way, the stress tolerance of the applicant can be evaluated by reflecting the stress tolerance in the diagnosis results.

[0094] The diagnosis unit can estimate the applicant's emotions and adjust the display method of the diagnosis results based on the estimated emotions of the applicant. For example, if the applicant is nervous, the diagnosis unit provides a simple, highly visible display method. For example, if the applicant is nervous, the diagnosis unit provides a simple, highly visible display method. Furthermore, if the applicant is relaxed, the diagnosis unit can provide a display method including detailed information. For example, if the applicant is relaxed, the diagnosis unit provides a display method including detailed information. Furthermore, if the applicant is feeling stressed, the diagnosis unit can provide a display method for stress reduction. For example, if the applicant is feeling stressed, the diagnosis unit provides a display method for stress reduction. This allows for adjusting the display method of the diagnosis results based on the applicant's emotions to provide a more appropriate display method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0095] The diagnosis unit can adjust the diagnosis results by specifically considering the geographical and cultural backgrounds of the applicant during the personality diagnosis. For example, the diagnosis unit adds questions that take into account the cultural background based on the applicant's place of origin. For example, the diagnosis unit adds questions that take into account the cultural background based on the applicant's place of origin. The diagnosis unit can also evaluate personality traits specific to a region based on the applicant's place of residence. For example, the diagnosis unit evaluates personality traits specific to a region based on the applicant's place of residence. The diagnosis unit can also provide diagnosis results that take into account the cultural background based on the applicant's nationality. For example, the diagnosis unit provides diagnosis results that take into account the cultural background based on the applicant's nationality. This makes it possible to provide more accurate diagnosis results by taking into account the geographical and cultural backgrounds.

[0096] The diagnostic unit can analyze the applicant's social media activity during the personality diagnosis and reflect it in the diagnosis results. The diagnostic unit, for example, analyzes the content of the applicant's social media posts to evaluate personality traits. For example, the diagnostic unit analyzes the content of the applicant's social media posts to evaluate personality traits. The diagnostic unit can also analyze the applicant's friendships on social media to evaluate sociability. For example, the diagnostic unit can analyze the applicant's friendships on social media to evaluate sociability. The diagnostic unit can also analyze the frequency of the applicant's social media activity to evaluate proactiveness. For example, the diagnostic unit can analyze the frequency of the applicant's social media activity to evaluate proactiveness. In this way, by analyzing social media activity, more accurate diagnostic results can be provided.

[0097] The diagnosis unit can customize the diagnosis algorithm by reflecting the applicant's past feedback when conducting a personality diagnosis. The diagnosis unit adjusts the personality diagnosis algorithm based on, for example, the applicant's past job evaluations. For example, the diagnosis unit adjusts the personality diagnosis algorithm based on the applicant's past job evaluations. The diagnosis unit can also customize the personality diagnosis algorithm based on the applicant's past interview feedback. For example, the diagnosis unit customizes the personality diagnosis algorithm based on the applicant's past interview feedback. The diagnosis unit can also adjust the personality diagnosis algorithm based on feedback from the applicant's past colleagues. For example, the diagnosis unit adjusts the personality diagnosis algorithm based on feedback from the applicant's past colleagues. In this way, the accuracy of the diagnosis algorithm is improved by reflecting past feedback.

[0098] The visualization unit can estimate the emotions of the applicant and adjust the visualization method of the future prospects based on the estimated emotions of the applicant. For example, if the applicant is nervous, the visualization unit provides a simple and highly visible visualization method. For example, if the applicant is nervous, the visualization unit provides a simple and highly visible visualization method. Furthermore, if the applicant is relaxed, the visualization unit can provide a visualization method including detailed information. For example, if the applicant is relaxed, the visualization unit can provide a visualization method for stress reduction if the applicant is feeling stressed. For example, if the applicant is feeling stressed, the visualization unit provides a visualization method for stress reduction. In this way, by adjusting the visualization method based on the emotions of the applicant, a more appropriate visualization method can be provided. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0099] During visualization, the visualization unit can improve the accuracy of predicting future potential based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The visualization unit, for example, analyzes the applicant's past work experience to improve the accuracy of predicting future potential. For example, the visualization unit analyzes the applicant's past work experience to improve the accuracy of predicting future potential. The visualization unit can also improve the accuracy of predicting future potential based on the applicant's educational background. For example, the visualization unit can improve the accuracy of predicting future potential based on the applicant's educational background. The visualization unit can also improve the accuracy of predicting future potential based on the applicant's hobbies and interests. For example, the visualization unit analyzes the applicant's hobbies and interests to improve the accuracy of predicting future potential. As a result, the accuracy of predicting future potential is improved by using information such as the applicant's past work experience, educational background, and hobbies as a basis.

[0100] During visualization, the visualization unit can simulate the applicant's career path and present future career possibilities. The visualization unit, for example, simulates the applicant's future career path based on the applicant's past work experience. For example, the visualization unit simulates the applicant's future career path based on the applicant's past work experience. The visualization unit can also simulate the future career path based on the applicant's educational background. For example, the visualization unit simulates the applicant's future career path based on the applicant's educational background. The visualization unit can also simulate the future career path based on the applicant's hobbies and interests. For example, the visualization unit simulates the applicant's future career path based on the applicant's hobbies and interests. In this way, by simulating the career path, future career possibilities can be presented.

[0101] During visualization, the visualization unit can predict the evolution of the applicant's skill set and evaluate the applicant's ability to meet future skill demands. The visualization unit, for example, predicts the evolution of the skill set based on the applicant's past work experience. For example, the visualization unit predicts the evolution of the skill set based on the applicant's past work experience. The visualization unit can also predict the evolution of the skill set based on the applicant's educational background. For example, the visualization unit predicts the evolution of the skill set based on the applicant's educational background. The visualization unit can also predict the evolution of the skill set based on the applicant's hobbies and interests. For example, the visualization unit predicts the evolution of the skill set based on the applicant's hobbies and interests. In this way, by predicting the evolution of the skill set, it is possible to evaluate the applicant's ability to meet future skill demands.

[0102] The visualization unit can estimate the emotions of the applicant and adjust the display method of the applicant's future potential based on the estimated emotions of the applicant. For example, if the applicant is nervous, the visualization unit provides a simple, highly visible display method. For example, if the applicant is nervous, the visualization unit provides a simple, highly visible display method. The visualization unit can also provide a display method including detailed information if the applicant is relaxed. For example, if the applicant is relaxed, the visualization unit provides a display method including detailed information. The visualization unit can also provide a display method for stress reduction if the applicant is feeling stressed. For example, if the applicant is feeling stressed, the visualization unit provides a display method for stress reduction. This allows for adjusting the display method based on the applicant's emotions to provide a more appropriate display method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0103] During visualization, the visualization unit can evaluate future potential by specifically considering the applicant's geographical background and cultural background. For example, the visualization unit evaluates future potential taking into account the cultural background based on the applicant's place of origin. For example, the visualization unit evaluates future potential taking into account the cultural background based on the applicant's place of origin. The visualization unit can also evaluate region-specific future potential based on the applicant's place of residence. For example, the visualization unit evaluates region-specific future potential based on the applicant's place of residence. The visualization unit can also evaluate future potential taking into account the cultural background based on the applicant's nationality. For example, the visualization unit evaluates future potential taking into account the cultural background based on the applicant's nationality. This makes it possible to evaluate future potential more accurately by taking into account the geographical background and cultural background.

[0104] The visualization unit can analyze the applicant's social media activity during visualization and reflect it in the evaluation of future potential. The visualization unit, for example, analyzes the content of the applicant's posts on social media and evaluates future potential. For example, the visualization unit analyzes the content of the applicant's posts on social media and evaluates future potential. The visualization unit can also analyze the applicant's friendships on social media and evaluate their sociability. For example, the visualization unit can analyze the applicant's friendships on social media and evaluate their sociability. The visualization unit can also analyze the frequency of the applicant's social media activity and evaluate their proactiveness. For example, the visualization unit can analyze the frequency of the applicant's social media activity and evaluate their proactiveness. In this way, analyzing social media activity enables a more accurate evaluation of future potential.

[0105] The visualization unit can customize the visualization algorithm by reflecting the applicant's past feedback during visualization. The visualization unit adjusts the visualization algorithm based on, for example, the applicant's past job evaluations. For example, the visualization unit adjusts the visualization algorithm based on the applicant's past job evaluations. The visualization unit can also customize the visualization algorithm based on the applicant's past interview feedback. For example, the visualization unit customizes the visualization algorithm based on the applicant's past interview feedback. The visualization unit can also adjust the visualization algorithm based on feedback from the applicant's past colleagues. For example, the visualization unit adjusts the visualization algorithm based on feedback from the applicant's past colleagues. In this way, the accuracy of the visualization algorithm is improved by reflecting the past feedback.

[0106] The complementing unit can estimate the applicant's emotions and adjust the method of providing complementary information to the interviewer based on the estimated applicant's emotions. For example, if the applicant is nervous, the complementing unit provides the interviewer with information to help the applicant relax. For example, if the applicant is nervous, the complementing unit provides the interviewer with information to help the applicant relax. The complementing unit can also provide the interviewer with detailed information if the applicant is relaxed. For example, if the applicant is relaxed, the complementing unit provides the interviewer with detailed information. The complementing unit can also provide the interviewer with information to reduce stress if the applicant is feeling stressed. For example, if the applicant is feeling stressed, the complementing unit provides the interviewer with information to reduce stress. This allows for adjusting the method of providing complementary information based on the applicant's emotions, thereby providing more appropriate information. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0107] When providing the complementary information, the complementing unit can improve the accuracy of the complementary information based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The complementing unit improves the accuracy of the complementary information, for example, based on the applicant's past work experience. For example, the complementing unit improves the accuracy of the complementary information based on the applicant's past work experience. The complementing unit can also improve the accuracy of the complementary information based on the applicant's educational background. For example, the complementing unit improves the accuracy of the complementary information based on the applicant's educational background. The complementing unit can also improve the accuracy of the complementary information based on the applicant's hobbies and interests. For example, the complementing unit improves the accuracy of the complementary information based on the applicant's hobbies and interests. In this way, the accuracy of the complementary information is improved by using information such as the applicant's past work experience, educational background, and hobbies as the basis.

[0108] When providing the supplementary information, the completion unit can analyze the applicant's response patterns and suggest questions to the interviewer. The completion unit, for example, analyzes the applicant's response patterns and suggests specific questions to the interviewer. For example, the completion unit analyzes the applicant's response patterns and suggests specific questions to the interviewer. The completion unit can also analyze the applicant's response patterns and suggest follow-up questions to the interviewer. For example, the completion unit analyzes the applicant's response patterns and suggests follow-up questions to the interviewer. The completion unit can also analyze the applicant's response patterns and suggest probing questions to the interviewer. For example, the completion unit analyzes the applicant's response patterns and suggests probing questions to the interviewer. In this way, appropriate questions to the interviewer can be suggested by analyzing the response patterns.

[0109] When providing the supplementary information, the supplementary unit can evaluate the stress level of the applicant and provide advice to the interviewer. For example, the supplementary unit evaluates the stress level of the applicant and provides advice to the interviewer to help them relax. For example, the supplementary unit evaluates the stress level of the applicant and provides advice to the interviewer to help them relax. The supplementary unit can also evaluate the stress level of the applicant and suggest questions to the interviewer to reduce stress. For example, the supplementary unit evaluates the stress level of the applicant and suggests questions to the interviewer to reduce stress. The supplementary unit can also evaluate the stress level of the applicant and advise the interviewer to set up an appropriate interview environment. For example, the supplementary unit evaluates the stress level of the applicant and advises the interviewer to set up an appropriate interview environment. In this way, by evaluating the stress level, appropriate advice can be provided to the interviewer.

[0110] The completion unit can estimate the applicant's emotions and adjust the display method of the supplementary information based on the estimated emotions of the applicant. For example, if the applicant is nervous, the completion unit provides a simple, highly visible display method. For example, if the applicant is nervous, the completion unit provides a simple, highly visible display method. The completion unit can also provide a display method including detailed information if the applicant is relaxed. For example, if the applicant is relaxed, the completion unit provides a display method including detailed information. The completion unit can also provide a display method for stress reduction if the applicant is stressed. For example, if the applicant is stressed, the completion unit provides a display method for stress reduction. This allows for adjusting the display method based on the applicant's emotions to provide a more appropriate display method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0111] When providing the complementary information, the complementary unit can provide the complementary information by specifically taking into consideration the geographical background and cultural background of the applicant. For example, the complementary unit provides complementary information that takes into consideration the cultural background based on the applicant's place of origin. For example, the complementary unit provides complementary information that takes into consideration the cultural background based on the applicant's place of origin. The complementary unit can also provide region-specific complementary information based on the applicant's place of residence. For example, the complementary unit provides region-specific complementary information based on the applicant's place of residence. The complementary unit can also provide complementary information that takes into consideration the cultural background based on the applicant's nationality. For example, the complementary unit provides complementary information that takes into consideration the cultural background based on the applicant's nationality. This makes it possible to provide more accurate complementary information by taking into consideration the geographical background and cultural background.

[0112] When providing the supplemental information, the supplemental unit can analyze the applicant's social media activity and reflect it in the supplemental information. For example, the supplemental unit analyzes the content of the applicant's posts on social media and reflects it in the supplemental information. For example, the supplemental unit analyzes the content of the applicant's posts on social media and reflects it in the supplemental information. The supplemental unit can also analyze the applicant's friendships on social media and provide supplemental information regarding sociability. For example, the supplemental unit can analyze the applicant's friendships on social media and provide supplemental information regarding sociability. The supplemental unit can also analyze the frequency of the applicant's social media activity and provide supplemental information regarding proactivity. For example, the supplemental unit can analyze the frequency of the applicant's social media activity and provide supplemental information regarding proactivity. In this way, more accurate supplemental information can be provided by analyzing social media activity.

[0113] When providing the complementary information, the complementing unit can customize the method of providing the complementary information by reflecting the applicant's past feedback. The complementing unit adjusts the method of providing the complementary information based on, for example, the applicant's past job evaluations. For example, the complementing unit adjusts the method of providing the complementary information based on the applicant's past job evaluations. The complementing unit can also customize the method of providing the complementary information based on the applicant's past interview feedback. For example, the complementing unit customizes the method of providing the complementary information based on the applicant's past interview feedback. The complementing unit can also adjust the method of providing the complementary information based on feedback from the applicant's past colleagues. For example, the complementing unit adjusts the method of providing the complementary information based on feedback from the applicant's past colleagues. In this way, by reflecting past feedback, the accuracy of the method of providing the complementary information is improved.

[0114] The ranking unit can estimate the emotions of the applicants and adjust the ranking display method based on the estimated emotions of the applicants. For example, if the applicant is nervous, the ranking unit provides a simple and highly visible ranking display method. For example, if the applicant is nervous, the ranking unit provides a simple and highly visible ranking display method. Furthermore, if the applicant is relaxed, the ranking unit can provide a ranking display method that includes detailed information. For example, if the applicant is relaxed, the ranking unit can provide a ranking display method that includes detailed information. Furthermore, if the applicant is stressed, the ranking unit can provide a ranking display method for stress reduction. For example, if the applicant is stressed, the ranking unit provides a ranking display method for stress reduction. This allows for adjusting the display method based on the emotions of the applicants to provide a more appropriate ranking display method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0115] When creating rankings, the ranking unit can improve the accuracy of the rankings based on specific information such as the applicants' past work experience, educational background, hobbies, and special skills. The ranking unit, for example, improves the accuracy of the rankings based on the applicants' past work experience. For example, the ranking unit improves the accuracy of the rankings based on the applicants' past work experience. The ranking unit can also improve the accuracy of the rankings based on the applicants' educational background. For example, the ranking unit improves the accuracy of the rankings based on the applicants' educational background. The ranking unit can also improve the accuracy of the rankings based on the applicants' hobbies and interests. For example, the ranking unit improves the accuracy of the rankings based on the applicants' hobbies and interests. In this way, the accuracy of the rankings is improved by using information such as the applicants' past work experience, educational background, and hobbies.

[0116] When creating rankings, the ranking unit can analyze applicants' response patterns and evaluate the reliability of the rankings. The ranking unit, for example, analyzes applicants' response patterns and creates highly reliable rankings. For example, the ranking unit analyzes applicants' response patterns and creates highly reliable rankings. The ranking unit can also compare applicants' response patterns with other applicants and create highly reliable rankings. For example, the ranking unit compares applicants' response patterns with other applicants and create highly reliable rankings. The ranking unit can also analyze applicants' response patterns over time and create highly reliable rankings. For example, the ranking unit analyzes applicants' response patterns over time and creates highly reliable rankings. In this way, highly reliable rankings can be created by analyzing response patterns.

[0117] When creating rankings, the ranking unit can evaluate the stress levels of applicants and reflect them in the rankings. For example, the ranking unit evaluates the stress levels of applicants and reflects them in the rankings. For example, the ranking unit evaluates the stress levels of applicants and reflects them in the rankings. The ranking unit can also compare the stress levels of applicants with other applicants and reflect them in the rankings. For example, the ranking unit can compare the stress levels of applicants with other applicants and reflect them in the rankings. The ranking unit can also analyze the stress levels of applicants over time and reflect them in the rankings. For example, the ranking unit can analyze the stress levels of applicants over time and reflect them in the rankings. In this way, by evaluating stress levels, more accurate rankings can be created.

[0118] The ranking unit can estimate the emotions of the applicant and adjust the ranking priority based on the estimated emotions of the applicant. For example, if the applicant is nervous, the ranking unit adjusts the priority for relaxation. For example, if the applicant is nervous, the ranking unit adjusts the priority for relaxation. The ranking unit can also adjust the priority including detailed information if the applicant is relaxed. For example, if the applicant is relaxed, the ranking unit adjusts the priority including detailed information. The ranking unit can also adjust the priority for stress reduction if the applicant is stressed. For example, if the applicant is stressed, the ranking unit adjusts the priority for stress reduction. This allows for adjusting the priority based on the emotions of the applicant, thereby providing a more appropriate ranking. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0119] When creating rankings, the ranking unit can create rankings that specifically take into consideration the geographical and cultural backgrounds of applicants. For example, the ranking unit creates rankings that take into consideration cultural backgrounds based on the applicants' hometowns. For example, the ranking unit creates rankings that take into consideration cultural backgrounds based on the applicants' hometowns. The ranking unit can also create region-specific rankings based on the applicants' places of residence. For example, the ranking unit creates region-specific rankings based on the applicants' places of residence. The ranking unit can also create rankings that take into consideration cultural backgrounds based on the applicants' nationalities. For example, the ranking unit creates rankings that take into consideration cultural backgrounds based on the applicants' nationalities. In this way, by taking into consideration geographical and cultural backgrounds, more accurate rankings can be created.

[0120] When creating rankings, the ranking unit can analyze the social media activities of applicants and reflect them in the rankings. For example, the ranking unit analyzes the content of applicants' posts on social media and reflects them in the rankings. For example, the ranking unit analyzes the content of applicants' posts on social media and reflects them in the rankings. The ranking unit can also analyze the applicants' friendships on social media and create a ranking related to sociability. For example, the ranking unit can analyze the applicants' friendships on social media and create a ranking related to sociability. The ranking unit can also analyze the frequency of the applicants' social media activities and create a ranking related to proactivity. For example, the ranking unit can analyze the applicants' social media activities and create a ranking related to proactivity. In this way, by analyzing social media activities, more accurate rankings can be created.

[0121] When creating rankings, the ranking unit can customize the ranking algorithm by reflecting past feedback from applicants. The ranking unit, for example, adjusts the ranking algorithm based on past job evaluations of applicants. For example, the ranking unit adjusts the ranking algorithm based on past job evaluations of applicants. The ranking unit can also customize the ranking algorithm based on past interview feedback of applicants. For example, the ranking unit customizes the ranking algorithm based on past interview feedback of applicants. The ranking unit can also adjust the ranking algorithm based on feedback from past colleagues of applicants. For example, the ranking unit adjusts the ranking algorithm based on feedback from past colleagues of applicants. In this way, the accuracy of the ranking algorithm is improved by reflecting past feedback.

[0122] The matching unit can estimate the applicant's emotions and adjust the display method of the industry matching based on the estimated emotions of the applicant. For example, if the applicant is nervous, the matching unit provides a simple and highly visible industry matching display method. For example, if the applicant is nervous, the matching unit provides a simple and highly visible industry matching display method. Furthermore, if the applicant is relaxed, the matching unit can provide an industry matching display method including detailed information. For example, if the applicant is relaxed, the matching unit provides an industry matching display method including detailed information. Furthermore, if the applicant is stressed, the matching unit can provide an industry matching display method for stress reduction. For example, if the applicant is stressed, the matching unit provides an industry matching display method for stress reduction. This allows for adjusting the display method based on the applicant's emotions to provide a more appropriate industry matching display method. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0123] During matching, the matching unit can improve the accuracy of industry matching based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The matching unit improves the accuracy of industry matching based on, for example, the applicant's past work experience. For example, the matching unit improves the accuracy of industry matching based on the applicant's past work experience. The matching unit can also improve the accuracy of industry matching based on the applicant's educational background. For example, the matching unit improves the accuracy of industry matching based on the applicant's educational background. The matching unit can also improve the accuracy of industry matching based on the applicant's hobbies and interests. For example, the matching unit improves the accuracy of industry matching based on the applicant's hobbies and interests. In this way, the accuracy of industry matching is improved by using information such as the applicant's past work experience, educational background, and hobbies.

[0124] The matching unit can analyze the applicant's response patterns during matching and evaluate the reliability of industry matching. The matching unit, for example, analyzes the applicant's response patterns and performs highly reliable industry matching. For example, the matching unit analyzes the applicant's response patterns and performs highly reliable industry matching. The matching unit can also compare the applicant's response patterns with other applicants and perform highly reliable industry matching. For example, the matching unit can compare the applicant's response patterns with other applicants and perform highly reliable industry matching. The matching unit can also analyze the applicant's response patterns on a time axis and perform highly reliable industry matching. For example, the matching unit analyzes the applicant's response patterns on a time axis and performs highly reliable industry matching. In this way, highly reliable industry matching can be performed by analyzing the response patterns.

[0125] The matching unit can evaluate the stress level of an applicant during matching and reflect it in industry matching. The matching unit, for example, evaluates the stress level of an applicant and reflects it in industry matching. For example, the matching unit evaluates the stress level of an applicant and reflects it in industry matching. The matching unit can also compare the stress level of an applicant with other applicants and reflect it in industry matching. For example, the matching unit can compare the stress level of an applicant with other applicants and reflect it in industry matching. The matching unit can also analyze the stress level of an applicant over time and reflect it in industry matching. For example, the matching unit can analyze the stress level of an applicant over time and reflect it in industry matching. In this way, by evaluating the stress level, more accurate industry matching can be performed.

[0126] The matching unit can estimate the emotions of the applicant and adjust the priority of industry matching based on the estimated emotions of the applicant. For example, if the applicant is nervous, the matching unit adjusts the priority for relaxation. For example, if the applicant is nervous, the matching unit adjusts the priority for relaxation. The matching unit can also adjust the priority including detailed information if the applicant is relaxed. For example, if the applicant is relaxed, the matching unit adjusts the priority including detailed information. The matching unit can also adjust the priority for stress reduction if the applicant is stressed. For example, if the applicant is stressed, the matching unit adjusts the priority for stress reduction. This allows for adjusting the priority based on the emotions of the applicant, thereby providing more appropriate industry matching. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0127] When matching, the matching unit can perform industry matching by specifically considering the geographical background and cultural background of the applicant. For example, the matching unit performs industry matching that takes cultural background into consideration based on the applicant's place of origin. For example, the matching unit performs industry matching that takes cultural background into consideration based on the applicant's place of origin. The matching unit can also perform region-specific industry matching based on the applicant's place of residence. For example, the matching unit performs region-specific industry matching based on the applicant's place of residence. The matching unit can also perform industry matching that takes cultural background into consideration based on the applicant's nationality. For example, the matching unit performs industry matching that takes cultural background into consideration based on the applicant's nationality. In this way, more accurate industry matching can be performed by taking geographical background and cultural background into consideration.

[0128] The matching unit can analyze the applicant's social media activity during matching and reflect it in industry matching. The matching unit, for example, analyzes the content of the applicant's posts on social media and reflects it in industry matching. For example, the matching unit analyzes the content of the applicant's posts on social media and reflects it in industry matching. The matching unit can also analyze the applicant's friendships on social media and perform industry matching related to sociability. For example, the matching unit can analyze the applicant's friendships on social media and perform industry matching related to sociability. The matching unit can also analyze the frequency of the applicant's social media activity and perform industry matching related to proactiveness. For example, the matching unit can analyze the applicant's frequency of social media activity and perform industry matching related to proactiveness. In this way, more accurate industry matching can be performed by analyzing social media activity.

[0129] The matching unit can customize the industry matching algorithm by reflecting the applicant's past feedback when matching. The matching unit adjusts the industry matching algorithm based on, for example, the applicant's past job evaluations. For example, the matching unit adjusts the industry matching algorithm based on the applicant's past job evaluations. The matching unit can also customize the industry matching algorithm based on the applicant's past interview feedback. For example, the matching unit customizes the industry matching algorithm based on the applicant's past interview feedback. The matching unit can also adjust the industry matching algorithm based on feedback from the applicant's past colleagues. For example, the matching unit adjusts the industry matching algorithm based on feedback from the applicant's past colleagues. In this way, the accuracy of the industry matching algorithm is improved by reflecting past feedback.

[0130] The compatibility unit can estimate the applicant's emotions and adjust the compatibility check display method based on the estimated emotions of the applicant. For example, if the applicant is nervous, the compatibility unit provides a simple, highly visible compatibility check display method. For example, if the applicant is nervous, the compatibility unit provides a simple, highly visible compatibility check display method. The compatibility unit can also provide a compatibility check display method that includes detailed information if the applicant is relaxed. For example, if the applicant is relaxed, the compatibility unit provides a compatibility check display method that includes detailed information. The compatibility unit can also provide a compatibility check display method for stress reduction if the applicant is stressed. For example, if the applicant is stressed, the compatibility unit provides a compatibility check display method for stress reduction. This allows for adjusting the display method based on the applicant's emotions to provide a more appropriate compatibility check display method. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0131] When checking compatibility, the compatibility section can improve the accuracy of the compatibility check based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. The compatibility section, for example, improves the accuracy of the compatibility check based on the applicant's past work experience. For example, the compatibility section improves the accuracy of the compatibility check based on the applicant's past work experience. The compatibility section can also improve the accuracy of the compatibility check based on the applicant's educational background. For example, the compatibility section improves the accuracy of the compatibility check based on the applicant's educational background. The compatibility section can also improve the accuracy of the compatibility check based on the applicant's hobbies and interests. For example, the compatibility section improves the accuracy of the compatibility check based on the applicant's hobbies and interests. In this way, the accuracy of the compatibility check is improved by using information such as the applicant's past work experience, educational background, and hobbies.

[0132] The compatibility section can analyze the applicant's response patterns during a compatibility check and evaluate the reliability of the compatibility check. The compatibility section, for example, analyzes the applicant's response patterns and performs a highly reliable compatibility check. For example, the compatibility section analyzes the applicant's response patterns and performs a highly reliable compatibility check. The compatibility section can also compare the applicant's response patterns with other applicants and perform a highly reliable compatibility check. For example, the compatibility section compares the applicant's response patterns with other applicants and performs a highly reliable compatibility check. The compatibility section can also analyze the applicant's response patterns over time and perform a highly reliable compatibility check. For example, the compatibility section analyzes the applicant's response patterns over time and performs a highly reliable compatibility check. In this way, a highly reliable compatibility check can be performed by analyzing the response patterns.

[0133] The compatibility section can evaluate the stress level of the applicant during the compatibility check and reflect it in the compatibility check. The compatibility section, for example, evaluates the stress level of the applicant and reflects it in the compatibility check. For example, the compatibility section evaluates the stress level of the applicant and reflects it in the compatibility check. The compatibility section can also compare the stress level of the applicant with other applicants and reflect it in the compatibility check. For example, the compatibility section can compare the stress level of the applicant with other applicants and reflect it in the compatibility check. The compatibility section can also analyze the stress level of the applicant over time and reflect it in the compatibility check. For example, the compatibility section can analyze the stress level of the applicant over time and reflect it in the compatibility check. In this way, by evaluating the stress level, a more accurate compatibility check can be performed.

[0134] The compatibility unit can estimate the applicant's emotions and adjust the priority of the compatibility check based on the estimated emotions of the applicant. For example, if the applicant is nervous, the compatibility unit adjusts the priority for relaxation. For example, if the applicant is nervous, the compatibility unit adjusts the priority for relaxation. The compatibility unit can also adjust the priority including detailed information if the applicant is relaxed. For example, if the applicant is relaxed, the compatibility unit adjusts the priority including detailed information. The compatibility unit can also adjust the priority for stress reduction if the applicant is stressed. For example, if the applicant is stressed, the compatibility unit adjusts the priority for stress reduction. This allows for adjusting the priority based on the applicant's emotions to provide a more appropriate compatibility check. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI.

[0135] When performing a compatibility check, the compatibility department can perform a compatibility check that specifically takes into account the applicant's geographical background and cultural background. For example, the compatibility department performs a compatibility check that takes into account the applicant's cultural background based on the applicant's place of origin. For example, the compatibility department performs a compatibility check that takes into account the applicant's cultural background based on the applicant's place of origin. The compatibility department can also perform a region-specific compatibility check based on the applicant's place of residence. For example, the compatibility department can perform a region-specific compatibility check based on the applicant's place of residence. The compatibility department can also perform a compatibility check that takes into account the applicant's cultural background based on the applicant's nationality. For example, the compatibility department can perform a compatibility check that takes into account the applicant's cultural background based on the applicant's nationality. This allows for a more accurate compatibility check by taking into account the geographical background and cultural background.

[0136] The compatibility section can analyze the applicant's social media activity during the compatibility check and reflect the results in the compatibility check. The compatibility section, for example, analyzes the applicant's social media posts and reflects the results in the compatibility check. For example, the compatibility section analyzes the applicant's social media posts and reflects the results in the compatibility check. The compatibility section can also analyze the applicant's social media friendships and perform a compatibility check regarding sociability. For example, the compatibility section can analyze the applicant's social media friendships and perform a compatibility check regarding sociability. The compatibility section can also analyze the frequency of the applicant's social media activity and perform a compatibility check regarding proactiveness. For example, the compatibility section can analyze the applicant's social media activity and perform a compatibility check regarding proactiveness. In this way, a more accurate compatibility check can be performed by analyzing social media activity.

[0137] The compatibility unit can customize the compatibility check algorithm by reflecting the applicant's past feedback when checking compatibility. The compatibility unit, for example, adjusts the compatibility check algorithm based on the applicant's past job evaluations. For example, the compatibility unit adjusts the compatibility check algorithm based on the applicant's past job evaluations. The compatibility unit can also customize the compatibility check algorithm based on the applicant's past interview feedback. For example, the compatibility unit customizes the compatibility check algorithm based on the applicant's past interview feedback. The compatibility unit can also adjust the compatibility check algorithm based on feedback from the applicant's past colleagues. For example, the compatibility unit adjusts the compatibility check algorithm based on feedback from the applicant's past colleagues. In this way, the accuracy of the compatibility check algorithm is improved by reflecting past feedback. === Hard Collateral 1-1 === Each of the multiple elements, including the above-mentioned diagnosis unit, visualization unit, completion unit, ranking unit, matching unit, and compatibility unit, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the diagnosis unit is implemented by the control unit 46A of the smart device 14 and performs a personality diagnosis based on the applicant's resume information and questionnaire results. The visualization unit is implemented by the specific processing unit 290 of the data processing device 12 and visualizes the applicant's future potential based on the information obtained by the diagnosis unit. The completion unit is implemented, for example, by the control unit 46A of the smart device 14 and provides the visualized information to the interviewer. The ranking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and displays the applicant's aptitude in a ranking format. The matching unit is implemented, for example, by the control unit 46A of the smart device 14 and matches the applicant with an industry or job type based on the results of the applicant's personality diagnosis. The compatibility unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and checks the compatibility between the applicant and the interviewer. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned diagnosis unit, visualization unit, completion unit, ranking unit, matching unit, and compatibility unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the diagnosis unit is realized by the control unit 46A of the smart glasses 214 and performs a personality diagnosis based on the applicant's resume information and questionnaire results. The visualization unit is realized by the specific processing unit 290 of the data processing device 12 and visualizes the applicant's future potential based on the information obtained by the diagnosis unit. The completion unit is realized by the control unit 46A of the smart glasses 214 and provides the visualized information to the interviewer. The ranking unit is realized by the specific processing unit 290 of the data processing device 12 and displays the applicant's aptitude in a ranking format. The matching unit is realized by the control unit 46A of the smart glasses 214 and matches the applicant with an industry or job type based on the results of the applicant's personality diagnosis. The compatibility unit is realized by the specific processing unit 290 of the data processing device 12 and checks the compatibility between the applicant and the interviewer. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned diagnosis unit, visualization unit, completion unit, ranking unit, matching unit, and compatibility unit, is implemented, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the diagnosis unit is implemented by the control unit 46A of the headset-type terminal 314 and performs a personality diagnosis based on the applicant's resume information and questionnaire results. The visualization unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the applicant's future potential based on the information obtained by the diagnosis unit. The completion unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and provides the visualized information to the interviewer. The ranking unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and displays the applicant's aptitude in a ranking format. The matching unit is implemented, for example, by the control unit 46A of the headset-type terminal 314 and matches the applicant with an industry or job type based on the results of the applicant's personality diagnosis. The compatibility unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and checks the compatibility between the applicant and the interviewer. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned diagnosis unit, visualization unit, completion unit, ranking unit, matching unit, and compatibility unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the diagnosis unit is realized by the control unit 46A of the robot 414 and performs a personality diagnosis based on the applicant's resume information and questionnaire results. The visualization unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and visualizes the applicant's future potential based on the information obtained by the diagnosis unit. The completion unit is realized, for example, by the control unit 46A of the robot 414 and provides the visualized information to the interviewer. The ranking unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and displays the applicant's aptitude in a ranking format. The matching unit is realized, for example, by the control unit 46A of the robot 414 and matches the applicant with an industry or job type based on the results of the applicant's personality diagnosis. The compatibility unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and checks the compatibility between the applicant and the interviewer.

[0138] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0139] The diagnostic unit can evaluate an applicant's stress tolerance based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills. For example, the diagnostic unit can analyze the applicant's past work experience to evaluate stress tolerance. The diagnostic unit can also evaluate stress tolerance related to academic performance and field of major based on the applicant's educational background. Furthermore, the diagnostic unit can evaluate stress tolerance based on the applicant's hobbies and special skills. This allows for a more accurate personality diagnosis by evaluating the applicant's stress tolerance.

[0140] The visualization unit can visualize the applicant's leadership ability based on the information obtained by the diagnosis unit. For example, the visualization unit can display the applicant's leadership ability in a graph based on the applicant's past work experience. The visualization unit can also display the applicant's leadership ability in a chart based on the applicant's educational background and hobbies. Furthermore, the visualization unit can visualize the applicant's leadership ability using simulation results. In this way, by visualizing the applicant's leadership ability, the applicant's future potential can be understood.

[0141] The complementing unit can evaluate the applicant's communication skills based on the information visualized by the visualization unit. For example, the complementing unit can provide the interviewer with a report on the applicant's communication skills based on the applicant's personality assessment results and future potential assessment. The complementing unit can also evaluate the applicant's communication skills using a dashboard display. Furthermore, the complementing unit can evaluate the applicant's communication skills using AI. This allows the interviewer to check the applicant's communication skills and more accurately determine the applicant's suitability during the interview.

[0142] The diagnosis unit can estimate the emotions of the applicant and evaluate the motivation of the applicant based on the estimated emotions of the applicant. For example, if the applicant is nervous, the diagnosis unit can add questions to relax the applicant and evaluate the motivation. Also, if the applicant is relaxed, the diagnosis unit can add more detailed questions to refine the motivation. Furthermore, if the applicant is feeling stressed, the diagnosis unit can add questions to reduce stress and evaluate the motivation. This allows for a more accurate personality diagnosis by evaluating the applicant's motivation based on their emotions.

[0143] The visualization unit can visualize the applicant's creativity based on the information obtained by the diagnosis unit. For example, the visualization unit can display the applicant's creativity in a graph based on the applicant's past work experience. The visualization unit can also display the applicant's creativity in a chart based on the applicant's educational background and hobbies. Furthermore, the visualization unit can visualize the applicant's creativity using simulation results. In this way, by visualizing the applicant's creativity, the applicant's future potential can be understood.

[0144] The complementing unit can evaluate the applicant's problem-solving ability based on the information visualized by the visualization unit. For example, the complementing unit can provide the interviewer with a report on the applicant's problem-solving ability based on the applicant's personality assessment results and future potential assessment. The complementing unit can also evaluate the applicant's problem-solving ability using a dashboard display. Furthermore, the complementing unit can evaluate the applicant's problem-solving ability using AI. This allows the interviewer to check the applicant's problem-solving ability and more accurately determine the applicant's suitability during the interview.

[0145] The diagnosis unit can estimate the applicant's emotions and evaluate the applicant's adaptability based on the estimated emotions of the applicant. For example, if the applicant is nervous, the diagnosis unit can add questions to relax the applicant and evaluate the applicant's adaptability. Also, if the applicant is relaxed, the diagnosis unit can add more detailed questions to refine the applicant's adaptability. Furthermore, if the applicant is feeling stressed, the diagnosis unit can add questions to reduce stress and evaluate the applicant's adaptability. This allows for a more accurate personality diagnosis by evaluating the applicant's adaptability based on the applicant's emotions.

[0146] The visualization unit can visualize the teamwork ability of the applicant based on the information obtained by the diagnosis unit. For example, the visualization unit can display the teamwork ability in a graph based on the applicant's past work experience. The visualization unit can also display the teamwork ability in a chart based on the applicant's educational background and hobbies. Furthermore, the visualization unit can visualize the teamwork ability of the applicant using simulation results. In this way, by visualizing the applicant's teamwork ability, the applicant's future potential can be understood.

[0147] The supplementation unit can evaluate the applicant's risk management ability based on the information visualized by the visualization unit. For example, the supplementation unit can provide the interviewer with a report on the applicant's risk management ability based on the applicant's personality assessment results and future potential assessment. The supplementation unit can also evaluate the applicant's risk management ability using a dashboard display. Furthermore, the supplementation unit can evaluate the applicant's risk management ability using AI. This allows the interviewer to check the applicant's risk management ability and more accurately determine the applicant's suitability during the interview.

[0148] The diagnosis unit can estimate the applicant's emotions and evaluate the applicant's stress tolerance based on the estimated emotions. For example, if the applicant is nervous, the diagnosis unit can add questions to relax the applicant and evaluate the applicant's stress tolerance. If the applicant is relaxed, the diagnosis unit can add more detailed questions to refine the applicant's stress tolerance. Furthermore, if the applicant is feeling stressed, the diagnosis unit can add questions to reduce stress and evaluate the applicant's stress tolerance. This allows for a more accurate personality diagnosis by evaluating the applicant's stress tolerance based on the applicant's emotions.

[0149] The processing flow of the second embodiment will be briefly explained below.

[0150] Step 1: The diagnostic department performs a personality diagnosis based on pre-input information. The pre-input information includes the applicant's resume information and questionnaire results. The diagnostic department diagnoses the applicant's personality using psychological tests, behavioral analysis, or AI. For example, the applicant's resume information is input into the AI, which then performs a personality diagnosis. Step 2: The visualization unit visualizes future potential based on the information obtained by the diagnosis unit. Graphs, charts, simulation results, etc. are used to visualize future potential. Based on the information obtained by the diagnosis unit, the visualization unit displays the applicant's future potential in graphs or visualizes it using simulation results. It is also possible to visualize the applicant's future potential using AI. Step 3: The complementing unit uses the information visualized by the visualization unit as complementary information for the interviewer. Methods of providing complementary information include reports to the interviewer and displaying it on a dashboard. The complementing unit provides the information visualized by the visualization unit to the interviewer in the form of a report, or displays the information on a dashboard. It can also provide information to the interviewer using AI.

[0151] 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.

[0152] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<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.

[0153] 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.

[0154] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0155] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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).

[0161] 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.

[0162] 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.

[0163] 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.

[0164] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0165] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0171] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] 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).

[0177] 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.

[0178] 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.

[0179] 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.

[0180] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0181] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0187] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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).

[0193] 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.

[0194] 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.

[0195] 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.

[0196] 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.

[0197] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0198] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0199] 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.

[0200] 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.

[0201] 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.

[0202] 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.

[0203] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0204] 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.

[0205] 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.

[0206] 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.

[0207] 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).

[0208] 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.

[0209] 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."

[0210] 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.

[0211] 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.

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] [Explanation of symbols]

[0223] 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 diagnostic section that performs personality diagnosis based on information input in advance, a visualization unit that specifically visualizes future prospects based on the information obtained by the diagnosis unit; a complementing unit that utilizes the information visualized by the visualization unit as complementary information for an interviewer. A system characterized by:

2. Equipped with a ranking section that displays rankings 2. The system of claim 1.

3. Equipped with a matching department that performs industry matching 2. The system of claim 1.

4. Equipped with a compatibility section that checks compatibility 2. The system of claim 1.

5. The diagnostic unit A personality assessment is conducted based on specific information such as the applicant's past work experience, educational background, hobbies, and special skills.

2. The system of claim 1.

6. The visualization unit Visualizing the applicant's future potential based on the information obtained by the diagnostic section 2. The system of claim 1.

7. The complementing unit The interviewer checks the information visualized by the visualization unit and uses it as a reference during the interview.

2. The system of claim 1.

8. The diagnostic unit Estimate the applicant's emotions and adjust the personality test results based on the estimated emotions of the applicant 2. The system of claim 1.

9. The diagnostic unit Collect additional data to improve the accuracy of personality assessments based on specific information such as applicants' past work experience, educational background, hobbies, and special skills.

2. The system of claim 1.

10. The diagnostic unit Analyze the applicant's response patterns during the personality test and evaluate the consistency of their responses 2. The system of claim 1.

11. The diagnostic unit During the personality test, the applicant's stress level is evaluated and their stress tolerance is reflected in the test results.

2. The system of claim 1.

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