System

Abstract

An object of a system according to an embodiment is to provide a virtual scenario that allows a user to practice a realistic interview.SOLUTION: In one embodiment, a system comprises an interview scenario generator, a multimedia integrator, a feedback learner, and a progress tracker. The interview scenario generator supports multiple interview types. The multimedia integration unit integrates at least one multimedia element of text, voice, and video. The feedback learning unit learns feedback of the user. The progress tracker tracks and analyzes the progress of the user.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 lack the ability to create virtual scenarios and track progress for users to practice realistic interviews, leaving room for improvement.

[0005] The system according to the embodiment aims to provide a virtual scenario in which a user can practice a realistic interview. [Means for solving the problem]

[0006] The system according to the embodiment includes an interview scenario generator, a multimedia integration unit, a feedback learning unit, and a progress tracking unit. The interview scenario generator supports multiple interview types. The multimedia integration unit integrates at least one multimedia element of text, audio, and video. The feedback learning unit learns user feedback. The progress tracking unit tracks and analyzes user progress. [Effects of the Invention]

[0007] The system according to the embodiment can provide a virtual scenario in which a user can practice a realistic interview. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) An interview practice platform according to an embodiment of the present invention is a system that uses generative AI to create virtual interview scenarios and allows users to practice for realistic interviews. This allows users to practice for realistic interviews and improve their interview skills.

[0029] An interview practice platform according to an embodiment includes an interview scenario generation unit, a multimedia integration unit, a feedback learning unit, and a progress tracking unit. The interview scenario generation unit supports multiple interview types. For example, the generation AI generates scenarios corresponding to various interview types, such as technical interviews, behavioral interviews, and case study interviews. The interview scenario generation unit also allows a user to select a desired interview type and practice based on the selected scenario. For example, the generation AI generates an interview scenario based on prompts including the user's desired interview type and details of the scenario. The multimedia integration unit integrates at least one multimedia element from text, audio, and video. For example, the generation AI provides a scenario in which the interviewer's questions are played back audibly and the user answers audibly. Furthermore, in a video interview scenario, a video of the interviewer is displayed and the user can answer via a camera. This allows the user to have a more realistic interview experience. The feedback learning unit learns user feedback. For example, the generation AI learns the user's answers and feedback and incorporates them into the next interview scenario. For example, if a user has difficulty with a particular question, the generation AI learns that information and repeatedly asks similar questions in the next scenario, thereby improving the user's skills. The progress tracking unit tracks and analyzes the user's progress. For example, the generation AI records how the user answered each question and the extent of skill improvement, and provides analysis results. This allows the user to understand their own progress and identify areas for improvement. This allows the interview practice platform according to the embodiment to enable users to practice for realistic interviews and improve their interview skills. For example, in a technical interview scenario, the user practices solving programming problems, and the generation AI evaluates the answers. In a behavioral interview scenario, the user answers questions based on their past experience, and the generation AI analyzes the answers and provides feedback.

[0030] The interview scenario generation unit can analyze a user's past interview history and generate an interview scenario optimized for each individual user. In the interview scenario generation unit, for example, the generation AI analyzes the user's past interview history and understands the answer trends for specific questions. For example, the user's strengths and weaknesses are identified based on the answer history for programming questions in technical interviews. In addition, the interview scenario generation unit generates an interview scenario optimized for each individual user based on the user's past interview history. For example, the answer history in behavioral interviews is analyzed and questions that the user previously found difficult to answer are re-asked. In addition, the interview scenario generation unit allows the generation AI to learn the user's past interview history and reflect this in the next interview scenario. For example, a scenario including points that the user should improve is generated based on the answer history in case study interviews. This makes it possible to provide an interview scenario optimized for the user.

[0031] The interview scenario generation unit can evaluate user answers in real time and provide feedback on the spot. In the interview scenario generation unit, for example, the generation AI analyzes user answers in real time and provides instant feedback. For example, in response to answers to programming questions in a technical interview, the efficiency and accuracy of the code is evaluated. In addition, the interview scenario generation unit evaluates user answers during the interview scenario and points out areas for improvement on the spot. For example, in response to answers in a behavioral interview, it points out a lack of specificity or logic. In addition, the interview scenario generation unit builds a system in which the generation AI evaluates user answers in real time and provides feedback. For example, in response to answers in a case study interview, it evaluates the problem-solving approach and the quality of the proposal. This makes it possible to provide feedback in real time.

[0032] The interview scenario generation unit generates interview scenarios specialized for different industries or occupations, allowing users to practice interviews for specific industries or occupations. For example, the interview scenario generation unit generates interview scenarios specialized for different industries or occupations using a generation AI, allowing users to practice interviews for specific industries or occupations. For example, it provides a technical interview scenario for the IT industry. The interview scenario generation unit also generates interview scenarios specialized for different industries or occupations, allowing users to practice interviews for their desired industry or occupation. For example, it provides a behavioral interview scenario for a marketing position. The interview scenario generation unit also builds a system in which the generation AI generates interview scenarios specialized for different industries or occupations, allowing users to practice interviews for specific industries or occupations. For example, it provides a case study interview scenario for the financial industry. This makes it possible to provide interview practice for specific industries or occupations.

[0033] The multimedia integration unit can analyze the user's voice data and provide feedback on areas for improvement in pronunciation or intonation. For example, the multimedia integration unit uses a generation AI to analyze the user's voice data and point out areas for improvement in pronunciation or intonation. For example, if the pronunciation of a particular word is unclear, the unit can suggest the correct pronunciation method. The multimedia integration unit also builds a system in which the generation AI uses the user's voice data to provide feedback on areas for improvement in pronunciation and intonation. For example, if the intonation is flat, the unit can provide advice on adding intonation. The multimedia integration unit also uses a generation AI to analyze the user's voice data in real time and provide feedback on areas for improvement in pronunciation and intonation. For example, the unit can evaluate the clarity of pronunciation and the appropriateness of intonation in response to answers during an interview. This allows the unit to provide feedback on areas for improvement in the user's pronunciation and intonation.

[0034] The multimedia integration unit can analyze a user's video data and point out areas for improvement in gaze or posture. In the multimedia integration unit, for example, the generation AI analyzes the user's video data and points out areas for improvement in gaze or posture. For example, if the gaze is unstable during an interview, the importance of eye contact can be explained. The multimedia integration unit also builds a system in which the generation AI uses the user's video data to provide feedback on areas for improvement in gaze or posture. For example, if posture is poor, advice on correcting posture is provided. The multimedia integration unit also analyzes the user's video data in real time and provides feedback on areas for improvement in gaze or posture. For example, the appropriateness of gaze movements and posture during an interview can be evaluated and areas for improvement can be pointed out. This makes it possible to point out areas for improvement in the user's gaze and posture.

[0035] The multimedia integration unit can subtitle the user's answers in real time and provide visual feedback. The multimedia integration unit, for example, builds a system in which a generation AI subtitles the user's answers in real time and provides visual feedback. For example, the multimedia integration unit converts the user's voice answers into text and displays it on a screen. The multimedia integration unit also subtitles the user's answers during an interview scenario and provides visual feedback. For example, the content of the user's answers is displayed in real time, making them easier to check. The multimedia integration unit also subtitles the user's answers in real time and provides visual feedback. For example, answers during an interview are converted into text, allowing the user to review their answers. This makes it possible to subtitle the user's answers in real time and provide visual feedback.

[0036] The multimedia integration unit generates an interactive interview scenario using multimedia elements, and the user can progress by selecting options within the scenario. For example, the multimedia integration unit constructs a system in which a generation AI generates an interactive interview scenario using multimedia elements, and the user progresses by selecting options within the scenario. For example, it provides a scenario in which the next question changes depending on the option selected by the user. The multimedia integration unit also generates an interactive interview scenario, and the scenario progresses by the user selecting options within the scenario. For example, it provides a scenario in which the interviewer's response changes depending on the option selected by the user. The multimedia integration unit also generates an interactive interview scenario using multimedia elements, and the scenario progresses by the user selecting options within the scenario. For example, it provides a system in which the next question or the development of the scenario changes based on the option selected by the user. This makes it possible to provide an interactive interview scenario.

[0037] The feedback learning unit can analyze user feedback and propose a study plan optimized for each individual user. In the feedback learning unit, for example, the generation AI analyzes user feedback and proposes a study plan optimized for each individual user. For example, it provides a plan that focuses on practicing questions that the user is weak at. The feedback learning unit also builds a system in which the generation AI proposes an optimal study plan based on user feedback. For example, it analyzes the user's answer history and proposes specific practice content to improve skills. The feedback learning unit also allows the generation AI to learn the user's feedback and reflect it in the next interview scenario. For example, if the user is weak at a particular question, it provides a plan that repeatedly asks that question. This makes it possible to provide a study plan optimized for the user.

[0038] The feedback learning unit can adjust the difficulty of the interview scenario based on user feedback and provide practice that suits the user's skill level. For example, the generation AI in the feedback learning unit analyzes the user's feedback and adjusts the difficulty of the interview scenario. For example, questions that the user finds too easy are changed to more difficult questions. The feedback learning unit also builds a system in which the generation AI adjusts the difficulty of the interview scenario based on user feedback. For example, the difficulty of questions is dynamically changed according to the user's skill level. The feedback learning unit also allows the generation AI to learn the user's feedback and adjust the difficulty of the next interview scenario. For example, questions that the user finds difficult are repeatedly asked to help improve skills. This makes it possible to provide practice that suits the user's skill level.

[0039] The feedback learning unit can perform comparative analysis with other users based on the user's feedback and evaluate the relative skill level. In the feedback learning unit, for example, the generation AI analyzes the user's feedback and performs comparative analysis with other users. For example, the quality of answers to the same question is compared and the relative skill level is evaluated. The feedback learning unit also builds a system in which the generation AI performs comparative analysis with other users based on the user's feedback. For example, the user's answer history is compared with other users and the skill level is evaluated. In addition, the feedback learning unit allows the generation AI to learn the user's feedback and perform comparative analysis with other users. For example, based on the content of the user's answer, the user's answer is compared with other users' answers to the same question and the relative skill level is evaluated. This makes it possible to evaluate the user's relative skill level.

[0040] The feedback learning unit can automatically generate new interview scenarios based on user feedback and provide practice that meets the user's needs. In the feedback learning unit, for example, the generation AI analyzes the user's feedback and automatically generates new interview scenarios. For example, it generates a scenario that includes questions that the user finds difficult and provides practice. The feedback learning unit also builds a system in which the generation AI automatically generates new interview scenarios based on user feedback. For example, it analyzes the user's answer history and generates scenarios for skill improvement. The feedback learning unit also learns the user's feedback and automatically generates new interview scenarios. For example, if the user has difficulty with a particular question, it generates a scenario that repeatedly asks that question. This makes it possible to provide practice that meets the user's needs.

[0041] The progress tracking unit can track the user's progress in detail and analyze patterns of skill improvement. In the progress tracking unit, for example, the generation AI tracks the user's progress in detail and analyzes patterns of skill improvement. For example, it records how the user answered which questions and identifies trends in skill improvement. The progress tracking unit also builds a system in which the generation AI analyzes patterns of skill improvement based on the user's progress. For example, it analyzes the user's answer history and provides specific advice for skill improvement. The progress tracking unit also tracks the user's progress and analyzes patterns of skill improvement. For example, it records how much skill improvement the user has seen in response to specific questions and provides the analysis results. This makes it possible to analyze patterns of user skill improvement.

[0042] The progress tracking unit can suggest the next practice step based on the user's progress and support effective learning. In the progress tracking unit, for example, the generation AI analyzes the user's progress and suggests the next practice step. For example, it suggests steps to focus on practicing for questions that the user has difficulty with. The progress tracking unit also builds a system in which the generation AI suggests the next practice step based on the user's progress. For example, it analyzes the user's answer history and suggests specific practice content to improve skills. The progress tracking unit also tracks the user's progress and suggests the next practice step. For example, if the user has difficulty with a particular question, it suggests a step to repeatedly ask that question. This can support the user's effective learning.

[0043] The progress tracking unit can compare the user's progress with other users and evaluate the relative skill level. In the progress tracking unit, for example, the generation AI analyzes the user's progress and performs a comparative analysis with other users. For example, the quality of answers to the same question is compared to evaluate the relative skill level. The progress tracking unit also builds a system in which the generation AI performs a comparative analysis with other users based on the user's progress. For example, the user's answer history is compared with other users to evaluate the skill level. The progress tracking unit also tracks the user's progress and performs a comparative analysis with other users. For example, based on the content of the user's answers, the user's answers are compared with other users' answers to the same question to evaluate the relative skill level. This makes it possible to evaluate the user's relative skill level.

[0044] The progress tracking unit can suggest new interview scenarios based on the user's progress and promote the improvement of the user's skills. In the progress tracking unit, for example, the generation AI analyzes the user's progress and proposes new interview scenarios. For example, it generates scenarios including questions that the user finds difficult and provides practice. The progress tracking unit also builds a system in which the generation AI proposes new interview scenarios based on the user's progress. For example, it analyzes the user's answer history and proposes specific scenarios for skill improvement. The progress tracking unit also tracks the user's progress and proposes new interview scenarios. For example, if the user finds a particular question difficult, it proposes a scenario in which that question is repeatedly asked. This can promote the improvement of the user's skills.

[0045] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0046] The interview practice platform may further include a non-verbal evaluation unit that evaluates the user's non-verbal communication skills. For example, the non-verbal evaluation unit may analyze the user's facial expressions and gestures and provide appropriate non-verbal communication advice. For example, the non-verbal evaluation unit may evaluate the frequency and timing at which the user smiles during the interview and provide guidance on how to use smiles appropriately. The non-verbal evaluation unit may also evaluate the naturalness and consistency of the user's gestures and provide advice on reducing excessive or unnatural movements. Furthermore, the non-verbal evaluation unit may analyze the user's posture and eye movement and provide guidance on the importance of eye contact with the interviewer and appropriate posture. This allows the user to improve their non-verbal communication skills.

[0047] The interview practice platform may further include a cultural adaptation unit that generates an interview scenario that corresponds to the user's cultural background. For example, the cultural adaptation unit provides an appropriate interview scenario taking into account the user's country of origin and cultural background. For example, the cultural adaptation unit may provide questions based on business etiquette and communication styles in different cultural spheres. The cultural adaptation unit may also generate a scenario to help the user prepare for a cross-cultural interview. For example, the cultural adaptation unit may provide a scenario that includes differences in cross-cultural communication and points to note. Furthermore, the cultural adaptation unit may provide feedback that corresponds to the user's cultural background and instruct the user on appropriate ways to respond in a cross-cultural interview. This allows the user to prepare for a cross-cultural interview.

[0048] The interview practice platform may further include a health management unit that generates an interview scenario that takes the user's health condition into consideration. For example, the health management unit provides an appropriate interview scenario based on the user's health condition and physical condition. For example, if the user is tired, it may present questions that allow the user to concentrate for a short period of time. The health management unit may also monitor the user's health condition and suggest appropriate breaks and relaxation methods. For example, it may provide advice on stretching or deep breathing to relax after a long period of interview practice. Furthermore, the health management unit may provide feedback according to the user's health condition and advice on how to conduct interview practice while maintaining good health. This allows the user to conduct effective interview practice while taking their health into consideration.

[0049] The interview practice platform may further include a career planning unit that generates interview scenarios according to the user's career goals. For example, the career planning unit provides appropriate interview scenarios based on the user's career goals and desired job type. For example, if the user is aiming for a managerial position, the career planning unit may present questions related to leadership and management. The career planning unit may also provide feedback according to the user's career goals and advice on how to achieve those goals. For example, the career planning unit may suggest a practice plan for the user to improve specific skills. Furthermore, the career planning unit may provide a long-term interview practice plan based on the user's career goals. This allows the user to practice for interviews according to their career goals.

[0050] The interview practice platform can further include a learning style adaptation unit that generates an interview scenario according to the user's learning style. For example, the learning style adaptation unit provides an appropriate interview scenario based on the user's learning style and preferences. For example, questions using diagrams and graphs are presented to a visual learner. Also, audio questions are presented to an auditory learner. Furthermore, the learning style adaptation unit provides feedback according to the user's learning style and suggests effective study methods. For example, if the user is a visual learner, feedback using visual aids is provided. The progress of the interview scenario can also be adjusted based on the user's learning style. This makes it possible to provide interview practice according to the user's learning style.

[0051] The interview practice platform can further customize the content of the interview scenario based on the user's progress. For example, the progress tracking unit analyzes the user's progress and provides an appropriate interview scenario. For example, if the user has difficulty with a particular question, that question can be repeatedly asked. The difficulty of the interview scenario can also be adjusted according to the user's progress. For example, if the user shows improvement in skills, more difficult questions can be asked. The progress of the interview scenario can also be adjusted based on the user's progress. For example, a scenario can be provided that helps the user improve a particular skill. In this way, interview scenarios can be provided that correspond to the user's progress.

[0052] The processing flow of the first embodiment will be briefly explained below.

[0053] Step 1: The interview scenario generator supports multiple interview types. For example, the generation AI generates scenarios for various interview types, such as technical interviews, behavioral interviews, and case study interviews. Users can also select their desired interview type and practice based on that scenario. The generation AI generates an interview scenario based on prompts containing details of the user's desired interview type and scenario. Step 2: The multimedia integration unit integrates at least one multimedia element from text, audio, and video. For example, the generation AI provides a scenario in which the interviewer's questions are played back aloud and the user answers aloud. In addition, in a video interview scenario, a video of the interviewer is displayed and the user can answer through a camera. This allows the user to have a more realistic interview experience. Step 3: The feedback learning unit learns the user's feedback. For example, the generation AI learns the user's answers and feedback and reflects them in the next interview scenario. If the user has difficulty answering a particular question, the generation AI will learn that information and repeatedly ask similar questions in the next scenario, thereby improving the user's skills. Step 4: The progress tracking section tracks and analyzes the user's progress. For example, the generation AI records which questions the user answered, how they answered them, and how much their skills improved, and provides the analysis results. This allows the user to understand their own progress and identify areas for improvement.

[0054] (Example 2) An interview practice platform according to an embodiment of the present invention is a system that uses generative AI to create virtual interview scenarios and allows users to practice for realistic interviews. This allows users to practice for realistic interviews and improve their interview skills.

[0055] An interview practice platform according to an embodiment includes an interview scenario generation unit, a multimedia integration unit, a feedback learning unit, and a progress tracking unit. The interview scenario generation unit supports multiple interview types. For example, the generation AI generates scenarios corresponding to various interview types, such as technical interviews, behavioral interviews, and case study interviews. The interview scenario generation unit also allows a user to select a desired interview type and practice based on the selected scenario. For example, the generation AI generates an interview scenario based on prompts including the user's desired interview type and details of the scenario. The multimedia integration unit integrates at least one multimedia element from text, audio, and video. For example, the generation AI provides a scenario in which the interviewer's questions are played back audibly and the user answers audibly. Furthermore, in a video interview scenario, a video of the interviewer is displayed and the user can answer via a camera. This allows the user to have a more realistic interview experience. The feedback learning unit learns user feedback. For example, the generation AI learns the user's answers and feedback and incorporates them into the next interview scenario. For example, if a user has difficulty with a particular question, the generation AI learns that information and repeatedly asks similar questions in the next scenario, thereby improving the user's skills. The progress tracking unit tracks and analyzes the user's progress. For example, the generation AI records how the user answered each question and the extent of skill improvement, and provides analysis results. This allows the user to understand their own progress and identify areas for improvement. This allows the interview practice platform according to the embodiment to enable users to practice for realistic interviews and improve their interview skills. For example, in a technical interview scenario, the user practices solving programming problems, and the generation AI evaluates the answers. In a behavioral interview scenario, the user answers questions based on their past experience, and the generation AI analyzes the answers and provides feedback.

[0056] The interview scenario generation unit can analyze a user's past interview history and generate an interview scenario optimized for each individual user. In the interview scenario generation unit, for example, the generation AI analyzes the user's past interview history and understands the answer trends for specific questions. For example, the user's strengths and weaknesses are identified based on the answer history for programming questions in technical interviews. In addition, the interview scenario generation unit generates an interview scenario optimized for each individual user based on the user's past interview history. For example, the answer history in behavioral interviews is analyzed and questions that the user previously found difficult to answer are re-asked. In addition, the interview scenario generation unit allows the generation AI to learn the user's past interview history and reflect this in the next interview scenario. For example, a scenario including points that the user should improve is generated based on the answer history in case study interviews. This makes it possible to provide an interview scenario optimized for the user.

[0057] The interview scenario generation unit can evaluate user answers in real time and provide feedback on the spot. In the interview scenario generation unit, for example, the generation AI analyzes user answers in real time and provides instant feedback. For example, in response to answers to programming questions in a technical interview, the efficiency and accuracy of the code is evaluated. In addition, the interview scenario generation unit evaluates user answers during the interview scenario and points out areas for improvement on the spot. For example, in response to answers in a behavioral interview, it points out a lack of specificity or logic. In addition, the interview scenario generation unit builds a system in which the generation AI evaluates user answers in real time and provides feedback. For example, in response to answers in a case study interview, it evaluates the problem-solving approach and the quality of the proposal. This makes it possible to provide feedback in real time.

[0058] The interview scenario generation unit uses the emotion estimation function to generate an interview scenario according to the user's emotional state and adjust the stress level. The interview scenario generation unit, for example, uses the emotion estimation function to analyze the user's emotional state in real time and generate an interview scenario according to the stress level. For example, if the user is nervous, it asks questions that will help the user relax. The interview scenario generation unit also provides an interview scenario in which the generation AI adjusts the stress level based on the user's emotional state. For example, if the user is relaxed, it asks more difficult questions. The interview scenario generation unit also uses the emotion estimation function to generate an interview scenario according to the user's emotional state and builds a system that adjusts the stress level. For example, if the user is feeling stressed, it provides a scenario that includes a lot of positive feedback. This makes it possible to provide an interview scenario according to the user's emotional state.

[0059] The interview scenario generation unit generates interview scenarios specialized for different industries or occupations, allowing users to practice interviews for specific industries or occupations. For example, the interview scenario generation unit generates interview scenarios specialized for different industries or occupations using a generation AI, allowing users to practice interviews for specific industries or occupations. For example, it provides a technical interview scenario for the IT industry. The interview scenario generation unit also generates interview scenarios specialized for different industries or occupations, allowing users to practice interviews for their desired industry or occupation. For example, it provides a behavioral interview scenario for a marketing position. The interview scenario generation unit also builds a system in which the generation AI generates interview scenarios specialized for different industries or occupations, allowing users to practice interviews for specific industries or occupations. For example, it provides a case study interview scenario for the financial industry. This makes it possible to provide interview practice for specific industries or occupations.

[0060] The multimedia integration unit can analyze the user's voice data and provide feedback on areas for improvement in pronunciation or intonation. For example, the multimedia integration unit uses a generation AI to analyze the user's voice data and point out areas for improvement in pronunciation or intonation. For example, if the pronunciation of a particular word is unclear, the unit can suggest the correct pronunciation method. The multimedia integration unit also builds a system in which the generation AI uses the user's voice data to provide feedback on areas for improvement in pronunciation and intonation. For example, if the intonation is flat, the unit can provide advice on adding intonation. The multimedia integration unit also uses a generation AI to analyze the user's voice data in real time and provide feedback on areas for improvement in pronunciation and intonation. For example, the unit can evaluate the clarity of pronunciation and the appropriateness of intonation in response to answers during an interview. This allows the unit to provide feedback on areas for improvement in the user's pronunciation and intonation.

[0061] The multimedia integration unit can analyze a user's video data and point out areas for improvement in gaze or posture. In the multimedia integration unit, for example, the generation AI analyzes the user's video data and points out areas for improvement in gaze or posture. For example, if the gaze is unstable during an interview, the importance of eye contact can be explained. The multimedia integration unit also builds a system in which the generation AI uses the user's video data to provide feedback on areas for improvement in gaze or posture. For example, if posture is poor, advice on correcting posture is provided. The multimedia integration unit also analyzes the user's video data in real time and provides feedback on areas for improvement in gaze or posture. For example, the appropriateness of gaze movements and posture during an interview can be evaluated and areas for improvement can be pointed out. This makes it possible to point out areas for improvement in the user's gaze and posture.

[0062] The multimedia integration unit can use the emotion estimation function to estimate emotions from the user's voice or video and provide feedback according to the emotions. For example, the multimedia integration unit uses the emotion estimation function to analyze emotions from the user's voice or video in real time and provide feedback according to the emotions. For example, if the user is nervous, advice to relax is provided. The multimedia integration unit also builds a system in which a generation AI estimates emotions based on the user's voice and video data and provides feedback according to the emotions. For example, if the user is confident, positive feedback is provided. The multimedia integration unit also uses the emotion estimation function to estimate emotions from the user's voice or video and provide feedback according to the emotions. For example, if the user is feeling stressed, advice to reduce stress is provided. This makes it possible to provide feedback according to the user's emotions.

[0063] The multimedia integration unit can subtitle the user's answers in real time and provide visual feedback. The multimedia integration unit, for example, builds a system in which a generation AI subtitles the user's answers in real time and provides visual feedback. For example, the multimedia integration unit converts the user's voice answers into text and displays it on a screen. The multimedia integration unit also subtitles the user's answers during an interview scenario and provides visual feedback. For example, the content of the user's answers is displayed in real time, making them easier to check. The multimedia integration unit also subtitles the user's answers in real time and provides visual feedback. For example, answers during an interview are converted into text, allowing the user to review their answers. This makes it possible to subtitle the user's answers in real time and provide visual feedback.

[0064] The multimedia integration unit generates an interactive interview scenario using multimedia elements, and the user can progress by selecting options within the scenario. For example, the multimedia integration unit constructs a system in which a generation AI generates an interactive interview scenario using multimedia elements, and the user progresses by selecting options within the scenario. For example, it provides a scenario in which the next question changes depending on the option selected by the user. The multimedia integration unit also generates an interactive interview scenario, and the scenario progresses by the user selecting options within the scenario. For example, it provides a scenario in which the interviewer's response changes depending on the option selected by the user. The multimedia integration unit also generates an interactive interview scenario using multimedia elements, and the scenario progresses by the user selecting options within the scenario. For example, it provides a system in which the next question or the development of the scenario changes based on the option selected by the user. This makes it possible to provide an interactive interview scenario.

[0065] The multimedia integration unit can use the emotion estimation function to monitor emotions from the user's voice or video in real time and adjust the progress of the scenario according to the emotions. For example, the multimedia integration unit uses the emotion estimation function to build a system that monitors emotions from the user's voice or video in real time and adjusts the progress of the scenario according to the emotions. For example, if the user is nervous, it will ask questions that will help the user relax. The multimedia integration unit also uses the generation AI to adjust the progress of the scenario based on the user's emotional state. For example, if the user is confident, it will ask more difficult questions. The multimedia integration unit also uses the emotion estimation function to monitor emotions from the user's voice or video in real time and adjust the progress of the scenario according to the emotions. For example, if the user is feeling stressed, it will provide a scenario that includes a lot of positive feedback. This makes it possible to adjust the progress of the scenario according to the user's emotions.

[0066] The feedback learning unit can analyze user feedback and propose a study plan optimized for each individual user. In the feedback learning unit, for example, the generation AI analyzes user feedback and proposes a study plan optimized for each individual user. For example, it provides a plan that focuses on practicing questions that the user is weak at. The feedback learning unit also builds a system in which the generation AI proposes an optimal study plan based on user feedback. For example, it analyzes the user's answer history and proposes specific practice content to improve skills. The feedback learning unit also allows the generation AI to learn the user's feedback and reflect it in the next interview scenario. For example, if the user is weak at a particular question, it provides a plan that repeatedly asks that question. This makes it possible to provide a study plan optimized for the user.

[0067] The feedback learning unit can adjust the difficulty of the interview scenario based on user feedback and provide practice that suits the user's skill level. For example, the generation AI in the feedback learning unit analyzes the user's feedback and adjusts the difficulty of the interview scenario. For example, questions that the user finds too easy are changed to more difficult questions. The feedback learning unit also builds a system in which the generation AI adjusts the difficulty of the interview scenario based on user feedback. For example, the difficulty of questions is dynamically changed according to the user's skill level. The feedback learning unit also allows the generation AI to learn the user's feedback and adjust the difficulty of the next interview scenario. For example, questions that the user finds difficult are repeatedly asked to help improve skills. This makes it possible to provide practice that suits the user's skill level.

[0068] The feedback learning unit can use the emotion estimation function to infer emotions from user feedback and provide feedback according to the emotions. The feedback learning unit, for example, uses the emotion estimation function to analyze emotions from user feedback in real time and provide feedback according to the emotions. For example, if the user is feeling anxious, an encouraging message is provided. The feedback learning unit also builds a system in which a generation AI infers emotions based on user feedback and provides feedback according to the emotions. For example, if the user is confident, positive feedback is provided. The feedback learning unit also uses the emotion estimation function to infer emotions from user feedback and provide feedback according to the emotions. For example, if the user is feeling stressed, advice to reduce stress is provided. This makes it possible to provide feedback according to the user's emotions.

[0069] The feedback learning unit can perform comparative analysis with other users based on the user's feedback and evaluate the relative skill level. In the feedback learning unit, for example, the generation AI analyzes the user's feedback and performs comparative analysis with other users. For example, the quality of answers to the same question is compared and the relative skill level is evaluated. The feedback learning unit also builds a system in which the generation AI performs comparative analysis with other users based on the user's feedback. For example, the user's answer history is compared with other users and the skill level is evaluated. In addition, the feedback learning unit allows the generation AI to learn the user's feedback and perform comparative analysis with other users. For example, based on the content of the user's answer, the user's answer is compared with other users' answers to the same question and the relative skill level is evaluated. This makes it possible to evaluate the user's relative skill level.

[0070] The feedback learning unit can automatically generate new interview scenarios based on user feedback and provide practice that meets the user's needs. In the feedback learning unit, for example, the generation AI analyzes the user's feedback and automatically generates new interview scenarios. For example, it generates a scenario that includes questions that the user finds difficult and provides practice. The feedback learning unit also builds a system in which the generation AI automatically generates new interview scenarios based on user feedback. For example, it analyzes the user's answer history and generates scenarios for skill improvement. The feedback learning unit also learns the user's feedback and automatically generates new interview scenarios. For example, if the user has difficulty with a particular question, it generates a scenario that repeatedly asks that question. This makes it possible to provide practice that meets the user's needs.

[0071] The feedback learning unit can use the emotion estimation function to analyze emotions from user feedback and provide learning advice according to the emotions. The feedback learning unit, for example, uses the emotion estimation function to analyze emotions from user feedback in real time and provide learning advice according to the emotions. For example, if the user is feeling anxious, an encouraging message is provided. The feedback learning unit also builds a system in which a generation AI analyzes emotions based on user feedback and provides learning advice according to the emotions. For example, if the user is confident, positive feedback is provided. The feedback learning unit also uses the emotion estimation function to analyze emotions from user feedback and provide learning advice according to the emotions. For example, if the user is feeling stressed, advice to reduce stress is provided. This makes it possible to provide learning advice according to the user's emotions.

[0072] The progress tracking unit can track the user's progress in detail and analyze patterns of skill improvement. In the progress tracking unit, for example, the generation AI tracks the user's progress in detail and analyzes patterns of skill improvement. For example, it records how the user answered which questions and identifies trends in skill improvement. The progress tracking unit also builds a system in which the generation AI analyzes patterns of skill improvement based on the user's progress. For example, it analyzes the user's answer history and provides specific advice for skill improvement. The progress tracking unit also tracks the user's progress and analyzes patterns of skill improvement. For example, it records how much skill improvement the user has seen in response to specific questions and provides the analysis results. This makes it possible to analyze patterns of user skill improvement.

[0073] The progress tracking unit can suggest the next practice step based on the user's progress and support effective learning. In the progress tracking unit, for example, the generation AI analyzes the user's progress and suggests the next practice step. For example, it suggests steps to focus on practicing for questions that the user has difficulty with. The progress tracking unit also builds a system in which the generation AI suggests the next practice step based on the user's progress. For example, it analyzes the user's answer history and suggests specific practice content to improve skills. The progress tracking unit also tracks the user's progress and suggests the next practice step. For example, if the user has difficulty with a particular question, it suggests a step to repeatedly ask that question. This can support the user's effective learning.

[0074] The progress tracking unit can use the emotion estimation function to analyze changes in emotions from the user's progress and provide feedback according to the emotions. The progress tracking unit, for example, uses the emotion estimation function to analyze changes in emotions from the user's progress in real time and provide feedback according to the emotions. For example, if the user is feeling anxious, an encouraging message is provided. The progress tracking unit also builds a system in which a generation AI analyzes changes in emotions based on the user's progress and provides feedback according to the emotions. For example, if the user is confident, positive feedback is provided. The progress tracking unit also uses the emotion estimation function to analyze changes in emotions from the user's progress and provide feedback according to the emotions. For example, if the user is feeling stressed, advice on how to reduce stress is provided. This makes it possible to provide feedback according to the user's emotions.

[0075] The progress tracking unit can compare the user's progress with other users and evaluate the relative skill level. In the progress tracking unit, for example, the generation AI analyzes the user's progress and performs a comparative analysis with other users. For example, the quality of answers to the same question is compared to evaluate the relative skill level. The progress tracking unit also builds a system in which the generation AI performs a comparative analysis with other users based on the user's progress. For example, the user's answer history is compared with other users to evaluate the skill level. The progress tracking unit also tracks the user's progress and performs a comparative analysis with other users. For example, based on the content of the user's answers, the user's answers are compared with other users' answers to the same question to evaluate the relative skill level. This makes it possible to evaluate the user's relative skill level.

[0076] The progress tracking unit can suggest new interview scenarios based on the user's progress and promote the improvement of the user's skills. In the progress tracking unit, for example, the generation AI analyzes the user's progress and proposes new interview scenarios. For example, it generates scenarios including questions that the user finds difficult and provides practice. The progress tracking unit also builds a system in which the generation AI proposes new interview scenarios based on the user's progress. For example, it analyzes the user's answer history and proposes specific scenarios for skill improvement. The progress tracking unit also tracks the user's progress and proposes new interview scenarios. For example, if the user finds a particular question difficult, it proposes a scenario in which that question is repeatedly asked. This can promote the improvement of the user's skills.

[0077] The progress tracking unit can use the emotion estimation function to monitor changes in emotions from the user's progress in real time and adjust the progress of the scenario according to the emotions. The progress tracking unit, for example, uses the emotion estimation function to monitor changes in emotions from the user's progress in real time and build a system to adjust the progress of the scenario according to the emotions. For example, if the user is nervous, questions that will help the user relax are presented. Furthermore, the progress tracking unit allows the generation AI to adjust the progress of the scenario based on the user's emotional state. For example, if the user is confident, more difficult questions are presented. Furthermore, the progress tracking unit can use the emotion estimation function to monitor changes in emotions from the user's progress in real time and adjust the progress of the scenario according to the emotions. For example, if the user is feeling stressed, a scenario containing a lot of positive feedback is presented. This makes it possible to adjust the progress of the scenario according to the user's emotions.

[0078] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0079] The interview practice platform may further include a non-verbal evaluation unit that evaluates the user's non-verbal communication skills. For example, the non-verbal evaluation unit may analyze the user's facial expressions and gestures and provide appropriate non-verbal communication advice. For example, the non-verbal evaluation unit may evaluate the frequency and timing at which the user smiles during the interview and provide guidance on how to use smiles appropriately. The non-verbal evaluation unit may also evaluate the naturalness and consistency of the user's gestures and provide advice on reducing excessive or unnatural movements. Furthermore, the non-verbal evaluation unit may analyze the user's posture and eye movement and provide guidance on the importance of eye contact with the interviewer and appropriate posture. This allows the user to improve their non-verbal communication skills.

[0080] The interview practice platform may further include a cultural adaptation unit that generates an interview scenario that corresponds to the user's cultural background. For example, the cultural adaptation unit provides an appropriate interview scenario taking into account the user's country of origin and cultural background. For example, the cultural adaptation unit may provide questions based on business etiquette and communication styles in different cultural spheres. The cultural adaptation unit may also generate a scenario to help the user prepare for a cross-cultural interview. For example, the cultural adaptation unit may provide a scenario that includes differences in cross-cultural communication and points to note. Furthermore, the cultural adaptation unit may provide feedback that corresponds to the user's cultural background and instruct the user on appropriate ways to respond in a cross-cultural interview. This allows the user to prepare for a cross-cultural interview.

[0081] The interview practice platform may further include a health management unit that generates an interview scenario that takes the user's health condition into consideration. For example, the health management unit provides an appropriate interview scenario based on the user's health condition and physical condition. For example, if the user is tired, it may present questions that allow the user to concentrate for a short period of time. The health management unit may also monitor the user's health condition and suggest appropriate breaks and relaxation methods. For example, it may provide advice on stretching or deep breathing to relax after a long period of interview practice. Furthermore, the health management unit may provide feedback according to the user's health condition and advice on how to conduct interview practice while maintaining good health. This allows the user to conduct effective interview practice while taking their health into consideration.

[0082] The interview practice platform can further adjust the difficulty of the interview scenario based on the user's emotional state. For example, by using the emotion estimation function, if the user is nervous, questions that will help the user relax are presented. On the other hand, if the user is confident, more difficult questions are presented. Furthermore, the progress of the interview scenario can be adjusted according to the user's emotional state. For example, if the user is feeling stressed, a scenario containing a lot of positive feedback is provided. The content of the interview scenario can also be changed based on the user's emotional state. For example, if the user is relaxed, a more challenging scenario is provided. In this way, an interview scenario that suits the user's emotional state can be provided.

[0083] The interview practice platform may further include a career planning unit that generates interview scenarios according to the user's career goals. For example, the career planning unit provides appropriate interview scenarios based on the user's career goals and desired job type. For example, if the user is aiming for a managerial position, the career planning unit may present questions related to leadership and management. The career planning unit may also provide feedback according to the user's career goals and advice on how to achieve those goals. For example, the career planning unit may suggest a practice plan for the user to improve specific skills. Furthermore, the career planning unit may provide a long-term interview practice plan based on the user's career goals. This allows the user to practice for interviews according to their career goals.

[0084] The interview practice platform can further adjust the content of the feedback based on the user's emotional state. For example, by using the emotion estimation function, if the user is feeling anxious, an encouraging message is provided. If the user is feeling confident, positive feedback is provided. Furthermore, the tone and content of the feedback can be changed according to the user's emotional state. For example, if the user is feeling stressed, advice on how to reduce stress is provided. The frequency and timing of feedback can also be adjusted based on the user's emotional state. For example, if the user is relaxed, detailed feedback is provided. In this way, feedback can be provided according to the user's emotional state.

[0085] The interview practice platform can further include a learning style adaptation unit that generates an interview scenario according to the user's learning style. For example, the learning style adaptation unit provides an appropriate interview scenario based on the user's learning style and preferences. For example, questions using diagrams and graphs are presented to a visual learner. Also, audio questions are presented to an auditory learner. Furthermore, the learning style adaptation unit provides feedback according to the user's learning style and suggests effective study methods. For example, if the user is a visual learner, feedback using visual aids is provided. The progress of the interview scenario can also be adjusted based on the user's learning style. This makes it possible to provide interview practice according to the user's learning style.

[0086] The interview practice platform can further adjust the interviewer's responses based on the user's emotional state. For example, by using the emotion estimation function, if the user is nervous, the interviewer's responses can be made gentler. Conversely, if the user is confident, the interviewer's responses can be made harsher. Furthermore, the tone and content of the interviewer's questions can be changed according to the user's emotional state. For example, if the user is feeling stressed, the interviewer's questions can be changed to more relaxing questions. The content of the interviewer's feedback can also be adjusted based on the user's emotional state. For example, if the user is relaxed, detailed feedback can be provided. This allows the interviewer's responses to be provided according to the user's emotional state.

[0087] The interview practice platform can further customize the content of the interview scenario based on the user's progress. For example, the progress tracking unit analyzes the user's progress and provides an appropriate interview scenario. For example, if the user has difficulty with a particular question, that question can be repeatedly asked. The difficulty of the interview scenario can also be adjusted according to the user's progress. For example, if the user shows improvement in skills, more difficult questions can be asked. The progress of the interview scenario can also be adjusted based on the user's progress. For example, a scenario can be provided that helps the user improve a particular skill. In this way, interview scenarios can be provided that correspond to the user's progress.

[0088] The interview practice platform can further adjust the progress of the interview scenario based on the user's emotional state. For example, by using the emotion estimation function, if the user is nervous, questions that will help the user relax are presented. On the other hand, if the user is confident, more difficult questions are presented. Furthermore, the progress of the interview scenario can be adjusted according to the user's emotional state. For example, if the user is feeling stressed, a scenario containing a lot of positive feedback is provided. The content of the interview scenario can also be changed based on the user's emotional state. For example, if the user is relaxed, a more challenging scenario is provided. In this way, an interview scenario that suits the user's emotional state can be provided.

[0089] The processing flow of the second embodiment will be briefly explained below.

[0090] Step 1: The interview scenario generator supports multiple interview types. For example, the generation AI generates scenarios for various interview types, such as technical interviews, behavioral interviews, and case study interviews. Users can also select their desired interview type and practice based on that scenario. The generation AI generates an interview scenario based on prompts containing details of the user's desired interview type and scenario. Step 2: The multimedia integration unit integrates at least one multimedia element from text, audio, and video. For example, the generation AI provides a scenario in which the interviewer's questions are played back aloud and the user answers aloud. In addition, in a video interview scenario, a video of the interviewer is displayed and the user can answer through a camera. This allows the user to have a more realistic interview experience. Step 3: The feedback learning unit learns the user's feedback. For example, the generation AI learns the user's answers and feedback and reflects them in the next interview scenario. If the user has difficulty answering a particular question, the generation AI will learn that information and repeatedly ask similar questions in the next scenario, thereby improving the user's skills. Step 4: The progress tracking section tracks and analyzes the user's progress. For example, the generation AI records which questions the user answered, how they answered them, and how much their skills improved, and provides the analysis results. This allows the user to understand their own progress and identify areas for improvement.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0119] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0125] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0135] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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).

[0144] 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.

[0145] 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."

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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]

[0158] 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 generative AI to create virtual interview scenarios and provides a platform where users can practice for real interviews, an interview scenario generation unit that supports multiple interview types; a multimedia integration unit for integrating at least one multimedia element of text, audio, and video; a feedback learning unit that learns user feedback; a progress tracking unit that tracks and analyzes the user's progress. A system characterized by:

2. The interview scenario generation unit Generate interview scenarios specific to different industries or job types, allowing the user to practice interviewing for specific industries or job types.

2. The system of claim 1.

3. The multimedia integration unit Analyzing the user's voice data and providing feedback on improvements to pronunciation or intonation 2. The system of claim 1.

4. The feedback learning unit Analyze the user's feedback and propose a learning plan optimized for each individual user 2. The system of claim 1.

5. The progress tracking unit: Tracking the user's progress in detail and analyzing patterns of skill development 2. The system of claim 1.

6. The interview scenario generation unit An interview scenario is generated according to the emotional state of the user, and the stress level is adjusted.

2. The system of claim 1.

Images