System

Abstract

An object of a system according to an embodiment is to improve the efficiency of an interview process.SOLUTION: A system includes an interaction unit, an analysis unit, an evaluation unit, a selection unit, and a specification unit. The dialogue unit performs a dialogue with the candidate. The analysis unit analyzes the dialogue content obtained by the dialogue unit. The evaluation unit evaluates the strengths and characteristics of the candidates based on the contents analyzed by the analysis unit. The selection unit selects an appropriate second interviewer based on the strengths and characteristics evaluated by the evaluation unit. The specifying unit specifies an optimal position based on the strength and the characteristic evaluated by the evaluating unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, the interview process requires a lot of time and human resources, which makes the selection process less efficient.

[0005] The system according to the embodiment aims to improve the efficiency of the interview process. [Means for solving the problem]

[0006] The system according to the embodiment includes a dialogue unit, an analysis unit, an evaluation unit, a selection unit, and an identification unit. The dialogue unit engages in dialogue with the candidate. The analysis unit analyzes the content of the dialogue obtained by the dialogue unit. The evaluation unit evaluates the strengths and characteristics of the candidate based on the content analyzed by the analysis unit. The selection unit selects an appropriate candidate for the second interview based on the strengths and characteristics evaluated by the evaluation unit. The identification unit identifies the optimal position based on the strengths and characteristics evaluated by the evaluation unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of the interview process. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The interview process efficiency system according to an embodiment of the present invention uses generative AI to accurately evaluate candidates. This system accurately identifies a candidate's strengths and characteristics by analyzing their thinking style, personality, facial expressions, and tone of voice during dialogue with the candidate and determining their authenticity. It also selects appropriate second interviewers, identifies the most suitable position within the organization based on company data, maximizes the candidate's capabilities, and identifies compatibility and synergy with existing employees. This allows the interview process efficiency system to streamline a company's interview process and accurately evaluate candidates.

[0029] An interview process efficiency improvement system according to an embodiment includes a dialogue unit, an analysis unit, an evaluation unit, a selection unit, and an identification unit. The dialogue unit engages in dialogue with candidates. For example, the dialogue unit asks questions to the candidates and collects their responses. The dialogue unit can also monitor the candidates' facial expressions and tone of voice in real time. The analysis unit analyzes the dialogue content obtained by the dialogue unit. For example, the analysis unit analyzes the candidates' responses using text analysis technology. The analysis unit can also analyze the candidates' facial expressions and tone of voice using emotion analysis technology. The evaluation unit evaluates the candidates' strengths and characteristics based on the content analyzed by the analysis unit. For example, the evaluation unit evaluates the candidates' skills and experience and determines their suitability. The evaluation unit can also evaluate the candidates' personality traits. The selection unit selects an appropriate second interviewer based on the strengths and characteristics evaluated by the evaluation unit. For example, the selection unit selects a technical department staff member as the second interviewer for a candidate with technical skills. The selection unit can also select the most suitable interviewer based on the candidates' characteristics. The identification unit identifies the most suitable position based on the strengths and characteristics evaluated by the evaluation unit. For example, the identification unit analyzes which department or position is most suitable for the candidate based on the candidate's skills and experience. The identification unit can also compare company data with candidate data to identify the most suitable position. As a result, the interview process efficiency improvement system according to the embodiment can efficiently and accurately evaluate candidates, select appropriate second interviewers, and identify the most suitable position.

[0030] The analysis unit can compare the analysis results of the candidate's answers with past interview data to evaluate the candidate's consistency and reliability. For example, the analysis unit uses a generation AI to analyze the candidate's answers and compares the results with past interview data. For example, it refers to answers given to similar questions in the past and evaluates the consistency of the candidate's answers. The analysis unit can also compare the answers with past interview data to evaluate the reliability of the candidate's answers. For example, it checks whether the candidate's answers are consistent with past data and evaluates their reliability. This enables a more accurate evaluation by evaluating the candidate's consistency and reliability.

[0031] The analysis unit cross-references the analysis results of a candidate's answers with the data of other candidates and can perform a relative evaluation of the candidate. For example, the analysis unit has the generation AI analyze the candidate's answers and cross-reference them with the data of other candidates. For example, it compares them with the answers of other candidates to the same question and performs a relative evaluation. The analysis unit can also compare the candidate's skills and characteristics with other candidates. For example, it compares the candidate's skill set with other candidates and performs a relative evaluation. This makes it possible to perform a relative evaluation of the candidate by comparing them with other candidates.

[0032] The analysis unit can translate candidate responses in real time, enabling the evaluation of international candidates. For example, the analysis unit uses a generative AI to translate candidate responses in real time, enabling the evaluation of international candidates. For example, responses in English can be translated into Japanese so that Japanese interviewers can evaluate them. The analysis unit can also support multiple languages. For example, even if a candidate responds in a different language, the responses can be translated and evaluated in real time. This makes it possible to evaluate international candidates by translating in real time.

[0033] The analysis unit can visualize the candidate's answers to enable the interviewer to intuitively understand. For example, the analysis unit uses a generative AI to visualize the candidate's answers to enable the interviewer to intuitively understand. For example, the answers can be displayed in graphs or charts. The analysis unit can also visually emphasize the key points of the answers. For example, important keywords and phrases can be highlighted. This visualization allows the interviewer to intuitively understand.

[0034] The evaluation unit can automatically create a skill matrix based on the candidate's response data and visualize the candidate's skill set. For example, the evaluation unit uses a generation AI to analyze the candidate's response data and automatically create a skill matrix. For example, it organizes and visualizes the candidate's skills by category. The evaluation unit can also visually display the skill level and years of experience. For example, in the skill matrix, the level of each skill is displayed in a different color. In this way, by visualizing the skill matrix, the candidate's skill set can be intuitively understood.

[0035] The evaluation department can predict a candidate's career path by analyzing the candidate's past work history and project data. For example, the evaluation department uses a generative AI to analyze a candidate's past work history and predict their career path. For example, it can propose a future career path based on the candidate's past job content and job title. The evaluation department can also analyze a candidate's project data and predict their career path. For example, it can propose a future career path based on the results and roles of the projects in which the candidate was involved. This can support the candidate's future growth by predicting their career path.

[0036] The assessment department can redefine a candidate's strengths and characteristics in a form that is applicable to different industries and job types. For example, the assessment department uses generative AI to analyze a candidate's strengths and characteristics and redefine them in a form that is applicable to different industries and job types. For example, it evaluates the applicability of technical skills to other industries. The assessment department can also apply a candidate's characteristics to different job types. For example, it applies leadership skills to managerial positions. This allows the candidate's range of application to be expanded by redefining them in a form that is applicable to different industries and job types.

[0037] The evaluation department can utilize the strengths and characteristics of candidates for team building and leadership training. For example, the evaluation department uses generative AI to analyze the strengths and characteristics of candidates and utilize them for team building. For example, it proposes the optimal team composition based on the candidate's leadership skills. The evaluation department can also be utilized for leadership training. For example, it proposes a leadership training program based on the candidate's characteristics. This can be utilized for team building and leadership training to improve organizational performance.

[0038] The selection department can optimize the schedule of second-stage interviewers based on the strengths and characteristics of candidates. For example, the selection department uses generative AI to analyze the strengths and characteristics of candidates, select the most suitable second-stage interviewers, and automatically optimize their schedules. For example, a candidate with high technical skills could be assigned a technical department staff member. The selection department can also adjust the schedule taking into account the interviewer's available time. For example, it can check the interviewer's schedule in real time and set the optimal interview time. This can improve the efficiency of the interview process by optimizing the schedule of second-stage interviewers.

[0039] The selection unit can generate a specific list of questions for the second interviewer based on the candidate's evaluation data. For example, the selection unit uses a generation AI to analyze the candidate's evaluation data and generate a specific list of questions for the second interviewer. For example, the selection unit may list detailed questions related to technical skills. The selection unit can also customize the list of questions based on the candidate's characteristics. For example, the selection unit may include questions that correspond to the candidate's personality traits. This allows the generation of a specific list of questions, thereby improving the quality of the second interview.

[0040] The selection department can apply the selection process for second-stage interviewers to different departments and positions, optimizing the interview process across the entire organization. For example, the selection department uses generative AI to analyze the selection process for second-stage interviewers and apply it to different departments and positions. For example, it can apply it not only to the technical department, but also to the sales and management departments. The selection department can also optimize the selection process for different positions. For example, it selects the most suitable interviewer for managerial or specialist positions. This enables efficient talent selection by optimizing the interview process across the entire organization.

[0041] The selection department can compare the selection results of second-stage interviewees with other companies and industries to perform benchmarking. For example, the selection department uses the generative AI to analyze the selection results of second-stage interviewees and compare them with other companies and industries. For example, it compares them with the interview processes of other companies in the same industry to perform benchmarking. The selection department can also identify areas for improvement in the selection process. For example, it can improve the selection process by referring to successful examples from other companies. In this way, benchmarking can identify areas for improvement in the selection process.

[0042] The Identification Department can automatically propose career plans based on a candidate's skills and experience, supporting their long-term growth. For example, the Identification Department uses generative AI to analyze a candidate's skills and experience and automatically propose a career plan. For example, it proposes the optimal career path based on the candidate's skill set. The Identification Department can also propose plans based on the candidate's career goals. For example, if a candidate is aiming for a managerial position, it will propose a skill development plan for that purpose. In this way, by proposing a career plan, it is possible to support the candidate's long-term growth.

[0043] The Identification Department can evaluate suitability for projects and tasks within the organization based on candidate data. For example, the Identification Department uses generative AI to analyze candidate data and evaluate suitability for projects and tasks within the organization. For example, candidates with high technical skills can be assigned technical projects. The Identification Department can also evaluate suitability based on candidates' experience and skills. For example, candidates' suitability is evaluated based on data on projects in which they have been successful in the past. This makes it possible to optimally assign candidates by evaluating their suitability for projects and tasks.

[0044] The Identification Department can apply a candidate's aptitude to offices in different regions or countries to optimize global human resource allocation. For example, the Identification Department uses generative AI to analyze a candidate's aptitude and apply it to offices in different regions or countries. For example, a candidate with high technical skills may be assigned to an overseas technical department. The Identification Department can also evaluate a candidate's cultural adaptability. For example, it analyzes how a candidate will adapt to different cultures and proposes the optimal placement. This allows the optimization of global human resource allocation to improve international competitiveness.

[0045] The Identification Department can support diversity in work styles by applying a candidate's aptitude to remote work or flexible work positions. For example, the Identification Department uses generative AI to analyze a candidate's aptitude and apply them to remote work or flexible work positions. For example, a candidate with high technical skills may be placed in a remote work technical department. The Identification Department can also propose positions taking into account the candidate's preferred working style. For example, if a candidate requests flexible working hours, the Identification Department will propose a position that meets that preference. This can improve candidate satisfaction and productivity by supporting diversity in work styles.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The analysis unit can identify the candidate's learning style based on the candidate's answers. For example, it can analyze whether the candidate prefers visual or auditory information. The analysis unit can also evaluate the candidate's learning speed and comprehension. For example, it can analyze how quickly the candidate can understand new information. By identifying the candidate's learning style, it can suggest an appropriate training program.

[0048] The evaluation department can evaluate the candidate's stress tolerance based on the candidate's response data. For example, it can analyze how the candidate reacts in stressful situations. The evaluation department can also evaluate the candidate's stress management skills. For example, it can analyze how the candidate relieves stress. This allows the evaluation of the candidate's stress tolerance to suggest suitable positions.

[0049] The selection department can evaluate the candidate's cultural adaptability based on the candidate's response data. For example, it can analyze how the candidate adapts to different cultures. The selection department can also evaluate the candidate's experience in a multicultural environment. For example, it can analyze how the candidate has worked in a multicultural environment in the past. This allows the selection department to evaluate the candidate's cultural adaptability and propose them for an international position.

[0050] The evaluation department can evaluate the candidate's leadership style based on the candidate's response data. For example, it can analyze how the candidate leads a team. The evaluation department can also evaluate the candidate's leadership skills. For example, it can analyze how the candidate makes decisions. By evaluating the candidate's leadership style, it can propose an appropriate leadership position.

[0051] The evaluation department can evaluate the candidate's creativity based on the candidate's response data. For example, it can analyze how the candidate generates new ideas. The evaluation department can also evaluate the candidate's problem-solving ability. For example, it can analyze how the candidate solves complex problems. This allows the evaluation of the candidate's creativity to suggest appropriate creative positions.

[0052] The evaluation department can evaluate the candidate's adaptability based on the candidate's response data. For example, it can analyze how the candidate adapts to a new environment. The evaluation department can also evaluate the candidate's flexibility. For example, it can analyze how the candidate responds to change. This allows the evaluation of the candidate's adaptability to suggest suitable positions.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The dialogue unit engages in dialogue with the candidate. For example, the dialogue unit asks the candidate questions and collects their responses. The dialogue unit can also monitor the candidate's facial expressions and tone of voice in real time. Step 2: The analysis unit analyzes the dialogue content obtained by the dialogue unit. For example, the analysis unit may analyze the candidate's responses using text analysis technology. The analysis unit may also analyze the candidate's facial expressions and tone of voice using emotion analysis technology. Step 3: The evaluation unit evaluates the candidate's strengths and characteristics based on the analysis by the analysis unit. For example, the evaluation unit evaluates the candidate's skills and experience to determine their suitability. The evaluation unit can also evaluate the candidate's personality traits. Step 4: The selection department selects the appropriate second interviewer based on the strengths and characteristics assessed by the evaluation department. For example, the selection department may select a technical department staff member as the second interviewer for a candidate with technical skills. The selection department may also select the most suitable interviewer based on the candidate's characteristics. Step 5: The Identification Department identifies the best fit for a candidate based on the strengths and characteristics assessed by the Evaluation Department. For example, the Identification Department analyzes which department or position is most suitable for the candidate based on their skills and experience. The Identification Department can also match company data with candidate data to identify the best fit for a candidate.

[0055] (Example 2) The interview process efficiency system according to an embodiment of the present invention uses generative AI to accurately evaluate candidates. This system accurately identifies a candidate's strengths and characteristics by analyzing their thinking style, personality, facial expressions, and tone of voice during dialogue with the candidate and determining their authenticity. It also selects appropriate second interviewers, identifies the most suitable position within the organization based on company data, maximizes the candidate's capabilities, and identifies compatibility and synergy with existing employees. This allows the interview process efficiency system to streamline a company's interview process and accurately evaluate candidates.

[0056] An interview process efficiency improvement system according to an embodiment includes a dialogue unit, an analysis unit, an evaluation unit, a selection unit, and an identification unit. The dialogue unit engages in dialogue with candidates. For example, the dialogue unit asks questions to the candidates and collects their responses. The dialogue unit can also monitor the candidates' facial expressions and tone of voice in real time. The analysis unit analyzes the dialogue content obtained by the dialogue unit. For example, the analysis unit analyzes the candidates' responses using text analysis technology. The analysis unit can also analyze the candidates' facial expressions and tone of voice using emotion analysis technology. The evaluation unit evaluates the candidates' strengths and characteristics based on the content analyzed by the analysis unit. For example, the evaluation unit evaluates the candidates' skills and experience and determines their suitability. The evaluation unit can also evaluate the candidates' personality traits. The selection unit selects an appropriate second interviewer based on the strengths and characteristics evaluated by the evaluation unit. For example, the selection unit selects a technical department staff member as the second interviewer for a candidate with technical skills. The selection unit can also select the most suitable interviewer based on the candidates' characteristics. The identification unit identifies the most suitable position based on the strengths and characteristics evaluated by the evaluation unit. For example, the identification unit analyzes which department or position is most suitable for the candidate based on the candidate's skills and experience. The identification unit can also compare company data with candidate data to identify the most suitable position. As a result, the interview process efficiency improvement system according to the embodiment can efficiently and accurately evaluate candidates, select appropriate second interviewers, and identify the most suitable position.

[0057] The analysis unit can compare the analysis results of the candidate's answers with past interview data to evaluate the candidate's consistency and reliability. For example, the analysis unit uses a generation AI to analyze the candidate's answers and compares the results with past interview data. For example, it refers to answers given to similar questions in the past and evaluates the consistency of the candidate's answers. The analysis unit can also compare the answers with past interview data to evaluate the reliability of the candidate's answers. For example, it checks whether the candidate's answers are consistent with past data and evaluates their reliability. This enables a more accurate evaluation by evaluating the candidate's consistency and reliability.

[0058] The analysis unit cross-references the analysis results of a candidate's answers with the data of other candidates and can perform a relative evaluation of the candidate. For example, the analysis unit has the generation AI analyze the candidate's answers and cross-reference them with the data of other candidates. For example, it compares them with the answers of other candidates to the same question and performs a relative evaluation. The analysis unit can also compare the candidate's skills and characteristics with other candidates. For example, it compares the candidate's skill set with other candidates and performs a relative evaluation. This makes it possible to perform a relative evaluation of the candidate by comparing them with other candidates.

[0059] The analysis unit uses an emotion estimation function to analyze the emotions of candidates when they answer, and can determine the authenticity of their answers based on changes in their emotions. For example, the analysis unit uses a generation AI to analyze the emotions of candidates when they answer, and determines the authenticity of their answers based on changes in their emotions. For example, if their emotions change significantly during the answer, the authenticity will be suspected. The analysis unit can also analyze the candidate's facial expressions and tone of voice to detect changes in their emotions. For example, if a candidate is nervous or lying, there will be a noticeable change in their emotions. This allows for more accurate evaluation by determining the authenticity of their answers based on changes in their emotions.

[0060] The analysis unit can translate candidate responses in real time, enabling the evaluation of international candidates. For example, the analysis unit uses a generative AI to translate candidate responses in real time, enabling the evaluation of international candidates. For example, responses in English can be translated into Japanese so that Japanese interviewers can evaluate them. The analysis unit can also support multiple languages. For example, even if a candidate responds in a different language, the responses can be translated and evaluated in real time. This makes it possible to evaluate international candidates by translating in real time.

[0061] The analysis unit can visualize the candidate's answers to enable the interviewer to intuitively understand. For example, the analysis unit uses a generative AI to visualize the candidate's answers to enable the interviewer to intuitively understand. For example, the answers can be displayed in graphs or charts. The analysis unit can also visually emphasize the key points of the answers. For example, important keywords and phrases can be highlighted. This visualization allows the interviewer to intuitively understand.

[0062] The analysis unit uses the emotion estimation function to analyze the interviewer's emotional response to the candidate's answers and can eliminate interviewer bias. For example, the analysis unit uses the generative AI's emotion estimation function to analyze the interviewer's emotional response to the candidate's answers and eliminate bias. For example, if the interviewer has negative emotions, the influence of those emotions can be eliminated. The analysis unit can also monitor the interviewer's emotional responses in real time. For example, if the interviewer has a prejudice against a particular candidate, the analysis unit can detect that emotional response and prevent it from affecting the evaluation. This eliminates interviewer bias, enabling a fairer evaluation.

[0063] The evaluation unit can automatically create a skill matrix based on the candidate's response data and visualize the candidate's skill set. For example, the evaluation unit uses a generation AI to analyze the candidate's response data and automatically create a skill matrix. For example, it organizes and visualizes the candidate's skills by category. The evaluation unit can also visually display the skill level and years of experience. For example, in the skill matrix, the level of each skill is displayed in a different color. In this way, by visualizing the skill matrix, the candidate's skill set can be intuitively understood.

[0064] The evaluation department can predict a candidate's career path by analyzing the candidate's past work history and project data. For example, the evaluation department uses a generative AI to analyze a candidate's past work history and predict their career path. For example, it can propose a future career path based on the candidate's past job content and job title. The evaluation department can also analyze a candidate's project data and predict their career path. For example, it can propose a future career path based on the results and roles of the projects in which the candidate was involved. This can support the candidate's future growth by predicting their career path.

[0065] The evaluation unit can use the emotion estimation function to analyze the emotions contained in the candidate's answers and identify their emotional strengths and weaknesses. For example, the generation AI can use the emotion estimation function to analyze the emotions contained in the candidate's answers and identify their emotional strengths and weaknesses. For example, if there is strong positive emotion, it can be determined to be an emotional strength. The evaluation unit can also determine if there is strong negative emotion, it can be determined to be an emotional weakness. For example, it can analyze how the candidate reacts to stress and identify their emotional strengths and weaknesses. This allows for a more accurate evaluation of the candidate's aptitude by identifying their emotional strengths and weaknesses.

[0066] The assessment department can redefine a candidate's strengths and characteristics in a form that is applicable to different industries and job types. For example, the assessment department uses generative AI to analyze a candidate's strengths and characteristics and redefine them in a form that is applicable to different industries and job types. For example, it evaluates the applicability of technical skills to other industries. The assessment department can also apply a candidate's characteristics to different job types. For example, it applies leadership skills to managerial positions. This allows the candidate's range of application to be expanded by redefining them in a form that is applicable to different industries and job types.

[0067] The evaluation department can utilize the strengths and characteristics of candidates for team building and leadership training. For example, the evaluation department uses generative AI to analyze the strengths and characteristics of candidates and utilize them for team building. For example, it proposes the optimal team composition based on the candidate's leadership skills. The evaluation department can also be utilized for leadership training. For example, it proposes a leadership training program based on the candidate's characteristics. This can be utilized for team building and leadership training to improve organizational performance.

[0068] The selection department can optimize the schedule of second-stage interviewers based on the strengths and characteristics of candidates. For example, the selection department uses generative AI to analyze the strengths and characteristics of candidates, select the most suitable second-stage interviewers, and automatically optimize their schedules. For example, a candidate with high technical skills could be assigned a technical department staff member. The selection department can also adjust the schedule taking into account the interviewer's available time. For example, it can check the interviewer's schedule in real time and set the optimal interview time. This can improve the efficiency of the interview process by optimizing the schedule of second-stage interviewers.

[0069] The selection unit can generate a specific list of questions for the second interviewer based on the candidate's evaluation data. For example, the selection unit uses a generation AI to analyze the candidate's evaluation data and generate a specific list of questions for the second interviewer. For example, the selection unit may list detailed questions related to technical skills. The selection unit can also customize the list of questions based on the candidate's characteristics. For example, the selection unit may include questions that correspond to the candidate's personality traits. This allows the generation of a specific list of questions, thereby improving the quality of the second interview.

[0070] The selection unit can use the emotion estimation function to analyze the emotional compatibility between the candidate and the second interviewer and select the most suitable interviewer. For example, the selection unit uses the generative AI emotion estimation function to analyze the emotional compatibility between the candidate and the second interviewer and select the most suitable interviewer. For example, it selects interviewers with a high number of positive emotional responses. The selection unit can also evaluate the emotional match between the candidate and interviewer. For example, if the candidate is relaxed, it checks whether the interviewer is also relaxed. This allows for a better interview experience by analyzing emotional compatibility.

[0071] The selection department can apply the selection process for second-stage interviewers to different departments and positions, optimizing the interview process across the entire organization. For example, the selection department uses generative AI to analyze the selection process for second-stage interviewers and apply it to different departments and positions. For example, it can apply it not only to the technical department, but also to the sales and management departments. The selection department can also optimize the selection process for different positions. For example, it selects the most suitable interviewer for managerial or specialist positions. This enables efficient talent selection by optimizing the interview process across the entire organization.

[0072] The selection department can compare the selection results of second-stage interviewees with other companies and industries to perform benchmarking. For example, the selection department uses the generative AI to analyze the selection results of second-stage interviewees and compare them with other companies and industries. For example, it compares them with the interview processes of other companies in the same industry to perform benchmarking. The selection department can also identify areas for improvement in the selection process. For example, it can improve the selection process by referring to successful examples from other companies. In this way, benchmarking can identify areas for improvement in the selection process.

[0073] The selection department can use the emotion estimation function to analyze the candidate's emotional response to the selection of the second interviewer and identify areas for improvement in the interview process. For example, the selection department uses the emotion estimation function to analyze the candidate's emotional response to the selection of the second interviewer and identify areas for improvement in the interview process. For example, if there are few positive emotional responses, the selection department can review the selection process. The selection department can also collect candidate feedback and identify areas for improvement based on the emotional responses. For example, the selection department can analyze how the candidate feels about the interview process and suggest areas for improvement. This can provide a better interview experience by identifying areas for improvement in the interview process.

[0074] The Identification Department can automatically propose career plans based on a candidate's skills and experience, supporting their long-term growth. For example, the Identification Department uses generative AI to analyze a candidate's skills and experience and automatically propose a career plan. For example, it proposes the optimal career path based on the candidate's skill set. The Identification Department can also propose plans based on the candidate's career goals. For example, if a candidate is aiming for a managerial position, it will propose a skill development plan for that purpose. In this way, by proposing a career plan, it is possible to support the candidate's long-term growth.

[0075] The Identification Department can evaluate suitability for projects and tasks within the organization based on candidate data. For example, the Identification Department uses generative AI to analyze candidate data and evaluate suitability for projects and tasks within the organization. For example, candidates with high technical skills can be assigned technical projects. The Identification Department can also evaluate suitability based on candidates' experience and skills. For example, candidates' suitability is evaluated based on data on projects in which they have been successful in the past. This makes it possible to optimally assign candidates by evaluating their suitability for projects and tasks.

[0076] The identification unit can use the emotion estimation function to identify the position for which the candidate feels the most positive. For example, the identification unit uses the emotion estimation function to identify the position for which the candidate feels the most positive. For example, the identification unit suggests the optimal position based on the candidate's emotion score. The identification unit can also monitor the candidate's emotional responses in real time. For example, if the candidate expresses positive emotions toward a particular position, the identification unit suggests that position. This makes it possible to improve candidate satisfaction by identifying the position for which the candidate feels the most positive.

[0077] The Identification Department can apply a candidate's aptitude to offices in different regions or countries to optimize global human resource allocation. For example, the Identification Department uses generative AI to analyze a candidate's aptitude and apply it to offices in different regions or countries. For example, a candidate with high technical skills may be assigned to an overseas technical department. The Identification Department can also evaluate a candidate's cultural adaptability. For example, it analyzes how a candidate will adapt to different cultures and proposes the optimal placement. This allows the optimization of global human resource allocation to improve international competitiveness.

[0078] The Identification Department can support diversity in work styles by applying a candidate's aptitude to remote work or flexible work positions. For example, the Identification Department uses generative AI to analyze a candidate's aptitude and apply them to remote work or flexible work positions. For example, a candidate with high technical skills may be placed in a remote work technical department. The Identification Department can also propose positions taking into account the candidate's preferred working style. For example, if a candidate requests flexible working hours, the Identification Department will propose a position that meets that preference. This can improve candidate satisfaction and productivity by supporting diversity in work styles.

[0079] The identification department can use the emotion estimation function to analyze the emotional reactions of existing employees to the candidate's aptitude and optimize team harmony. For example, the generation AI uses the emotion estimation function to analyze the emotional reactions of existing employees to the candidate's aptitude and optimize team harmony. For example, if there are many positive emotional reactions, it determines that team harmony will be achieved. The identification department can also make suggestions to improve team harmony if there are many negative emotional reactions. For example, it can analyze whether a specific candidate is compatible with existing employees and propose the optimal team composition. This can improve organizational performance by optimizing team harmony.

[0080] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0081] The analysis unit can identify the candidate's learning style based on the candidate's answers. For example, it can analyze whether the candidate prefers visual or auditory information. The analysis unit can also evaluate the candidate's learning speed and comprehension. For example, it can analyze how quickly the candidate can understand new information. By identifying the candidate's learning style, it can suggest an appropriate training program.

[0082] The evaluation department can evaluate the candidate's stress tolerance based on the candidate's response data. For example, it can analyze how the candidate reacts in stressful situations. The evaluation department can also evaluate the candidate's stress management skills. For example, it can analyze how the candidate relieves stress. This allows the evaluation of the candidate's stress tolerance to suggest suitable positions.

[0083] The selection department can evaluate the candidate's cultural adaptability based on the candidate's response data. For example, it can analyze how the candidate adapts to different cultures. The selection department can also evaluate the candidate's experience in a multicultural environment. For example, it can analyze how the candidate has worked in a multicultural environment in the past. This allows the selection department to evaluate the candidate's cultural adaptability and propose them for an international position.

[0084] The analysis unit uses the emotion estimation function to analyze the emotions expressed when a candidate answers questions, allowing it to assess the candidate's motivation. For example, it analyzes the emotions a candidate expresses in response to a specific question. The analysis unit can also monitor changes in a candidate's motivation in real time. For example, it can analyze how interested a candidate is in a particular topic. This allows it to assess a candidate's motivation and suggest suitable positions.

[0085] The evaluation department can evaluate the candidate's leadership style based on the candidate's response data. For example, it can analyze how the candidate leads a team. The evaluation department can also evaluate the candidate's leadership skills. For example, it can analyze how the candidate makes decisions. By evaluating the candidate's leadership style, it can propose an appropriate leadership position.

[0086] The analysis unit can use the emotion estimation function to analyze the emotions expressed by candidates when answering questions and assess their stress levels. For example, it can analyze the emotions expressed by candidates in response to stressful questions. The analysis unit can also monitor changes in a candidate's stress level in real time. For example, it can analyze how stressed a candidate feels in a particular situation. This allows the system to assess the candidate's stress level and provide appropriate support.

[0087] The evaluation department can evaluate the candidate's creativity based on the candidate's response data. For example, it can analyze how the candidate generates new ideas. The evaluation department can also evaluate the candidate's problem-solving ability. For example, it can analyze how the candidate solves complex problems. This allows the evaluation of the candidate's creativity to suggest appropriate creative positions.

[0088] The analysis unit uses the emotion estimation function to analyze the emotions expressed by candidates when they answer questions, and can evaluate the candidate's communication skills. For example, it analyzes how the candidate expresses their emotions. The analysis unit can also monitor the candidate's communication style in real time. For example, it analyzes how the candidate interacts with others. This allows the system to evaluate the candidate's communication skills and suggest suitable positions.

[0089] The evaluation department can evaluate the candidate's adaptability based on the candidate's response data. For example, it can analyze how the candidate adapts to a new environment. The evaluation department can also evaluate the candidate's flexibility. For example, it can analyze how the candidate responds to change. This allows the evaluation of the candidate's adaptability to suggest suitable positions.

[0090] The analysis unit can use the emotion estimation function to analyze the emotions expressed by candidates when they answer questions and evaluate their self-awareness. For example, it can analyze how well a candidate understands their own emotions. The analysis unit can also monitor changes in a candidate's self-awareness in real time. For example, it can analyze how self-aware a candidate is in a specific situation. This allows the system to evaluate a candidate's self-awareness and recommend suitable positions.

[0091] The processing flow of the second embodiment will be briefly explained below.

[0092] Step 1: The dialogue unit engages in dialogue with the candidate. For example, the dialogue unit asks the candidate questions and collects their responses. The dialogue unit can also monitor the candidate's facial expressions and tone of voice in real time. Step 2: The analysis unit analyzes the dialogue content obtained by the dialogue unit. For example, the analysis unit may analyze the candidate's responses using text analysis technology. The analysis unit may also analyze the candidate's facial expressions and tone of voice using emotion analysis technology. Step 3: The evaluation unit evaluates the candidate's strengths and characteristics based on the analysis by the analysis unit. For example, the evaluation unit evaluates the candidate's skills and experience to determine their suitability. The evaluation unit can also evaluate the candidate's personality traits. Step 4: The selection department selects the appropriate second interviewer based on the strengths and characteristics assessed by the evaluation department. For example, the selection department may select a technical department staff member as the second interviewer for a candidate with technical skills. The selection department may also select the most suitable interviewer based on the candidate's characteristics. Step 5: The Identification Department identifies the best fit for a candidate based on the strengths and characteristics assessed by the Evaluation Department. For example, the Identification Department analyzes which department or position is most suitable for the candidate based on their skills and experience. The Identification Department can also match company data with candidate data to identify the best fit for a candidate.

[0093] 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.

[0094] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0095] 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.

[0096] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0097] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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).

[0102] 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.

[0103] 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.

[0104] 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.

[0105] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0106] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0107] 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.

[0108] 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.

[0109] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0110] 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.

[0111] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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).

[0117] 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.

[0118] 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.

[0119] 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.

[0120] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0121] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0122] 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.

[0123] 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.

[0124] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0125] 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.

[0126] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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).

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0137] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0138] 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.

[0139] 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.

[0140] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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).

[0146] 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 "reaction" domain, where sensation is dominant. The right half of the emotion map lists emotions belonging to the "situation" domain, where situational awareness is dominant.

[0147] 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."

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0160] 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 dialogue section that engages in dialogue with candidates; an analysis unit that analyzes the dialogue content obtained by the dialogue unit; an evaluation unit that evaluates the strengths and characteristics of the candidate based on the content analyzed by the analysis unit; a selection unit that selects appropriate second interviewees based on the strengths and characteristics evaluated by the evaluation unit; an identification unit that identifies an optimal position based on the strengths and characteristics evaluated by the evaluation unit; A system characterized by:

2. The analysis unit Real-time translation of said candidate responses to enable evaluation of international candidates 2. The system of claim 1.

3. The evaluation unit A skills matrix is ​​automatically created based on the candidate's response data, and the candidate's skill set is visualized.

2. The system of claim 1.

4. The selection unit Optimize the schedule of the second interviewers based on the strengths and characteristics of the candidate.

2. The system of claim 1.

5. The identification unit Automatically propose career plans based on the candidate's skills and experience to support their long-term growth 2. The system of claim 1.

6. The analysis unit Analyzing the emotions of the candidate when answering and determining the truthfulness based on the change in emotions 2. The system of claim 1.

7. The evaluation unit Analyzing the emotions contained in the candidate's responses to identify the candidate's emotional strengths and weaknesses 2. The system of claim 1.

8. The selection unit Analyzing the emotional compatibility between the candidate and the second interviewer and selecting the most suitable interviewer 2. The system of claim 1.

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