System

Abstract

An object of the system according to the embodiment is to improve efficiency of a process of an interview or training and to improve effects of employment activity and human resource development of a company.SOLUTION: A system according to an embodiment includes an interview system, a training system, and a robot. The interviewing system leverages the generated AI. The training system leverages the generated AI. The robot conducts the interview and the training based on the questions and the training program generated by the interview system and the training system.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, support from other departments was required for the interview and training processes, making it difficult to recruit and train personnel efficiently.

[0005] The system according to the embodiment aims to streamline the interview and training processes and improve the effectiveness of companies' recruitment activities and human resource development. [Means for solving the problem]

[0006] A system according to an embodiment includes an interview system, a training system, and a robot. The interview system utilizes a generation AI. The training system utilizes a generation AI. The robot conducts interviews and training based on questions and a training program generated by the interview system and the training system. [Effects of the Invention]

[0007] The system according to the embodiment can streamline the interview and training processes and improve the effectiveness of a company's recruitment activities and human resource development. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The AI ​​interview system and training system according to the embodiment of the present invention utilizes generative AI to conduct interviews and training for all job types, including sales, technical, and code interviews. As a result, the AI ​​interview system and training system can significantly improve the efficiency and effectiveness of corporate recruitment activities and human resource development.

[0029] An AI interview system and training system according to an embodiment includes an interview system, a training system, and a robot. The interview system analyzes information about an applicant and generates appropriate questions. For example, for an applicant for a sales position, the system generates a question such as, "Tell me about your past sales experience." For an applicant for a technical position, the system generates a question such as, "Please explain your knowledge of a specific programming language." The training system analyzes the skill level and learning progress of a trainee and generates an appropriate training program. For example, for a trainee for a sales position, the system suggests "training to learn basic customer service skills." For a trainee for a technical position, the system suggests "a course to learn the basics of a specific programming language." The robot conducts interviews and training based on the questions and training program generated by the interview system and training system. For example, the robot asks questions to the applicant and records the answers. The robot also provides training to the trainee and monitors their progress. As a result, the AI ​​interview system and training system according to the embodiment can significantly improve the efficiency and effectiveness of a company's recruitment activities and human resource development. For example, the quality of interviews can be maintained uniformly, and applicants can be accurately evaluated. Additionally, training programs are optimized for each individual trainee, improving learning effectiveness, allowing companies to recruit and develop better talent more efficiently.

[0030] An interview system can analyze an applicant's past interview data and generate questions optimized for each individual applicant. For example, an interview system collects an applicant's past interview data, and a generation AI analyzes that data to identify the applicant's strengths and weaknesses. For example, it generates questions that are optimal for an applicant based on the answers and evaluations from past interviews. The interview system also analyzes an applicant's past interview data and generates questions related to specific skills and experience. For example, it generates detailed questions related to projects the applicant has previously worked on. The interview system also evaluates the applicant's growth and changes based on the applicant's past interview data and generates questions accordingly. For example, it generates questions to check whether the applicant has overcome weaknesses from past interviews. This makes it possible to improve the quality of interviews by generating questions optimized for the applicant.

[0031] An interview system can analyze an applicant's nonverbal communication and adjust the questions based on that. For example, an interview system could analyze an applicant's facial expressions and gestures during an interview in real time, and a generative AI could adjust the questions based on that data. For example, if an applicant is nervous, it could generate questions to relax them. The interview system could also analyze an applicant's nonverbal communication to assess the applicant's confidence and interest. For example, it could ask more in-depth questions about topics the applicant is talking about confidently. The interview system could also analyze an applicant's facial expressions and gestures to understand the applicant's emotional state. For example, if the applicant seems confused, it could simplify the question or add an explanation. In this way, the quality of interviews can be improved by analyzing an applicant's nonverbal communication.

[0032] The training system can analyze the learning style of trainees and generate a training program optimized for each individual trainee. The training system, for example, analyzes the learning style of trainees and generates a training program optimized for each individual trainee. For example, visual learning materials are provided to trainees who prefer visual learning. The training system also suggests an appropriate training method based on the trainee's learning style. For example, hands-on training is provided to trainees who prefer practical learning. The training system also analyzes the learning style of trainees and provides learning pace and materials tailored to each individual trainee. For example, online courses are provided to trainees who prefer self-study. In this way, the learning effectiveness can be improved by analyzing the learning style of trainees and generating an optimized training program.

[0033] The training system can provide real-time feedback according to the trainee's progress based on the trainee's past learning history. For example, the training system analyzes the trainee's past learning history and provides real-time feedback according to the trainee's progress. For example, if a particular skill is lacking, additional learning materials are provided. The training system also evaluates the trainee's progress based on the trainee's learning history and provides appropriate feedback. For example, it may offer praise or suggest areas for improvement depending on the degree of goal achievement. The training system also analyzes the trainee's past learning history, monitors the trainee's progress in real time, and provides feedback as needed. For example, it may provide support if there is a delay in learning. In this way, the learning effectiveness can be improved by providing real-time feedback based on the trainee's past learning history.

[0034] The robot has the ability to converse naturally with applicants and trainees, enabling smooth communication. The robot, for example, has the ability to converse naturally, enabling smooth communication with applicants and trainees. For example, it uses natural language processing technology to provide appropriate answers to applicants' questions. The robot also incorporates voice recognition technology to smoothly converse with applicants and trainees. For example, it accurately recognizes what the applicant says and provides an appropriate response. The robot also incorporates emotion recognition technology to provide natural conversational capabilities. For example, it analyzes the emotional state of the applicant and converses accordingly. This gives the robot the ability to converse naturally, enabling smooth communication with applicants and trainees.

[0035] Robots can analyze applicant and trainee feedback in real time and respond immediately. Robots can, for example, build a system that analyzes applicant and trainee feedback in real time and responds immediately. For example, it can provide appropriate feedback immediately in response to an applicant's answers. Robots can also monitor trainee progress in real time and provide feedback as needed. For example, it can provide additional explanations if a trainee is having difficulty understanding. Robots can also analyze applicant and trainee feedback and adjust the progress of interviews and training based on the results. For example, it can generate the next question depending on the applicant's answer. This makes it possible to improve the quality of interviews and training by analyzing feedback in real time and responding immediately.

[0036] The robot can be adapted to different industries and occupations, meeting a wide range of interview and training needs. For example, to accommodate different industries and occupations, the robot's generative AI learns the characteristics of each industry and occupation, and conducts interviews and training based on that. For example, it can provide specialized support for the medical or IT industry. In addition, to accommodate different occupations, the robot provides interviews and training that take into account the skill sets and knowledge of each occupation. For example, it asks questions and provides training that is specialized for sales or technical jobs. In addition, to accommodate different industries and occupations, the robot analyzes the trends and needs of each industry and occupation, and provides interviews and training based on that. This allows the robot to accommodate different industries and occupations, meeting a wide range of interview and training needs.

[0037] Robots can be equipped with multilingual capabilities to support global recruitment activities and training. For example, robots can be equipped with multilingual capabilities to support global recruitment activities. For example, they can conduct interviews in multiple languages, such as English, Japanese, and Chinese. Robots with multilingual capabilities can also facilitate communication with applicants and trainees who speak different languages. For example, they can ask questions in the applicant's native language. In order to provide a robot with multilingual capabilities, generative AI learns the characteristics and cultural background of each language and responds based on that. In this way, having multilingual capabilities can support global recruitment activities and training.

[0038] The system can analyze recruitment activity data and generate an optimal recruitment strategy. For example, the system builds a system that analyzes recruitment activity data and generates an optimal recruitment strategy. For example, it analyzes what strategies were effective based on past recruitment data. The system also analyzes applicant characteristics and trends based on the recruitment activity data and proposes an appropriate recruitment strategy. For example, it generates a strategy targeting applicants with a specific skill set. The system also analyzes recruitment activity data in real time and dynamically generates an optimal recruitment strategy. For example, it adjusts the strategy according to the application status of applicants and market fluctuations. In this way, by analyzing recruitment activity data and generating an optimal recruitment strategy, it is possible to improve the efficiency and effectiveness of recruitment activities.

[0039] The system can propose optimized career paths for individual employees based on human resource development data. For example, the system analyzes human resource development data and builds a system that proposes optimized career paths for individual employees. For example, it proposes appropriate career paths based on an employee's skills and experience. The system also proposes career paths based on the employee's growth and goals based on the human resource development data. For example, it proposes steps to acquire specific skills. The system also analyzes human resource development data in real time and dynamically proposes optimized career paths for individual employees. For example, it adjusts career paths according to the employee's progress and degree of goal achievement. In this way, it is possible to support employee growth by proposing optimized career paths based on human resource development data.

[0040] The system can integrate data on recruitment activities and human resource development to formulate a comprehensive human resource strategy. For example, the system builds a system that integrates data on recruitment activities and human resource development to formulate a comprehensive human resource strategy. For example, it centrally manages recruitment data and human resource development data and formulates a strategy. The system also proposes a comprehensive human resource strategy based on the data on recruitment activities and human resource development. For example, it hires personnel with a specific skill set and formulates a subsequent development plan. The system also integrates data on recruitment activities and human resource development in real time to dynamically formulate a human resource strategy. For example, it adjusts the strategy according to the recruitment status and development progress. In this way, by integrating data on recruitment activities and human resource development and formulating a comprehensive human resource strategy, a company's human resource management can be made more efficient.

[0041] The system can visualize the results of recruitment activities and human resource development, and streamline reporting to management. For example, the system builds a system that visualizes the results of recruitment activities and human resource development, and streamlines reporting to management. For example, a dashboard can be used to make the results understandable at a glance. The system also visualizes the results based on recruitment activity and human resource development data, and reports to management. For example, graphs and charts can be used to visually display the results. The system also visualizes the results of recruitment activities and human resource development in real time, and streamlines reporting to management. For example, it provides reports that are automatically generated on a regular basis. This makes it possible to visualize the results of recruitment activities and human resource development, and streamlines reporting to management, thereby supporting corporate decision-making.

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

[0043] The interview system can also analyze an applicant's past work history and generate questions based on the applicant's career path. For example, if an applicant has leadership experience in the past, it can generate detailed questions about that experience. Also, if an applicant has extensive experience in a particular industry, it can generate specific questions related to that industry. Furthermore, based on the applicant's past work history, it can generate questions about challenges the applicant faced and how they overcame them. This can improve the quality of interviews by generating questions based on the applicant's work history.

[0044] The interview system can also analyze an applicant's social media activity and generate questions based on the applicant's interests and values. For example, if an applicant frequently posts about a particular topic, it can generate questions related to that topic. It can also generate relevant questions based on the communities and groups the applicant participates in. Furthermore, it can evaluate the applicant's values ​​and interests based on their social media activity and generate questions accordingly. This can improve the quality of interviews by generating questions based on the applicant's social media activity.

[0045] The interview system can also analyze an applicant's language usage patterns and generate questions based on the applicant's communication style. For example, if an applicant uses a lot of concrete language, it can generate questions related to those words. Also, if an applicant prefers abstract expressions, it can generate questions related to those expressions. Furthermore, it can evaluate the applicant's thought process and problem-solving ability based on the applicant's language usage patterns and generate questions accordingly. In this way, the quality of interviews can be improved by generating questions based on the applicant's language usage patterns.

[0046] The training system can also analyze trainees' health data and adjust the training program. For example, it can adjust the training schedule based on the trainee's sleep patterns and exercise habits. It can also monitor the trainee's stress level and suggest relaxation activities as needed. Furthermore, it can adjust the content and methods of training based on the trainee's health data to optimize the trainee's health. This makes it possible to improve learning effectiveness by analyzing trainees' health data and adjusting the training program.

[0047] The training system can also analyze trainees' hobbies and interests and generate training programs based on them. For example, if a trainee is interested in a particular sport, it can provide training related to that sport. If a trainee is interested in music or art, it can provide related learning materials. Furthermore, it can adjust the content and methods of training based on the trainee's hobbies and interests, thereby increasing the trainee's motivation. In this way, by analyzing trainees' hobbies and interests and generating training programs based on them, it is possible to improve learning effectiveness.

[0048] The robot can also take into account the cultural backgrounds of applicants and trainees and respond accordingly. For example, if an applicant has a different cultural background, it can generate questions that take that culture into consideration. Also, if a trainee has a different cultural background, it can provide training content that is appropriate for that culture. Furthermore, the robot can adjust the method and tone of communication to take cultural backgrounds into consideration. This can improve the quality of communication by responding in a way that takes into account the cultural backgrounds of applicants and trainees.

[0049] The robot can also dynamically adjust the content of interviews and training sessions based on applicant and trainee feedback. For example, if an applicant has difficulty with a particular question, it can simplify that question. Or, if a trainee shows interest in a particular training session, it can strengthen that training. Furthermore, it can adjust the progress of interviews and training sessions based on applicant and trainee feedback to provide an optimal experience. This allows for quality improvement by dynamically adjusting the content of interviews and training sessions based on applicant and trainee feedback.

[0050] The processing flow of the first embodiment will be briefly explained below.

[0051] Step 1: The interview system analyzes the applicant's information and generates appropriate questions. For example, for a sales applicant, it might generate a question such as "Tell me about your past sales experience," and for a technical applicant, it might generate a question such as "Please explain your knowledge of a specific programming language." Step 2: The training system analyzes the trainee's skill level and learning progress and generates an appropriate training program. For example, it suggests "training to learn basic customer service skills" for a sales trainee, and "a course to learn the basics of a specific programming language" for a technical trainee. Step 3: The robot conducts interviews and training based on the questions and training programs generated by the interview system and training system. For example, the robot asks questions to applicants and records their answers. The robot also provides training to trainees and monitors their progress.

[0052] (Example 2) The AI ​​interview system and training system according to the embodiment of the present invention utilizes generative AI to conduct interviews and training for all job types, including sales, technical, and code interviews. As a result, the AI ​​interview system and training system can significantly improve the efficiency and effectiveness of corporate recruitment activities and human resource development.

[0053] An AI interview system and training system according to an embodiment includes an interview system, a training system, and a robot. The interview system analyzes information about an applicant and generates appropriate questions. For example, for an applicant for a sales position, the system generates a question such as, "Tell me about your past sales experience." For an applicant for a technical position, the system generates a question such as, "Please explain your knowledge of a specific programming language." The training system analyzes the skill level and learning progress of a trainee and generates an appropriate training program. For example, for a trainee for a sales position, the system suggests "training to learn basic customer service skills." For a trainee for a technical position, the system suggests "a course to learn the basics of a specific programming language." The robot conducts interviews and training based on the questions and training program generated by the interview system and training system. For example, the robot asks questions to the applicant and records the answers. The robot also provides training to the trainee and monitors their progress. As a result, the AI ​​interview system and training system according to the embodiment can significantly improve the efficiency and effectiveness of a company's recruitment activities and human resource development. For example, the quality of interviews can be maintained uniformly, and applicants can be accurately evaluated. Additionally, training programs are optimized for each individual trainee, improving learning effectiveness, allowing companies to recruit and develop better talent more efficiently.

[0054] An interview system can analyze an applicant's past interview data and generate questions optimized for each individual applicant. For example, an interview system collects an applicant's past interview data, and a generation AI analyzes that data to identify the applicant's strengths and weaknesses. For example, it generates questions that are optimal for an applicant based on the answers and evaluations from past interviews. The interview system also analyzes an applicant's past interview data and generates questions related to specific skills and experience. For example, it generates detailed questions related to projects the applicant has previously worked on. The interview system also evaluates the applicant's growth and changes based on the applicant's past interview data and generates questions accordingly. For example, it generates questions to check whether the applicant has overcome weaknesses from past interviews. This makes it possible to improve the quality of interviews by generating questions optimized for the applicant.

[0055] An interview system can analyze an applicant's nonverbal communication and adjust the questions based on that. For example, an interview system could analyze an applicant's facial expressions and gestures during an interview in real time, and a generative AI could adjust the questions based on that data. For example, if an applicant is nervous, it could generate questions to relax them. The interview system could also analyze an applicant's nonverbal communication to assess the applicant's confidence and interest. For example, it could ask more in-depth questions about topics the applicant is talking about confidently. The interview system could also analyze an applicant's facial expressions and gestures to understand the applicant's emotional state. For example, if the applicant seems confused, it could simplify the question or add an explanation. In this way, the quality of interviews can be improved by analyzing an applicant's nonverbal communication.

[0056] The interview system can use the emotion estimation function to analyze the emotional state of an applicant in real time and generate questions that reduce stress. For example, the interview system uses the emotion estimation function to analyze the stress level of an applicant in real time and generate questions that reduce stress. For example, it inserts small talk or easy questions to help the applicant relax. The interview system also analyzes the emotional state of an applicant and adjusts the difficulty of questions if stress is increasing. For example, it avoids difficult questions and generates questions that are easy for the applicant to answer. The interview system also uses the emotion estimation function to monitor the emotional state of an applicant and provide feedback to reduce stress. For example, it adjusts the progress of the interview so that the applicant can relax. In this way, the quality of the interview can be improved by analyzing the emotional state of an applicant and generating questions that reduce stress.

[0057] The training system can analyze the learning style of trainees and generate a training program optimized for each individual trainee. The training system, for example, analyzes the learning style of trainees and generates a training program optimized for each individual trainee. For example, visual learning materials are provided to trainees who prefer visual learning. The training system also suggests an appropriate training method based on the trainee's learning style. For example, hands-on training is provided to trainees who prefer practical learning. The training system also analyzes the learning style of trainees and provides learning pace and materials tailored to each individual trainee. For example, online courses are provided to trainees who prefer self-study. In this way, the learning effectiveness can be improved by analyzing the learning style of trainees and generating an optimized training program.

[0058] The training system can provide real-time feedback according to the trainee's progress based on the trainee's past learning history. For example, the training system analyzes the trainee's past learning history and provides real-time feedback according to the trainee's progress. For example, if a particular skill is lacking, additional learning materials are provided. The training system also evaluates the trainee's progress based on the trainee's learning history and provides appropriate feedback. For example, it may offer praise or suggest areas for improvement depending on the degree of goal achievement. The training system also analyzes the trainee's past learning history, monitors the trainee's progress in real time, and provides feedback as needed. For example, it may provide support if there is a delay in learning. In this way, the learning effectiveness can be improved by providing real-time feedback based on the trainee's past learning history.

[0059] The training system can use the emotion estimation function to generate training content to maintain trainee motivation. For example, the training system uses the emotion estimation function to analyze the trainee's motivation in real time and adjust the training content based on the results. For example, if motivation is declining, interesting content is added. The training system also analyzes the trainee's emotional state and provides feedback to maintain motivation. For example, encouraging messages are sent to elicit positive emotions. The training system also uses the emotion estimation function to generate training programs to increase trainee motivation. For example, goal setting and a reward system that creates a sense of accomplishment are introduced. In this way, the training system can generate training content to maintain trainee motivation, thereby improving learning effectiveness.

[0060] The robot has the ability to converse naturally with applicants and trainees, enabling smooth communication. The robot, for example, has the ability to converse naturally, enabling smooth communication with applicants and trainees. For example, it uses natural language processing technology to provide appropriate answers to applicants' questions. The robot also incorporates voice recognition technology to smoothly converse with applicants and trainees. For example, it accurately recognizes what the applicant says and provides an appropriate response. The robot also incorporates emotion recognition technology to provide natural conversational capabilities. For example, it analyzes the emotional state of the applicant and converses accordingly. This gives the robot the ability to converse naturally, enabling smooth communication with applicants and trainees.

[0061] Robots can analyze applicant and trainee feedback in real time and respond immediately. Robots can, for example, build a system that analyzes applicant and trainee feedback in real time and responds immediately. For example, it can provide appropriate feedback immediately in response to an applicant's answers. Robots can also monitor trainee progress in real time and provide feedback as needed. For example, it can provide additional explanations if a trainee is having difficulty understanding. Robots can also analyze applicant and trainee feedback and adjust the progress of interviews and training based on the results. For example, it can generate the next question depending on the applicant's answer. This makes it possible to improve the quality of interviews and training by analyzing feedback in real time and responding immediately.

[0062] The robot can be adapted to different industries and occupations, meeting a wide range of interview and training needs. For example, to accommodate different industries and occupations, the robot's generative AI learns the characteristics of each industry and occupation, and conducts interviews and training based on that. For example, it can provide specialized support for the medical or IT industry. In addition, to accommodate different occupations, the robot provides interviews and training that take into account the skill sets and knowledge of each occupation. For example, it asks questions and provides training that is specialized for sales or technical jobs. In addition, to accommodate different industries and occupations, the robot analyzes the trends and needs of each industry and occupation, and provides interviews and training based on that. This allows the robot to accommodate different industries and occupations, meeting a wide range of interview and training needs.

[0063] Robots can be equipped with multilingual capabilities to support global recruitment activities and training. For example, robots can be equipped with multilingual capabilities to support global recruitment activities. For example, they can conduct interviews in multiple languages, such as English, Japanese, and Chinese. Robots with multilingual capabilities can also facilitate communication with applicants and trainees who speak different languages. For example, they can ask questions in the applicant's native language. In order to provide a robot with multilingual capabilities, generative AI learns the characteristics and cultural background of each language and responds based on that. In this way, having multilingual capabilities can support global recruitment activities and training.

[0064] The robot can use the emotion estimation function to provide feedback according to the emotions of applicants and trainees. For example, the robot uses the emotion estimation function to analyze the emotional state of applicants and trainees in real time and provide feedback based on the results. For example, if an applicant is nervous, the robot provides feedback to relax them. The robot also monitors the emotional state of trainees and provides feedback to maintain their motivation. For example, if a trainee is tired, the robot gives words of encouragement. The robot also uses the emotion estimation function to provide feedback according to the emotions of applicants and trainees. For example, it gives words of praise to elicit positive emotions. In this way, the quality of interviews and training can be improved by using the emotion estimation function to provide feedback according to the emotions of applicants and trainees.

[0065] The system can analyze recruitment activity data and generate an optimal recruitment strategy. For example, the system builds a system that analyzes recruitment activity data and generates an optimal recruitment strategy. For example, it analyzes what strategies were effective based on past recruitment data. The system also analyzes applicant characteristics and trends based on the recruitment activity data and proposes an appropriate recruitment strategy. For example, it generates a strategy targeting applicants with a specific skill set. The system also analyzes recruitment activity data in real time and dynamically generates an optimal recruitment strategy. For example, it adjusts the strategy according to the application status of applicants and market fluctuations. In this way, by analyzing recruitment activity data and generating an optimal recruitment strategy, it is possible to improve the efficiency and effectiveness of recruitment activities.

[0066] The system can propose optimized career paths for individual employees based on human resource development data. For example, the system analyzes human resource development data and builds a system that proposes optimized career paths for individual employees. For example, it proposes appropriate career paths based on an employee's skills and experience. The system also proposes career paths based on the employee's growth and goals based on the human resource development data. For example, it proposes steps to acquire specific skills. The system also analyzes human resource development data in real time and dynamically proposes optimized career paths for individual employees. For example, it adjusts career paths according to the employee's progress and degree of goal achievement. In this way, it is possible to support employee growth by proposing optimized career paths based on human resource development data.

[0067] The system can use the emotion estimation function to generate measures to maintain employee motivation. For example, the system uses the emotion estimation function to analyze employee motivation in real time and generate measures based on the results. For example, the system may send encouraging messages to employees whose motivation is declining. The system also analyzes employees' emotional states and suggests measures to maintain their motivation. For example, it may suggest events or activities that will elicit positive emotions. The system also uses the emotion estimation function to generate measures to increase employee motivation. For example, it may introduce goal setting or a reward system that will create a sense of accomplishment. In this way, the work environment can be improved by using the emotion estimation function to generate measures to maintain employee motivation.

[0068] The system can integrate data on recruitment activities and human resource development to formulate a comprehensive human resource strategy. For example, the system builds a system that integrates data on recruitment activities and human resource development to formulate a comprehensive human resource strategy. For example, it centrally manages recruitment data and human resource development data and formulates a strategy. The system also proposes a comprehensive human resource strategy based on the data on recruitment activities and human resource development. For example, it hires personnel with a specific skill set and formulates a subsequent development plan. The system also integrates data on recruitment activities and human resource development in real time to dynamically formulate a human resource strategy. For example, it adjusts the strategy according to the recruitment status and development progress. In this way, by integrating data on recruitment activities and human resource development and formulating a comprehensive human resource strategy, a company's human resource management can be made more efficient.

[0069] The system can visualize the results of recruitment activities and human resource development, and streamline reporting to management. For example, the system builds a system that visualizes the results of recruitment activities and human resource development, and streamlines reporting to management. For example, a dashboard can be used to make the results understandable at a glance. The system also visualizes the results based on recruitment activity and human resource development data, and reports to management. For example, graphs and charts can be used to visually display the results. The system also visualizes the results of recruitment activities and human resource development in real time, and streamlines reporting to management. For example, it provides reports that are automatically generated on a regular basis. This makes it possible to visualize the results of recruitment activities and human resource development, and streamlines reporting to management, thereby supporting corporate decision-making.

[0070] The system can use the emotion estimation function to provide support according to employees' emotions and improve the work environment. For example, the system uses the emotion estimation function to analyze employees' emotional states in real time and provide support based on the results. For example, the system suggests counseling for employees experiencing increased stress. The system also analyzes employees' emotional states and suggests measures to improve the work environment. For example, the system suggests team building activities to bring out positive emotions. The system also uses the emotion estimation function to provide support according to employees' emotions and improve the work environment. For example, the system suggests taking a break to refresh based on the emotional state. In this way, the system can use the emotion estimation function to provide support according to employees' emotions and improve the work environment, thereby increasing employee satisfaction.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The interview system can also analyze an applicant's past work history and generate questions based on the applicant's career path. For example, if an applicant has leadership experience in the past, it can generate detailed questions about that experience. Also, if an applicant has extensive experience in a particular industry, it can generate specific questions related to that industry. Furthermore, based on the applicant's past work history, it can generate questions about challenges the applicant faced and how they overcame them. This can improve the quality of interviews by generating questions based on the applicant's work history.

[0073] The interview system can also analyze an applicant's social media activity and generate questions based on the applicant's interests and values. For example, if an applicant frequently posts about a particular topic, it can generate questions related to that topic. It can also generate relevant questions based on the communities and groups the applicant participates in. Furthermore, it can evaluate the applicant's values ​​and interests based on their social media activity and generate questions accordingly. This can improve the quality of interviews by generating questions based on the applicant's social media activity.

[0074] The interview system can also analyze an applicant's language usage patterns and generate questions based on the applicant's communication style. For example, if an applicant uses a lot of concrete language, it can generate questions related to those words. Also, if an applicant prefers abstract expressions, it can generate questions related to those expressions. Furthermore, it can evaluate the applicant's thought process and problem-solving ability based on the applicant's language usage patterns and generate questions accordingly. In this way, the quality of interviews can be improved by generating questions based on the applicant's language usage patterns.

[0075] The interview system can also use emotion estimation to evaluate an applicant's motivation and generate questions that will motivate them. For example, if an applicant shows a strong interest in a particular topic, it can generate questions related to that topic. It can also generate questions about the applicant's past achievements to elicit their confidence. It can also analyze the applicant's emotional state and generate questions that elicit positive emotions. This can improve the quality of interviews by generating questions that motivate applicants.

[0076] The training system can also analyze trainees' health data and adjust the training program. For example, it can adjust the training schedule based on the trainee's sleep patterns and exercise habits. It can also monitor the trainee's stress level and suggest relaxation activities as needed. Furthermore, it can adjust the content and methods of training based on the trainee's health data to optimize the trainee's health. This makes it possible to improve learning effectiveness by analyzing trainees' health data and adjusting the training program.

[0077] The training system can also analyze trainees' hobbies and interests and generate training programs based on them. For example, if a trainee is interested in a particular sport, it can provide training related to that sport. If a trainee is interested in music or art, it can provide related learning materials. Furthermore, it can adjust the content and methods of training based on the trainee's hobbies and interests, thereby increasing the trainee's motivation. In this way, by analyzing trainees' hobbies and interests and generating training programs based on them, it is possible to improve learning effectiveness.

[0078] The training system can also use emotion estimation to monitor trainees' stress levels and generate training content to reduce stress. For example, if a trainee is feeling stressed, it can suggest relaxation activities or breaks. It can also analyze the trainee's emotional state and provide feedback to reduce stress. Furthermore, it can adjust the training schedule and content based on the trainee's stress level to reduce stress. This makes it possible to improve learning effectiveness by monitoring trainees' stress levels and generating training content to reduce stress.

[0079] The robot can also take into account the cultural backgrounds of applicants and trainees and respond accordingly. For example, if an applicant has a different cultural background, it can generate questions that take that culture into consideration. Also, if a trainee has a different cultural background, it can provide training content that is appropriate for that culture. Furthermore, the robot can adjust the method and tone of communication to take cultural backgrounds into consideration. This can improve the quality of communication by responding in a way that takes into account the cultural backgrounds of applicants and trainees.

[0080] The robot can also dynamically adjust the content of interviews and training sessions based on applicant and trainee feedback. For example, if an applicant has difficulty with a particular question, it can simplify that question. Or, if a trainee shows interest in a particular training session, it can strengthen that training. Furthermore, it can adjust the progress of interviews and training sessions based on applicant and trainee feedback to provide an optimal experience. This allows for quality improvement by dynamically adjusting the content of interviews and training sessions based on applicant and trainee feedback.

[0081] The robot can also use its emotion estimation function to engage in dialogue that reflects the emotions of applicants and trainees. For example, if an applicant is nervous, it can engage in dialogue to help them relax. If a trainee is tired, it can offer words of encouragement. It can also analyze the emotional state of applicants and trainees and engage in dialogue that elicits positive emotions. This allows the quality of interviews and training to be improved by using the emotion estimation function to engage in dialogue that reflects the emotions of applicants and trainees.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: The interview system analyzes the applicant's information and generates appropriate questions. For example, for a sales applicant, it might generate a question such as "Tell me about your past sales experience," and for a technical applicant, it might generate a question such as "Please explain your knowledge of a specific programming language." Step 2: The training system analyzes the trainee's skill level and learning progress and generates an appropriate training program. For example, it suggests "training to learn basic customer service skills" for a sales trainee, and "a course to learn the basics of a specific programming language" for a technical trainee. Step 3: The robot conducts interviews and training based on the questions and training programs generated by the interview system and training system. For example, the robot asks questions to applicants and records their answers. The robot also provides training to trainees and monitors their progress.

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0097] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0098] 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.

[0099] 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.

[0100] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0110] 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.

[0111] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0112] In the headset type terminal 314, 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 headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0113] 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.

[0114] 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.

[0115] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0128] In the robot 414, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The robot 414 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the specific processing unit 290 using these models.

[0129] 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.

[0130] 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.

[0131] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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]

[0151] 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. An interview system that utilizes generative AI, A training system that utilizes generative AI, a robot that conducts interviews and training based on questions and training programs generated by the interview system and the training system. A system characterized by:

2. The interview system includes: Analyzes applicants' past interview data and generates questions optimized for each individual applicant 2. The system of claim 1.

3. The interview system includes: Analyze applicants' non-verbal communication and tailor your questions accordingly 2. The system of claim 1.

4. The interview system includes: Analyzing applicants' emotional state in real time and generating questions that reduce stress 2. The system of claim 1.

5. The training system includes: Analyze trainees' learning styles and generate training programs optimized for each individual trainee 2. The system of claim 1.

6. The training system includes: Provide real-time feedback based on trainees' progress based on their past learning history 2. The system of claim 1.

7. The training system includes: Generate training content to keep trainees motivated 2. The system of claim 1.

8. The robot Ability to naturally converse with applicants and trainees and facilitate smooth communication 2. The system of claim 1.

Images