system

Abstract

The system according to this embodiment aims to provide personalized interview practice and immediate feedback regardless of location or time. [Solution] The system according to the embodiment comprises a reception unit, a feedback unit, and a personalization unit. The reception unit allows the user to open the app and conduct a mock interview with the AI ​​in a conversational format. The feedback unit provides real-time feedback based on the information received by the reception unit. The personalization unit personalizes the questions based on the feedback provided by the feedback unit.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, there is a problem that it is difficult to provide individualized interview practice and immediate feedback regardless of location or time.

[0005] The system according to the embodiment aims to provide individualized interview practice and immediate feedback regardless of location or time.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, a feedback unit, and a personalization unit. The reception unit allows the user to open the app and conduct a mock interview with the AI ​​in a conversational format. The feedback unit provides real-time feedback based on the information received by the reception unit. The personalization unit personalizes the questions based on the feedback provided by the feedback unit. [Effects of the Invention]

[0007] The system according to this embodiment can provide personalized interview practice and immediate feedback regardless of location or time. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10] This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applied to the communication I / F include wireless communication standards such as 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark).

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0025] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI ​​agent system according to an embodiment of the present invention is a system that provides personalized interview practice and immediate feedback to students seeking employment and working professionals considering a career change, regardless of location or time. This system provides real-time feedback and analyzes the user's strengths and weaknesses by allowing the user to open the app and conduct a mock interview with the AI ​​in a conversational format. The feedback is based on a personalized optimization algorithm using natural language processing technology and deep learning. This allows the user to practice repeatedly and approach interviews with confidence. For example, the user opens the app and conducts a mock interview with the AI ​​in a conversational format. During this interview, the user speaks to the AI ​​about their work history, motivations, etc. For example, the user answers questions such as, "Please introduce yourself," or "What are your strengths?" This information is input into the AI. Next, the AI ​​analyzes the input information and provides real-time feedback. The AI ​​analyzes the user's answers and identifies strengths and weaknesses. For example, it evaluates whether the user's answers are specific, logical, and use appropriate language. This allows the user to objectively understand their own interview skills. Furthermore, the AI ​​personalizes the questions based on a personalized optimization algorithm using deep learning. For example, the next question is adjusted based on the user's past answer history and feedback. This allows users to practice in a way that suits them. This system allows users to practice repeatedly regardless of location or time. For example, they can open the app and practice during their commute or breaks. In addition, objective and detailed feedback from the AI ​​allows users to understand their strengths and weaknesses and prepare for interviews efficiently. In this way, the Personal AI Interview Coach revolutionizes interview preparation by providing students seeking employment and working professionals considering a career change with personalized interview practice and instant feedback, regardless of location or time. As a result, the AI ​​agent system allows users to receive personalized interview practice and instant feedback regardless of location or time.

[0029] The AI ​​agent system according to this embodiment comprises a reception unit, a feedback unit, and a personalization unit. The reception unit allows the user to open an app and conduct a mock interview with the AI ​​in a conversational format. The reception unit can conduct conversations in formats such as text-based, voice-based, or video-based. The feedback unit provides real-time feedback based on the information received by the reception unit. The feedback unit, for example, analyzes the user's responses and identifies strengths and weaknesses. The feedback unit evaluates whether the user's responses are specific, logical, and use appropriate language based on a personalization algorithm using natural language processing technology and deep learning. The feedback unit analyzes the user's responses and provides real-time feedback. The feedback unit analyzes the user's responses and identifies strengths and weaknesses. The feedback unit evaluates whether the user's responses are specific, logical, and use appropriate language. The personalization unit personalizes the questions based on the feedback provided by the feedback unit. The personalization unit adjusts the next questions based on the user's past response history and feedback. The personalization unit personalizes the questions based on a deep learning-based personalization algorithm. For example, the personalization unit adjusts the next question based on the user's past answer history and feedback. This allows the AI ​​agent system to provide users with personalized interview practice and immediate feedback, regardless of location or time.

[0030] The reception desk allows users to open the app and conduct mock interviews with AI in a conversational format. The reception desk can conduct these conversations in various formats, such as text-based, voice-based, and video-based. Specifically, in text-based conversations, users answer questions using a keyboard, and the AI ​​provides feedback in text. In voice-based conversations, users answer questions verbally using a microphone, and the AI ​​analyzes the answers using speech recognition technology and provides feedback in voice or text. In video-based conversations, users answer questions video-wise using a camera, and the AI ​​analyzes the answers using facial recognition and speech recognition technology and provides feedback in video or text. This allows the reception desk to select the most suitable conversation format according to the user's preferences and circumstances. The reception desk also records the user's responses in real time, making them available later for the feedback and personalization departments. Furthermore, the reception desk securely stores the user's responses and implements appropriate security measures to protect privacy. This ensures that the reception desk provides an environment where users can conduct mock interviews with peace of mind.

[0031] The Feedback Department provides real-time feedback based on information received by the Reception Department. For example, the Feedback Department analyzes user responses to identify strengths and weaknesses. Specifically, it evaluates whether user responses are specific, logical, and use appropriate language based on personalized optimization algorithms using natural language processing and deep learning. For example, if a user responds, "I am good at teamwork," the Feedback Department analyzes the response and advises adding specific anecdotes or achievements. Similarly, if a user responds, "I have problem-solving skills," the Feedback Department analyzes the response and instructs the user to elaborate on the specific problem-solving process and results. Furthermore, the Feedback Department suggests appropriate language and expressions based on the user's responses. For example, if a user responds, "I will do my best," the Feedback Department advises revising the expression to a more specific and emphasized one, such as "I will spare no effort to achieve my goals." In this way, the Feedback Department supports users in providing more specific and logical responses. The Feedback Department also accumulates user responses and encourages continuous improvement by referring to past feedback history. This allows the feedback unit to provide effective feedback to help users improve themselves through mock interviews.

[0032] The Personalization Department personalizes the questions based on feedback provided by the Feedback Department. Specifically, the next questions are adjusted based on the user's past answer history and feedback. For example, if a user was unable to provide specific answers to questions about "leadership experience" in the past, the Personalization Department will focus on leadership-related questions in the next mock interview. Also, if a user receives high marks for questions about "problem-solving ability," the Personalization Department will ask more advanced problem-solving questions to further highlight that strength. The Personalization Department analyzes the user's answer history and feedback based on a personalization algorithm using deep learning, and generates questions tailored to the user's strengths and weaknesses. This allows the Personalization Department to provide optimal questions for the user to improve themselves. The Personalization Department also monitors the user's progress and provides feedback and advice at the appropriate time. For example, if a user improves a specific skill within a certain period, the Personalization Department evaluates that achievement and provides advice for the next step. This allows the Personalization Department to provide effective support for the user to continuously improve themselves.

[0033] The analysis department identifies the user's strengths and weaknesses. For example, the analysis department analyzes the user's responses to identify strengths and weaknesses. The analysis department evaluates whether the user's responses are specific, logical, and use appropriate language based on a personalized optimization algorithm using natural language processing technology and deep learning. For example, the analysis department analyzes the user's responses to identify strengths and weaknesses. The analysis department analyzes the user's responses to identify strengths and weaknesses. The analysis department evaluates whether the user's responses are specific, logical, and use appropriate language. By identifying the user's strengths and weaknesses, more effective feedback can be provided. The specific definition and identification methods for strengths and weaknesses are based on, for example, skill sets and behavioral characteristics. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input the user's responses into a generating AI and have the generating AI perform the identification of strengths and weaknesses.

[0034] The adjustment unit adjusts the next question based on the user's past answer history and feedback. The adjustment unit, for example, analyzes the user's past answer history and adjusts the next question. The adjustment unit adjusts the next question based on the user's past answer history and feedback, based on a personal optimization algorithm using deep learning. The adjustment unit, for example, analyzes the user's past answer history and adjusts the next question. The adjustment unit adjusts the next question based on the user's past answer history and feedback. The adjustment unit, for example, analyzes the user's past answer history and adjusts the next question. This allows for more personalized practice by adjusting questions based on the user's past answer history and feedback. The specific content and storage method of the past answer history are determined based on, for example, the type of answer and the storage period. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's past answer history into a generating AI and have the generating AI perform the adjustment of the question content.

[0035] The Practice Section allows users to practice regardless of location or time. For example, users can open the app and practice during their commute or breaks. The Practice Section adjusts the next questions based on the user's past answer history and feedback, using a personalization algorithm based on deep learning. This allows users to practice regardless of location or time, enabling them to efficiently prepare for interviews. The specific scope and conditions of "regardless of location or time" are based on factors such as internet connectivity and device type. Some or all of the above-described processes in the Practice Section may be performed using AI or not. For example, the Practice Section can input the user's practice content into a generating AI and have the generating AI adjust the practice method.

[0036] The reception department analyzes the user's past interview history and selects the optimal reception method. For example, the reception department proposes the most effective reception method based on the user's past interview history. The reception department prioritizes specific question patterns based on the user's past interview history. The reception department analyzes the user's past interview history and proposes the optimal interview format. In this way, the optimal reception method can be selected by analyzing the user's past interview history. The specific criteria and selection method for the optimal reception method are based on, for example, the user's attribute information and past behavioral history. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's past interview history into a generating AI and have the generating AI select the optimal reception method.

[0037] The reception desk filters applications for mock interviews based on the user's current work situation and areas of interest. For example, the reception desk prioritizes receiving relevant questions based on the user's current work situation. The reception desk filters questions related to specific industries based on the user's areas of interest. The reception desk combines the user's work situation and areas of interest to suggest the most relevant questions. This allows for the provision of more relevant questions by filtering based on the user's current work situation and areas of interest. The specific definition and identification methods for work situation and areas of interest are based on, for example, work history, topics of interest, etc. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's work situation and areas of interest into a generating AI and have the generating AI perform the filtering of question content.

[0038] The reception department prioritizes accepting mock interview requests by considering the user's geographical location information and selecting the most relevant interview content. For example, the reception department prioritizes regional questions based on the user's current location. The reception department also prioritizes questions related to specific companies or industries based on the user's geographical location information. The reception department proposes the most suitable interview content, taking the user's geographical location information into consideration. This ensures that highly relevant interview content is provided by considering the user's geographical location information. The specific methods for acquiring and using geographical location information include, for example, GPS data and location information services. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of interview content.

[0039] The reception department analyzes the user's social media activity when they register for a mock interview and accepts relevant interview content. For example, the reception department prioritizes accepting relevant questions based on the user's social media activity. The reception department analyzes the user's social media activity and suggests questions based on specific interests and concerns. The reception department proposes the most suitable interview content, taking into account the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant interview content. The specific content and analysis methods of social media activity are based on, for example, the content of posts, the number of followers, etc. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input data on the user's social media activity into a generating AI and have the generating AI perform the filtering of interview content.

[0040] The feedback unit adjusts the level of detail of the feedback based on the user's response when providing feedback. For example, if the user's response is specific, the feedback unit provides detailed feedback. If the user's response is vague, the feedback unit provides concise feedback. The feedback unit adjusts the level of detail of the feedback appropriately according to the user's response. This allows for the provision of more appropriate feedback by adjusting the level of detail based on the user's response. The specific criteria and adjustment methods for the level of detail of the feedback are based on, for example, detailed explanations or concise summaries. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's response into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0041] The Feedback Unit applies different feedback algorithms depending on the user's work history when providing feedback. For example, if the user has extensive work experience, the Feedback Unit provides detailed feedback. If the user has limited work experience, the Feedback Unit provides basic feedback. The Feedback Unit applies an appropriate feedback algorithm according to the user's work history. This allows for the provision of more appropriate feedback by applying a feedback algorithm according to the user's work history. The specific types and application methods of the feedback algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above processing in the Feedback Unit may be performed using AI or not. For example, the Feedback Unit can input the user's work history data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0042] The feedback unit determines the priority of feedback based on the timing of the user's response when providing feedback. For example, the feedback unit provides priority feedback based on the user's most recent responses. The feedback unit determines the appropriate priority of feedback according to the timing of the user's response. The feedback unit provides optimal feedback, taking into account the timing of the user's response. This allows for the provision of more appropriate feedback by determining the priority of feedback based on the timing of the user's response. The specific method and criteria for determining the priority of feedback are based on, for example, importance, relevance, etc. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user response timing data into a generating AI and have the generating AI perform the determination of the priority of feedback.

[0043] The feedback unit adjusts the order of feedback based on user relevance when providing feedback. For example, the feedback unit prioritizes providing highly relevant feedback based on the user's responses. The feedback unit adjusts the order of appropriate feedback according to user relevance. The feedback unit provides optimal feedback considering user relevance. This allows for the provision of more appropriate feedback by adjusting the order of feedback based on user relevance. The specific method and criteria for determining the order of feedback are based on factors such as importance and relevance. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the order of feedback.

[0044] The personalization unit adjusts the questions based on the user's past answers during the personalization process. For example, the personalization unit prioritizes relevant questions based on the user's past answers. The personalization unit analyzes the user's past answers and suggests the most appropriate questions. The personalization unit adjusts the questions appropriately according to the user's past answers. This allows for the provision of more appropriate questions by adjusting the questions based on the user's past answers. The specific content and storage method of past answers are determined based on, for example, the type of answer and the storage period. Some or all of the above processing in the personalization unit may be performed using AI or not. For example, the personalization unit can input the user's past answers into a generating AI and have the generating AI perform the question adjustments.

[0045] The personalization unit applies different question algorithms to the user's work history during the personalization process. For example, if the user has extensive work experience, the personalization unit will ask detailed questions. If the user has limited work experience, the personalization unit will ask basic questions. The personalization unit applies the appropriate question algorithm according to the user's work history. This allows for the provision of more appropriate questions by applying the question algorithm according to the user's work history. The specific types and application methods of the question algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above processing in the personalization unit may be performed using AI, or it may be performed without AI. For example, the personalization unit can input the user's work history data into a generating AI and have the generating AI perform the application of the question algorithm.

[0046] The personalization unit prioritizes questions based on the timing of the user's responses during the personalization process. For example, the personalization unit prioritizes questions based on the user's most recent responses. The personalization unit determines the appropriate priority of questions according to the timing of the user's responses. The personalization unit presents the most appropriate questions, taking into account the timing of the user's responses. This allows for the provision of more appropriate questions by prioritizing questions based on the timing of the user's responses. The specific methods and criteria for determining the priority of questions are based on factors such as importance and relevance. Some or all of the above-described processes in the personalization unit may be performed using AI or not. For example, the personalization unit can input user response timing data into a generating AI and have the generating AI perform the determination of question priorities.

[0047] The personalization unit adjusts the order of questions based on user relevance during the personalization process. For example, the personalization unit prioritizes asking questions that are highly relevant based on the user's answers. The personalization unit adjusts the order of questions appropriately according to user relevance. The personalization unit asks the most relevant questions, taking user relevance into consideration. This allows for the provision of more appropriate questions by adjusting the order of questions based on user relevance. The specific method and criteria for determining the order of questions are based on factors such as importance and relevance. Some or all of the above-described processes in the personalization unit may be performed using AI or not. For example, the personalization unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the question order.

[0048] The analysis department identifies strengths and weaknesses based on the user's past responses during the analysis. For example, the analysis department identifies strengths and weaknesses based on the user's past responses. The analysis department analyzes the user's past responses and evaluates specific skills and abilities. The analysis department identifies appropriate strengths and weaknesses according to the user's past responses. This allows for more appropriate analysis by identifying strengths and weaknesses based on the user's past responses. The specific content and storage method of past responses are determined based on, for example, the type of response and the storage period. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input the user's past responses into a generating AI and have the generating AI perform the identification of strengths and weaknesses.

[0049] The Analysis Department applies different analysis algorithms depending on the user's work history during analysis. For example, if the user has extensive work experience, the Analysis Department performs a detailed analysis. If the user has limited work experience, the Analysis Department performs a basic analysis. The Analysis Department applies the appropriate analysis algorithm according to the user's work history. This allows for more accurate analysis by applying the analysis algorithm according to the user's work history. The specific types and application methods of the analysis algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above-mentioned processes in the Analysis Department may be performed using AI, or they may be performed without AI. For example, the Analysis Department can input the user's work history data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0050] The analysis department determines the priority of strengths and weaknesses based on the timing of the user's responses during the analysis. For example, the analysis department identifies strengths and weaknesses preferentially based on the content the user has recently responded to. The analysis department determines the appropriate priority of strengths and weaknesses according to the timing of the user's responses. The analysis department identifies the optimal strengths and weaknesses, taking into account the timing of the user's responses. This allows for more appropriate analysis by determining the priority of strengths and weaknesses based on the timing of the user's responses. The specific methods and criteria for determining the priority of strengths and weaknesses are based on factors such as importance and relevance. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input user response timing data into a generating AI and have the generating AI perform the determination of the priority of strengths and weaknesses.

[0051] The adjustment unit adjusts the question content based on the user's past answer history during the adjustment process. For example, the adjustment unit prioritizes and presents relevant questions based on the user's past answer history. The adjustment unit analyzes the user's past answer history and proposes the most appropriate questions. The adjustment unit adjusts the question content appropriately according to the user's past answer history. This allows for the provision of more appropriate questions by adjusting the question content based on the user's past answer history. The specific content and storage method of the past answer history are determined based on, for example, the type of answer and the storage period. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's past answer history into a generating AI and have the generating AI perform the question content adjustment.

[0052] The adjustment unit applies different adjustment algorithms during the adjustment process, depending on the user's work history. For example, if the user has extensive work experience, the adjustment unit will ask detailed questions. If the user has limited work experience, the adjustment unit will ask basic questions. The adjustment unit applies the appropriate adjustment algorithm according to the user's work history. This allows for the provision of more appropriate questions by applying the adjustment algorithm according to the user's work history. The specific types and application methods of the adjustment algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above processing in the adjustment unit may be performed using AI, or it may be performed without AI. For example, the adjustment unit can input the user's work history data into a generating AI and have the generating AI execute the application of the adjustment algorithm.

[0053] The adjustment unit determines the priority of questions based on the timing of the user's responses during the adjustment process. For example, the adjustment unit prioritizes questions based on the user's most recent responses. The adjustment unit determines the appropriate priority of questions according to the timing of the user's responses. The adjustment unit presents the most appropriate questions, taking into account the timing of the user's responses. This allows for the provision of more appropriate questions by determining the priority of questions based on the timing of the user's responses. The specific methods and criteria for determining the priority of questions are based on factors such as importance and relevance. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user response timing data into a generating AI and have the generating AI perform the determination of question priorities.

[0054] The practice unit adjusts the practice content based on the user's past practice history during practice. For example, the practice unit prioritizes providing relevant practice content based on the user's past practice history. The practice unit analyzes the user's past practice history and proposes the optimal practice content. The practice unit adjusts the practice content appropriately according to the user's past practice history. This allows for more appropriate practice to be provided by adjusting the practice content based on the user's past practice history. The specific content and storage method of past practice history are determined based on, for example, the type of practice and the storage period. Some or all of the above processes in the practice unit may be performed using AI or not. For example, the practice unit can input the user's past practice history into a generating AI and have the generating AI perform the adjustment of the practice content.

[0055] The practice unit applies different practice algorithms to the user during practice sessions, depending on the user's work experience. For example, if the user has extensive work experience, the practice unit provides detailed practice content. If the user has limited work experience, the practice unit provides basic practice content. The practice unit applies the appropriate practice algorithm according to the user's work experience. This allows for more appropriate practice to be provided by applying the practice algorithm according to the user's work experience. The specific types and application methods of the practice algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above-described processes in the practice unit may be performed using AI, or they may not be performed using AI. For example, the practice unit can input the user's work experience data into a generating AI and have the generating AI execute the application of the practice algorithm.

[0056] The practice unit selects the optimal practice method during practice, taking into account the user's geographical location information. For example, the practice unit prioritizes providing relevant practice content based on the user's current location. The practice unit prioritizes providing practice content related to a specific industry based on the user's geographical location information. The practice unit proposes the optimal practice method, taking into account the user's geographical location information. In this way, the optimal practice method can be provided by taking into account the user's geographical location information. The specific methods for acquiring and using geographical location information are based on, for example, GPS data, location information services, etc. Some or all of the above processing in the practice unit may be performed using AI, or may not be performed using AI. For example, the practice unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of practice methods.

[0057] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0058] The reception department analyzes the user's past interview history and selects the optimal reception method. For example, it can suggest the most effective reception method based on the user's past interview history. It can also prioritize specific question patterns based on the user's past interview history. Furthermore, it can analyze the user's past interview history and suggest the optimal interview format. In this way, the optimal reception method can be selected by analyzing the user's past interview history. The specific criteria and selection method for the optimal reception method are based on, for example, the user's attribute information and past behavioral history. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the user's past interview history into a generating AI and have the generating AI select the optimal reception method.

[0059] The reception desk filters applications for mock interviews based on the user's current work situation and areas of interest. For example, it can prioritize relevant questions based on the user's current work situation. It can also filter questions related to a specific industry based on the user's areas of interest. It can even suggest optimal questions by combining the user's work situation and areas of interest. This allows for the provision of more relevant questions by filtering based on the user's current work situation and areas of interest. The specific definition and identification methods for work situation and areas of interest are based on, for example, work history and topics of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's work situation and areas of interest into a generating AI and have the generating AI perform the filtering of the questions.

[0060] The feedback unit adjusts the level of detail of the feedback based on the user's response when providing feedback. For example, if the user's response is specific, detailed feedback can be provided. If the user's response is vague, concise feedback can be provided. The appropriate level of detail of the feedback can be adjusted according to the user's response. This allows for the provision of more appropriate feedback by adjusting the level of detail based on the user's response. The specific criteria and adjustment methods for the level of detail of the feedback are based on, for example, detailed explanations or concise summaries. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's response into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0061] The personalization unit adjusts the questions based on the user's past responses during the personalization process. For example, it can prioritize relevant questions based on the user's past responses. It can also analyze the user's past responses and suggest the most appropriate questions. The questions can be adjusted appropriately according to the user's past responses. This allows for the provision of more appropriate questions by adjusting the questions based on the user's past responses. The specific content and storage method of past responses are determined based on, for example, the type of response and the storage period. Some or all of the above processing in the personalization unit may be performed using AI or not. For example, the personalization unit can input the user's past responses into a generating AI and have the generating AI perform the question adjustments.

[0062] The analysis unit applies different analysis algorithms depending on the user's work history during the analysis. For example, if the user has extensive work experience, a detailed analysis can be performed. If the user has limited work experience, a basic analysis can be performed. The appropriate analysis algorithm can be applied according to the user's work experience. This allows for more accurate analysis by applying the analysis algorithm according to the user's work experience. The specific types and application methods of the analysis algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the user's work history data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0063] The training unit selects the optimal training method during training, taking into account the user's geographical location. For example, it can prioritize providing relevant training content based on the user's current location. It can also prioritize providing training content related to a specific industry based on the user's geographical location. It can also suggest the optimal training method, taking the user's geographical location into consideration. This ensures that the optimal training method is provided by considering the user's geographical location. The specific methods for acquiring and using geographical location information are based on, for example, GPS data and location information services. Some or all of the above processing in the training unit may be performed using AI, or not. For example, the training unit can input the user's geographical location information into a generating AI and have the generating AI select the training method.

[0064] The following briefly describes the processing flow for example form 1.

[0065] Step 1: The reception desk has the user open the app and conduct a mock interview with the AI ​​in a conversational format. The reception desk can conduct the conversation in various formats, such as text-based, voice-based, or video-based. Step 2: The feedback department provides real-time feedback based on the information received by the reception department. For example, the feedback department analyzes the user's responses to identify strengths and weaknesses. The feedback department evaluates whether the user's responses are specific, logical, and use appropriate language based on a personalized optimization algorithm using natural language processing technology and deep learning. Step 3: The personalization unit personalizes the questions based on the feedback provided by the feedback unit. For example, the personalization unit adjusts the next question based on the user's past answer history and feedback. The personalization unit personalizes the questions based on a personal optimization algorithm using deep learning.

[0066] (Example of form 2) The AI ​​agent system according to an embodiment of the present invention is a system that provides personalized interview practice and immediate feedback to students seeking employment and working professionals considering a career change, regardless of location or time. This system provides real-time feedback and analyzes the user's strengths and weaknesses by allowing the user to open the app and conduct a mock interview with the AI ​​in a conversational format. The feedback is based on a personalized optimization algorithm using natural language processing technology and deep learning. This allows the user to practice repeatedly and approach interviews with confidence. For example, the user opens the app and conducts a mock interview with the AI ​​in a conversational format. During this interview, the user speaks to the AI ​​about their work history, motivations, etc. For example, the user answers questions such as, "Please introduce yourself," or "What are your strengths?" This information is input into the AI. Next, the AI ​​analyzes the input information and provides real-time feedback. The AI ​​analyzes the user's answers and identifies strengths and weaknesses. For example, it evaluates whether the user's answers are specific, logical, and use appropriate language. This allows the user to objectively understand their own interview skills. Furthermore, the AI ​​personalizes the questions based on a personalized optimization algorithm using deep learning. For example, the next question is adjusted based on the user's past answer history and feedback. This allows users to practice in a way that suits them. This system allows users to practice repeatedly regardless of location or time. For example, they can open the app and practice during their commute or breaks. In addition, objective and detailed feedback from the AI ​​allows users to understand their strengths and weaknesses and prepare for interviews efficiently. In this way, the Personal AI Interview Coach revolutionizes interview preparation by providing students seeking employment and working professionals considering a career change with personalized interview practice and instant feedback, regardless of location or time. As a result, the AI ​​agent system allows users to receive personalized interview practice and instant feedback regardless of location or time.

[0067] The AI ​​agent system according to this embodiment comprises a reception unit, a feedback unit, and a personalization unit. The reception unit allows the user to open an app and conduct a mock interview with the AI ​​in a conversational format. The reception unit can conduct conversations in formats such as text-based, voice-based, or video-based. The feedback unit provides real-time feedback based on the information received by the reception unit. The feedback unit, for example, analyzes the user's responses and identifies strengths and weaknesses. The feedback unit evaluates whether the user's responses are specific, logical, and use appropriate language based on a personalization algorithm using natural language processing technology and deep learning. The feedback unit analyzes the user's responses and provides real-time feedback. The feedback unit analyzes the user's responses and identifies strengths and weaknesses. The feedback unit evaluates whether the user's responses are specific, logical, and use appropriate language. The personalization unit personalizes the questions based on the feedback provided by the feedback unit. The personalization unit adjusts the next questions based on the user's past response history and feedback. The personalization unit personalizes the questions based on a deep learning-based personalization algorithm. For example, the personalization unit adjusts the next question based on the user's past answer history and feedback. This allows the AI ​​agent system to provide users with personalized interview practice and immediate feedback, regardless of location or time.

[0068] The reception desk allows users to open the app and conduct mock interviews with AI in a conversational format. The reception desk can conduct these conversations in various formats, such as text-based, voice-based, and video-based. Specifically, in text-based conversations, users answer questions using a keyboard, and the AI ​​provides feedback in text. In voice-based conversations, users answer questions verbally using a microphone, and the AI ​​analyzes the answers using speech recognition technology and provides feedback in voice or text. In video-based conversations, users answer questions video-wise using a camera, and the AI ​​analyzes the answers using facial recognition and speech recognition technology and provides feedback in video or text. This allows the reception desk to select the most suitable conversation format according to the user's preferences and circumstances. The reception desk also records the user's responses in real time, making them available later for the feedback and personalization departments. Furthermore, the reception desk securely stores the user's responses and implements appropriate security measures to protect privacy. This ensures that the reception desk provides an environment where users can conduct mock interviews with peace of mind.

[0069] The Feedback Department provides real-time feedback based on information received by the Reception Department. For example, the Feedback Department analyzes user responses to identify strengths and weaknesses. Specifically, it evaluates whether user responses are specific, logical, and use appropriate language based on personalized optimization algorithms using natural language processing and deep learning. For example, if a user responds, "I am good at teamwork," the Feedback Department analyzes the response and advises adding specific anecdotes or achievements. Similarly, if a user responds, "I have problem-solving skills," the Feedback Department analyzes the response and instructs the user to elaborate on the specific problem-solving process and results. Furthermore, the Feedback Department suggests appropriate language and expressions based on the user's responses. For example, if a user responds, "I will do my best," the Feedback Department advises revising the expression to a more specific and emphasized one, such as "I will spare no effort to achieve my goals." In this way, the Feedback Department supports users in providing more specific and logical responses. The Feedback Department also accumulates user responses and encourages continuous improvement by referring to past feedback history. This allows the feedback unit to provide effective feedback to help users improve themselves through mock interviews.

[0070] The Personalization Department personalizes the questions based on feedback provided by the Feedback Department. Specifically, the next questions are adjusted based on the user's past answer history and feedback. For example, if a user was unable to provide specific answers to questions about "leadership experience" in the past, the Personalization Department will focus on leadership-related questions in the next mock interview. Also, if a user receives high marks for questions about "problem-solving ability," the Personalization Department will ask more advanced problem-solving questions to further highlight that strength. The Personalization Department analyzes the user's answer history and feedback based on a personalization algorithm using deep learning, and generates questions tailored to the user's strengths and weaknesses. This allows the Personalization Department to provide optimal questions for the user to improve themselves. The Personalization Department also monitors the user's progress and provides feedback and advice at the appropriate time. For example, if a user improves a specific skill within a certain period, the Personalization Department evaluates that achievement and provides advice for the next step. This allows the Personalization Department to provide effective support for the user to continuously improve themselves.

[0071] The analysis department identifies the user's strengths and weaknesses. For example, the analysis department analyzes the user's responses to identify strengths and weaknesses. The analysis department evaluates whether the user's responses are specific, logical, and use appropriate language based on a personalized optimization algorithm using natural language processing technology and deep learning. For example, the analysis department analyzes the user's responses to identify strengths and weaknesses. The analysis department analyzes the user's responses to identify strengths and weaknesses. The analysis department evaluates whether the user's responses are specific, logical, and use appropriate language. By identifying the user's strengths and weaknesses, more effective feedback can be provided. The specific definition and identification methods for strengths and weaknesses are based on, for example, skill sets and behavioral characteristics. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input the user's responses into a generating AI and have the generating AI perform the identification of strengths and weaknesses.

[0072] The adjustment unit adjusts the next question based on the user's past answer history and feedback. The adjustment unit, for example, analyzes the user's past answer history and adjusts the next question. The adjustment unit adjusts the next question based on the user's past answer history and feedback, based on a personal optimization algorithm using deep learning. The adjustment unit, for example, analyzes the user's past answer history and adjusts the next question. The adjustment unit adjusts the next question based on the user's past answer history and feedback. The adjustment unit, for example, analyzes the user's past answer history and adjusts the next question. This allows for more personalized practice by adjusting questions based on the user's past answer history and feedback. The specific content and storage method of the past answer history are determined based on, for example, the type of answer and the storage period. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's past answer history into a generating AI and have the generating AI perform the adjustment of the question content.

[0073] The Practice Section allows users to practice regardless of location or time. For example, users can open the app and practice during their commute or breaks. The Practice Section adjusts the next questions based on the user's past answer history and feedback, using a personalization algorithm based on deep learning. This allows users to practice regardless of location or time, enabling them to efficiently prepare for interviews. The specific scope and conditions of "regardless of location or time" are based on factors such as internet connectivity and device type. Some or all of the above-described processes in the Practice Section may be performed using AI or not. For example, the Practice Section can input the user's practice content into a generating AI and have the generating AI adjust the practice method.

[0074] The reception desk estimates the user's emotions and adjusts the start time of the mock interview based on the estimated emotions. For example, if the user is nervous, the reception desk suggests a short break to help them relax before starting the mock interview. If the user is relaxed, the reception desk starts the mock interview immediately. If the user is in a hurry, the reception desk starts the mock interview quickly. This allows for more effective practice by adjusting the start time of the mock interview according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI adjust the start time of the mock interview.

[0075] The reception department analyzes the user's past interview history and selects the optimal reception method. For example, the reception department proposes the most effective reception method based on the user's past interview history. The reception department prioritizes specific question patterns based on the user's past interview history. The reception department analyzes the user's past interview history and proposes the optimal interview format. In this way, the optimal reception method can be selected by analyzing the user's past interview history. The specific criteria and selection method for the optimal reception method are based on, for example, the user's attribute information and past behavioral history. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input the user's past interview history into a generating AI and have the generating AI select the optimal reception method.

[0076] The reception desk filters applications for mock interviews based on the user's current work situation and areas of interest. For example, the reception desk prioritizes receiving relevant questions based on the user's current work situation. The reception desk filters questions related to specific industries based on the user's areas of interest. The reception desk combines the user's work situation and areas of interest to suggest the most relevant questions. This allows for the provision of more relevant questions by filtering based on the user's current work situation and areas of interest. The specific definition and identification methods for work situation and areas of interest are based on, for example, work history, topics of interest, etc. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's work situation and areas of interest into a generating AI and have the generating AI perform the filtering of question content.

[0077] The reception desk estimates the user's emotions and prioritizes the interview content based on the estimated emotions. For example, if the user is nervous, the reception desk starts with easy questions and gradually increases the difficulty. If the user is relaxed, the reception desk starts with difficult questions. If the user is in a hurry, the reception desk prioritizes important questions. This allows for more effective practice by prioritizing interview content according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input user emotion data into a generative AI and have the generative AI determine the priority of interview content.

[0078] The reception department prioritizes accepting mock interview requests by considering the user's geographical location information and selecting the most relevant interview content. For example, the reception department prioritizes regional questions based on the user's current location. The reception department also prioritizes questions related to specific companies or industries based on the user's geographical location information. The reception department proposes the most suitable interview content, taking the user's geographical location information into consideration. This ensures that highly relevant interview content is provided by considering the user's geographical location information. The specific methods for acquiring and using geographical location information include, for example, GPS data and location information services. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the user's geographical location information into a generating AI and have the generating AI perform the filtering of interview content.

[0079] The reception department analyzes the user's social media activity when they register for a mock interview and accepts relevant interview content. For example, the reception department prioritizes accepting relevant questions based on the user's social media activity. The reception department analyzes the user's social media activity and suggests questions based on specific interests and concerns. The reception department proposes the most suitable interview content, taking into account the user's social media activity. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant interview content. The specific content and analysis methods of social media activity are based on, for example, the content of posts, the number of followers, etc. Some or all of the above processing in the reception department may be performed using AI or not. For example, the reception department can input data on the user's social media activity into a generating AI and have the generating AI perform the filtering of interview content.

[0080] The feedback unit estimates the user's emotions and adjusts the way feedback is expressed based on the estimated emotions. For example, if the user is nervous, the feedback unit provides feedback in gentle words. If the user is relaxed, the feedback unit provides detailed feedback. If the user is in a hurry, the feedback unit provides concise feedback. By adjusting the way feedback is expressed according to the user's emotions, more effective feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not using AI. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the way feedback is expressed.

[0081] The feedback unit adjusts the level of detail of the feedback based on the user's response when providing feedback. For example, if the user's response is specific, the feedback unit provides detailed feedback. If the user's response is vague, the feedback unit provides concise feedback. The feedback unit adjusts the level of detail of the feedback appropriately according to the user's response. This allows for the provision of more appropriate feedback by adjusting the level of detail based on the user's response. The specific criteria and adjustment methods for the level of detail of the feedback are based on, for example, detailed explanations or concise summaries. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's response into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0082] The Feedback Unit applies different feedback algorithms depending on the user's work history when providing feedback. For example, if the user has extensive work experience, the Feedback Unit provides detailed feedback. If the user has limited work experience, the Feedback Unit provides basic feedback. The Feedback Unit applies an appropriate feedback algorithm according to the user's work history. This allows for the provision of more appropriate feedback by applying a feedback algorithm according to the user's work history. The specific types and application methods of the feedback algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above processing in the Feedback Unit may be performed using AI or not. For example, the Feedback Unit can input the user's work history data into a generating AI and have the generating AI execute the application of the feedback algorithm.

[0083] The feedback unit estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback unit provides short feedback. If the user is relaxed, the feedback unit provides long feedback. If the user is in a hurry, the feedback unit provides concise feedback. By adjusting the length of the feedback according to the user's emotions, more effective feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the length of the feedback.

[0084] The feedback unit determines the priority of feedback based on the timing of the user's response when providing feedback. For example, the feedback unit provides priority feedback based on the user's most recent responses. The feedback unit determines the appropriate priority of feedback according to the timing of the user's response. The feedback unit provides optimal feedback, taking into account the timing of the user's response. This allows for the provision of more appropriate feedback by determining the priority of feedback based on the timing of the user's response. The specific method and criteria for determining the priority of feedback are based on, for example, importance, relevance, etc. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user response timing data into a generating AI and have the generating AI perform the determination of the priority of feedback.

[0085] The feedback unit adjusts the order of feedback based on user relevance when providing feedback. For example, the feedback unit prioritizes providing highly relevant feedback based on the user's responses. The feedback unit adjusts the order of appropriate feedback according to user relevance. The feedback unit provides optimal feedback considering user relevance. This allows for the provision of more appropriate feedback by adjusting the order of feedback based on user relevance. The specific method and criteria for determining the order of feedback are based on factors such as importance and relevance. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the order of feedback.

[0086] The personalization unit estimates the user's emotions and adjusts the method of personalizing the questions based on the estimated emotions. For example, if the user is nervous, the personalization unit starts with easy questions and gradually increases the difficulty. If the user is relaxed, the personalization unit starts with difficult questions. If the user is in a hurry, the personalization unit prioritizes important questions. This allows for more effective practice by adjusting the method of personalizing the questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the personalization unit may be performed using AI or not. For example, the personalization unit can input user emotion data into a generative AI and have the generative AI adjust the method of personalizing the questions.

[0087] The personalization unit adjusts the questions based on the user's past answers during the personalization process. For example, the personalization unit prioritizes relevant questions based on the user's past answers. The personalization unit analyzes the user's past answers and suggests the most appropriate questions. The personalization unit adjusts the questions appropriately according to the user's past answers. This allows for the provision of more appropriate questions by adjusting the questions based on the user's past answers. The specific content and storage method of past answers are determined based on, for example, the type of answer and the storage period. Some or all of the above processing in the personalization unit may be performed using AI or not. For example, the personalization unit can input the user's past answers into a generating AI and have the generating AI perform the question adjustments.

[0088] The personalization unit applies different question algorithms to the user's work history during the personalization process. For example, if the user has extensive work experience, the personalization unit will ask detailed questions. If the user has limited work experience, the personalization unit will ask basic questions. The personalization unit applies the appropriate question algorithm according to the user's work history. This allows for the provision of more appropriate questions by applying the question algorithm according to the user's work history. The specific types and application methods of the question algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above processing in the personalization unit may be performed using AI, or it may be performed without AI. For example, the personalization unit can input the user's work history data into a generating AI and have the generating AI perform the application of the question algorithm.

[0089] The personalization unit estimates the user's emotions and adjusts the length of the questions based on the estimated emotions. For example, if the user is nervous, the personalization unit will ask short questions. If the user is relaxed, the personalization unit will ask long questions. If the user is in a hurry, the personalization unit will ask concise questions. By adjusting the length of the questions according to the user's emotions, more effective practice becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the personalization unit may be performed using AI or not using AI. For example, the personalization unit can input user emotion data into a generative AI and have the generative AI adjust the length of the questions.

[0090] The personalization unit prioritizes questions based on the timing of the user's responses during the personalization process. For example, the personalization unit prioritizes questions based on the user's most recent responses. The personalization unit determines the appropriate priority of questions according to the timing of the user's responses. The personalization unit presents the most appropriate questions, taking into account the timing of the user's responses. This allows for the provision of more appropriate questions by prioritizing questions based on the timing of the user's responses. The specific methods and criteria for determining the priority of questions are based on factors such as importance and relevance. Some or all of the above-described processes in the personalization unit may be performed using AI or not. For example, the personalization unit can input user response timing data into a generating AI and have the generating AI perform the determination of question priorities.

[0091] The personalization unit adjusts the order of questions based on user relevance during the personalization process. For example, the personalization unit prioritizes asking questions that are highly relevant based on the user's answers. The personalization unit adjusts the order of questions appropriately according to user relevance. The personalization unit asks the most relevant questions, taking user relevance into consideration. This allows for the provision of more appropriate questions by adjusting the order of questions based on user relevance. The specific method and criteria for determining the order of questions are based on factors such as importance and relevance. Some or all of the above-described processes in the personalization unit may be performed using AI or not. For example, the personalization unit can input user relevance data into a generating AI and have the generating AI perform the adjustment of the question order.

[0092] The analysis department estimates the user's emotions and adjusts the analysis method for strengths and weaknesses based on the estimated emotions. For example, if the user is nervous, the analysis department starts with easy questions and gradually increases the difficulty. If the user is relaxed, the analysis department starts with difficult questions. If the user is in a hurry, the analysis department prioritizes important questions. This allows for a more appropriate analysis by adjusting the analysis method for strengths and weaknesses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input user emotion data into a generative AI and have the generative AI adjust the analysis method for strengths and weaknesses.

[0093] The analysis department identifies strengths and weaknesses based on the user's past responses during the analysis. For example, the analysis department identifies strengths and weaknesses based on the user's past responses. The analysis department analyzes the user's past responses and evaluates specific skills and abilities. The analysis department identifies appropriate strengths and weaknesses according to the user's past responses. This allows for more appropriate analysis by identifying strengths and weaknesses based on the user's past responses. The specific content and storage method of past responses are determined based on, for example, the type of response and the storage period. Some or all of the above processing in the analysis department may be performed using AI or not. For example, the analysis department can input the user's past responses into a generating AI and have the generating AI perform the identification of strengths and weaknesses.

[0094] The Analysis Department applies different analysis algorithms depending on the user's work history during analysis. For example, if the user has extensive work experience, the Analysis Department performs a detailed analysis. If the user has limited work experience, the Analysis Department performs a basic analysis. The Analysis Department applies the appropriate analysis algorithm according to the user's work history. This allows for more accurate analysis by applying the analysis algorithm according to the user's work history. The specific types and application methods of the analysis algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above-mentioned processes in the Analysis Department may be performed using AI, or they may be performed without AI. For example, the Analysis Department can input the user's work history data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0095] The Analysis Department estimates the user's emotions and adjusts the display of strengths and weaknesses based on the estimated emotions. For example, if the user is nervous, the Analysis Department provides a simple and highly visible display. If the user is relaxed, the Analysis Department provides a display that includes detailed information. If the user is in a hurry, the Analysis Department provides a display that gets straight to the point. By adjusting the display of strengths and weaknesses according to the user's emotions, a more appropriate display becomes possible. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the Analysis Department may be performed using AI or not. For example, the Analysis Department can input user emotion data into a generative AI and have the generative AI adjust the display of strengths and weaknesses.

[0096] The analysis department determines the priority of strengths and weaknesses based on the timing of the user's responses during the analysis. For example, the analysis department identifies strengths and weaknesses preferentially based on the content the user has recently responded to. The analysis department determines the appropriate priority of strengths and weaknesses according to the timing of the user's responses. The analysis department identifies the optimal strengths and weaknesses, taking into account the timing of the user's responses. This allows for more appropriate analysis by determining the priority of strengths and weaknesses based on the timing of the user's responses. The specific methods and criteria for determining the priority of strengths and weaknesses are based on factors such as importance and relevance. Some or all of the above processes in the analysis department may be performed using AI or not. For example, the analysis department can input user response timing data into a generating AI and have the generating AI perform the determination of the priority of strengths and weaknesses.

[0097] The adjustment unit estimates the user's emotions and modifies the way the next questions are presented based on the estimated emotions. For example, if the user is nervous, the adjustment unit starts with easy questions and gradually increases the difficulty. If the user is relaxed, the adjustment unit starts with difficult questions. If the user is in a hurry, the adjustment unit prioritizes important questions. This allows for the provision of more appropriate questions by changing the way the next questions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into the generative AI and have the generative AI perform the modification of the question adjustment method.

[0098] The adjustment unit adjusts the question content based on the user's past answer history during the adjustment process. For example, the adjustment unit prioritizes and presents relevant questions based on the user's past answer history. The adjustment unit analyzes the user's past answer history and proposes the most appropriate questions. The adjustment unit adjusts the question content appropriately according to the user's past answer history. This allows for the provision of more appropriate questions by adjusting the question content based on the user's past answer history. The specific content and storage method of the past answer history are determined based on, for example, the type of answer and the storage period. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input the user's past answer history into a generating AI and have the generating AI perform the question content adjustment.

[0099] The adjustment unit applies different adjustment algorithms during the adjustment process, depending on the user's work history. For example, if the user has extensive work experience, the adjustment unit will ask detailed questions. If the user has limited work experience, the adjustment unit will ask basic questions. The adjustment unit applies the appropriate adjustment algorithm according to the user's work history. This allows for the provision of more appropriate questions by applying the adjustment algorithm according to the user's work history. The specific types and application methods of the adjustment algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above processing in the adjustment unit may be performed using AI, or it may be performed without AI. For example, the adjustment unit can input the user's work history data into a generating AI and have the generating AI execute the application of the adjustment algorithm.

[0100] The adjustment unit estimates the user's emotions and adjusts the order of the next questions based on the estimated emotions. For example, if the user is nervous, the adjustment unit starts with easy questions and gradually increases the difficulty. If the user is relaxed, the adjustment unit starts with difficult questions. If the user is in a hurry, the adjustment unit prioritizes important questions. This allows for the provision of more appropriate questions by adjusting the order of the next questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into a generative AI and have the generative AI perform the adjustment of the question order.

[0101] The adjustment unit determines the priority of questions based on the timing of the user's responses during the adjustment process. For example, the adjustment unit prioritizes questions based on the user's most recent responses. The adjustment unit determines the appropriate priority of questions according to the timing of the user's responses. The adjustment unit presents the most appropriate questions, taking into account the timing of the user's responses. This allows for the provision of more appropriate questions by determining the priority of questions based on the timing of the user's responses. The specific methods and criteria for determining the priority of questions are based on factors such as importance and relevance. Some or all of the above-described processes in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user response timing data into a generating AI and have the generating AI perform the determination of question priorities.

[0102] The practice unit estimates the user's emotions and adjusts the practice method based on the estimated emotions. For example, if the user is nervous, the practice unit suggests a short break to relax before resuming practice. If the user is relaxed, the practice unit starts practice immediately. If the user is in a hurry, the practice unit starts practice quickly. This allows for more effective practice by adjusting the practice method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the practice unit may be performed using AI or not. For example, the practice unit can input user emotion data into a generative AI and have the generative AI adjust the practice method.

[0103] The practice unit adjusts the practice content based on the user's past practice history during practice. For example, the practice unit prioritizes providing relevant practice content based on the user's past practice history. The practice unit analyzes the user's past practice history and proposes the optimal practice content. The practice unit adjusts the practice content appropriately according to the user's past practice history. This allows for more appropriate practice to be provided by adjusting the practice content based on the user's past practice history. The specific content and storage method of past practice history are determined based on, for example, the type of practice and the storage period. Some or all of the above processes in the practice unit may be performed using AI or not. For example, the practice unit can input the user's past practice history into a generating AI and have the generating AI perform the adjustment of the practice content.

[0104] The practice unit applies different practice algorithms to the user during practice sessions, depending on the user's work experience. For example, if the user has extensive work experience, the practice unit provides detailed practice content. If the user has limited work experience, the practice unit provides basic practice content. The practice unit applies the appropriate practice algorithm according to the user's work experience. This allows for more appropriate practice to be provided by applying the practice algorithm according to the user's work experience. The specific types and application methods of the practice algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above-described processes in the practice unit may be performed using AI, or they may not be performed using AI. For example, the practice unit can input the user's work experience data into a generating AI and have the generating AI execute the application of the practice algorithm.

[0105] The practice section estimates the user's emotions and determines the priority of practice based on the estimated emotions. For example, if the user is nervous, the practice section will start with easy exercises and gradually increase the difficulty. If the user is relaxed, the practice section will start with more difficult exercises. If the user is in a hurry, the practice section will prioritize providing important exercises. This allows for more effective practice by determining the priority of exercises according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the practice section may be performed using AI or not. For example, the practice section can input user emotion data into a generative AI and have the generative AI determine the priority of exercises.

[0106] The practice unit selects the optimal practice method during practice, taking into account the user's geographical location information. For example, the practice unit prioritizes providing relevant practice content based on the user's current location. The practice unit prioritizes providing practice content related to a specific industry based on the user's geographical location information. The practice unit proposes the optimal practice method, taking into account the user's geographical location information. In this way, the optimal practice method can be provided by taking into account the user's geographical location information. The specific methods for acquiring and using geographical location information are based on, for example, GPS data, location information services, etc. Some or all of the above processing in the practice unit may be performed using AI, or may not be performed using AI. For example, the practice unit can input the user's geographical location information into a generating AI and have the generating AI perform the selection of practice methods.

[0107] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0108] The reception department analyzes the user's past interview history and selects the optimal reception method. For example, it can suggest the most effective reception method based on the user's past interview history. It can also prioritize specific question patterns based on the user's past interview history. Furthermore, it can analyze the user's past interview history and suggest the optimal interview format. In this way, the optimal reception method can be selected by analyzing the user's past interview history. The specific criteria and selection method for the optimal reception method are based on, for example, the user's attribute information and past behavioral history. Some or all of the above processing in the reception department may be performed using AI, or not. For example, the reception department can input the user's past interview history into a generating AI and have the generating AI select the optimal reception method.

[0109] The analysis unit estimates the user's emotions and adjusts the analysis method for strengths and weaknesses based on the estimated emotions. For example, if the user is nervous, the analysis can start with easy questions and gradually increase in difficulty. If the user is relaxed, it is possible to start with more difficult questions. If the user is in a hurry, important questions can be prioritized. This allows for a more appropriate analysis by adjusting the analysis method for strengths and weaknesses according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI adjust the analysis method for strengths and weaknesses.

[0110] The adjustment unit estimates the user's emotions and modifies the way the next questions are presented based on the estimated emotions. For example, if the user is nervous, the system can start with easy questions and gradually increase the difficulty. If the user is relaxed, it can start with more difficult questions. If the user is in a hurry, important questions can be prioritized. This allows for the provision of more appropriate questions by changing the way the next questions are presented according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the adjustment unit may be performed using AI or not. For example, the adjustment unit can input user emotion data into the generative AI and have the generative AI modify the way the questions are presented.

[0111] The practice unit estimates the user's emotions and adjusts the practice method based on the estimated emotions. For example, if the user is nervous, it can suggest a short break to relax before resuming practice. If the user is relaxed, it can start practicing immediately. If the user is in a hurry, it can start practicing quickly. This allows for more effective practice by adjusting the practice method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the practice unit may be performed using AI or not. For example, the practice unit can input user emotion data into a generative AI and have the generative AI adjust the practice method.

[0112] The feedback unit estimates the user's emotions and adjusts the way feedback is expressed based on the estimated emotions. For example, if the user is nervous, the feedback can be provided in gentle language. If the user is relaxed, detailed feedback can be provided. If the user is in a hurry, concise feedback can be provided. In this way, more effective feedback can be provided by adjusting the way feedback is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI adjust the way feedback is expressed.

[0113] The reception desk filters applications for mock interviews based on the user's current work situation and areas of interest. For example, it can prioritize relevant questions based on the user's current work situation. It can also filter questions related to a specific industry based on the user's areas of interest. It can even suggest optimal questions by combining the user's work situation and areas of interest. This allows for the provision of more relevant questions by filtering based on the user's current work situation and areas of interest. The specific definition and identification methods for work situation and areas of interest are based on, for example, work history and topics of interest. Some or all of the above processing in the reception desk may be performed using AI or not. For example, the reception desk can input data on the user's work situation and areas of interest into a generating AI and have the generating AI perform the filtering of the questions.

[0114] The feedback unit adjusts the level of detail of the feedback based on the user's response when providing feedback. For example, if the user's response is specific, detailed feedback can be provided. If the user's response is vague, concise feedback can be provided. The appropriate level of detail of the feedback can be adjusted according to the user's response. This allows for the provision of more appropriate feedback by adjusting the level of detail based on the user's response. The specific criteria and adjustment methods for the level of detail of the feedback are based on, for example, detailed explanations or concise summaries. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input the user's response into a generating AI and have the generating AI perform the adjustment of the level of detail of the feedback.

[0115] The personalization unit adjusts the questions based on the user's past responses during the personalization process. For example, it can prioritize relevant questions based on the user's past responses. It can also analyze the user's past responses and suggest the most appropriate questions. The questions can be adjusted appropriately according to the user's past responses. This allows for the provision of more appropriate questions by adjusting the questions based on the user's past responses. The specific content and storage method of past responses are determined based on, for example, the type of response and the storage period. Some or all of the above processing in the personalization unit may be performed using AI or not. For example, the personalization unit can input the user's past responses into a generating AI and have the generating AI perform the question adjustments.

[0116] The analysis unit applies different analysis algorithms depending on the user's work history during the analysis. For example, if the user has extensive work experience, a detailed analysis can be performed. If the user has limited work experience, a basic analysis can be performed. The appropriate analysis algorithm can be applied according to the user's work experience. This allows for more accurate analysis by applying the analysis algorithm according to the user's work experience. The specific types and application methods of the analysis algorithms are based on, for example, rule-based or machine learning-based algorithms. Some or all of the above processing in the analysis unit may be performed using AI, or it may be performed without AI. For example, the analysis unit can input the user's work history data into a generating AI and have the generating AI execute the application of the analysis algorithm.

[0117] The training unit selects the optimal training method during training, taking into account the user's geographical location. For example, it can prioritize providing relevant training content based on the user's current location. It can also prioritize providing training content related to a specific industry based on the user's geographical location. It can also suggest the optimal training method, taking the user's geographical location into consideration. This ensures that the optimal training method is provided by considering the user's geographical location. The specific methods for acquiring and using geographical location information are based on, for example, GPS data and location information services. Some or all of the above processing in the training unit may be performed using AI, or not. For example, the training unit can input the user's geographical location information into a generating AI and have the generating AI select the training method.

[0118] The following briefly describes the processing flow for example form 2.

[0119] Step 1: The reception desk has the user open the app and conduct a mock interview with the AI ​​in a conversational format. The reception desk can conduct the conversation in various formats, such as text-based, voice-based, or video-based. Step 2: The feedback department provides real-time feedback based on the information received by the reception department. For example, the feedback department analyzes the user's responses to identify strengths and weaknesses. The feedback department evaluates whether the user's responses are specific, logical, and use appropriate language based on a personalized optimization algorithm using natural language processing technology and deep learning. Step 3: The personalization unit personalizes the questions based on the feedback provided by the feedback unit. For example, the personalization unit adjusts the next question based on the user's past answer history and feedback. The personalization unit personalizes the questions based on a personal optimization algorithm using deep learning.

[0120] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0121] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0122] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0123] Each of the multiple elements described above, including the reception unit, feedback unit, personalization unit, analysis unit, adjustment unit, and practice unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart device 14, where the user opens the app and conducts a mock interview with the AI ​​in a conversational format. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's answers are analyzed and feedback is provided in real time. The personalization unit is implemented by the specific processing unit 290 of the data processing unit 12, where the questions are adjusted based on the user's past answer history and feedback. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's strengths and weaknesses are identified. The adjustment unit is implemented by the control unit 46A of the smart device 14, where the next questions are adjusted based on the user's past answer history and feedback. The practice unit is implemented by the control unit 46A of the smart device 14, where the user can practice regardless of location or time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0124] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0125] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0126] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0127] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0128] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the 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 image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0130] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0131] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0132] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0133] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0134] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0135] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0136] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0137] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0138] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0139] Each of the multiple elements described above, including the reception unit, feedback unit, personalization unit, analysis unit, adjustment unit, and practice unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the smart glasses 214, where the user opens the app and conducts a mock interview with the AI ​​in a conversational format. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's answers are analyzed and feedback is provided in real time. The personalization unit is implemented by the specific processing unit 290 of the data processing unit 12, where the questions are adjusted based on the user's past answer history and feedback. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's strengths and weaknesses are identified. The adjustment unit is implemented by the control unit 46A of the smart glasses 214, where the next questions are adjusted based on the user's past answer history and feedback. The practice unit is implemented by the control unit 46A of the smart glasses 214, where the user can practice regardless of location or time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0140] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0141] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0142] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0143] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0144] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0145] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0146] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0147] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0148] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0149] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0150] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0151] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0152] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0153] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0154] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0155] Each of the multiple elements described above, including the reception unit, feedback unit, personalization unit, analysis unit, adjustment unit, and practice unit, is implemented by at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the headset terminal 314, where the user opens the app and conducts a mock interview with the AI ​​in a conversational format. The feedback unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's answers are analyzed and feedback is provided in real time. The personalization unit is implemented by the specific processing unit 290 of the data processing unit 12, where the questions are adjusted based on the user's past answer history and feedback. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12, where the user's strengths and weaknesses are identified. The adjustment unit is implemented by the control unit 46A of the headset terminal 314, where the next questions are adjusted based on the user's past answer history and feedback. The practice unit is implemented by the control unit 46A of the headset terminal 314, where the user can practice regardless of location or time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0156] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0157] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0158] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0159] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0160] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0161] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0162] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0163] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0164] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0165] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0166] Storage 32 stores the data generation model 58 and the 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 emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0167] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0169] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0170] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0171] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0172] Each of the multiple elements described above, including the reception unit, feedback unit, personalization unit, analysis unit, adjustment unit, and practice unit, is implemented by, for example, at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the control unit 46A of the robot 414, where the user opens the app and conducts a mock interview with the AI ​​in a conversational format. The feedback unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which analyzes the user's answers and provides feedback in real time. The personalization unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which adjusts the content of the questions based on the user's past answer history and feedback. The analysis unit is implemented by, for example, the specific processing unit 290 of the data processing unit 12, which identifies the user's strengths and weaknesses. The adjustment unit is implemented by, for example, the control unit 46A of the robot 414, which adjusts the next questions based on the user's past answer history and feedback. The practice unit is implemented by, for example, the control unit 46A of the robot 414, allowing the user to practice regardless of location or time. The correspondence between each part and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0173] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0174] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0175] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0176] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0177] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0178] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0179] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0180] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0181] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0182] 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.

[0183] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0184] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0185] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0186] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0187] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0188] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0189] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0190] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0191] (Note 1) The receptionist opens the app and conducts a mock interview with the AI ​​in a conversational format. Based on the information received by the aforementioned reception unit, a feedback unit provides real-time feedback, The system includes a personalization unit that personalizes the question content based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features. (Note 2) It includes an analysis department that identifies the strengths and weaknesses of users. The system described in Appendix 1, characterized by the features described herein. (Note 3) It includes an adjustment unit that adjusts the next question based on the user's past answer history and feedback. The system described in Appendix 1, characterized by the features described herein. (Note 4) The facility includes a practice area where users can practice regardless of location or time. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned reception unit is The system estimates the user's emotions and adjusts the start time of the mock interview based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned reception unit is Analyze the user's past interview history and select the most suitable application method. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is When registering for a mock interview, filtering is performed based on the user's current work situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is The system estimates the user's emotions and prioritizes the interview content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When users register for mock interviews, the system prioritizes accepting applications based on their geographical location and the likelihood of the interview being relevant to their situation. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is When a user registers for a mock interview, the system analyzes their social media activity and accepts relevant interview questions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned feedback unit is It estimates the user's emotions and adjusts how feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned feedback unit is When providing feedback, adjust the level of detail in the feedback based on the user's response. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned feedback unit is When providing feedback, different feedback algorithms are applied depending on the user's work history. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned feedback unit is It estimates the user's emotions and adjusts the length of the feedback based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned feedback unit is When providing feedback, we prioritize the feedback based on when the user responded. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned feedback unit is When providing feedback, the order of feedback will be adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 17) The personalization unit described above is It estimates the user's emotions and adjusts how questions are personalized based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 18) The personalization unit described above is When personalizing, the questions are adjusted based on the user's past responses. The system described in Appendix 1, characterized by the features described herein. (Note 19) The personalization unit described above is When personalizing, different question algorithms are applied depending on the user's work history. The system described in Appendix 1, characterized by the features described herein. (Note 20) The personalization unit described above is The system estimates the user's emotions and adjusts the length of the questions based on those emotions. The system described in Appendix 1, characterized by the features described herein. (Note 21) The personalization unit described above is When personalizing, the priority of questions is determined based on when the user responded. The system described in Appendix 1, characterized by the features described herein. (Note 22) The personalization unit described above is During personalization, the order of questions is adjusted based on user relevance. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned analysis unit is It estimates user emotions and adjusts the analysis of strengths and weaknesses based on the estimated user emotions. The system described in Appendix 2, characterized by the features described herein. (Note 24) The aforementioned analysis unit is During analysis, strengths and weaknesses are identified based on the user's past responses. The system described in Appendix 2, characterized by the features described herein. (Note 25) The aforementioned analysis unit is During analysis, different analysis algorithms are applied depending on the user's work history. The system described in Appendix 2, characterized by the features described herein. (Note 26) The aforementioned analysis unit is It estimates the user's emotions and adjusts how strengths and weaknesses are displayed based on those estimated emotions. The system described in Appendix 2, characterized by the features described herein. (Note 27) The aforementioned analysis unit is During the analysis, we prioritize strengths and weaknesses based on when users responded. The system described in Appendix 2, characterized by the features described herein. (Note 28) The adjustment unit is, It estimates the user's emotions and adjusts how the next questions are phrased based on those estimated emotions. The system described in Appendix 3, characterized by the features described herein. (Note 29) The adjustment unit is, During the adjustment process, the question content will be adjusted based on the user's past answer history. The system described in Appendix 3, characterized by the features described herein. (Note 30) The adjustment unit is, During the adjustment process, different adjustment algorithms are applied depending on the user's work history. The system described in Appendix 3, characterized by the features described herein. (Note 31) The adjustment unit is, The system estimates the user's emotions and adjusts the order of subsequent questions based on those emotions. The system described in Appendix 3, characterized by the features described herein. (Note 32) The adjustment unit is, During the adjustment process, the priority of questions is determined based on when the user responded. The system described in Appendix 3, characterized by the features described herein. (Note 33) The aforementioned practice unit, It estimates the user's emotions and adjusts the practice method based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 34) The aforementioned practice unit, During practice, the practice content is adjusted based on the user's past practice history. The system described in Appendix 4, characterized by the features described herein. (Note 35) The aforementioned practice unit, During practice, different practice algorithms are applied depending on the user's work experience. The system described in Appendix 4, characterized by the features described herein. (Note 36) The aforementioned practice unit, It estimates the user's emotions and determines the priority of practice based on the estimated user emotions. The system described in Appendix 4, characterized by the features described herein. (Note 37) The aforementioned practice unit, During practice, the system selects the optimal practice method by considering the user's geographical location. The system described in Appendix 4, characterized by the features described herein. [Explanation of Symbols]

[0192] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The receptionist opens the app and conducts a mock interview with the AI ​​in a conversational format. Based on the information received by the aforementioned reception unit, a feedback unit provides real-time feedback, The system includes a personalization unit that personalizes the question content based on the feedback provided by the aforementioned feedback unit. A system characterized by the following features.

2. It includes an analysis department that identifies the strengths and weaknesses of users. The system according to feature 1.

3. It includes an adjustment unit that adjusts the next question based on the user's past answer history and feedback. The system according to feature 1.

4. The facility includes a practice area where users can practice regardless of location or time. The system according to feature 1.

5. The aforementioned reception unit is The system estimates the user's emotions and adjusts the start time of the mock interview based on those emotions. The system according to feature 1.

6. The aforementioned reception unit is Analyze the user's past interview history and select the most suitable application method. The system according to feature 1.

7. The aforementioned reception unit is When registering for a mock interview, filtering is performed based on the user's current work situation and areas of interest. The system according to feature 1.

8. The aforementioned reception unit is The system estimates the user's emotions and prioritizes the interview content based on those estimated emotions. The system according to feature 1.

9. The aforementioned reception unit is When users register for mock interviews, the system prioritizes accepting applications based on their geographical location and the likelihood of the interview being relevant to their situation. The system according to feature 1.

10. The aforementioned reception unit is When a user registers for a mock interview, the system analyzes their social media activity and accepts relevant interview questions. The system according to feature 1.

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