System

Abstract

An object of a system according to an embodiment is to efficiently provide individual interview guidance.SOLUTION: A system includes an interview simulation unit, a feedback unit, and a customization unit. An interview simulation part performs interview simulation by the generated AI. The feedback unit provides personalized feedback based on the simulation result generated by the interview simulation unit. The customizing unit customizes the interview preparation based on the feedback provided by the feedback unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques make it difficult to provide individual face-to-face guidance efficiently, and there is room for improvement.

[0005] The system according to the embodiment aims to efficiently provide individual interview guidance. [Means for solving the problem]

[0006] The system according to the embodiment includes an interview simulation unit, a feedback unit, and a customization unit. The interview simulation unit performs an interview simulation using a generation AI. The feedback unit provides individual feedback based on the simulation results generated by the interview simulation unit. The customization unit customizes interview preparation based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to the embodiment can efficiently provide individual interview guidance. [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) An interview guidance system according to an embodiment of the present invention is a system for enabling test takers and job seekers to acquire skills for success in interviews. This system provides individual interview guidance using generative AI. As a result, the interview guidance system enables test takers and job seekers to effectively acquire skills for success in interviews.

[0029] An interview coaching system according to an embodiment includes an interview simulation unit, a feedback unit, and a customization unit. The interview simulation unit performs an interview simulation using a generation AI. For example, the generation AI performs a simulation based on prompts including questions that the candidate may be asked in an interview and the content of the candidate's self-introduction. The generation AI analyzes the candidate's answers using a text generation AI such as GPT-3 or BERT and provides appropriate example answers. The generation AI can also analyze how the candidate should respond to a question such as "Please introduce yourself" and provide appropriate example answers. The feedback unit provides individualized feedback based on the simulation results generated by the interview simulation unit. For example, after the candidate gives a self-introduction, the generation AI evaluates the content and suggests areas for improvement or strengthening. The generation AI can also analyze the candidate's answers and present specific points for improvement in bullet points. The customization unit customizes the interview preparation based on the feedback provided by the feedback unit. For example, the generation AI analyzes the candidate's resume and work history and suggests optimal interview preparation based on the content. Furthermore, the generation AI can provide interview strategies specialized for a specific industry or job type, for example. As a result, the interview guidance system according to the embodiment can effectively help test takers and job seekers acquire the skills they need to succeed in interviews.

[0030] The interview simulation unit can analyze the candidate's past interview history and optimize the simulation based on past failures and success patterns. For example, the interview simulation unit stores the candidate's past interview history in a database, and the generation AI analyzes that data. For example, the unit identifies the candidate's strengths and weaknesses based on the answers given in past interviews and the interviewer's evaluations, and optimizes the simulation. The interview simulation unit also analyzes recordings of the candidate's past interviews, and the generation AI extracts patterns of failure and success from the audio data. For example, it can analyze the tone and content of answers to specific questions and identify areas for improvement. The interview simulation unit also collects feedback from the candidate's past interviews, and the generation AI optimizes the simulation based on that feedback. For example, it can analyze specific comments and advice from interviewers and reflect them in the next simulation. This makes it possible to optimize the simulation based on past interview history.

[0031] The interview simulation unit can analyze the test taker's tone of voice and speaking habits to simulate more natural conversations. For example, the interview simulation unit records the test taker's tone of voice and speaking habits, and the generation AI analyzes the audio data. For example, it analyzes the pitch, speed, and intonation of the voice to simulate natural conversations. The interview simulation unit also analyzes specific speaking habits and patterns of the test taker, and the generation AI adjusts the simulation based on that data. For example, it can provide feedback to improve the repetition of specific words or the use of pauses. The interview simulation unit also analyzes the test taker's speaking habits in real time, and the generation AI provides feedback on the spot. For example, it can point out speaking habits and provide advice to encourage more natural conversations. This makes it possible to analyze the test taker's tone of voice and speaking habits to simulate natural conversations.

[0032] The interview simulation unit can provide interview simulations in different cultures and languages ​​to support international interview preparation. For example, the generation AI provides interview simulations in different languages. For example, it can perform interview simulations in multiple languages, such as English, French, and Chinese, to support international interview preparation. The interview simulation unit also simulates interviewers with different cultural backgrounds to enable candidates to prepare for international interviews. For example, it can provide simulations that include culturally specific questions and manners. The generation AI also analyzes interview styles in different countries and regions and performs simulations based on that data. For example, it can provide simulations that correspond to regional interview styles, such as those in the United States, Europe, and Asia. This makes it possible to provide interview simulations in different cultures and languages ​​to support international interview preparation.

[0033] The interview simulation unit can simulate a group interview and support a format in which multiple test takers participate simultaneously. In the interview simulation unit, for example, the generation AI has multiple test takers participate in the simulation at the same time, recreating the format of a group interview. For example, it asks questions that encourage discussion and cooperation among the test takers. In addition, in the group interview simulation, the generation AI assigns roles to each test taker and conducts an interview based on a specific scenario. For example, it can assign roles such as leader and follower and ask questions according to their roles. In addition, the interview simulation unit has the generation AI simulate a group interview and evaluate the communication skills of test takers. For example, it can analyze the degree of exchange of opinions and cooperation with other test takers and provide feedback. This makes it possible to simulate a group interview and support a format in which multiple test takers participate simultaneously.

[0034] The feedback section can analyze the examinee's answers in detail and present specific points for improvement in bullet points. For example, the feedback section uses a generation AI to analyze the examinee's answers and present specific points for improvement in bullet points. For example, it provides specific advice such as "Make your self-introduction more specific" or "Adjust your speaking speed." The feedback section also builds a system in which the generation AI analyzes the examinee's answers in detail and presents specific points for improvement in bullet points. For example, it can evaluate the content, structure, and expression of the answer and present specific points for improvement. The feedback section also uses a generation AI to analyze the examinee's answers and present points for improvement in bullet points, making it easier for the examinee to create a specific action plan. For example, it can provide advice such as "Include a specific anecdote in your next interview" or "Make eye contact." This allows the system to analyze the examinee's answers in detail and present specific points for improvement in bullet points.

[0035] The feedback unit can compare the test taker's answers with other success stories and provide feedback based on a benchmark. For example, the generation AI can compare the test taker's answers with other success stories and provide feedback based on a benchmark. For example, it can specifically indicate which parts are excellent and which parts need improvement by comparing them with past success stories. The feedback unit can also build a system in which the generation AI compares the test taker's answers with success stories and provides feedback based on a benchmark. For example, the test taker's answers can be evaluated by referring to the content and expression of the answers in the success stories. The feedback unit can also compare the test taker's answers with other success stories and provide specific feedback based on a benchmark. For example, it can indicate points such as "the success story includes specific anecdotes" and "the speech is clear." This makes it possible to compare the test taker's answers with other success stories and provide feedback based on a benchmark.

[0036] The feedback unit can provide feedback in video format, showing areas for improvement in a visually easy-to-understand manner. For example, the feedback unit can have the generation AI provide feedback in video format, showing areas for improvement in a visually easy-to-understand manner for test takers. For example, specific examples can be shown in video to visually explain how improvements should be made. The feedback unit can also build a system that provides feedback in video format, allowing test takers to grasp areas for improvement in a visually easy-to-understand manner. For example, video explanations and demonstrations can be provided. The feedback unit can also have the generation AI provide feedback in video format, allowing test takers to visually confirm specific areas for improvement. For example, model answers and improvement examples can be shown in video, allowing test takers to compare them with their own answers. This makes it possible to provide feedback in video format, showing areas for improvement in a visually easy-to-understand manner.

[0037] The feedback unit can provide feedback by incorporating the opinions of multiple experts and make improvement suggestions from multiple perspectives. For example, the generation AI can provide feedback by incorporating the opinions of multiple experts and make improvement suggestions from multiple perspectives. For example, the feedback unit can integrate the opinions of interviewers, career coaches, psychologists, etc. Furthermore, when providing feedback, the feedback unit can analyze the opinions of multiple experts and make specific improvement suggestions from multiple perspectives. For example, the feedback unit can provide feedback based on expert advice. Furthermore, the feedback unit can provide feedback by incorporating the opinions of multiple experts and enable the test taker to understand areas for improvement from multiple perspectives. For example, the feedback unit can provide specific advice based on expert opinions. This allows feedback to be provided by incorporating the opinions of multiple experts and make improvement suggestions from multiple perspectives.

[0038] The customization unit can analyze an examinee's past learning history and skill set and provide customized interview preparation based on that. For example, the customization unit uses a generation AI to analyze an examinee's past learning history and skill set and provide customized interview preparation based on that. For example, the customization unit asks questions specialized in a specific field based on the examinee's learning history. The customization unit also analyzes an examinee's skill set and builds a system in which the generation AI provides customized interview preparation based on that data. For example, the customization unit can ask questions that bring out the examinee's strengths. The customization unit can also use a generation AI to analyze an examinee's past learning history and skill set and provide customized interview preparation, allowing the examinee to effectively showcase their strengths. For example, the customization unit can ask specific questions related to a specific skill. This makes it possible to analyze an examinee's past learning history and skill set and provide customized interview preparation based on that.

[0039] The customization department can analyze the examinee's goals and motivations for applying in detail and propose specific interview strategies accordingly. For example, the customization department allows the generation AI to analyze the examinee's goals and motivations for applying in detail and propose specific interview strategies accordingly. For example, the customization department asks questions based on the examinee's motivations for applying and provides specific example answers. The customization department also builds a system in which the generation AI analyzes the examinee's goals and motivations for applying and proposes customized interview strategies based on that data. For example, the customization department can ask questions that draw out specific episodes related to the examinee's motivations for applying. The customization department also allows the generation AI to analyze the examinee's goals and motivations for applying in detail and propose customized interview strategies, allowing the examinee to effectively showcase their goals and motivations for applying. For example, the customization department can ask specific questions related to the examinee's motivations for applying. This allows the generation AI to analyze the examinee's goals and motivations for applying in detail and propose specific interview strategies accordingly.

[0040] The customization department can provide interview preparation specialized for different industries and occupations to meet the diverse needs of test takers. For example, the generation AI can provide interview preparation specialized for different industries and occupations to meet the diverse needs of test takers. For example, the generation AI can ask questions specialized for specific industries such as IT, healthcare, and finance. The customization department can also build a system that provides interview preparation specialized for different occupations, and the generation AI can ask specific questions tailored to the test taker's occupation. For example, questions specialized for occupations such as engineer, designer, and marketer can be asked. The customization department can also provide interview preparation specialized for different industries and occupations, allowing test takers to develop specific preparation tailored to their own industry or occupation. For example, it can run simulations that address industry-specific questions and topics. This makes it possible to provide interview preparation specialized for different industries and occupations to meet the diverse needs of test takers.

[0041] The customization unit can automatically generate a training plan that fits the examinee's schedule, supporting efficient learning. For example, the customization unit uses a generation AI to analyze the examinee's schedule and automatically generate a training plan based on that. For example, the customization unit proposes an interview practice schedule that fits the examinee's free time. The customization unit also builds a system that automatically generates a training plan that fits the examinee's schedule, and the generation AI supports efficient learning. For example, the customization unit can provide a plan for effective practice in a short amount of time. The customization unit also uses a generation AI to analyze the examinee's schedule and automatically generate a training plan, allowing the examinee to study efficiently at their own pace. For example, the customization unit can provide a short practice plan that fits a busy schedule. This allows the automatic generation of a training plan that fits the examinee's schedule, supporting efficient learning.

[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 guidance system can further include a "motivation enhancement unit." The motivation enhancement unit provides content to enhance the examinee's motivation. For example, it displays examples of successful interviews and inspirational messages. The motivation enhancement unit can also record small goals that the examinee achieves in the process of preparing for the interview and send a message of congratulations on the achievement. Furthermore, the motivation enhancement unit can provide encouraging messages and specific advice in response to any anxieties or questions the examinee may have about preparing for the interview. This increases the examinee's motivation and enables them to prepare for the interview more effectively.

[0044] The interview guidance system can further include a "health management section." The health management section monitors the examinee's health status and provides advice on maintaining optimal physical condition for interview preparation. For example, it records the examinee's sleep patterns and dietary habits and makes suggestions to promote healthy lifestyles. The health management section can also suggest ways for the examinee to relax if they feel stressed. The health management section can also introduce breathing techniques and simple exercises to help the examinee relax before the interview. This allows the examinee to approach the interview in a healthy state.

[0045] The interview guidance system can further include a "networking support section." The networking support section provides support for test takers to strengthen their networking within the industry. For example, it can hold online meetings and webinars with industry professionals, providing test takers with opportunities to ask questions and seek advice directly. The networking support section can also provide test takers with information about industry events and seminars that they should attend and encourage them to participate. The networking support section can also provide test takers with advice and techniques to expand their network within the industry. This allows test takers to strengthen their networking within the industry and increase their selling points in interviews.

[0046] The interview guidance system can further include a "career planning section." The career planning section sets the candidate's long-term career goals and provides interview preparation based on those goals. For example, it proposes specific steps and action plans according to the candidate's career goals. The career planning section can also provide the latest information and trends related to the industry and occupation the candidate is aiming for, helping the candidate to more specifically outline their career plan. Furthermore, the career planning section can provide advice on how to acquire the skills and qualifications necessary to achieve the candidate's career goals. This allows the candidate to develop specific measures to achieve their long-term career goals.

[0047] The interview guidance system can further include a "self-assessment section." The self-assessment section provides tools for test takers to objectively evaluate their own strengths and weaknesses. For example, it provides test takers with a list of questions or checklists for self-assessment, encouraging self-analysis. The self-assessment section can also provide test takers with advice to identify specific areas for improvement or strengthening based on the results of their self-assessment. Furthermore, the self-assessment section can provide test takers with feedback to compare the results of their self-assessment with other success stories and understand where they stand. This allows test takers to objectively evaluate themselves and prepare for the interview more effectively.

[0048] The processing flow of the first embodiment will be briefly explained below.

[0049] Step 1: The interview simulation unit uses a generation AI to perform an interview simulation. For example, the generation AI performs a simulation based on prompts including questions that the candidate may be asked in an interview and the content of the self-introduction. The generation AI uses text generation AI such as GPT-3 or BERT to analyze the candidate's answers and provide appropriate example answers. The generation AI can also analyze how the candidate should respond to the question "Please introduce yourself" and provide appropriate example answers. Step 2: The feedback section provides individualized feedback based on the simulation results generated by the interview simulation section. For example, after the candidate gives a self-introduction, the generation AI evaluates the content and suggests areas for improvement or strengthening. The generation AI can also analyze the candidate's answers and present specific points for improvement in bullet points. Step 3: The customization unit customizes the interview preparation based on the feedback provided by the feedback unit. For example, the generation AI analyzes the candidate's resume and CV and suggests optimal interview preparation based on their content. The generation AI can also provide interview preparation tailored to specific industries or job types.

[0050] (Example 2) An interview guidance system according to an embodiment of the present invention is a system for enabling test takers and job seekers to acquire skills for success in interviews. This system provides individual interview guidance using generative AI. As a result, the interview guidance system enables test takers and job seekers to effectively acquire skills for success in interviews.

[0051] An interview coaching system according to an embodiment includes an interview simulation unit, a feedback unit, and a customization unit. The interview simulation unit performs an interview simulation using a generation AI. For example, the generation AI performs a simulation based on prompts including questions that the candidate may be asked in an interview and the content of the candidate's self-introduction. The generation AI analyzes the candidate's answers using a text generation AI such as GPT-3 or BERT and provides appropriate example answers. The generation AI can also analyze how the candidate should respond to a question such as "Please introduce yourself" and provide appropriate example answers. The feedback unit provides individualized feedback based on the simulation results generated by the interview simulation unit. For example, after the candidate gives a self-introduction, the generation AI evaluates the content and suggests areas for improvement or strengthening. The generation AI can also analyze the candidate's answers and present specific points for improvement in bullet points. The customization unit customizes the interview preparation based on the feedback provided by the feedback unit. For example, the generation AI analyzes the candidate's resume and work history and suggests optimal interview preparation based on the content. Furthermore, the generation AI can provide interview strategies specialized for a specific industry or job type, for example. As a result, the interview guidance system according to the embodiment can effectively help test takers and job seekers acquire the skills they need to succeed in interviews.

[0052] The interview simulation unit can analyze the candidate's past interview history and optimize the simulation based on past failures and success patterns. For example, the interview simulation unit stores the candidate's past interview history in a database, and the generation AI analyzes that data. For example, the unit identifies the candidate's strengths and weaknesses based on the answers given in past interviews and the interviewer's evaluations, and optimizes the simulation. The interview simulation unit also analyzes recordings of the candidate's past interviews, and the generation AI extracts patterns of failure and success from the audio data. For example, it can analyze the tone and content of answers to specific questions and identify areas for improvement. The interview simulation unit also collects feedback from the candidate's past interviews, and the generation AI optimizes the simulation based on that feedback. For example, it can analyze specific comments and advice from interviewers and reflect them in the next simulation. This makes it possible to optimize the simulation based on past interview history.

[0053] The interview simulation unit can analyze the test taker's tone of voice and speaking habits to simulate more natural conversations. For example, the interview simulation unit records the test taker's tone of voice and speaking habits, and the generation AI analyzes the audio data. For example, it analyzes the pitch, speed, and intonation of the voice to simulate natural conversations. The interview simulation unit also analyzes specific speaking habits and patterns of the test taker, and the generation AI adjusts the simulation based on that data. For example, it can provide feedback to improve the repetition of specific words or the use of pauses. The interview simulation unit also analyzes the test taker's speaking habits in real time, and the generation AI provides feedback on the spot. For example, it can point out speaking habits and provide advice to encourage more natural conversations. This makes it possible to analyze the test taker's tone of voice and speaking habits to simulate natural conversations.

[0054] The interview simulation unit uses an emotion estimation function to analyze the emotional state of the examinee in real time and provide questions and feedback appropriate to the emotion. For example, the interview simulation unit analyzes the examinee's facial expressions and voice in real time, and the generation AI estimates the examinee's emotional state. For example, if the examinee is nervous, it asks questions to relax them. The interview simulation unit also uses the emotion estimation function to provide feedback appropriate to the examinee's emotional state. For example, it can provide compliments if the examinee has strong positive emotions, and words of encouragement if the examinee has strong negative emotions. The interview simulation unit also monitors the examinee's emotional state in real time, and the generation AI adjusts the simulation based on that data. For example, it can ask more difficult questions if the examinee's emotions are stable, and easier questions if the examinee's emotions are unstable. This makes it possible to provide questions and feedback appropriate to the examinee's emotional state.

[0055] The interview simulation unit can provide interview simulations in different cultures and languages ​​to support international interview preparation. For example, the generation AI provides interview simulations in different languages. For example, it can perform interview simulations in multiple languages, such as English, French, and Chinese, to support international interview preparation. The interview simulation unit also simulates interviewers with different cultural backgrounds to enable candidates to prepare for international interviews. For example, it can provide simulations that include culturally specific questions and manners. The generation AI also analyzes interview styles in different countries and regions and performs simulations based on that data. For example, it can provide simulations that correspond to regional interview styles, such as those in the United States, Europe, and Asia. This makes it possible to provide interview simulations in different cultures and languages ​​to support international interview preparation.

[0056] The interview simulation unit can simulate a group interview and support a format in which multiple test takers participate simultaneously. In the interview simulation unit, for example, the generation AI has multiple test takers participate in the simulation at the same time, recreating the format of a group interview. For example, it asks questions that encourage discussion and cooperation among the test takers. In addition, in the group interview simulation, the generation AI assigns roles to each test taker and conducts an interview based on a specific scenario. For example, it can assign roles such as leader and follower and ask questions according to their roles. In addition, the interview simulation unit has the generation AI simulate a group interview and evaluate the communication skills of test takers. For example, it can analyze the degree of exchange of opinions and cooperation with other test takers and provide feedback. This makes it possible to simulate a group interview and support a format in which multiple test takers participate simultaneously.

[0057] The interview simulation unit uses an emotion estimation function to provide a simulation environment that allows the examinee to relax, thereby reducing tension. For example, the interview simulation unit uses the emotion estimation function to analyze the examinee's state of tension in real time and provide a simulation environment that allows the examinee to relax. For example, it displays music or images that have a relaxing effect. The interview simulation unit also monitors the examinee's state of tension using the emotion estimation function, and the generation AI provides questions and feedback to help the examinee relax. For example, it can use light-hearted topics and jokes to reduce tension. The interview simulation unit also uses the generation AI to analyze the examinee's state of tension and customize a simulation environment that has a relaxing effect. For example, it can set background and audio that suits the examinee's preferences. This provides a simulation environment that allows the examinee to relax, thereby reducing tension.

[0058] The feedback section can analyze the examinee's answers in detail and present specific points for improvement in bullet points. For example, the feedback section uses a generation AI to analyze the examinee's answers and present specific points for improvement in bullet points. For example, it provides specific advice such as "Make your self-introduction more specific" or "Adjust your speaking speed." The feedback section also builds a system in which the generation AI analyzes the examinee's answers in detail and presents specific points for improvement in bullet points. For example, it can evaluate the content, structure, and expression of the answer and present specific points for improvement. The feedback section also uses a generation AI to analyze the examinee's answers and present points for improvement in bullet points, making it easier for the examinee to create a specific action plan. For example, it can provide advice such as "Include a specific anecdote in your next interview" or "Make eye contact." This allows the system to analyze the examinee's answers in detail and present specific points for improvement in bullet points.

[0059] The feedback unit can compare the test taker's answers with other success stories and provide feedback based on a benchmark. For example, the generation AI can compare the test taker's answers with other success stories and provide feedback based on a benchmark. For example, it can specifically indicate which parts are excellent and which parts need improvement by comparing them with past success stories. The feedback unit can also build a system in which the generation AI compares the test taker's answers with success stories and provides feedback based on a benchmark. For example, the test taker's answers can be evaluated by referring to the content and expression of the answers in the success stories. The feedback unit can also compare the test taker's answers with other success stories and provide specific feedback based on a benchmark. For example, it can indicate points such as "the success story includes specific anecdotes" and "the speech is clear." This makes it possible to compare the test taker's answers with other success stories and provide feedback based on a benchmark.

[0060] The feedback unit can use the emotion estimation function to analyze the emotional responses of the test taker and make suggestions for improvement based on their emotions. For example, the feedback unit can use the emotion estimation function to analyze the emotional responses of the test taker and make suggestions for improvement based on their emotions. For example, if the test taker is nervous, advice can be provided to relax. The feedback unit can also analyze the emotional responses of the test taker in real time, and the generation AI can make specific suggestions for improvement based on their emotions. For example, if the test taker has strong positive emotions, advice can be given to maintain the current style. The feedback unit can also use the emotion estimation function to analyze the emotional responses of the test taker and build a system that provides feedback based on their emotions. For example, if the test taker has strong negative emotions, it can gently point out areas for improvement. This makes it possible to analyze the emotional responses of the test taker and make suggestions for improvement based on their emotions.

[0061] The feedback unit can provide feedback in video format, showing areas for improvement in a visually easy-to-understand manner. For example, the feedback unit can have the generation AI provide feedback in video format, showing areas for improvement in a visually easy-to-understand manner for test takers. For example, specific examples can be shown in video to visually explain how improvements should be made. The feedback unit can also build a system that provides feedback in video format, allowing test takers to grasp areas for improvement in a visually easy-to-understand manner. For example, video explanations and demonstrations can be provided. The feedback unit can also have the generation AI provide feedback in video format, allowing test takers to visually confirm specific areas for improvement. For example, model answers and improvement examples can be shown in video, allowing test takers to compare them with their own answers. This makes it possible to provide feedback in video format, showing areas for improvement in a visually easy-to-understand manner.

[0062] The feedback unit can provide feedback by incorporating the opinions of multiple experts and make improvement suggestions from multiple perspectives. For example, the generation AI can provide feedback by incorporating the opinions of multiple experts and make improvement suggestions from multiple perspectives. For example, the feedback unit can integrate the opinions of interviewers, career coaches, psychologists, etc. Furthermore, when providing feedback, the feedback unit can analyze the opinions of multiple experts and make specific improvement suggestions from multiple perspectives. For example, the feedback unit can provide feedback based on expert advice. Furthermore, the feedback unit can provide feedback by incorporating the opinions of multiple experts and enable the test taker to understand areas for improvement from multiple perspectives. For example, the feedback unit can provide specific advice based on expert opinions. This allows feedback to be provided by incorporating the opinions of multiple experts and make improvement suggestions from multiple perspectives.

[0063] The feedback unit can use the emotion estimation function to make suggestions at times when the test taker is most receptive to feedback. For example, the feedback unit can use the emotion estimation function to make suggestions at times when the test taker is most receptive to feedback. For example, it can provide feedback when the test taker is relaxed. The feedback unit can also analyze the test taker's emotional state in real time, and the generation AI can make suggestions at times when the test taker is most receptive to feedback. For example, it can point out areas for improvement when positive emotions are strong. The feedback unit can also monitor the test taker's emotional state using the emotion estimation function, and build a system that makes suggestions at times when the test taker is most receptive to feedback. For example, it can suggest specific areas for improvement when the test taker is emotionally stable. This makes it possible to make suggestions at times when the test taker is most receptive to feedback.

[0064] The customization unit can analyze an examinee's past learning history and skill set and provide customized interview preparation based on that. For example, the customization unit uses a generation AI to analyze an examinee's past learning history and skill set and provide customized interview preparation based on that. For example, the customization unit asks questions specialized in a specific field based on the examinee's learning history. The customization unit also analyzes an examinee's skill set and builds a system in which the generation AI provides customized interview preparation based on that data. For example, the customization unit can ask questions that bring out the examinee's strengths. The customization unit can also use a generation AI to analyze an examinee's past learning history and skill set and provide customized interview preparation, allowing the examinee to effectively showcase their strengths. For example, the customization unit can ask specific questions related to a specific skill. This makes it possible to analyze an examinee's past learning history and skill set and provide customized interview preparation based on that.

[0065] The customization department can analyze the examinee's goals and motivations for applying in detail and propose specific interview strategies accordingly. For example, the customization department allows the generation AI to analyze the examinee's goals and motivations for applying in detail and propose specific interview strategies accordingly. For example, the customization department asks questions based on the examinee's motivations for applying and provides specific example answers. The customization department also builds a system in which the generation AI analyzes the examinee's goals and motivations for applying and proposes customized interview strategies based on that data. For example, the customization department can ask questions that draw out specific episodes related to the examinee's motivations for applying. The customization department also allows the generation AI to analyze the examinee's goals and motivations for applying in detail and propose customized interview strategies, allowing the examinee to effectively showcase their goals and motivations for applying. For example, the customization department can ask specific questions related to the examinee's motivations for applying. This allows the generation AI to analyze the examinee's goals and motivations for applying in detail and propose specific interview strategies accordingly.

[0066] The customization unit can use the emotion estimation function to provide flexible interview preparation measures according to the emotional state of the examinee. For example, the customization unit uses the emotion estimation function to analyze the emotional state of the examinee in real time and provide flexible interview preparation measures according to the emotions. For example, if the examinee is nervous, questions are asked to relax them. The customization unit also monitors the emotional state of the examinee and builds a system in which the generation AI provides flexible interview preparation measures based on that data. For example, more difficult questions can be asked when the examinee is emotionally stable. The customization unit also uses the emotion estimation function to provide flexible interview preparation measures according to the examinee's emotional state, allowing the examinee to proceed with interview preparation at their own pace. For example, easier questions can be asked when the examinee is emotionally unstable. This makes it possible to provide flexible interview preparation measures according to the examinee's emotional state.

[0067] The customization department can provide interview preparation specialized for different industries and occupations to meet the diverse needs of test takers. For example, the generation AI can provide interview preparation specialized for different industries and occupations to meet the diverse needs of test takers. For example, the generation AI can ask questions specialized for specific industries such as IT, healthcare, and finance. The customization department can also build a system that provides interview preparation specialized for different occupations, and the generation AI can ask specific questions tailored to the test taker's occupation. For example, questions specialized for occupations such as engineer, designer, and marketer can be asked. The customization department can also provide interview preparation specialized for different industries and occupations, allowing test takers to develop specific preparation tailored to their own industry or occupation. For example, it can run simulations that address industry-specific questions and topics. This makes it possible to provide interview preparation specialized for different industries and occupations to meet the diverse needs of test takers.

[0068] The customization unit can automatically generate a training plan that fits the examinee's schedule, supporting efficient learning. For example, the customization unit uses a generation AI to analyze the examinee's schedule and automatically generate a training plan based on that. For example, the customization unit proposes an interview practice schedule that fits the examinee's free time. The customization unit also builds a system that automatically generates a training plan that fits the examinee's schedule, and the generation AI supports efficient learning. For example, the customization unit can provide a plan for effective practice in a short amount of time. The customization unit also uses a generation AI to analyze the examinee's schedule and automatically generate a training plan, allowing the examinee to study efficiently at their own pace. For example, the customization unit can provide a short practice plan that fits a busy schedule. This allows the automatic generation of a training plan that fits the examinee's schedule, supporting efficient learning.

[0069] The customization unit can use the emotion estimation function to identify the time of day when a test taker can study most effectively and provide interview preparation tailored to that time of day. The customization unit, for example, can use the emotion estimation function to identify the time of day when a test taker can study most effectively and provide interview preparation tailored to that time of day. For example, the test taker can practice when the test taker is relaxed. The customization unit also builds a system that monitors the test taker's emotional state and uses a generation AI to identify the time of day when a test taker can study most effectively. For example, practice can be concentrated on times when emotions are stable. The customization unit can also use the emotion estimation function to identify the time of day when a test taker can study most effectively and provide interview preparation tailored to that time of day, allowing the test taker to study efficiently. For example, practice can be performed when emotions are positive. This allows the test taker to identify the time of day when a test taker can study most effectively and provide interview preparation tailored to that time of day.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The interview guidance system can further include a "motivation enhancement unit." The motivation enhancement unit provides content to enhance the examinee's motivation. For example, it displays examples of successful interviews and inspirational messages. The motivation enhancement unit can also record small goals that the examinee achieves in the process of preparing for the interview and send a message of congratulations on the achievement. Furthermore, the motivation enhancement unit can provide encouraging messages and specific advice in response to any anxieties or questions the examinee may have about preparing for the interview. This increases the examinee's motivation and enables them to prepare for the interview more effectively.

[0072] The interview guidance system can further include a "health management section." The health management section monitors the examinee's health status and provides advice on maintaining optimal physical condition for interview preparation. For example, it records the examinee's sleep patterns and dietary habits and makes suggestions to promote healthy lifestyles. The health management section can also suggest ways for the examinee to relax if they feel stressed. The health management section can also introduce breathing techniques and simple exercises to help the examinee relax before the interview. This allows the examinee to approach the interview in a healthy state.

[0073] The interview guidance system can further include a "networking support section." The networking support section provides support for test takers to strengthen their networking within the industry. For example, it can hold online meetings and webinars with industry professionals, providing test takers with opportunities to ask questions and seek advice directly. The networking support section can also provide test takers with information about industry events and seminars that they should attend and encourage them to participate. The networking support section can also provide test takers with advice and techniques to expand their network within the industry. This allows test takers to strengthen their networking within the industry and increase their selling points in interviews.

[0074] The interview guidance system can further include a "career planning section." The career planning section sets the candidate's long-term career goals and provides interview preparation based on those goals. For example, it proposes specific steps and action plans according to the candidate's career goals. The career planning section can also provide the latest information and trends related to the industry and occupation the candidate is aiming for, helping the candidate to more specifically outline their career plan. Furthermore, the career planning section can provide advice on how to acquire the skills and qualifications necessary to achieve the candidate's career goals. This allows the candidate to develop specific measures to achieve their long-term career goals.

[0075] The interview guidance system can further include a "self-assessment section." The self-assessment section provides tools for test takers to objectively evaluate their own strengths and weaknesses. For example, it provides test takers with a list of questions or checklists for self-assessment, encouraging self-analysis. The self-assessment section can also provide test takers with advice to identify specific areas for improvement or strengthening based on the results of their self-assessment. Furthermore, the self-assessment section can provide test takers with feedback to compare the results of their self-assessment with other success stories and understand where they stand. This allows test takers to objectively evaluate themselves and prepare for the interview more effectively.

[0076] The interview guidance system can also use an "emotion estimation function" to provide reminders based on the examinee's emotional state. For example, if the examinee is nervous, it can send advice and reminders to relax. Also, if the examinee is feeling positive, it can provide encouraging messages to help them maintain those emotions. Furthermore, the emotion estimation function can be used to send reminders at the optimal time for examinees to proceed with interview preparation. For example, sending an interview practice reminder when the examinee is relaxed can encourage effective practice. This allows the system to provide reminders according to the examinee's emotional state, enabling more effective interview preparation.

[0077] The interview guidance system can also use an "emotion estimation function" to provide customized feedback based on the examinee's emotional state. For example, if the examinee is nervous, specific advice on how to relax can be provided. Also, if the examinee is feeling positive, feedback to maintain that emotion can be provided. Furthermore, the emotion estimation function can be used to adjust the timing of feedback according to the examinee's emotional state. For example, by providing feedback when the examinee is relaxed, it can present areas for improvement in an easily accepted manner. This allows the system to provide customized feedback based on the examinee's emotional state, enabling more effective interview preparation.

[0078] The interview coaching system can also use an "emotion estimation function" to adjust the difficulty of the interview simulation based on the examinee's emotional state. For example, if the examinee is nervous, it can start with easy questions and gradually increase the difficulty. On the other hand, if the examinee is relaxed, it can ask more difficult questions. Furthermore, the emotion estimation function can also be used to customize the simulation scenario according to the examinee's emotional state. For example, if the examinee is feeling positive, it can provide a challenging scenario to encourage further growth. This allows the difficulty of the interview simulation to be adjusted based on the examinee's emotional state, providing effective practice.

[0079] The interview coaching system can also use an "emotion estimation function" to manage the progress of interview preparation based on the examinee's emotional state. For example, if the examinee is nervous, the progress can be adjusted to proceed slowly. On the other hand, if the examinee is relaxed, the progress can be sped up. Furthermore, the emotion estimation function can also be used to provide progress management feedback according to the examinee's emotional state. For example, if the examinee is feeling positive, feedback to maintain that emotion can be provided to increase motivation. This makes it possible to manage the progress of interview preparation based on the examinee's emotional state and support effective interview preparation.

[0080] The interview guidance system can also use an "emotion estimation function" to customize interview preparation based on the examinee's emotional state. For example, if the examinee is nervous, it can provide specific advice and simulations to help them relax. Also, if the examinee is feeling positive, it can provide feedback and simulations to help them maintain those emotions. Furthermore, by using the emotion estimation function to customize interview preparation based on the examinee's emotional state, it can also support the examinee in preparing for the interview at their own pace. This allows the system to customize interview preparation based on the examinee's emotional state and support effective interview preparation.

[0081] The processing flow of the second embodiment will be briefly explained below.

[0082] Step 1: The interview simulation unit uses a generation AI to perform an interview simulation. For example, the generation AI performs a simulation based on prompts including questions that the candidate may be asked in an interview and the content of the self-introduction. The generation AI uses text generation AI such as GPT-3 or BERT to analyze the candidate's answers and provide appropriate example answers. The generation AI can also analyze how the candidate should respond to the question "Please introduce yourself" and provide appropriate example answers. Step 2: The feedback section provides individualized feedback based on the simulation results generated by the interview simulation section. For example, after the candidate gives a self-introduction, the generation AI evaluates the content and suggests areas for improvement or strengthening. The generation AI can also analyze the candidate's answers and present specific points for improvement in bullet points. Step 3: The customization unit customizes the interview preparation based on the feedback provided by the feedback unit. For example, the generation AI analyzes the candidate's resume and CV and suggests optimal interview preparation based on their content. The generation AI can also provide interview preparation tailored to specific industries or job types.

[0083] 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.

[0084] 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.

[0085] 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.

[0086] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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).

[0092] 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.

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0102] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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."

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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]

[0150] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A system that uses generation AI to provide individual interview guidance, An interview simulation department that uses generative AI to conduct interview simulations, and a feedback unit that provides individual feedback based on the simulation results generated by the interview simulation unit; a customization unit that customizes interview preparation based on the feedback provided by the feedback unit. A system characterized by:

2. The interview simulation unit Analyze the candidate's past interview history and optimize the simulation based on past failures and success patterns 2. The system of claim 1.

3. The interview simulation unit Analyzes the test taker's tone of voice and speaking habits to simulate more natural conversations 2. The system of claim 1.

4. The interview simulation unit Analyzes test-taker's emotional state in real time and provides emotionally appropriate questions and feedback 2. The system of claim 1.

5. The interview simulation unit Providing interview simulations in different cultures and languages ​​to help prepare for international interviews 2. The system of claim 1.

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