system

Abstract

The system according to the embodiment aims to provide criteria for identifying high performers during interviews. [Solution] A system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a determination unit. The collection unit collects personal data from the first few years of employment. The collection unit collects data on interviewers. The analysis unit analyzes the data collected by the collection unit and extracts characteristics of high performers. The generation unit generates questions or evaluation criteria to be used during interviews based on the characteristics extracted by the analysis unit. The determination unit analyzes the candidate's answers in real time based on the questions or evaluation criteria generated by the generation unit and determines whether the candidate is a high performer.

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] Conventional techniques do not provide sufficient criteria for identifying high performers during interviews, which can result in low performers being hired.

[0005] The system according to the embodiment aims to provide criteria for identifying high performers during interviews. [Means for solving the problem]

[0006] The system according to the embodiment includes a collection unit, an analysis unit, a generation unit, and a determination unit. The collection unit collects personal data from the first few years of employment. The collection unit collects data on interviewers. The analysis unit analyzes the data collected by the collection unit and extracts characteristics of high performers. The generation unit generates questions or evaluation criteria to be used during interviews based on the characteristics extracted by the analysis unit. The determination unit analyzes the candidate's answers in real time based on the questions or evaluation criteria generated by the generation unit and determines whether the candidate is a high performer. [Effects of the Invention]

[0007] The system according to the embodiment can provide criteria for identifying high performers during interviews. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​interview system of this embodiment combines personal data from the first few years of employment with data from interviewers to develop high performers. This system aims to address the current situation of an increasing number of low performers who join companies primarily based on their academic background. Specifically, it consists of the following steps: First, personal data from the first few years of employment is collected. This data includes work performance, project results, evaluations from colleagues and superiors, and skill acquisition status. Next, interviewer data is collected. This data includes interview evaluations, questions, and interview progress. AI analyzes the collected data to extract characteristics of high performers. For example, it identifies specific skill sets, personality traits, and interview responses common to high performers. Next, the AI ​​generates questions and evaluation criteria to be used during interviews. Interviewers can then use the AI-generated questions to evaluate candidates. The AI ​​analyzes candidates' responses in real time and determines whether they are likely to become high performers. This system enables a comprehensive evaluation of not only academic background but also actual work performance, skills, personality traits, and other factors, making it possible to identify high performers. This is expected to reduce the number of low performers entering the company and improve overall corporate performance. This allows the AI ​​interview system to comprehensively evaluate not only academic background but also actual work performance, skills, personality traits, etc., making it possible to identify high performers.

[0029] An AI interview system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a determination unit. The collection unit collects personal data from employees over several years after joining the company. The personal data includes, but is not limited to, work performance, project results, evaluations from colleagues and superiors, and skill acquisition status. For example, the collection unit collects data such as sales and project completion rates to collect work performance. The collection unit can also collect data such as project success rates and delivery deadline adherence rates to collect project results. The collection unit can also collect data such as 360-degree evaluations and feedback content to collect evaluations from colleagues and superiors. For example, the collection unit collects data such as sales and project completion rates to collect work performance. The collection unit can also collect data such as project success rates and delivery deadline adherence rates to collect project results. The collection unit can also collect data such as 360-degree evaluations and feedback content to collect evaluations from colleagues and superiors. The analysis unit analyzes the collected data and extracts characteristics of high performers. The analysis unit analyzes data such as specific skill sets, personality traits, and interview answers. For example, the analysis unit analyzes data such as technical skills and communication skills to extract specific skill sets. The analysis unit can also analyze data such as leadership and cooperation to extract personality traits. The analysis unit can also analyze data such as the consistency and specificity of answers to analyze interview answers. The generation unit generates questions and evaluation criteria to be used in interviews based on the extracted characteristics. The generation unit generates, for example, question types and evaluation criteria items. For example, the generation unit generates questions related to specific skill sets. The generation unit can also generate evaluation criteria related to personality traits. The generation unit can also generate questions and evaluation criteria related to interview answers. The determination unit analyzes the candidate's answers in real time based on the generated questions and evaluation criteria and determines whether the candidate is a high performer.The determination unit sets, for example, the algorithm to be used and the timing of the analysis in order to analyze the candidate's answers in real time. For example, the determination unit sets, for example, the algorithm to be used and the timing of the analysis in order to analyze the candidate's answers in real time. As a result, the AI ​​interview system according to the embodiment can produce high performers by combining personal data from the first few years of employment with data from interviewers.

[0030] The collection unit can collect data on work performance, project results, evaluations from colleagues and superiors, and skill acquisition status. For example, the collection unit collects data such as sales and project completion rates to collect work performance. The collection unit can also collect data such as project success rates and delivery deadline adherence rates to collect project results. Furthermore, the collection unit can collect data such as 360-degree evaluations and feedback content to collect evaluations from colleagues and superiors. For example, the collection unit collects data such as sales and project completion rates to collect work performance. The collection unit can also collect data such as project success rates and delivery deadline adherence rates to collect project results. Furthermore, the collection unit can collect data such as 360-degree evaluations and feedback content to collect evaluations from colleagues and superiors. In this way, by collecting data such as work performance and project results, the characteristics of high performers can be more accurately extracted.

[0031] The collection unit can collect data on interview evaluations, question content, and interview progress methods. For example, the collection unit collects data such as interviewer evaluation sheets and interview feedback to collect interview evaluations. The collection unit can also collect data such as the types of questions and the intentions of the questions to collect question content. Furthermore, the collection unit can collect data such as the interview format and progress procedures to collect interview progress methods. For example, the collection unit collects data such as interviewer evaluation sheets and interview feedback to collect interview evaluations. The collection unit can also collect data such as the types of questions and the intentions of the questions to collect question content. Furthermore, the collection unit can collect data such as the interview format and progress procedures to collect interview progress methods. In this way, by collecting data such as interview evaluations and question content, the quality of interviews can be improved.

[0032] The analysis unit can analyze the collected data and extract characteristics of high performers, such as specific skill sets, personality traits, and interview answers. For example, the analysis unit analyzes data such as technical skills and communication skills to extract specific skill sets. The analysis unit can also analyze data such as leadership and cooperativeness to extract personality traits. Furthermore, the analysis unit can analyze data such as the consistency and specificity of answers to analyze the content of interview answers. For example, the analysis unit analyzes data such as technical skills and communication skills to extract specific skill sets. The analysis unit can also analyze data such as leadership and cooperativeness to extract personality traits. Furthermore, the analysis unit can analyze data such as the consistency and specificity of answers to analyze the content of interview answers. This allows the accuracy of interviews to be improved by extracting characteristics of high performers, such as specific skill sets and personality traits.

[0033] The generation unit can generate questions or evaluation criteria to be used during the interview based on the extracted features. The generation unit generates, for example, types of questions and items of evaluation criteria. For example, the generation unit generates questions related to a specific skill set. The generation unit can also generate evaluation criteria related to personality traits. Furthermore, the generation unit can generate questions and evaluation criteria related to the content of answers given during the interview. For example, the generation unit generates questions related to a specific skill set. The generation unit can also generate evaluation criteria related to personality traits. Furthermore, the generation unit can generate questions and evaluation criteria related to the content of answers given during the interview. In this way, by generating questions and evaluation criteria based on the extracted features, the quality of the interview can be improved.

[0034] The determination unit can analyze the candidate's answers in real time based on the generated questions and evaluation criteria, and determine whether or not the candidate is a high performer. For example, the determination unit sets the algorithm to be used, the timing of the analysis, and the like, in order to analyze the candidate's answers in real time. For example, the determination unit sets the algorithm to be used, the timing of the analysis, and the like, in order to analyze the candidate's answers in real time. In this way, by analyzing the candidate's answers in real time, it is possible to accurately determine the possibility of the candidate becoming a high performer.

[0035] The collection unit can expand the types of data it collects to include not only work performance but also participation in internal events and the frequency of internal communication. For example, the collection unit collects participation in internal events to evaluate the degree of team building and internal interaction. The collection unit can also collect the frequency of internal communication to evaluate relationships with colleagues and superiors. Furthermore, the collection unit can collect participation in internal training and seminars to evaluate motivation for skill improvement. For example, the collection unit collects participation in internal events to evaluate the degree of team building and internal interaction. The collection unit can also collect the frequency of internal communication to evaluate relationships with colleagues and superiors. The collection unit can also collect participation in internal training and seminars to evaluate motivation for skill improvement. In this way, by expanding the types of data, a more multifaceted evaluation is possible.

[0036] When collecting data, the collection unit can optimize the collection method by taking into account the success rate and failure rate of the user's past projects. For example, the collection unit collects detailed data for a user with a high success rate in past projects and analyzes the factors behind success. The collection unit can also collect data for identifying the causes of failure for a user with a high failure rate in past projects. Furthermore, the collection unit can adjust the frequency and timing of data collection based on the success rate and failure rate of the projects. For example, the collection unit collects detailed data for a user with a high success rate in past projects and analyzes the factors behind success. The collection unit can also collect data for identifying the causes of failure for a user with a high failure rate in past projects. Furthermore, the collection unit can adjust the frequency and timing of data collection based on the success rate and failure rate of the projects. In this way, the accuracy of data collection is improved by taking into account the success rate and failure rate of past projects.

[0037] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is on a business trip, the collection unit prioritizes collecting work performance and activity status at the business trip destination. Furthermore, when the user is working remotely, the collection unit can also collect work performance and communication status at home. Furthermore, when the user is in the office, the collection unit can also collect work performance and frequency of internal communication at the office. For example, when the user is on a business trip, the collection unit prioritizes collecting work performance and activity status at the business trip destination. Furthermore, when the user is working remotely, the collection unit can also collect work performance and communication status at home. Furthermore, when the user is in the office, the collection unit can also collect work performance and frequency of internal communication at the office. In this way, highly relevant data can be efficiently collected by taking into account the geographical location information.

[0038] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can analyze the user's social media activity status and collect work-related information. The collection unit can also analyze the user's comments and posts on social media to collect data related to work performance and skills. Furthermore, the collection unit can analyze the user's social media network and collect information on internal and external communication status. For example, the collection unit can analyze the user's social media activity status and collect information related to work. The collection unit can also analyze the user's comments and posts on social media to collect data related to work performance and skills. Furthermore, the collection unit can analyze the user's social media network and collect information on internal and external communication status. In this way, work-related information can be efficiently collected by analyzing social media activity.

[0039] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. For example, the analysis unit analyzes the interrelationships between work performance and project results to identify success factors. The analysis unit can also analyze the interrelationships between evaluations from colleagues and superiors and skill acquisition status to improve the reliability of evaluations. Furthermore, the analysis unit can analyze the interrelationships between participation in in-house events and work performance to evaluate the effectiveness of team building. For example, the analysis unit analyzes the interrelationships between work performance and project results to identify success factors. The analysis unit can also analyze the interrelationships between evaluations from colleagues and superiors and skill acquisition status to improve the reliability of evaluations. Furthermore, the analysis unit can analyze the interrelationships between participation in in-house events and work performance to evaluate the effectiveness of team building. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between data.

[0040] The analysis unit can perform the analysis while taking into account the attribute information of the data submitter. For example, the analysis unit analyzes work performance and project results while taking into account the submitter's job title and job content. The analysis unit can also improve the reliability of the evaluation by taking into account the submitter's years of experience and skill level. Furthermore, the analysis unit can analyze the frequency and quality of internal communications while taking into account the submitter's department and team. For example, the analysis unit analyzes work performance and project results while taking into account the submitter's job title and job content. The analysis unit can also improve the reliability of the evaluation by taking into account the submitter's years of experience and skill level. Furthermore, the analysis unit can analyze the frequency and quality of internal communications while taking into account the submitter's department and team. In this way, the reliability of the analysis is improved by taking into account the submitter's attribute information.

[0041] The analysis unit can perform the analysis while taking into account the geographic distribution of data. For example, the analysis unit analyzes work performance and project results while taking into account the geographic distribution of the user's workplaces and business trip destinations. The analysis unit can also analyze work performance and communication status while taking into account the user's remote work situation. Furthermore, the analysis unit can analyze work performance and the frequency of internal communication while taking into account the user's movement patterns within the office. For example, the analysis unit analyzes work performance and project results while taking into account the geographic distribution of the user's workplaces and business trip destinations. The analysis unit can also analyze work performance and communication status while taking into account the user's remote work situation. Furthermore, the analysis unit can analyze work performance and the frequency of internal communication while taking into account the user's movement patterns within the office. In this way, the accuracy of the analysis is improved by taking into account the geographic distribution.

[0042] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to work performance and project results. The analysis unit can also improve the reliability of the evaluation by referring to literature related to evaluations from colleagues and superiors. Furthermore, the analysis unit can evaluate the effectiveness of skill development by referring to literature related to skill acquisition status. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to work performance and project results. The analysis unit can also improve the reliability of the evaluation by referring to literature related to evaluations from colleagues and superiors. Furthermore, the analysis unit can evaluate the effectiveness of skill development by referring to literature related to skill acquisition status. In this way, the accuracy of the analysis is improved by referring to related literature.

[0043] When generating questions and evaluation criteria, the generation unit can refer to the candidate's past interview history to generate appropriate questions. For example, the generation unit refers to the candidate's past interview history to avoid asking the same questions. The generation unit can also generate questions related to specific skills or experience from the candidate's past interview history. Furthermore, the generation unit can analyze the candidate's past interview history to generate the most effective questions. For example, the generation unit refers to the candidate's past interview history to avoid asking the same questions. The generation unit can also generate questions related to specific skills or experience from the candidate's past interview history. Furthermore, the generation unit can analyze the candidate's past interview history to generate the most effective questions. In this way, optimal questions can be generated by referring to the past interview history.

[0044] The generation unit can generate questions and evaluation criteria by taking into account attribute information of the candidate. The generation unit generates appropriate questions by taking into account, for example, the candidate's educational background and work history. The generation unit can also generate evaluation criteria by taking into account the candidate's skill set and experience. Furthermore, the generation unit can generate appropriate questions and evaluation criteria by taking into account the candidate's personality traits. For example, the generation unit generates appropriate questions by taking into account the candidate's educational background and work history. The generation unit can also generate evaluation criteria by taking into account the candidate's skill set and experience. Furthermore, the generation unit can generate appropriate questions and evaluation criteria by taking into account the candidate's personality traits. In this way, appropriate questions and evaluation criteria can be generated by taking into account the candidate's attribute information.

[0045] When generating questions and evaluation criteria, the generation unit can generate appropriate questions by taking into account the geographical location information of the candidate. The generation unit can generate appropriate questions by taking into account, for example, geographical information of the candidate's workplace or business trip destination. The generation unit can also generate appropriate questions by taking into account the candidate's remote work situation. Furthermore, the generation unit can generate appropriate questions by taking into account the candidate's movement pattern within the office. For example, the generation unit can generate appropriate questions by taking into account geographical information of the candidate's workplace or business trip destination. The generation unit can also generate appropriate questions by taking into account the candidate's remote work situation. Furthermore, the generation unit can generate appropriate questions by taking into account the candidate's movement pattern within the office. In this way, optimal questions can be generated by taking into account geographical location information.

[0046] When generating questions and evaluation criteria, the generation unit can improve the accuracy of generation by referring to relevant industry trends. For example, the generation unit generates appropriate questions by referring to the latest trends in the industry. The generation unit can also generate evaluation criteria by referring to industry best practices. Furthermore, the generation unit can generate questions related to the latest technologies and skills in the industry. For example, the generation unit generates appropriate questions by referring to the latest trends in the industry. The generation unit can also generate evaluation criteria by referring to industry best practices. Furthermore, the generation unit can generate questions related to the latest technologies and skills in the industry. In this way, by referring to industry trends, the accuracy of generation is improved.

[0047] When analyzing a candidate's answer, the judgment unit can improve the accuracy of the judgment by referring to the answer patterns of past high performers. For example, the judgment unit analyzes the candidate's answer based on the answer patterns of past high performers. The judgment unit can also analyze by emphasizing parts that match the answer patterns of high performers. Furthermore, the judgment unit can also evaluate the reliability of the candidate's answer by referring to the answer patterns of high performers. For example, the judgment unit analyzes the candidate's answer based on the answer patterns of past high performers. The judgment unit can also analyze by emphasizing parts that match the answer patterns of high performers. Furthermore, the judgment unit can evaluate the reliability of the candidate's answer by referring to the answer patterns of high performers. In this way, by referring to the answer patterns of past high performers, the accuracy of the judgment is improved.

[0048] When analyzing the answers of the candidates, the determination unit can make a determination by taking into account the candidate's attribute information. The determination unit can evaluate the reliability of the answers by taking into account, for example, the candidate's educational background and work history. The determination unit can also evaluate the appropriateness of the answers by taking into account the candidate's skill set and experience. Furthermore, the determination unit can evaluate the consistency of the answers by taking into account the candidate's personality traits. For example, the determination unit can evaluate the reliability of the answers by taking into account the candidate's educational background and work history. The determination unit can also evaluate the appropriateness of the answers by taking into account the candidate's skill set and experience. Furthermore, the determination unit can evaluate the consistency of the answers by taking into account the candidate's personality traits. In this way, by taking into account the candidate's attribute information, the reliability of the determination is improved.

[0049] When analyzing the candidate's answer, the determination unit can make a determination by taking into account the candidate's geographic location information. The determination unit can evaluate the appropriateness of the answer by taking into account, for example, geographic information of the candidate's workplace or business trip destination. The determination unit can also evaluate the reliability of the answer by taking into account the candidate's remote work situation. Furthermore, the determination unit can evaluate the consistency of the answer by taking into account the candidate's movement pattern within the office. For example, the determination unit can evaluate the appropriateness of the answer by taking into account geographic information of the candidate's workplace or business trip destination. The determination unit can also evaluate the reliability of the answer by taking into account the candidate's remote work situation. Furthermore, the determination unit can evaluate the consistency of the answer by taking into account the candidate's movement pattern within the office. In this way, by taking into account the geographic location information, the accuracy of the determination is improved.

[0050] When analyzing the candidate's answers, the judgment unit can improve the accuracy of the judgment by referring to relevant industry trends. For example, the judgment unit can refer to the latest industry trends to evaluate the appropriateness of the answers. The judgment unit can also refer to industry best practices to evaluate the reliability of the answers. Furthermore, the judgment unit can evaluate answers related to the latest technologies and skills in the industry. For example, the judgment unit can refer to the latest industry trends to evaluate the appropriateness of the answers. The judgment unit can also refer to industry best practices to evaluate the reliability of the answers. Furthermore, the judgment unit can evaluate answers related to the latest technologies and skills in the industry. In this way, by referring to industry trends, the accuracy of the judgment is improved.

[0051] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0052] The collection unit can also collect user health data and analyze the correlation with work performance and skill acquisition status. For example, the collection unit can collect the user's sleep patterns and exercise habits and analyze the correlation with work performance. The collection unit can also collect the user's eating habits and analyze the correlation with skill acquisition status. Furthermore, the collection unit can collect the user's stress level and analyze the correlation with the project success rate. In this way, collecting health data enables more multifaceted evaluation.

[0053] The analysis unit can also predict future work performance based on the user's past work performance. For example, it can analyze the success and failure rates of past projects to predict the success rate of future projects. It can also analyze past skill acquisition status to predict the possibility of future skill acquisition. It can also analyze past evaluations from colleagues and superiors to predict future evaluations. This allows for more effective human resource evaluation by predicting future work performance based on past data.

[0054] The generation unit can also customize questions to be used during interviews based on the user's past interview results. For example, questions that received high marks in past interviews can be reused. Also, questions that received low marks in past interviews can be improved. Furthermore, questions related to specific skills or personality traits can be generated based on the past interview results. This allows the quality of interviews to be improved by customizing questions based on the past interview results.

[0055] The collection unit can also analyze a user's social media activity and analyze its correlation with work performance and skill acquisition status. For example, it can collect a user's comments and posts on social media and analyze the correlation with work performance. It can also collect a user's social media network and analyze its correlation with skill acquisition status. It can also collect the frequency of a user's social media activity and analyze its correlation with project success rates. This allows for a more multifaceted evaluation by analyzing social media activity.

[0056] The collection unit can also collect data related to work performance and skill acquisition status, taking into account the user's geographical location information. For example, if the user is on a business trip, the collection unit can collect work performance and activity status at the business trip destination. If the user is working remotely, the collection unit can also collect work performance and communication status at home. Furthermore, if the user is in the office, the collection unit can also collect work performance at the office and the frequency of internal communication. In this way, by taking into account the geographical location information, highly relevant data can be collected efficiently.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The collection department collects personal data from employees over the first few years of employment. This personal data includes information such as work performance, project outcomes, evaluations from colleagues and superiors, and skill acquisition status. For example, to collect work performance, data such as sales figures and project completion rates can be collected. To collect project outcomes, data such as project success rates and delivery deadline adherence rates can also be collected. Furthermore, to collect evaluations from colleagues and superiors, data such as 360-degree evaluations and feedback content can also be collected. Step 2: The collection department collects interviewer data, including interview evaluation criteria and past interview results. Step 3: The analysis department analyzes the collected data and extracts the characteristics of high performers. The analysis department analyzes data such as specific skill sets, personality traits, and interview responses. For example, data such as technical skills, communication skills, leadership, teamwork, and consistency and specificity of responses are analyzed. Step 4: The generator generates questions or evaluation criteria to be used during the interview based on the extracted features. The generator generates question types and evaluation criteria items. For example, it generates questions related to a specific skill set, evaluation criteria related to personality traits, and questions and evaluation criteria related to the answers given during the interview. Step 5: The evaluation unit analyzes the candidate's answers in real time based on the generated questions or evaluation criteria and determines whether or not the candidate is a high performer. The evaluation unit sets the algorithm to be used, the timing of the analysis, etc., in order to analyze the candidate's answers in real time.

[0059] (Example 2) The AI ​​interview system of this embodiment combines personal data from the first few years of employment with data from interviewers to develop high performers. This system aims to address the current situation of an increasing number of low performers who join companies primarily based on their academic background. Specifically, it consists of the following steps: First, personal data from the first few years of employment is collected. This data includes work performance, project results, evaluations from colleagues and superiors, and skill acquisition status. Next, interviewer data is collected. This data includes interview evaluations, questions, and interview progress. AI analyzes the collected data to extract characteristics of high performers. For example, it identifies specific skill sets, personality traits, and interview responses common to high performers. Next, the AI ​​generates questions and evaluation criteria to be used during interviews. Interviewers can then use the AI-generated questions to evaluate candidates. The AI ​​analyzes candidates' responses in real time and determines whether they are likely to become high performers. This system enables a comprehensive evaluation of not only academic background but also actual work performance, skills, personality traits, and other factors, making it possible to identify high performers. This is expected to reduce the number of low performers entering the company and improve overall corporate performance. This allows the AI ​​interview system to comprehensively evaluate not only academic background but also actual work performance, skills, personality traits, etc., making it possible to identify high performers.

[0060] An AI interview system according to an embodiment includes a collection unit, an analysis unit, a generation unit, and a determination unit. The collection unit collects personal data from employees over several years after joining the company. The personal data includes, but is not limited to, work performance, project results, evaluations from colleagues and superiors, and skill acquisition status. For example, the collection unit collects data such as sales and project completion rates to collect work performance. The collection unit can also collect data such as project success rates and delivery deadline adherence rates to collect project results. The collection unit can also collect data such as 360-degree evaluations and feedback content to collect evaluations from colleagues and superiors. For example, the collection unit collects data such as sales and project completion rates to collect work performance. The collection unit can also collect data such as project success rates and delivery deadline adherence rates to collect project results. The collection unit can also collect data such as 360-degree evaluations and feedback content to collect evaluations from colleagues and superiors. The analysis unit analyzes the collected data and extracts characteristics of high performers. The analysis unit analyzes data such as specific skill sets, personality traits, and interview answers. For example, the analysis unit analyzes data such as technical skills and communication skills to extract specific skill sets. The analysis unit can also analyze data such as leadership and cooperation to extract personality traits. The analysis unit can also analyze data such as the consistency and specificity of answers to analyze interview answers. The generation unit generates questions and evaluation criteria to be used in interviews based on the extracted characteristics. The generation unit generates, for example, question types and evaluation criteria items. For example, the generation unit generates questions related to specific skill sets. The generation unit can also generate evaluation criteria related to personality traits. The generation unit can also generate questions and evaluation criteria related to interview answers. The determination unit analyzes the candidate's answers in real time based on the generated questions and evaluation criteria and determines whether the candidate is a high performer.The determination unit sets, for example, the algorithm to be used and the timing of the analysis in order to analyze the candidate's answers in real time. For example, the determination unit sets, for example, the algorithm to be used and the timing of the analysis in order to analyze the candidate's answers in real time. As a result, the AI ​​interview system according to the embodiment can produce high performers by combining personal data from the first few years of employment with data from interviewers.

[0061] The collection unit can collect data on work performance, project results, evaluations from colleagues and superiors, and skill acquisition status. For example, the collection unit collects data such as sales and project completion rates to collect work performance. The collection unit can also collect data such as project success rates and delivery deadline adherence rates to collect project results. Furthermore, the collection unit can collect data such as 360-degree evaluations and feedback content to collect evaluations from colleagues and superiors. For example, the collection unit collects data such as sales and project completion rates to collect work performance. The collection unit can also collect data such as project success rates and delivery deadline adherence rates to collect project results. Furthermore, the collection unit can collect data such as 360-degree evaluations and feedback content to collect evaluations from colleagues and superiors. In this way, by collecting data such as work performance and project results, the characteristics of high performers can be more accurately extracted.

[0062] The collection unit can collect data on interview evaluations, question content, and interview progress methods. For example, the collection unit collects data such as interviewer evaluation sheets and interview feedback to collect interview evaluations. The collection unit can also collect data such as the types of questions and the intentions of the questions to collect question content. Furthermore, the collection unit can collect data such as the interview format and progress procedures to collect interview progress methods. For example, the collection unit collects data such as interviewer evaluation sheets and interview feedback to collect interview evaluations. The collection unit can also collect data such as the types of questions and the intentions of the questions to collect question content. Furthermore, the collection unit can collect data such as the interview format and progress procedures to collect interview progress methods. In this way, by collecting data such as interview evaluations and question content, the quality of interviews can be improved.

[0063] The analysis unit can analyze the collected data and extract characteristics of high performers, such as specific skill sets, personality traits, and interview answers. For example, the analysis unit analyzes data such as technical skills and communication skills to extract specific skill sets. The analysis unit can also analyze data such as leadership and cooperativeness to extract personality traits. Furthermore, the analysis unit can analyze data such as the consistency and specificity of answers to analyze the content of interview answers. For example, the analysis unit analyzes data such as technical skills and communication skills to extract specific skill sets. The analysis unit can also analyze data such as leadership and cooperativeness to extract personality traits. Furthermore, the analysis unit can analyze data such as the consistency and specificity of answers to analyze the content of interview answers. This allows the accuracy of interviews to be improved by extracting characteristics of high performers, such as specific skill sets and personality traits.

[0064] The generation unit can generate questions or evaluation criteria to be used during the interview based on the extracted features. The generation unit generates, for example, types of questions and items of evaluation criteria. For example, the generation unit generates questions related to a specific skill set. The generation unit can also generate evaluation criteria related to personality traits. Furthermore, the generation unit can generate questions and evaluation criteria related to the content of answers given during the interview. For example, the generation unit generates questions related to a specific skill set. The generation unit can also generate evaluation criteria related to personality traits. Furthermore, the generation unit can generate questions and evaluation criteria related to the content of answers given during the interview. In this way, by generating questions and evaluation criteria based on the extracted features, the quality of the interview can be improved.

[0065] The determination unit can analyze the candidate's answers in real time based on the generated questions and evaluation criteria, and determine whether or not the candidate is a high performer. For example, the determination unit sets the algorithm to be used, the timing of the analysis, and the like, in order to analyze the candidate's answers in real time. For example, the determination unit sets the algorithm to be used, the timing of the analysis, and the like, in order to analyze the candidate's answers in real time. In this way, by analyzing the candidate's answers in real time, it is possible to accurately determine the possibility of the candidate becoming a high performer.

[0066] The collection unit can estimate the user's emotions and adjust the timing of data collection based on the estimated user's emotions. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can adjust the timing of data collection so as not to interfere with work. Furthermore, if the user is tired, the collection unit can temporarily stop data collection and resume it after the user has rested. For example, if the user is feeling stressed, the collection unit can reduce the frequency of data collection and collect data when the user is relaxed. Furthermore, if the user is concentrating, the collection unit can adjust the timing of data collection so as not to interfere with work. Furthermore, if the user is tired, the collection unit can temporarily stop data collection and resume it after the user has rested. Thus, by adjusting the timing of data collection based on the user's emotions, the efficiency of data collection can be improved.

[0067] The collection unit can expand the types of data it collects to include not only work performance but also participation in internal events and the frequency of internal communication. For example, the collection unit collects participation in internal events to evaluate the degree of team building and internal interaction. The collection unit can also collect the frequency of internal communication to evaluate relationships with colleagues and superiors. Furthermore, the collection unit can collect participation in internal training and seminars to evaluate motivation for skill improvement. For example, the collection unit collects participation in internal events to evaluate the degree of team building and internal interaction. The collection unit can also collect the frequency of internal communication to evaluate relationships with colleagues and superiors. The collection unit can also collect participation in internal training and seminars to evaluate motivation for skill improvement. In this way, by expanding the types of data, a more multifaceted evaluation is possible.

[0068] When collecting data, the collection unit can optimize the collection method by taking into account the success rate and failure rate of the user's past projects. For example, the collection unit collects detailed data for a user with a high success rate in past projects and analyzes the factors behind success. The collection unit can also collect data for identifying the causes of failure for a user with a high failure rate in past projects. Furthermore, the collection unit can adjust the frequency and timing of data collection based on the success rate and failure rate of the projects. For example, the collection unit collects detailed data for a user with a high success rate in past projects and analyzes the factors behind success. The collection unit can also collect data for identifying the causes of failure for a user with a high failure rate in past projects. Furthermore, the collection unit can adjust the frequency and timing of data collection based on the success rate and failure rate of the projects. In this way, the accuracy of data collection is improved by taking into account the success rate and failure rate of past projects.

[0069] The collection unit can estimate the user's emotions and determine the priority of data to be collected based on the estimated user's emotions. For example, when the user is feeling stressed, the collection unit prioritizes collecting data related to stress reduction. Furthermore, when the user is relaxed, the collection unit can also collect detailed data such as work performance and skill acquisition status. Furthermore, when the user is concentrating, the collection unit can also prioritize collecting data related to project progress and results. For example, when the user is feeling stressed, the collection unit prioritizes collecting data related to stress reduction. Furthermore, when the user is relaxed, the collection unit can also collect detailed data such as work performance and skill acquisition status. Furthermore, when the user is concentrating, the collection unit can also prioritize collecting data related to project progress and results. In this way, by determining the priority of data based on the user's emotions, the efficiency of data collection is improved.

[0070] When collecting data, the collection unit can prioritize collecting highly relevant data by taking into account the user's geographical location information. For example, when the user is on a business trip, the collection unit prioritizes collecting work performance and activity status at the business trip destination. Furthermore, when the user is working remotely, the collection unit can also collect work performance and communication status at home. Furthermore, when the user is in the office, the collection unit can also collect work performance and frequency of internal communication at the office. For example, when the user is on a business trip, the collection unit prioritizes collecting work performance and activity status at the business trip destination. Furthermore, when the user is working remotely, the collection unit can also collect work performance and communication status at home. Furthermore, when the user is in the office, the collection unit can also collect work performance and frequency of internal communication at the office. In this way, highly relevant data can be efficiently collected by taking into account the geographical location information.

[0071] When collecting data, the collection unit can analyze the user's social media activities and collect related data. For example, the collection unit can analyze the user's social media activity status and collect work-related information. The collection unit can also analyze the user's comments and posts on social media to collect data related to work performance and skills. Furthermore, the collection unit can analyze the user's social media network and collect information on internal and external communication status. For example, the collection unit can analyze the user's social media activity status and collect information related to work. The collection unit can also analyze the user's comments and posts on social media to collect data related to work performance and skills. Furthermore, the collection unit can analyze the user's social media network and collect information on internal and external communication status. In this way, work-related information can be efficiently collected by analyzing social media activity.

[0072] The analysis unit can estimate the user's emotions and adjust the analysis algorithm based on the estimated user's emotions. For example, if the user is feeling stressed, the analysis unit can focus on data related to stress reduction during analysis. Furthermore, if the user is relaxed, the analysis unit can analyze detailed data and extract information useful for improving skills and work performance. Furthermore, if the user is concentrating, the analysis unit can focus on data related to project progress and results during analysis. For example, if the user is feeling stressed, the analysis unit can focus on data related to stress reduction during analysis. Furthermore, if the user is relaxed, the analysis unit can analyze detailed data and extract information useful for improving skills and work performance. Furthermore, if the user is concentrating, the analysis unit can focus on data related to project progress and results during analysis. In this way, by adjusting the analysis algorithm based on the user's emotions, the accuracy of the analysis is improved.

[0073] The analysis unit can improve the accuracy of the analysis by taking into account the interrelationships between data during analysis. For example, the analysis unit analyzes the interrelationships between work performance and project results to identify success factors. The analysis unit can also analyze the interrelationships between evaluations from colleagues and superiors and skill acquisition status to improve the reliability of evaluations. Furthermore, the analysis unit can analyze the interrelationships between participation in in-house events and work performance to evaluate the effectiveness of team building. For example, the analysis unit analyzes the interrelationships between work performance and project results to identify success factors. The analysis unit can also analyze the interrelationships between evaluations from colleagues and superiors and skill acquisition status to improve the reliability of evaluations. Furthermore, the analysis unit can analyze the interrelationships between participation in in-house events and work performance to evaluate the effectiveness of team building. In this way, the accuracy of the analysis is improved by taking into account the interrelationships between data.

[0074] The analysis unit can perform the analysis while taking into account the attribute information of the data submitter. For example, the analysis unit analyzes work performance and project results while taking into account the submitter's job title and job content. The analysis unit can also improve the reliability of the evaluation by taking into account the submitter's years of experience and skill level. Furthermore, the analysis unit can analyze the frequency and quality of internal communications while taking into account the submitter's department and team. For example, the analysis unit analyzes work performance and project results while taking into account the submitter's job title and job content. The analysis unit can also improve the reliability of the evaluation by taking into account the submitter's years of experience and skill level. Furthermore, the analysis unit can analyze the frequency and quality of internal communications while taking into account the submitter's department and team. In this way, the reliability of the analysis is improved by taking into account the submitter's attribute information.

[0075] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user's emotions. For example, when the user is feeling stressed, the analysis unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method including detailed information. Furthermore, when the user is concentrating, the analysis unit can also provide a display method that focuses on the main points. For example, when the user is feeling stressed, the analysis unit provides a simple, highly visible display method. Furthermore, when the user is relaxed, the analysis unit can also provide a display method that includes detailed information. Furthermore, when the user is concentrating, the analysis unit can also provide a display method that focuses on the main points. In this way, by adjusting the display method based on the user's emotions, the visibility of the analysis results is improved.

[0076] The analysis unit can perform the analysis while taking into account the geographic distribution of data. For example, the analysis unit analyzes work performance and project results while taking into account the geographic distribution of the user's workplaces and business trip destinations. The analysis unit can also analyze work performance and communication status while taking into account the user's remote work situation. Furthermore, the analysis unit can analyze work performance and the frequency of internal communication while taking into account the user's movement patterns within the office. For example, the analysis unit analyzes work performance and project results while taking into account the geographic distribution of the user's workplaces and business trip destinations. The analysis unit can also analyze work performance and communication status while taking into account the user's remote work situation. Furthermore, the analysis unit can analyze work performance and the frequency of internal communication while taking into account the user's movement patterns within the office. In this way, the accuracy of the analysis is improved by taking into account the geographic distribution.

[0077] During analysis, the analysis unit can improve the accuracy of the analysis by referring to literature related to the data. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to work performance and project results. The analysis unit can also improve the reliability of the evaluation by referring to literature related to evaluations from colleagues and superiors. Furthermore, the analysis unit can evaluate the effectiveness of skill development by referring to literature related to skill acquisition status. For example, the analysis unit can improve the accuracy of the analysis by referring to literature related to work performance and project results. The analysis unit can also improve the reliability of the evaluation by referring to literature related to evaluations from colleagues and superiors. Furthermore, the analysis unit can evaluate the effectiveness of skill development by referring to literature related to skill acquisition status. In this way, the accuracy of the analysis is improved by referring to related literature.

[0078] The generation unit can estimate the user's emotions and adjust the expression method of the generated questions and evaluation criteria based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit generates simple and easy-to-understand questions and evaluation criteria. Furthermore, when the user is relaxed, the generation unit can generate detailed questions and evaluation criteria. Furthermore, when the user is concentrating, the generation unit can generate questions and evaluation criteria that get to the point. For example, when the user is feeling stressed, the generation unit generates simple and easy-to-understand questions and evaluation criteria. Furthermore, when the user is relaxed, the generation unit can generate detailed questions and evaluation criteria. Furthermore, when the user is concentrating, the generation unit can generate questions and evaluation criteria that get to the point. In this way, by adjusting the expression method based on the user's emotions, understanding of the questions and evaluation criteria is improved.

[0079] When generating questions and evaluation criteria, the generation unit can refer to the candidate's past interview history to generate appropriate questions. For example, the generation unit refers to the candidate's past interview history to avoid asking the same questions. The generation unit can also generate questions related to specific skills or experience from the candidate's past interview history. Furthermore, the generation unit can analyze the candidate's past interview history to generate the most effective questions. For example, the generation unit refers to the candidate's past interview history to avoid asking the same questions. The generation unit can also generate questions related to specific skills or experience from the candidate's past interview history. Furthermore, the generation unit can analyze the candidate's past interview history to generate the most effective questions. In this way, optimal questions can be generated by referring to the past interview history.

[0080] The generation unit can generate questions and evaluation criteria by taking into account attribute information of the candidate. The generation unit generates appropriate questions by taking into account, for example, the candidate's educational background and work history. The generation unit can also generate evaluation criteria by taking into account the candidate's skill set and experience. Furthermore, the generation unit can generate appropriate questions and evaluation criteria by taking into account the candidate's personality traits. For example, the generation unit generates appropriate questions by taking into account the candidate's educational background and work history. The generation unit can also generate evaluation criteria by taking into account the candidate's skill set and experience. Furthermore, the generation unit can generate appropriate questions and evaluation criteria by taking into account the candidate's personality traits. In this way, appropriate questions and evaluation criteria can be generated by taking into account the candidate's attribute information.

[0081] The generation unit can estimate the user's emotions and determine the priority of questions and evaluation criteria to be generated based on the estimated user's emotions. For example, when the user is feeling stressed, the generation unit can prioritize generating important questions. Furthermore, when the user is relaxed, the generation unit can also prioritize generating detailed questions. Furthermore, when the user is concentrating, the generation unit can also prioritize generating questions that get to the point. For example, when the user is feeling stressed, the generation unit can prioritize generating important questions. Furthermore, when the user is relaxed, the generation unit can also prioritize generating detailed questions. Furthermore, when the user is concentrating, the generation unit can also prioritize generating questions that get to the point. In this way, by determining the priority based on the user's emotions, it is possible to prioritize generating important questions and evaluation criteria.

[0082] When generating questions and evaluation criteria, the generation unit can generate appropriate questions by taking into account the geographical location information of the candidate. The generation unit can generate appropriate questions by taking into account, for example, geographical information of the candidate's workplace or business trip destination. The generation unit can also generate appropriate questions by taking into account the candidate's remote work situation. Furthermore, the generation unit can generate appropriate questions by taking into account the candidate's movement pattern within the office. For example, the generation unit can generate appropriate questions by taking into account geographical information of the candidate's workplace or business trip destination. The generation unit can also generate appropriate questions by taking into account the candidate's remote work situation. Furthermore, the generation unit can generate appropriate questions by taking into account the candidate's movement pattern within the office. In this way, optimal questions can be generated by taking into account geographical location information.

[0083] When generating questions and evaluation criteria, the generation unit can improve the accuracy of generation by referring to relevant industry trends. For example, the generation unit generates appropriate questions by referring to the latest trends in the industry. The generation unit can also generate evaluation criteria by referring to industry best practices. Furthermore, the generation unit can generate questions related to the latest technologies and skills in the industry. For example, the generation unit generates appropriate questions by referring to the latest trends in the industry. The generation unit can also generate evaluation criteria by referring to industry best practices. Furthermore, the generation unit can generate questions related to the latest technologies and skills in the industry. In this way, by referring to industry trends, the accuracy of generation is improved.

[0084] The determination unit can estimate the user's emotions and adjust the analysis method of the candidate's answers based on the estimated user's emotions. For example, if the user is feeling stressed, the determination unit uses a simple and easy-to-understand analysis method. Furthermore, if the user is relaxed, the determination unit can also use a detailed analysis method. Furthermore, if the user is concentrating, the determination unit can also use an analysis method that focuses on the main points. For example, if the user is feeling stressed, the determination unit uses a simple and easy-to-understand analysis method. Furthermore, if the user is relaxed, the determination unit can also use a detailed analysis method. Furthermore, if the user is concentrating, the determination unit can also use an analysis method that focuses on the main points. In this way, by adjusting the analysis method based on the user's emotions, the accuracy of the analysis is improved.

[0085] When analyzing a candidate's answer, the judgment unit can improve the accuracy of the judgment by referring to the answer patterns of past high performers. For example, the judgment unit analyzes the candidate's answer based on the answer patterns of past high performers. The judgment unit can also analyze by emphasizing parts that match the answer patterns of high performers. Furthermore, the judgment unit can also evaluate the reliability of the candidate's answer by referring to the answer patterns of high performers. For example, the judgment unit analyzes the candidate's answer based on the answer patterns of past high performers. The judgment unit can also analyze by emphasizing parts that match the answer patterns of high performers. Furthermore, the judgment unit can evaluate the reliability of the candidate's answer by referring to the answer patterns of high performers. In this way, by referring to the answer patterns of past high performers, the accuracy of the judgment is improved.

[0086] When analyzing the answers of the candidates, the determination unit can make a determination by taking into account the candidate's attribute information. The determination unit can evaluate the reliability of the answers by taking into account, for example, the candidate's educational background and work history. The determination unit can also evaluate the appropriateness of the answers by taking into account the candidate's skill set and experience. Furthermore, the determination unit can evaluate the consistency of the answers by taking into account the candidate's personality traits. For example, the determination unit can evaluate the reliability of the answers by taking into account the candidate's educational background and work history. The determination unit can also evaluate the appropriateness of the answers by taking into account the candidate's skill set and experience. Furthermore, the determination unit can evaluate the consistency of the answers by taking into account the candidate's personality traits. In this way, by taking into account the candidate's attribute information, the reliability of the determination is improved.

[0087] The determination unit can estimate the user's emotions and determine the priority of the candidates' answers based on the estimated user's emotions. For example, when the user is feeling stressed, the determination unit prioritizes analyzing important answers. Furthermore, when the user is relaxed, the determination unit can also prioritize analyzing detailed answers. Furthermore, when the user is concentrating, the determination unit can also prioritize analyzing answers that emphasize the main points. For example, when the user is feeling stressed, the determination unit prioritizes analyzing important answers. Furthermore, when the user is relaxed, the determination unit can also prioritize analyzing detailed answers. Furthermore, when the user is concentrating, the determination unit can also prioritize analyzing answers that emphasize the main points. In this way, by determining the priority based on the user's emotions, it is possible to prioritize analyzing important answers.

[0088] When analyzing the candidate's answer, the determination unit can make a determination by taking into account the candidate's geographic location information. The determination unit can evaluate the appropriateness of the answer by taking into account, for example, geographic information of the candidate's workplace or business trip destination. The determination unit can also evaluate the reliability of the answer by taking into account the candidate's remote work situation. Furthermore, the determination unit can evaluate the consistency of the answer by taking into account the candidate's movement pattern within the office. For example, the determination unit can evaluate the appropriateness of the answer by taking into account geographic information of the candidate's workplace or business trip destination. The determination unit can also evaluate the reliability of the answer by taking into account the candidate's remote work situation. Furthermore, the determination unit can evaluate the consistency of the answer by taking into account the candidate's movement pattern within the office. In this way, by taking into account the geographic location information, the accuracy of the determination is improved.

[0089] When analyzing the candidate's answers, the judgment unit can improve the accuracy of the judgment by referring to relevant industry trends. For example, the judgment unit can refer to the latest industry trends to evaluate the appropriateness of the answers. The judgment unit can also refer to industry best practices to evaluate the reliability of the answers. Furthermore, the judgment unit can evaluate answers related to the latest technologies and skills in the industry. For example, the judgment unit can refer to the latest industry trends to evaluate the appropriateness of the answers. The judgment unit can also refer to industry best practices to evaluate the reliability of the answers. Furthermore, the judgment unit can evaluate answers related to the latest technologies and skills in the industry. In this way, by referring to industry trends, the accuracy of the judgment is improved. === Hard Collateral 1-1 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and determination unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the collection unit collects personal data using the camera 42 and microphone 38B of the smart device 14 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts characteristics of high performers. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates questions and evaluation criteria to be used during interviews based on the extracted characteristics. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the candidate's answers in real time and determines whether the candidate is a high performer. === Hard Collateral 1-2 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and determination unit, described above, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the collection unit collects personal data using the camera 42 and microphone 238 of the smart glasses 214 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts characteristics of high performers. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates questions and evaluation criteria to be used during interviews based on the extracted characteristics. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the candidate's answers in real time and determines whether the candidate is a high performer. === Hard Collateral 1-3 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and determination unit, described above, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the collection unit collects personal data using the camera 42 and microphone 238 of the headset-type terminal 314 and transmits the data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts characteristics of high performers. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates questions and evaluation criteria to be used during interviews based on the extracted characteristics. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the candidate's answers in real time and determines whether the candidate is a high performer. === Hard Collateral 1-4 === Each of the multiple elements, including the collection unit, analysis unit, generation unit, and determination unit, described above, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the collection unit collects personal data using the camera 42 and microphone 238 of the robot 414 and transmits the collected data to the data processing device 12 via the control unit 46A. The analysis unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the collected data and extracts characteristics of high performers. The generation unit, realized, for example, by the specific processing unit 290 of the data processing device 12, generates questions and evaluation criteria to be used during interviews based on the extracted characteristics. The determination unit, realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the answers of candidates in real time and determines whether or not they are high performers.

[0090] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0091] The analysis unit can also estimate the user's emotions and adjust the analysis priority based on the estimated user's emotions. For example, if the user is feeling stressed, data related to stress reduction can be analyzed with priority. Also, if the user is relaxed, detailed data can be analyzed with priority. Furthermore, if the user is concentrating, data related to the progress and results of a project can be analyzed with priority. In this way, adjusting the analysis priority based on the user's emotions improves the efficiency and accuracy of the analysis.

[0092] The collection unit can also collect user health data and analyze the correlation with work performance and skill acquisition status. For example, the collection unit can collect the user's sleep patterns and exercise habits and analyze the correlation with work performance. The collection unit can also collect the user's eating habits and analyze the correlation with skill acquisition status. Furthermore, the collection unit can collect the user's stress level and analyze the correlation with the project success rate. In this way, collecting health data enables more multifaceted evaluation.

[0093] The generation unit can also estimate the user's emotions and adjust the difficulty of the questions to be generated based on the estimated user's emotions. For example, if the user is feeling stressed, simple questions can be generated. If the user is relaxed, detailed questions can be generated. Furthermore, if the user is concentrating, more difficult questions can be generated. In this way, the quality of the interview can be improved by adjusting the difficulty of questions based on the user's emotions.

[0094] The analysis unit can also predict future work performance based on the user's past work performance. For example, it can analyze the success and failure rates of past projects to predict the success rate of future projects. It can also analyze past skill acquisition status to predict the possibility of future skill acquisition. It can also analyze past evaluations from colleagues and superiors to predict future evaluations. This allows for more effective human resource evaluation by predicting future work performance based on past data.

[0095] The collection unit can also estimate the user's emotions and adjust the type of data to be collected based on the estimated user emotions. For example, if the user is feeling stressed, data related to stress reduction can be collected. If the user is relaxed, detailed data such as work performance and skill acquisition status can be collected. Furthermore, if the user is concentrating, data related to the progress and results of a project can be collected. In this way, adjusting the type of data to be collected based on the user's emotions improves the efficiency of data collection.

[0096] The generation unit can also customize questions to be used during interviews based on the user's past interview results. For example, questions that received high marks in past interviews can be reused. Also, questions that received low marks in past interviews can be improved. Furthermore, questions related to specific skills or personality traits can be generated based on the past interview results. This allows the quality of interviews to be improved by customizing questions based on the past interview results.

[0097] The determination unit can also estimate the user's emotions and adjust the analysis method of the candidate's answers based on the estimated user's emotions. For example, if the user is feeling stressed, a simple and easy-to-understand analysis method can be used. If the user is relaxed, a detailed analysis method can be used. Furthermore, if the user is concentrating, a more concise analysis method can be used. In this way, by adjusting the analysis method based on the user's emotions, the accuracy of the analysis can be improved.

[0098] The collection unit can also analyze a user's social media activity and analyze its correlation with work performance and skill acquisition status. For example, it can collect a user's comments and posts on social media and analyze the correlation with work performance. It can also collect a user's social media network and analyze its correlation with skill acquisition status. It can also collect the frequency of a user's social media activity and analyze its correlation with project success rates. This allows for a more multifaceted evaluation by analyzing social media activity.

[0099] The analysis unit can also estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is feeling stressed, a simple, highly visible display method can be provided. If the user is relaxed, a display method including detailed information can be provided. Furthermore, if the user is concentrating, a display method that focuses on the main points can be provided. In this way, the visibility of the analysis results can be improved by adjusting the display method based on the user's emotions.

[0100] The collection unit can also collect data related to work performance and skill acquisition status, taking into account the user's geographical location information. For example, if the user is on a business trip, the collection unit can collect work performance and activity status at the business trip destination. If the user is working remotely, the collection unit can also collect work performance and communication status at home. Furthermore, if the user is in the office, the collection unit can also collect work performance at the office and the frequency of internal communication. In this way, by taking into account the geographical location information, highly relevant data can be collected efficiently.

[0101] The processing flow of the second embodiment will be briefly explained below.

[0102] Step 1: The collection department collects personal data from employees over the first few years of employment. This personal data includes information such as work performance, project outcomes, evaluations from colleagues and superiors, and skill acquisition status. For example, to collect work performance, data such as sales figures and project completion rates can be collected. To collect project outcomes, data such as project success rates and delivery deadline adherence rates can also be collected. Furthermore, to collect evaluations from colleagues and superiors, data such as 360-degree evaluations and feedback content can also be collected. Step 2: The collection department collects interviewer data, including interview evaluation criteria and past interview results. Step 3: The analysis department analyzes the collected data and extracts the characteristics of high performers. The analysis department analyzes data such as specific skill sets, personality traits, and interview responses. For example, data such as technical skills, communication skills, leadership, teamwork, and consistency and specificity of responses are analyzed. Step 4: The generator generates questions or evaluation criteria to be used during the interview based on the extracted features. The generator generates question types and evaluation criteria items. For example, it generates questions related to a specific skill set, evaluation criteria related to personality traits, and questions and evaluation criteria related to the answers given during the interview. Step 5: The evaluation unit analyzes the candidate's answers in real time based on the generated questions or evaluation criteria and determines whether or not the candidate is a high performer. The evaluation unit sets the algorithm to be used, the timing of the analysis, etc., in order to analyze the candidate's answers in real time.

[0103] 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.

[0104] 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 the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0105] 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.

[0106] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0107] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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).

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0121] 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.

[0122] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0137] 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.

[0138] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0139] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0140] 7, a 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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).

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0154] 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.

[0155] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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).

[0160] 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.

[0161] 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."

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] [Explanation of symbols]

[0175] 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 collection department that collects personal data from employees for several years after joining the company. a collection unit that collects interviewer data; an analysis unit that analyzes the data collected by the collection unit and extracts characteristics of high performers; a generation unit that generates questions or evaluation criteria to be used during an interview based on the features extracted by the analysis unit; a determination unit that analyzes the answers of the candidates in real time based on the questions or evaluation criteria generated by the generation unit and determines whether or not the candidates are high performers. A system characterized by:

2. The collecting unit Collect data on work performance, project outcomes, peer and manager evaluations, and skill development 2. The system of claim 1.

3. The collecting unit Collect data on interview evaluations, questions asked, and interview process 2. The system of claim 1.

4. The analysis unit Analyze the collected data to identify specific skill sets, personality traits, and interview responses that distinguish high performers 2. The system of claim 1.

5. The generation unit Generate interview questions or assessment criteria based on the extracted features 2. The system of claim 1.

6. The determination unit Analyze candidates' answers in real time based on generated questions and evaluation criteria to determine whether they are high performers 2. The system of claim 1.

7. The collecting unit Inferring user emotions and adjusting the timing of data collection based on the estimated user emotions 2. The system of claim 1.

8. The collecting unit Expand the types of data collected beyond just work performance to include attendance at company events and frequency of internal communications.

2. The system of claim 1.

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