System

Abstract

An object of a system according to an embodiment is to efficiently select candidates matching a corporate culture.SOLUTION: A system includes a question item generation part, an answer collection part, and an answer analysis part. The question item generation unit generates a question item reflecting a corporate culture. The answer collecting unit collects the answers of the candidates and the employees to the question items generated by the question item generating unit. The answer analysis unit analyzes the answers of the candidates and the employees collected by the answer collection unit and calculates a matching degree.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to screen out candidates who did not match the company culture during recruitment activities, and there was a problem of increased costs due to mismatches.

[0005] The system according to the embodiment aims to efficiently select candidates who match the company culture. [Means for solving the problem]

[0006] The system according to the embodiment includes a question generation unit, a response collection unit, and a response analysis unit. The question generation unit generates questions that reflect the corporate culture. The response collection unit collects responses from candidates and employees to the questions generated by the question generation unit. The response analysis unit analyzes the responses from candidates and employees collected by the response collection unit and calculates the degree of matching. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently select candidates who match the company culture. [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) A recruitment activity support system according to an embodiment of the present invention uses a generation AI to calculate the degree of match between a candidate and a corporate culture, preventing increased costs due to interviews with candidates who do not match the corporate culture and preventing mismatches after hiring. This allows the recruitment activity support system to efficiently select candidates who match the corporate culture and prevent mismatches after hiring.

[0029] A recruitment support system according to an embodiment includes a question generation unit, a response collection unit, and a response analysis unit. The question generation unit generates questions that reflect a corporate culture. For example, the generation AI generates questions based on prompts containing instructions about the corporate culture and values. The generation AI generates questions such as, "What do you value most when working in a team?" and "How do you deal with difficult situations?" The response collection unit collects responses from candidates and employees to the generated questions. For example, the response collection unit presents the questions to candidates and employees and collects their responses. The response collection unit can collect responses using, for example, an online form or a questionnaire system. The response analysis unit analyzes the collected responses from candidates and employees and calculates a matching degree. For example, the generation AI compares the response trends of employees with the responses of candidates to calculate a matching degree. For example, if an employee's response trend is, "I value being cooperative," the generation AI analyzes whether the candidate's responses also show a similar tendency. This enables the recruitment support system according to an embodiment to efficiently select candidates who match the corporate culture. For example, by prioritizing candidates with a high degree of match for interviews, you can hire people who fit your company culture.

[0030] The question generator can analyze a company's past successes and failures and generate questions based on the results. For example, the question generator can use generation AI to analyze a company's past project data and extract successes and failures. For example, it can identify commonalities between successful projects and causes of unsuccessful projects and generate questions based on these. This makes it possible to generate questions based on a company's past successes and failures.

[0031] The question generator can present different scenarios and situations and generate questions based on them. The question generator, for example, uses a generation AI to present different business scenarios and situations and generate questions based on them. For example, it can present a scenario such as an emergency or a project delay and generate questions asking how to respond to it. This makes it possible to generate questions based on different scenarios and situations.

[0032] The question generator can generate questions from a global perspective by referring to the corporate cultures of different industries and cultural spheres. For example, the question generator can use generation AI to analyze the corporate cultures of different industries and generate questions based on that. For example, it can generate questions that reflect the differences in corporate culture between technology companies and service industries. This allows questions to be generated from a global perspective.

[0033] The question item generation unit can analyze the candidate's resume and work history and generate individually customized questions based on the analysis. The question item generation unit can, for example, use a generation AI to analyze the candidate's resume and work history and generate individually customized questions based on the analysis. For example, it can generate questions related to the candidate's past work experience. This makes it possible to generate individually customized questions based on the candidate's resume and work history.

[0034] The answer collection unit uses a generation AI to automatically generate detailed feedback on the candidate's answers, thereby enabling a deeper understanding of the candidate. The answer collection unit, for example, uses a generation AI to automatically generate detailed feedback on the candidate's answers. For example, based on the content of the candidate's answers, it provides feedback that points out specific areas for improvement and strengths. This allows for the automatic generation of detailed feedback on the candidate's answers, thereby enabling a deeper understanding of the candidate.

[0035] The answer collection unit can analyze the candidate's non-verbal responses and evaluate the credibility of the answers. The answer collection unit can, for example, use a generative AI to analyze the candidate's non-verbal responses and evaluate the credibility of the answers. For example, the answer collection unit can analyze the candidate's facial expressions and tone of voice to determine the veracity of the answers. This makes it possible to analyze the candidate's non-verbal responses and evaluate the credibility of the answers.

[0036] The response collection unit can analyze the candidate's social media posts and published articles to collect additional information. The response collection unit can, for example, use generative AI to analyze the candidate's social media posts to collect additional information. For example, it can analyze the content of past posts to understand the candidate's interests and concerns. This makes it possible to analyze the candidate's social media posts and published articles to collect additional information.

[0037] The answer collection unit can analyze the candidate's past projects and achievements and evaluate the quality of the answer based thereon. The answer collection unit can, for example, use generative AI to analyze the candidate's past project data and evaluate the quality of the answer based thereon. For example, it can evaluate the success rate and role of projects in which the candidate was involved. This makes it possible to analyze the candidate's past projects and achievements and evaluate the quality of the answer based thereon.

[0038] The response analysis unit can refer to past recruitment data and identify successful matching patterns. The response analysis unit, for example, uses a generation AI to analyze past recruitment data and identify successful matching patterns. For example, it compares the response trends of employees previously hired with the response trends of successful candidates. This makes it possible to refer to past recruitment data and identify successful matching patterns.

[0039] The answer analysis unit provides a detailed interpretation of the candidate's answers and can evaluate their compatibility with the corporate culture from multiple angles. The answer analysis unit, for example, uses a generative AI to provide a detailed interpretation of the candidate's answers and builds a system that evaluates their compatibility with the corporate culture from multiple angles. For example, it analyzes the content of the candidate's answers and evaluates their adaptability to the corporate culture. This allows for a detailed interpretation of the candidate's answers and a multifaceted evaluation of their compatibility with the corporate culture.

[0040] The answer analysis unit can collect feedback from other employees on the candidate's answers and reevaluate the degree of matching based on that. The answer analysis unit, for example, uses a generative AI to collect feedback from other employees on the candidate's answers and builds a system that reevaluates the degree of matching based on that. For example, it evaluates the candidate's suitability based on the opinions of employees. This makes it possible to collect feedback from other employees on the candidate's answers and reevaluate the degree of matching based on that.

[0041] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0042] The recruitment support system can further include a hobby analysis unit that analyzes candidates' hobbies and interests. The hobby analysis unit extracts hobbies and interests from candidates' resumes and social media posts, for example, and evaluates the degree of match with the corporate culture based on the extracted information. For example, in a company with many candidates who enjoy outdoor activities, candidates with similar hobbies can be prioritized. This allows candidates who fit the corporate culture to be selected based on their hobbies and interests.

[0043] The recruitment activity support system can further include a stress assessment unit that evaluates the candidate's stress tolerance. The stress assessment unit evaluates the candidate's stress tolerance, for example, by asking questions about difficult situations the candidate has experienced in the past and how they dealt with them. For example, it analyzes what stressful situations the candidate has faced in the past and how they dealt with them. This makes it possible to select candidates who fit the company culture based on their stress tolerance.

[0044] The recruitment activity support system can further include a leadership evaluation unit that evaluates the leadership skills of candidates. The leadership evaluation unit evaluates leadership skills, for example, through questions about the candidate's past leadership experience. For example, it analyzes what kind of projects the candidate has demonstrated leadership in and what results they have achieved. This makes it possible to select candidates who fit the corporate culture based on their leadership skills.

[0045] The recruitment activity support system may further include a creativity evaluation unit that evaluates the creativity of candidates. The creativity evaluation unit evaluates creativity, for example, through questions about the candidate's past experience proposing creative solutions. For example, it analyzes what creative solutions the candidate proposed to what problems. This makes it possible to select candidates who fit the company culture based on their creativity.

[0046] The recruitment activity support system may further include a communication evaluation unit that evaluates the communication skills of candidates. The communication evaluation unit evaluates communication skills, for example, through questions about how the candidate has communicated with team members in the past. For example, it may analyze how the candidate summarized opinions within a team and led a project to success. This makes it possible to select candidates who fit the corporate culture based on their communication skills.

[0047] The processing flow of the first embodiment will be briefly explained below.

[0048] Step 1: The question generator generates questions that reflect the company culture. For example, the generator generates questions based on prompts that include instructions about the company culture and values. Specifically, it generates questions such as, "What do you value most when working in a team?" and "How do you deal with difficult situations?" Step 2: The response collection unit collects responses from candidates and employees to the generated questions. For example, the unit presents questions to candidates and employees and collects responses using an online form or questionnaire system. Step 3: The response analysis unit analyzes the collected responses of candidates and employees and calculates the degree of matching. For example, the generation AI compares the response trends of employees with the responses of candidates to calculate the degree of matching. Specifically, if the response trend of employees is something like "I value being cooperative," it analyzes whether the responses of candidates also show a similar tendency.

[0049] (Example 2) A recruitment activity support system according to an embodiment of the present invention uses a generation AI to calculate the degree of match between a candidate and a corporate culture, preventing increased costs due to interviews with candidates who do not match the corporate culture and preventing mismatches after hiring. This allows the recruitment activity support system to efficiently select candidates who match the corporate culture and prevent mismatches after hiring.

[0050] A recruitment support system according to an embodiment includes a question generation unit, a response collection unit, and a response analysis unit. The question generation unit generates questions that reflect a corporate culture. For example, the generation AI generates questions based on prompts containing instructions about the corporate culture and values. The generation AI generates questions such as, "What do you value most when working in a team?" and "How do you deal with difficult situations?" The response collection unit collects responses from candidates and employees to the generated questions. For example, the response collection unit presents the questions to candidates and employees and collects their responses. The response collection unit can collect responses using, for example, an online form or a questionnaire system. The response analysis unit analyzes the collected responses from candidates and employees and calculates a matching degree. For example, the generation AI compares the response trends of employees with the responses of candidates to calculate a matching degree. For example, if an employee's response trend is, "I value being cooperative," the generation AI analyzes whether the candidate's responses also show a similar tendency. This enables the recruitment support system according to an embodiment to efficiently select candidates who match the corporate culture. For example, by prioritizing candidates with a high degree of match for interviews, you can hire people who fit your company culture.

[0051] The question generator can analyze a company's past successes and failures and generate questions based on the results. For example, the question generator can use generation AI to analyze a company's past project data and extract successes and failures. For example, it can identify commonalities between successful projects and causes of unsuccessful projects and generate questions based on these. This makes it possible to generate questions based on a company's past successes and failures.

[0052] The question generator can present different scenarios and situations and generate questions based on them. The question generator, for example, uses a generation AI to present different business scenarios and situations and generate questions based on them. For example, it can present a scenario such as an emergency or a project delay and generate questions asking how to respond to it. This makes it possible to generate questions based on different scenarios and situations.

[0053] The question item generation unit can use the emotion estimation function to customize question items based on the candidate's emotions. For example, the question item generation unit uses the emotion estimation function to analyze the candidate's emotions toward questions in real time and customize question items based on those emotions. For example, if the candidate is nervous, a question to relax the candidate can be added. This allows the question items to be customized based on the candidate's emotions.

[0054] The question generator can generate questions from a global perspective by referring to the corporate cultures of different industries and cultural spheres. For example, the question generator can use generation AI to analyze the corporate cultures of different industries and generate questions based on that. For example, it can generate questions that reflect the differences in corporate culture between technology companies and service industries. This allows questions to be generated from a global perspective.

[0055] The question item generation unit can analyze the candidate's resume and work history and generate individually customized questions based on the analysis. The question item generation unit can, for example, use a generation AI to analyze the candidate's resume and work history and generate individually customized questions based on the analysis. For example, it can generate questions related to the candidate's past work experience. This makes it possible to generate individually customized questions based on the candidate's resume and work history.

[0056] The question item generation unit can use the emotion estimation function to monitor in real time how candidates feel about questions and generate question items that elicit positive emotions. The question item generation unit, for example, can use the emotion estimation function to monitor in real time how candidates feel about questions and generate questions that elicit positive emotions based on those emotions. For example, it can add questions that candidates can answer with confidence. This makes it possible to monitor candidates' emotions in real time and generate question items that elicit positive emotions.

[0057] The answer collection unit uses a generation AI to automatically generate detailed feedback on the candidate's answers, thereby enabling a deeper understanding of the candidate. The answer collection unit, for example, uses a generation AI to automatically generate detailed feedback on the candidate's answers. For example, based on the content of the candidate's answers, it provides feedback that points out specific areas for improvement and strengths. This allows for the automatic generation of detailed feedback on the candidate's answers, thereby enabling a deeper understanding of the candidate.

[0058] The answer collection unit can analyze the candidate's non-verbal responses and evaluate the credibility of the answers. The answer collection unit can, for example, use a generative AI to analyze the candidate's non-verbal responses and evaluate the credibility of the answers. For example, the answer collection unit can analyze the candidate's facial expressions and tone of voice to determine the veracity of the answers. This makes it possible to analyze the candidate's non-verbal responses and evaluate the credibility of the answers.

[0059] The answer collection unit can use the emotion estimation function to analyze the emotional reactions of the candidate to the answers and evaluate the candidate's adaptability to the corporate culture. The answer collection unit, for example, uses the emotion estimation function to analyze the emotional reactions of the candidate to the answers and evaluate the candidate's adaptability to the corporate culture. For example, it determines whether the candidate has positive emotions. This makes it possible to analyze the candidate's emotional reactions and evaluate the candidate's adaptability to the corporate culture.

[0060] The response collection unit can analyze the candidate's social media posts and published articles to collect additional information. The response collection unit can, for example, use generative AI to analyze the candidate's social media posts to collect additional information. For example, it can analyze the content of past posts to understand the candidate's interests and concerns. This makes it possible to analyze the candidate's social media posts and published articles to collect additional information.

[0061] The answer collection unit can analyze the candidate's past projects and achievements and evaluate the quality of the answer based thereon. The answer collection unit can, for example, use generative AI to analyze the candidate's past project data and evaluate the quality of the answer based thereon. For example, it can evaluate the success rate and role of projects in which the candidate was involved. This makes it possible to analyze the candidate's past projects and achievements and evaluate the quality of the answer based thereon.

[0062] The answer collection unit uses the emotion estimation function to monitor changes in emotions when candidates answer in real time, and encourages them to give emotionally stable answers. The answer collection unit, for example, uses the emotion estimation function to monitor changes in emotions when candidates answer in real time, and builds a system that encourages them to give emotionally stable answers. For example, if a candidate is nervous, it provides feedback to help them relax. This makes it possible to monitor changes in emotions in real time and encourages them to give emotionally stable answers.

[0063] The response analysis unit can refer to past recruitment data and identify successful matching patterns. The response analysis unit, for example, uses a generation AI to analyze past recruitment data and identify successful matching patterns. For example, it compares the response trends of employees previously hired with the response trends of successful candidates. This makes it possible to refer to past recruitment data and identify successful matching patterns.

[0064] The answer analysis unit provides a detailed interpretation of the candidate's answers and can evaluate their compatibility with the corporate culture from multiple angles. The answer analysis unit, for example, uses a generative AI to provide a detailed interpretation of the candidate's answers and builds a system that evaluates their compatibility with the corporate culture from multiple angles. For example, it analyzes the content of the candidate's answers and evaluates their adaptability to the corporate culture. This allows for a detailed interpretation of the candidate's answers and a multifaceted evaluation of their compatibility with the corporate culture.

[0065] The answer analysis unit uses the emotion estimation function to analyze the emotional match between employees and candidates, and can identify candidates who are easy to empathize with emotionally. The answer analysis unit, for example, uses the emotion estimation function to analyze the emotional match between employees and candidates, and builds a system that identifies candidates who are easy to empathize with emotionally. For example, the emotion scores of employees and candidates are compared. This makes it possible to analyze the emotional match between employees and candidates, and identify candidates who are easy to empathize with emotionally.

[0066] The answer analysis unit can collect feedback from other employees on the candidate's answers and reevaluate the degree of matching based on that. The answer analysis unit, for example, uses a generative AI to collect feedback from other employees on the candidate's answers and builds a system that reevaluates the degree of matching based on that. For example, it evaluates the candidate's suitability based on the opinions of employees. This makes it possible to collect feedback from other employees on the candidate's answers and reevaluate the degree of matching based on that.

[0067] The answer analysis unit uses the emotion estimation function to monitor employees' emotional reactions to the candidate's answers in real time, and can identify candidates who elicit a positive emotional response. The answer analysis unit, for example, uses the emotion estimation function to monitor employees' emotional reactions to the candidate's answers in real time, and builds a system to identify candidates who elicit a positive emotional response. For example, the answer analysis unit evaluates candidates based on the employee's emotion score. This makes it possible to monitor employees' emotional reactions to the candidate's answers in real time, and identify candidates who elicit a positive emotional response.

[0068] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0069] The recruitment support system can further include a hobby analysis unit that analyzes candidates' hobbies and interests. The hobby analysis unit extracts hobbies and interests from candidates' resumes and social media posts, for example, and evaluates the degree of match with the corporate culture based on the extracted information. For example, in a company with many candidates who enjoy outdoor activities, candidates with similar hobbies can be prioritized. This allows candidates who fit the corporate culture to be selected based on their hobbies and interests.

[0070] The recruitment activity support system can further include a stress assessment unit that evaluates the candidate's stress tolerance. The stress assessment unit evaluates the candidate's stress tolerance, for example, by asking questions about difficult situations the candidate has experienced in the past and how they dealt with them. For example, it analyzes what stressful situations the candidate has faced in the past and how they dealt with them. This makes it possible to select candidates who fit the company culture based on their stress tolerance.

[0071] The recruitment activity support system can further include a leadership evaluation unit that evaluates the leadership skills of candidates. The leadership evaluation unit evaluates leadership skills, for example, through questions about the candidate's past leadership experience. For example, it analyzes what kind of projects the candidate has demonstrated leadership in and what results they have achieved. This makes it possible to select candidates who fit the corporate culture based on their leadership skills.

[0072] The recruitment activity support system may further include a creativity evaluation unit that evaluates the creativity of candidates. The creativity evaluation unit evaluates creativity, for example, through questions about the candidate's past experience proposing creative solutions. For example, it analyzes what creative solutions the candidate proposed to what problems. This makes it possible to select candidates who fit the company culture based on their creativity.

[0073] The recruitment activity support system may further include a communication evaluation unit that evaluates the communication skills of candidates. The communication evaluation unit evaluates communication skills, for example, through questions about how the candidate has communicated with team members in the past. For example, it may analyze how the candidate summarized opinions within a team and led a project to success. This makes it possible to select candidates who fit the corporate culture based on their communication skills.

[0074] The recruitment activity support system can further include an interview adjustment unit that estimates the candidate's emotions and adjusts the progress of the interview based on the estimated emotions. For example, the interview adjustment unit adds questions to relax the candidate if the candidate is nervous, and asks more in-depth questions if the candidate is relaxed. This allows the progress of the interview to be adjusted based on the candidate's emotions, resulting in a more accurate evaluation.

[0075] The recruitment activity support system may further include a feedback providing unit that estimates the candidate's emotions and provides feedback based on the estimated emotions. For example, if the candidate answers with confidence, the feedback providing unit provides feedback that reinforces the candidate's confidence, and if the candidate feels anxious, the feedback providing unit provides feedback that reduces the candidate's anxiety. This allows the candidate to receive appropriate feedback based on their emotions and improve their performance.

[0076] The recruitment activity support system can further include a question adjustment unit that estimates the candidate's emotions and dynamically changes the questions based on the estimated emotions. For example, the question adjustment unit adds questions to relax the candidate if the candidate is nervous, and asks more in-depth questions if the candidate is relaxed. This allows the questions to be dynamically changed based on the candidate's emotions, resulting in a more accurate evaluation.

[0077] The recruitment activity support system can further include an interview progress unit that estimates the candidate's emotions and adjusts the progress of the interview based on the estimated emotions. For example, the interview progress unit adds questions to relax the candidate if the candidate is nervous, and asks more in-depth questions if the candidate is relaxed. This allows the progress of the interview to be adjusted based on the candidate's emotions, resulting in a more accurate evaluation.

[0078] The recruitment activity support system may further include a feedback providing unit that estimates the candidate's emotions and provides feedback based on the estimated emotions. For example, if the candidate answers with confidence, the feedback providing unit provides feedback that reinforces the candidate's confidence, and if the candidate feels anxious, the feedback providing unit provides feedback that reduces the candidate's anxiety. This allows the candidate to receive appropriate feedback based on their emotions and improve their performance.

[0079] The processing flow of the second embodiment will be briefly explained below.

[0080] Step 1: The question generator generates questions that reflect the company culture. For example, the generator generates questions based on prompts that include instructions about the company culture and values. Specifically, it generates questions such as, "What do you value most when working in a team?" and "How do you deal with difficult situations?" Step 2: The response collection unit collects responses from candidates and employees to the generated questions. For example, the unit presents questions to candidates and employees and collects responses using an online form or questionnaire system. Step 3: The response analysis unit analyzes the collected responses of candidates and employees and calculates the degree of matching. For example, the generation AI compares the response trends of employees with the responses of candidates to calculate the degree of matching. Specifically, if the response trend of employees is something like "I value being cooperative," it analyzes whether the responses of candidates also show a similar tendency.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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).

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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.

[0104] 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).

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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."

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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]

[0148] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a question item generation unit that generates question items that reflect the corporate culture; a response collection unit that collects responses from candidates and employees to the questions generated by the question generation unit; a response analysis unit that analyzes the responses of the candidate and the employee collected by the response collection unit and calculates the degree of matching; A system characterized by:

2. The question item generation unit Analyze past successes and failures of companies and generate the above questions based on that.

2. The system of claim 1.

3. The question item generation unit Generate questions from a global perspective, taking into account corporate cultures in different industries and cultural areas.

2. The system of claim 1.

4. The response collection unit The generation AI is used to automatically generate detailed feedback on the candidate's answers, thereby deepening understanding of the candidate.

2. The system of claim 1.

5. The answer analysis unit Look at past recruitment data to identify successful match patterns 2. The system of claim 1.

6. The question item generation unit Customize the questions based on the candidate's feelings 2. The system of claim 1.

7. The response collection unit Analyzing the candidate's emotional response to the answers and assessing their fit with the corporate culture.

2. The system of claim 1.

8. The answer analysis unit Analyzing the emotional match between the employee and the candidate, and identifying the candidate with whom the employee is likely to empathize emotionally.

2. The system of claim 1.

Images