system

Abstract

We provide the system. [Solution] A terminal device that accepts voice input, A speech recognition means for converting received audio into text, A natural language processing method that analyzes the converted text and detects inconsistencies and errors, A scoring method that grades the responses and calculates a score based on the analysis results, A feedback generation means that generates feedback for improvement based on the analysis results and scores, A display means for notifying the user of the generated feedback, A system that includes this.

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 modern society, interviews in entrance examinations, job hunting activities, etc. play an important role, but due to limited practical interview practice opportunities, there is a lack of means to efficiently improve skills. Against this background, there is a need for a system that can provide high-quality individual interview practice.

Means for Solving the Problems

[0005] The system of the present invention includes terminal means for receiving voice input, voice recognition means for converting the received voice into text, natural language processing means for analyzing the text and detecting inconsistencies and errors, scoring means for scoring the answers and calculating a score, feedback generation means for generating feedback that specifies areas for improvement, and display means for notifying the user of the generated feedback, thereby providing interview practice optimized for each individual user and supporting skill improvement.

[0006] A "device that accepts voice input" refers to a device that allows users to input information using their voice.

[0007] "Speech recognition means" refers to the technology or process of converting speech into digital text.

[0008] "Natural language processing methods" refer to technologies that analyze and process human language mechanically.

[0009] "Scoring means" refers to a technology or process that generates an evaluation score based on analyzed information.

[0010] "Feedback generation means" refers to technologies that provide users with improvement suggestions and information based on analysis results.

[0011] "Display means" refers to a technology or device that visually presents generated feedback or information to the user. [Brief explanation of the drawing]

[0012] [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]It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] It 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] It is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] It 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] It shows an emotion map to which a plurality of emotions are mapped. [Figure 10] It shows an emotion map to which a plurality of emotions are mapped. [Figure 11] It is a sequence diagram showing the processing flow of the data processing system in Example 1. [Figure 12] It is a sequence diagram showing the processing flow of the data processing system in Application Example 1. [Figure 13] It is a sequence diagram showing the processing flow of the data processing system in Example 2 when an emotion engine is combined. [Figure 14] It is a sequence diagram showing the processing flow of the data processing system in Application Example 2 when an emotion engine is combined.

Embodiments for Carrying Out the Invention

[0013] Hereinafter, an example of an embodiment of a system according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0014] First, the language used in the following description will be explained.

[0015] In the following embodiments, the labeled processor (hereinafter simply referred to as "processor") may be a single arithmetic unit or a combination of multiple arithmetic units. Also, the processor may be a single type of arithmetic unit or a combination of multiple types of arithmetic units. Examples of arithmetic units include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), and the like.

[0016] In the following embodiments, the labeled RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a work memory by the processor.

[0017] In the following embodiments, the labeled storage is one or more non-volatile storage devices that store various programs and various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), or magnetic tapes, etc.

[0018] In the following embodiments, the labeled communication I / F (Interface) is an interface including a communication processor and an antenna, etc. The communication I / F controls communication between multiple computers. Examples of communication standards applied to the communication I / F include wireless communication standards including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), or Bluetooth (registered trademark), etc.

[0019] 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 A alone, or B alone, or a combination of A and B. Furthermore, in this specification, the same concept as "A and / or B" applies when expressing three or more things linked by "and / or."

[0020] [First Embodiment]

[0021] Figure 1 shows an example of the configuration of the data processing system 10 according to the first embodiment.

[0022] As shown in Figure 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.

[0023] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0024] The smart device 14 comprises a computer 36, a reception 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 reception device 38, output device 40, and camera 42 are also connected to the bus 52.

[0025] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, etc., and receives user input. The touch panel 38A receives user input by detecting contact with an object (e.g., a pen or finger). The microphone 38B receives user input 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 device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0026] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user 20 by outputting the data in a form perceptible to the user 20 (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.

[0027] 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.

[0028] Figure 2 shows an example of the main functions of the data processing device 12 and the smart device 14.

[0029] 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.

[0030] The 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.

[0031] In the smart device 14, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The reception output program 60 is used in conjunction with a specific processing program 56 by the data processing system 10. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0032] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0033] This invention provides a system that utilizes voice input to offer users high-quality interview practice. This system supports interview practice through the cooperation of the user, terminal, and server. Specific embodiments are described below.

[0034] The user first logs into the system via a terminal and selects the type of interview they want to practice. The terminal sends a request to the server, which generates interview questions based on information in its database. The generated questions are sent to the terminal and presented to the user.

[0035] The user answers the presented questions using the device's microphone. The device records the audio and sends it to the server in real time. The server converts this audio into text using speech recognition and analyzes the answers using natural language processing. During the analysis process, the server detects inconsistencies and errors in the answers and calculates an evaluation score for each answer using a scoring system.

[0036] Next, the server uses a feedback generation mechanism to generate feedback based on the evaluation results and detected areas for improvement. The feedback includes suggestions to improve the specificity and logical consistency of the responses. The generated feedback is sent to the terminal and notified to the user.

[0037] Based on this feedback, users can improve their answers. By repeating this process, users can efficiently improve their interview skills. This embodiment allows users to receive high-quality evaluations and feedback, preparing them for actual interviews.

[0038] The following describes the processing flow.

[0039] Step 1:

[0040] The user operates a device to log in to the interview practice system and selects the type of interview they want to practice. The device then sends this selection information to the server.

[0041] Step 2:

[0042] The server receives the selection information and searches the database for the corresponding question data. The server uses a question generation algorithm to generate a specific question and sends it to the terminal.

[0043] Step 3:

[0044] The terminal presents the user with a question received from the server, either verbally or in text. The user then answers the presented question verbally.

[0045] Step 4:

[0046] The terminal records the user's voice input in real time and transfers the audio data to the server.

[0047] Step 5:

[0048] The server converts the received audio data into text using speech recognition technology. The converted text is then analyzed using natural language processing technology to detect inconsistencies and errors.

[0049] Step 6:

[0050] Based on the analysis results, the server uses a scoring algorithm to evaluate the responses and calculate a score.

[0051] Step 7:

[0052] The server generates feedback based on the evaluation results and analyzed areas for improvement. This feedback includes specific improvement suggestions.

[0053] Step 8:

[0054] The device receives feedback from the server and displays it to the user. After reviewing the feedback, the user can improve their answers and start a new practice session.

[0055] (Example 1)

[0056] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0057] In interview practice, there is a problem in that it is difficult for users to objectively evaluate and improve their own answers. In particular, there is a lack of systems that provide effective feedback for skill improvement using voice input. Solving this problem will allow users to prepare for interviews more efficiently.

[0058] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0059] In this invention, the server includes a speech conversion means, a natural language processing means, and a solution generation means. This enables the conversion of the user's voice input into text, as well as detailed response analysis and feedback provision.

[0060] "Voice input" is a method in which users provide data to an information processing device using their voice.

[0061] An "information processing device" refers to an electronic device used to process voice and text input and perform judgments and evaluations.

[0062] "Speech conversion means" refers to technologies and devices for converting speech data into text data.

[0063] "Natural language processing methods" are technologies used to analyze text data and understand its content and meaning.

[0064] "Evaluation methods" refer to the process of quantifying and ranking user responses based on analyzed data.

[0065] The "solution generation method" is a function that creates suggestions and advice to improve the user's response based on the evaluation results.

[0066] "Presentation means" refers to the means of displaying or notifying the user of the generated feedback.

[0067] "Data transmission means" refers to the functions and technologies for sending and receiving data between an information processing device and a computing device.

[0068] A "dialogue mechanism" is a system that allows users to try again or make corrections based on feedback.

[0069] This invention is an interview practice support system that utilizes voice input. The system primarily operates through the cooperation of the user, terminal, and server. A specific embodiment of this system is described below.

[0070] First, the user logs in to the terminal, which is an information processing device. Logging in is done using an information processing device such as a PC or smartphone, and accessing it via an internet connection. The user then selects the type of interview they want to practice from the displayed selection menu. For example, they can choose "job interview for experienced professionals" or "new graduate interview for entry-level positions."

[0071] Subsequently, the terminal transmits the user's selection information to the server using a communication method as data for question generation. The server retrieves appropriate questions from its database and uses a generation AI model to create interview questions. The questions are returned to the terminal and presented visually to the user. When the user answers the presented questions verbally, they record their answers using the terminal's voice input function. For example, a question like "Please tell me what you learned from your recent project" might be used.

[0072] The audio data is immediately sent to the server, which uses speech recognition software as a speech-to-text conversion tool to convert the audio into text. Then, natural language processing tools are used to analyze the text and identify inconsistencies and areas for improvement in the user's response. Once the analysis is complete, the server calculates a score for the response using an evaluation tool, and based on this result, a solution generation tool creates feedback.

[0073] The feedback is notified to the device and presented to the user as specific areas for improvement. For example, suggestions such as "Please add specific examples to your answer" may be made. Based on this feedback, the user can reconsider their answer and revise it if necessary.

[0074] This system functions as a practical tool to help users efficiently improve their interview skills and effectively support interview preparation. By using the prompt "Please give specific examples that demonstrate your understanding of the question," the AI ​​model generates detailed feedback.

[0075] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0076] Step 1:

[0077] The user logs into the system using their terminal. The login screen prompts the user to enter their user ID and password. The entered information is sent to the server, which verifies the user information against its database and performs authentication. Upon successful authentication, a menu for selecting the interview type is displayed on the terminal.

[0078] Step 2:

[0079] The user selects the interview type on their device. The selected information is sent to the server as necessary information for using the AI ​​model generated from the device. Based on the received data, the server retrieves relevant interview question data from its database.

[0080] Step 3:

[0081] The server uses a generative AI model to generate interview questions based on the acquired data. This AI model constructs the questions using prompts. The generated questions are sent from the server to the terminal and presented to the user in text format.

[0082] Step 4:

[0083] The user answers the presented questions using the device's microphone. The device records the voice input and sends it to the server. In this process, the device samples the voice data in digital format and sends it to the server as a file.

[0084] Step 5:

[0085] The server uses speech recognition software to convert audio data into text data. This conversion involves analyzing the audio waveform data and converting it into a string of characters. The converted text is then analyzed by natural language processing tools.

[0086] Step 6:

[0087] The server uses natural language processing to analyze the text data. During this process, it identifies inconsistencies and logical inconsistencies within the responses. The analysis results are then input into the next evaluation step.

[0088] Step 7:

[0089] The server calculates a score for the response based on the analysis results using an evaluation tool. This evaluation assigns points based on criteria such as logic, specificity, and fluency. The calculated score is then used to generate subsequent feedback.

[0090] Step 8:

[0091] The server uses a solution generation mechanism to generate feedback based on the evaluation score and text analysis results. This feedback includes specific suggestions for improvement. The generated feedback is then sent from the server to the terminal.

[0092] Step 9:

[0093] The terminal notifies and displays feedback received from the server to the user. The user then uses the provided feedback to improve their answers. By analyzing this feedback and re-responding as needed, the goal is to improve interview skills.

[0094] (Application Example 1)

[0095] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart device 14 will be referred to as the "terminal."

[0096] In today's technologically advanced world, it is crucial to provide individual users with opportunities to improve their communication skills. However, traditional interview practice methods are repetitive and lack the immediate, specific feedback needed. Therefore, there is a need to develop systems that enable efficient and high-quality interview practice at home.

[0097] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0098] In this invention, the server includes a device for acquiring audio information, a speech conversion means for converting the acquired audio information to generate text information, and a natural language processing means for analyzing the generated text information and detecting inconsistencies or omissions in the arguments. This allows users to receive real-time feedback and effectively practice interviews at home.

[0099] A "device for acquiring voice information" is a device that receives voice input from a user and records that information.

[0100] "Voice conversion means" refers to a device or software that has the function of processing acquired voice information and converting it into text information.

[0101] "Natural language analysis means" refers to techniques or methods for analyzing textual information and detecting illogical or inconsistent content.

[0102] The "evaluation method" is a mechanism that numerically evaluates user responses based on information obtained through natural language processing.

[0103] A "guideline generation method" is a system that generates advice and suggestions to improve the user's response based on the results calculated by the evaluation method.

[0104] A "display device" is a device used to visually present generated guidelines or other information to the user.

[0105] A "human-machine interaction device" is an interface technology that enables two-way information exchange between a user and a machine.

[0106] A "communication device" is a communication infrastructure used to send or receive data from a terminal to a remote server.

[0107] The system of this invention is designed to support a user's interview practice based on voice information. The system consists of a device for acquiring voice information, a voice conversion means, a natural language processing means, an evaluation means, a guideline generation means, a display device, and a human-machine interaction device.

[0108] The server receives audio information from the user and converts it into text using a speech-to-text conversion tool. Existing speech recognition software, such as Google® Speech-to-Text, is used for this process. After the audio is converted into text, the content is analyzed by a natural language processing tool. This analysis utilizes natural language processing tools such as spaCy or NLTK. The analysis detects illogical inconsistencies and contradictions in the response, and the results are numerically evaluated by an evaluation tool.

[0109] Furthermore, the terminal uses a guideline generation mechanism to generate suggestions for improving the user's answers based on the evaluation results. These suggestions are provided to the user via a display device, allowing the user to practice their answers again based on these suggestions. For example, prompts such as "Please tell me specific ways to solve difficult situations in the workplace" are used. This system enables users to experience effective and high-quality interview practice from the comfort of their homes.

[0110] This invention allows users to improve their interview skills at their own pace, anytime, anywhere.

[0111] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0112] Step 1:

[0113] The user accesses the terminal, logs into the system, and selects the type of interview they want to practice. This selection information is entered, and the terminal sends a request to the server. The server generates questions based on the received information and sends them to the terminal.

[0114] Step 2:

[0115] The terminal displays a question sent from the server to the user. The user answers the presented question verbally. This voice input is acquired by the terminal and sent to the server as an audio file.

[0116] Step 3:

[0117] The server uses a speech conversion device to convert the audio file received from the terminal into text information. The converted text information is output and used as data for the next analysis process.

[0118] Step 4:

[0119] The server analyzes the textual information using natural language processing tools and evaluates the logic and consistency of the response. Inconsistencies and deficiencies are identified as a result of the analysis, and this information is passed to the evaluation tool.

[0120] Step 5:

[0121] The server numerically evaluates the user's responses based on the analysis results using an evaluation method. The output here is represented as an evaluation score and becomes data for generating feedback.

[0122] Step 6:

[0123] The server uses a guidance generation mechanism with the evaluation score as input to generate specific suggestions for improving the user's response. These suggestions are sent to the terminal via a display device for the user to receive.

[0124] Step 7:

[0125] Users can practice again based on the displayed suggestions and repeat the same process as needed. This allows for continuous improvement.

[0126] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0127] This invention combines a voice input system with an emotion engine to provide users with more comprehensive interview practice. This system analyzes voice to recognize not only the content of the user's responses but also their emotional state, thereby supporting improved expressiveness during interviews. Specific embodiments are described below.

[0128] First, the user accesses the system through a terminal and selects the type of interview they want to practice. The terminal sends the user's selection to the server, which then generates questions. These questions are sent to the terminal and presented to the user either verbally or as text.

[0129] When a user responds verbally, the device sends the audio data to the server. The server converts this data into text using speech recognition and analyzes it using natural language processing. The server also uses an emotion engine to recognize the user's emotional state from the audio. This emotion analysis allows the system to evaluate how the emotional expression during practice affects the interviewer.

[0130] Based on the analysis results, the server evaluates the responses using a scoring system and generates feedback using a feedback generation system. This feedback includes not only areas for improvement in the responses themselves, but also advice on how to express emotions. For example, if tension is detected, specific suggestions such as "Relax and showcase your strengths more" will be given.

[0131] Finally, the generated feedback is notified to the user via their device. Based on this feedback, the user can improve both the content and emotional expression of their interview responses. By repeating this process, the user can develop richer and more expressive interview skills.

[0132] The following describes the processing flow.

[0133] Step 1:

[0134] The user accesses the system using a device and selects the type of interview they want to practice. The device then sends the selected information to the server.

[0135] Step 2:

[0136] Based on the information received by the server, it searches the database for question data. The server generates a question and sends it to the terminal.

[0137] Step 3:

[0138] The device presents the received question to the user via voice or text. The user then answers the question via voice.

[0139] Step 4:

[0140] The device records the user's voice response and prepares to send it to the server in real time.

[0141] Step 5:

[0142] The server converts the received audio data into text using speech recognition technology. Simultaneously, it analyzes the user's emotional state from the audio using an emotion engine.

[0143] Step 6:

[0144] The server analyzes the text obtained through speech recognition using natural language processing tools to detect inconsistencies and errors in the content.

[0145] Step 7:

[0146] The server evaluates the responses using a scoring method based on the results of text analysis and sentiment analysis, and calculates a score.

[0147] Step 8:

[0148] Based on the analysis results and scores, the server generates feedback using a feedback generation mechanism. This feedback includes suggestions for improving the answers and advice on expressing emotions.

[0149] Step 9:

[0150] The device receives the generated feedback from the server and displays it to the user. The user can review the feedback and practice again based on the areas for improvement.

[0151] (Example 2)

[0152] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart device 14 as the "terminal".

[0153] In modern communication and evaluation methods, providing comprehensive feedback that includes not only information retrieval from voice but also recognition of emotional states is a challenging task. Especially when practicing for interviews or improving communication skills, not only the content of the user's responses but also their emotional expression are crucial elements. However, existing technologies struggle to accurately recognize a user's emotional state and provide feedback based on that understanding.

[0154] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0155] In this invention, the server includes an information terminal means for receiving voice input, an emotion analysis means for evaluating emotions and recognizing emotional states based on analysis results, and a feedback creation method means for generating feedback for improvement based on the analysis results and emotional states. This enables comprehensive interview practice support by combining information obtained from voice with the emotional state at that time.

[0156] A "voice input-enabled information terminal" is a device that accepts voice information emitted by a user and processes that information as digital data.

[0157] A "speech recognition device for converting speech to text" is a device that analyzes received speech data and converts it into textual information.

[0158] A "natural language processing device" is a processing device that analyzes converted text data and understands its meaning and context.

[0159] "Emotion analysis means" refers to a technical means that analyzes data obtained from the user's voice to determine their emotional state.

[0160] An "evaluation tool" is a device that quantitatively scores the user's responses and evaluates their quality based on the analyzed data and emotional state.

[0161] A "feedback generation method" is a technical technique that generates responses indicating areas for improvement based on analysis results and evaluations.

[0162] An "information display means" is a device that visually presents information in order to notify the user of the generated feedback.

[0163] "Information and communication means" refers to communication technologies for sending and receiving data between information terminals and central processing units.

[0164] An "interaction device" is a technological device that requires effective interaction when the user refers to feedback and practices again.

[0165] This invention is a system that improves users' communication skills through voice input. Specifically, it has functions to receive voice input, convert it to text, perform sentiment analysis, and generate feedback.

[0166] First, the user inputs voice using a device. The device acts as a hub for sending the voice data to the server. The server then uses a speech recognition device to convert the voice data into text. A language model is used to improve accuracy during this process. The converted text data is analyzed by natural language processing tools to detect meaning and errors.

[0167] Next, the server uses emotion analysis tools to analyze the emotion data extracted from the voice. This analysis allows the server to understand the user's emotional state and determine, for example, whether they are tense or relaxed.

[0168] Based on these analysis results, the server quantitatively evaluates the user's responses using evaluation tools. This evaluation is based on the quality of the content and the expression of emotion. Then, feedback is generated that encourages the user to improve, according to the feedback generation method. An example of such feedback is, "Relax and showcase your strengths more."

[0169] Finally, the generated feedback is sent to the terminal via an information display device and notified to the user. The user can then use this feedback to improve their communication skills. An example of a prompt might be, "Generate feedback for a user who is nervous when asked 'Please introduce yourself' in a sales job interview."

[0170] This system is designed to support the improvement of comprehensive communication skills and will serve as a valuable practice tool for users.

[0171] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0172] Step 1:

[0173] The user accesses the interview practice system through their device. The user selects the type of interview they wish to practice and enters this selection into their device. The device then sends this information to a server, requesting that appropriate questions be generated based on the selected interview type. The input is the interview type, and the output is the request data sent to the server. Specific actions include the user tapping on options on the screen to enter their selection data into the fields.

[0174] Step 2:

[0175] The server uses information received from the terminal to generate relevant questions using a generative AI model. The server constructs a set of questions that match the interview format selected by the user and sends the generated questions to the terminal. The input is the interview type information received from the terminal, and the output is the generated question data. Specifically, the AI ​​model performs text generation and format conversion for requests.

[0176] Step 3:

[0177] The terminal receives questions sent from the server and presents them to the user. When the user answers the interview questions by voice, the terminal acquires the audio data. In this step, the input is the question data from the server and the user's voice, and the output is the recorded audio data. Specifically, the terminal's microphone function is used to capture the audio.

[0178] Step 4:

[0179] The terminal compresses the recorded audio data and sends it to the server. The server receives this audio data and converts it to text using a speech recognition device. The input is the user's audio data, and the output is text data generated by speech recognition. Specifically, the process involves digital processing of the audio data on the server and conversion by the recognition engine.

[0180] Step 5:

[0181] The server processes the converted text data using natural language processing (NLP) to analyze the user's responses. In addition, it analyzes emotional information extracted from the speech using sentiment analysis tools. The input is text data, and the output is the resulting understanding of the content and emotional information. Specific operations include grammar checking and emotional pattern identification by the analysis engine.

[0182] Step 6:

[0183] The server quantitatively evaluates the user's responses using an evaluation tool based on the analyzed data. Using the evaluation results, it generates feedback for the user using a feedback creation method. The input is the analysis results and emotional state data, and the output is the generated feedback. Specifically, it performs numerical scoring based on evaluation criteria and creates feedback statements.

[0184] Step 7:

[0185] The server sends the generated feedback to the terminal, which then notifies the user. The user reviews the feedback and aims to improve their interview skills based on its content. The input is the generated feedback data, and the output is the display of the feedback. Specifically, the process involves displaying the feedback text on the terminal's display for the user to review.

[0186] (Application Example 2)

[0187] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as a "server" and the smart device 14 as a "terminal".

[0188] In modern interview practice, it is a challenging task for users to receive comprehensive feedback on not only the content of their statements but also their emotional expression and attitude. Traditional systems lack emotional analysis, making it difficult to adequately assess the impact a user's demeanor and emotional state have on the interviewer. This makes it difficult for users to achieve the desired results in actual interviews.

[0189] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0190] In this invention, the server includes emotion analysis means for recognizing emotional states from voice, feedback generation means for generating information for improvement based on the analysis results and emotion analysis, and display means for notifying the user of the generated information. This enables accurate evaluation of the user's voice and the associated emotional state, allowing for more improved interview practice.

[0191] A "voice input receiving information processing device" is a device that acquires the content spoken by a user as data and processes it within the system.

[0192] "A speech recognition method for converting into a sequence of symbols" is a method for converting received speech data into a sequence of characters or symbols, and is implemented using speech recognition technology.

[0193] "Natural language processing means for analyzing converted symbol sequences and detecting logical errors" refers to processing technology for analyzing strings obtained through speech recognition and detecting semantic errors and grammatical inconsistencies.

[0194] An "evaluation method for evaluating a response and calculating a numerical value" is a method for objectively evaluating the content and structure of an analyzed response and expressing the results as a numerical value.

[0195] A "feedback generation method for generating information for improvement" is a technique that generates advice and suggestions for improvement based on evaluation results to enhance the user's speech.

[0196] "A means of displaying generated information to the user" refers to a method for conveying feedback information to the user in an easy-to-understand manner, and can be used for screen displays, audio notifications, etc.

[0197] "An emotion analysis method that recognizes emotional states from voice" is a method that identifies emotional states and changes based on the user's voice data.

[0198] This invention is a system that provides comprehensive feedback to users when they practice for interviews by combining voice input and emotion analysis. This is realized by a system that includes: an information processing device that accepts voice input; a voice recognition means for converting it into a sequence of symbols; a natural language processing means for analyzing the converted sequence of symbols and detecting logical errors; an evaluation means for evaluating the response and calculating a numerical value; a feedback generation means for generating information for improvement; a display means for notifying the user of the generated information; and an emotion analysis means for recognizing emotional states from voice.

[0199] An information processing device that accepts voice input acts as a terminal, acquiring the user's spoken content as data. This data is converted into a sequence of symbols via a cloud server using speech recognition software (e.g., Google Speech-to-Text API). The converted sequence of symbols is then analyzed using natural language processing techniques (e.g., Python's NLTK toolkit) to detect logical errors in the response.

[0200] Furthermore, the server performs a numerical evaluation of the analyzed response using an evaluation tool. Based on this evaluation, a feedback generation tool generates information for improvement. The feedback also takes emotional states into consideration, and for sentiment analysis, for example, the Emotion API of Azure Cognitive Services can be used. The generated information is notified to the user's terminal by a display tool.

[0201] For example, if a user responds nervously to the question, "Please introduce yourself," the system can provide feedback such as, "Relax more and try talking about your hobbies."

[0202] An example of a prompt for the generative AI model is, "Analyze the user's current emotions from this audio data and provide feedback in a cheerful tone." This is used to recognize the user's emotional state and provide appropriate feedback accordingly.

[0203] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0204] Step 1:

[0205] The user begins voice input. The terminal receives voice data from the user. This input voice data is used for subsequent processing.

[0206] Step 2:

[0207] The device sends voice input to the server. The server uses speech recognition technology to convert the voice data into a sequence of symbols. This process uses the Google Speech-to-Text API to obtain the voice data as text. The output text is used in the next parsing step.

[0208] Step 3:

[0209] The server analyzes the converted text using natural language processing techniques. It uses the Python natural language processing toolkit NLTK to detect grammatical and logical errors in the text. The analysis results are then fed into a subsequent evaluation step.

[0210] Step 4:

[0211] Based on the analysis results, the server evaluates the response using evaluation tools and quantifies it. The evaluation results output numerical values ​​for the accuracy and expressiveness of the response. This allows for a quantitative evaluation of the quality of the user's speech.

[0212] Step 5:

[0213] The server recognizes the user's emotional state from the voice data. Using the Azure Cognitive Services Emotion API, it identifies emotions from voice tone and speed. This analysis includes the type and intensity of emotion, which is then used to generate subsequent feedback.

[0214] Step 6:

[0215] The server generates feedback that includes areas for improvement based on accurate response evaluation and sentiment analysis results. The feedback generation mechanism uses a generation AI model to create appropriate prompt sentences and provide advice such as relaxation.

[0216] Step 7:

[0217] The generated feedback information is notified to the terminal via a display device. The user reviews this feedback and works to improve their speech. In this way, interview practice progresses more effectively.

[0218] 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.

[0219] Data generation model 58 is a 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> ), Gemini (registered trademark) (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0220] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart device 14.

[0221] [Second Embodiment]

[0222] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0223] 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.

[0224] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0225] 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.

[0226] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0227] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0228] 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.

[0229] 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 using the processor 28. The storage 32 stores the specific processing program 56.

[0230] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0231] The 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.

[0232] In the smart glasses 214, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0233] Next, the identification processing performed by the identification processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0234] This invention provides a system that utilizes voice input to offer users high-quality interview practice. This system supports interview practice through the cooperation of the user, terminal, and server. Specific embodiments are described below.

[0235] The user first logs into the system via a terminal and selects the type of interview they want to practice. The terminal sends a request to the server, which generates interview questions based on information in its database. The generated questions are sent to the terminal and presented to the user.

[0236] The user answers the presented questions using the device's microphone. The device records the audio and sends it to the server in real time. The server converts this audio into text using speech recognition and analyzes the answers using natural language processing. During the analysis process, the server detects inconsistencies and errors in the answers and calculates an evaluation score for each answer using a scoring system.

[0237] Next, the server uses a feedback generation mechanism to generate feedback based on the evaluation results and detected areas for improvement. The feedback includes suggestions to improve the specificity and logical consistency of the responses. The generated feedback is sent to the terminal and notified to the user.

[0238] Based on this feedback, users can improve their answers. By repeating this process, users can efficiently improve their interview skills. This embodiment allows users to receive high-quality evaluations and feedback, preparing them for actual interviews.

[0239] The following describes the processing flow.

[0240] Step 1:

[0241] The user operates a device to log in to the interview practice system and selects the type of interview they want to practice. The device then sends this selection information to the server.

[0242] Step 2:

[0243] The server receives the selection information and searches the database for the corresponding question data. The server uses a question generation algorithm to generate a specific question and sends it to the terminal.

[0244] Step 3:

[0245] The terminal presents the user with a question received from the server, either verbally or in text. The user then answers the presented question verbally.

[0246] Step 4:

[0247] The terminal records the user's voice input in real time and transfers the audio data to the server.

[0248] Step 5:

[0249] The server converts the received audio data into text using speech recognition technology. The converted text is then analyzed using natural language processing technology to detect inconsistencies and errors.

[0250] Step 6:

[0251] Based on the analysis results, the server uses a scoring algorithm to evaluate the responses and calculate a score.

[0252] Step 7:

[0253] The server generates feedback based on the evaluation results and analyzed areas for improvement. This feedback includes specific improvement suggestions.

[0254] Step 8:

[0255] The device receives feedback from the server and displays it to the user. After reviewing the feedback, the user can improve their answers and start a new practice session.

[0256] (Example 1)

[0257] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0258] In interview practice, there is a problem in that it is difficult for users to objectively evaluate and improve their own answers. In particular, there is a lack of systems that provide effective feedback for skill improvement using voice input. Solving this problem will allow users to prepare for interviews more efficiently.

[0259] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0260] In this invention, the server includes a speech conversion means, a natural language processing means, and a solution generation means. This enables the conversion of the user's voice input into text, as well as detailed response analysis and feedback provision.

[0261] "Voice input" is a method in which users provide data to an information processing device using their voice.

[0262] An "information processing device" refers to an electronic device used to process voice and text input and perform judgments and evaluations.

[0263] "Speech conversion means" refers to technologies and devices for converting speech data into text data.

[0264] "Natural language processing methods" are technologies used to analyze text data and understand its content and meaning.

[0265] "Evaluation methods" refer to the process of quantifying and ranking user responses based on analyzed data.

[0266] The "solution generation method" is a function that creates suggestions and advice to improve the user's response based on the evaluation results.

[0267] "Presentation means" refers to the means of displaying or notifying the user of the generated feedback.

[0268] "Data transmission means" refers to the functions and technologies for sending and receiving data between an information processing device and a computing device.

[0269] A "dialogue mechanism" is a system that allows users to try again or make corrections based on feedback.

[0270] This invention is an interview practice support system that utilizes voice input. The system primarily operates through the cooperation of the user, terminal, and server. A specific embodiment of this system is described below.

[0271] First, the user logs in to the terminal, which is an information processing device. Logging in is done using an information processing device such as a PC or smartphone, and accessing it via an internet connection. The user then selects the type of interview they want to practice from the displayed selection menu. For example, they can choose "job interview for experienced professionals" or "new graduate interview for entry-level positions."

[0272] Subsequently, the terminal transmits the user's selection information to the server using a communication method as data for question generation. The server retrieves appropriate questions from its database and uses a generation AI model to create interview questions. The questions are returned to the terminal and presented visually to the user. When the user answers the presented questions verbally, they record their answers using the terminal's voice input function. For example, a question like "Please tell me what you learned from your recent project" might be used.

[0273] The audio data is immediately sent to the server, which uses speech recognition software as a speech-to-text conversion tool to convert the audio into text. Then, natural language processing tools are used to analyze the text and identify inconsistencies and areas for improvement in the user's response. Once the analysis is complete, the server calculates a score for the response using an evaluation tool, and based on this result, a solution generation tool creates feedback.

[0274] The feedback is notified to the device and presented to the user as specific areas for improvement. For example, suggestions such as "Please add specific examples to your answer" may be made. Based on this feedback, the user can reconsider their answer and revise it if necessary.

[0275] This system functions as a practical tool for users to efficiently improve their interview skills and effectively supports interview preparation. By using the prompt sentence "Please give a specific example showing your understanding of the question content", detailed feedback is generated by the AI model.

[0276] The flow of the specific process in Example 1 will be described using FIG. 11.

[0277] Step 1:

[0278] The user uses the terminal to log in to the system. On the login screen, the input of the user ID and password is required. The input information is sent to the server, and the server checks in the database whether the user information is correct and performs authentication. When the authentication is successful, a selection menu for the interview type is displayed on the terminal.

[0279] Step 2:

[0280] The user selects the type of interview on the terminal. The selected information is sent to the server as the information necessary for using the generation AI model from the terminal. Based on the received data, the server acquires relevant interview question data from the database.

[0281] Step 3:

[0282] Based on the acquired data, the server utilizes the generation AI model to generate interview questions. This AI model constructs questions using the prompt sentence. The generated questions are sent from the server to the terminal and presented to the user in text form.

[0283] Step 4:

[0284] For the presented questions, the user answers by voice using the microphone of the terminal. The terminal records the voice input and sends this to the server. In this process, the terminal samples the voice data in digital form and sends it to the server as a file.

[0285] Step 5:

[0286] The server uses speech recognition software to convert speech data into text data. In this conversion, the waveform data of the speech is analyzed and converted into a character string. The converted text is then analyzed for content by natural language processing means.

[0287] Step 6:

[0288] The server uses natural language processing means to analyze the text data. In this process, the work of identifying contradictions and logical inconsistencies in the answer is carried out. The result of the analysis is input into the next evaluation step.

[0289] Step 7:

[0290] The server uses evaluation means to calculate the score of the answer based on the analysis result. In this evaluation, scores are given based on criteria such as logic, specificity, and fluency. The calculated score is used for the next feedback generation.

[0291] Step 8:

[0292] The server uses solution generation means to generate feedback based on the evaluation score and the text analysis result. The feedback includes specific proposals for improvement. The generated feedback is sent from the server to the terminal.

[0293] Step 9:

[0294] The terminal notifies and displays the feedback received from the server to the user. The user attempts to improve the answer based on the presented feedback. By analyzing this feedback and making a re-response if necessary, the user aims to improve their interview skills.

[0295] (Application Example 1)

[0296] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0297] In today's technologically advanced world, it is crucial to provide individual users with opportunities to improve their communication skills. However, traditional interview practice methods are repetitive and lack the immediate, specific feedback needed. Therefore, there is a need to develop systems that enable efficient and high-quality interview practice at home.

[0298] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0299] In this invention, the server includes a device for acquiring audio information, a speech conversion means for converting the acquired audio information to generate text information, and a natural language processing means for analyzing the generated text information and detecting inconsistencies or omissions in the arguments. This allows users to receive real-time feedback and effectively practice interviews at home.

[0300] A "device for acquiring voice information" is a device that receives voice input from a user and records that information.

[0301] "Voice conversion means" refers to a device or software that has the function of processing acquired voice information and converting it into text information.

[0302] "Natural language analysis means" refers to techniques or methods for analyzing textual information and detecting illogical or inconsistent content.

[0303] The "evaluation method" is a mechanism that numerically evaluates user responses based on information obtained through natural language processing.

[0304] The "guideline generation means" is a mechanism that generates advice and proposals for improving the user's answer based on the result calculated by the evaluation means.

[0305] The "display device" is a device for visually presenting the generated guidelines and other information to the user.

[0306] The "human-machine interaction device" is an interface technology for enabling two-way information exchange between the user and the machine.

[0307] The "communication device" is a communication infrastructure for transmitting or receiving data from the terminal to a server at a remote location.

[0308] The system of this invention is for assisting the user's interview practice based on voice information. This system is composed of a device for acquiring voice information, voice conversion means, natural language analysis means, evaluation means, guideline generation means, display device, and human-machine interaction device.

[0309] The server receives the voice information obtained from the user and converts it into character information using the voice conversion means. In this case, existing voice recognition software such as Google Speech-to-Text is used. After the voice is converted into character information, the content is analyzed by the natural language analysis means. For this analysis, natural language processing tools such as spaCy or NLTK are used. The analysis detects the lack of logic and contradictions in the answer, and the evaluation means numerically evaluates the result.

[0310] Also, the terminal uses the guideline generation means to generate proposals for improving the user's answer based on the evaluation result. This proposal is provided to the user through the display device, and the user can refer to it and practice answering again. As a specific example, a prompt sentence such as "Please teach me specific methods to solve difficult situations at work." is used. With this system, the user can experience effective and high-quality interview practice while staying at home.

[0311] This invention allows users to improve their interview skills at their own pace, anytime, anywhere.

[0312] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0313] Step 1:

[0314] The user accesses the terminal, logs into the system, and selects the type of interview they want to practice. This selection information is entered, and the terminal sends a request to the server. The server generates questions based on the received information and sends them to the terminal.

[0315] Step 2:

[0316] The terminal displays a question sent from the server to the user. The user answers the presented question verbally. This voice input is acquired by the terminal and sent to the server as an audio file.

[0317] Step 3:

[0318] The server uses a speech conversion device to convert the audio file received from the terminal into text information. The converted text information is output and used as data for the next analysis process.

[0319] Step 4:

[0320] The server analyzes the textual information using natural language processing tools and evaluates the logic and consistency of the response. Inconsistencies and deficiencies are identified as a result of the analysis, and this information is passed to the evaluation tool.

[0321] Step 5:

[0322] The server numerically evaluates the user's responses based on the analysis results using an evaluation method. The output here is represented as an evaluation score and becomes data for generating feedback.

[0323] Step 6:

[0324] The server uses a guidance generation mechanism with the evaluation score as input to generate specific suggestions for improving the user's response. These suggestions are sent to the terminal via a display device for the user to receive.

[0325] Step 7:

[0326] Users can practice again based on the displayed suggestions and repeat the same process as needed. This allows for continuous improvement.

[0327] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0328] This invention combines a voice input system with an emotion engine to provide users with more comprehensive interview practice. This system analyzes voice to recognize not only the content of the user's responses but also their emotional state, thereby supporting improved expressiveness during interviews. Specific embodiments are described below.

[0329] First, the user accesses the system through a terminal and selects the type of interview they want to practice. The terminal sends the user's selection to the server, which then generates questions. These questions are sent to the terminal and presented to the user either verbally or as text.

[0330] When a user responds verbally, the device sends the audio data to the server. The server converts this data into text using speech recognition and analyzes it using natural language processing. The server also uses an emotion engine to recognize the user's emotional state from the audio. This emotion analysis allows the system to evaluate how the emotional expression during practice affects the interviewer.

[0331] Based on the analysis results, the server evaluates the responses using a scoring system and generates feedback using a feedback generation system. This feedback includes not only areas for improvement in the responses themselves, but also advice on how to express emotions. For example, if tension is detected, specific suggestions such as "Relax and showcase your strengths more" will be given.

[0332] Finally, the generated feedback is notified to the user via their device. Based on this feedback, the user can improve both the content and emotional expression of their interview responses. By repeating this process, the user can develop richer and more expressive interview skills.

[0333] The following describes the processing flow.

[0334] Step 1:

[0335] The user accesses the system using a device and selects the type of interview they want to practice. The device then sends the selected information to the server.

[0336] Step 2:

[0337] Based on the information received by the server, it searches the database for question data. The server generates a question and sends it to the terminal.

[0338] Step 3:

[0339] The device presents the received question to the user via voice or text. The user then answers the question via voice.

[0340] Step 4:

[0341] The device records the user's voice response and prepares to send it to the server in real time.

[0342] Step 5:

[0343] The server converts the received audio data into text using speech recognition technology. Simultaneously, it analyzes the user's emotional state from the audio using an emotion engine.

[0344] Step 6:

[0345] The server analyzes the text obtained through speech recognition using natural language processing tools to detect inconsistencies and errors in the content.

[0346] Step 7:

[0347] The server evaluates the responses using a scoring method based on the results of text analysis and sentiment analysis, and calculates a score.

[0348] Step 8:

[0349] Based on the analysis results and scores, the server generates feedback using a feedback generation mechanism. This feedback includes suggestions for improving the answers and advice on expressing emotions.

[0350] Step 9:

[0351] The device receives the generated feedback from the server and displays it to the user. The user can review the feedback and practice again based on the areas for improvement.

[0352] (Example 2)

[0353] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the smart glasses 214 will be referred to as the "terminal".

[0354] In modern communication and evaluation methods, providing comprehensive feedback that includes not only information retrieval from voice but also recognition of emotional states is a challenging task. Especially when practicing for interviews or improving communication skills, not only the content of the user's responses but also their emotional expression are crucial elements. However, existing technologies struggle to accurately recognize a user's emotional state and provide feedback based on that understanding.

[0355] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0356] In this invention, the server includes an information terminal means for receiving voice input, an emotion analysis means for evaluating emotions and recognizing emotional states based on analysis results, and a feedback creation method means for generating feedback for improvement based on the analysis results and emotional states. This enables comprehensive interview practice support by combining information obtained from voice with the emotional state at that time.

[0357] A "voice input-enabled information terminal" is a device that accepts voice information emitted by a user and processes that information as digital data.

[0358] A "speech recognition device for converting speech to text" is a device that analyzes received speech data and converts it into textual information.

[0359] A "natural language processing device" is a processing device that analyzes converted text data and understands its meaning and context.

[0360] "Emotion analysis means" refers to a technical means that analyzes data obtained from the user's voice to determine their emotional state.

[0361] An "evaluation tool" is a device that quantitatively scores the user's responses and evaluates their quality based on the analyzed data and emotional state.

[0362] A "feedback generation method" is a technical technique that generates responses indicating areas for improvement based on analysis results and evaluations.

[0363] An "information display means" is a device that visually presents information in order to notify the user of the generated feedback.

[0364] "Information and communication means" refers to communication technologies for sending and receiving data between information terminals and central processing units.

[0365] An "interaction device" is a technological device that requires effective interaction when the user refers to feedback and practices again.

[0366] This invention is a system that improves users' communication skills through voice input. Specifically, it has functions to receive voice input, convert it to text, perform sentiment analysis, and generate feedback.

[0367] First, the user inputs voice using a device. The device acts as a hub for sending the voice data to the server. The server then uses a speech recognition device to convert the voice data into text. A language model is used to improve accuracy during this process. The converted text data is analyzed by natural language processing tools to detect meaning and errors.

[0368] Next, the server uses emotion analysis tools to analyze the emotion data extracted from the voice. This analysis allows the server to understand the user's emotional state and determine, for example, whether they are tense or relaxed.

[0369] Based on these analysis results, the server quantitatively evaluates the user's responses using evaluation tools. This evaluation is based on the quality of the content and the expression of emotion. Then, feedback is generated that encourages the user to improve, according to the feedback generation method. An example of such feedback is, "Relax and showcase your strengths more."

[0370] Finally, the generated feedback is sent to the terminal via an information display device and notified to the user. The user can then use this feedback to improve their communication skills. An example of a prompt might be, "Generate feedback for a user who is nervous when asked 'Please introduce yourself' in a sales job interview."

[0371] This system is designed to support the improvement of comprehensive communication skills and will serve as a valuable practice tool for users.

[0372] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0373] Step 1:

[0374] The user accesses the interview practice system through their device. The user selects the type of interview they wish to practice and enters this selection into their device. The device then sends this information to a server, requesting that appropriate questions be generated based on the selected interview type. The input is the interview type, and the output is the request data sent to the server. Specific actions include the user tapping on options on the screen to enter their selection data into the fields.

[0375] Step 2:

[0376] The server uses information received from the terminal to generate relevant questions using a generative AI model. The server constructs a set of questions that match the interview format selected by the user and sends the generated questions to the terminal. The input is the interview type information received from the terminal, and the output is the generated question data. Specifically, the AI ​​model performs text generation and format conversion for requests.

[0377] Step 3:

[0378] The terminal receives questions sent from the server and presents them to the user. When the user answers the interview questions by voice, the terminal acquires the audio data. In this step, the input is the question data from the server and the user's voice, and the output is the recorded audio data. Specifically, the terminal's microphone function is used to capture the audio.

[0379] Step 4:

[0380] The terminal compresses the recorded audio data and sends it to the server. The server receives this audio data and converts it to text using a speech recognition device. The input is the user's audio data, and the output is text data generated by speech recognition. Specifically, the process involves digital processing of the audio data on the server and conversion by the recognition engine.

[0381] Step 5:

[0382] The server processes the converted text data using natural language processing (NLP) to analyze the user's responses. In addition, it analyzes emotional information extracted from the speech using sentiment analysis tools. The input is text data, and the output is the resulting understanding of the content and emotional information. Specific operations include grammar checking and emotional pattern identification by the analysis engine.

[0383] Step 6:

[0384] The server quantitatively evaluates the user's responses using an evaluation tool based on the analyzed data. Using the evaluation results, it generates feedback for the user using a feedback creation method. The input is the analysis results and emotional state data, and the output is the generated feedback. Specifically, it performs numerical scoring based on evaluation criteria and creates feedback statements.

[0385] Step 7:

[0386] The server sends the generated feedback to the terminal, which then notifies the user. The user reviews the feedback and aims to improve their interview skills based on its content. The input is the generated feedback data, and the output is the display of the feedback. Specifically, the process involves displaying the feedback text on the terminal's display for the user to review.

[0387] (Application Example 2)

[0388] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the smart glasses 214 will be referred to as the "terminal."

[0389] In modern interview practice, it is a challenging task for users to receive comprehensive feedback on not only the content of their statements but also their emotional expression and attitude. Traditional systems lack emotional analysis, making it difficult to adequately assess the impact a user's demeanor and emotional state have on the interviewer. This makes it difficult for users to achieve the desired results in actual interviews.

[0390] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0391] In this invention, the server includes emotion analysis means for recognizing emotional states from voice, feedback generation means for generating information for improvement based on the analysis results and emotion analysis, and display means for notifying the user of the generated information. This enables accurate evaluation of the user's voice and the associated emotional state, allowing for more improved interview practice.

[0392] A "voice input receiving information processing device" is a device that acquires the content spoken by a user as data and processes it within the system.

[0393] "A speech recognition method for converting into a sequence of symbols" is a method for converting received speech data into a sequence of characters or symbols, and is implemented using speech recognition technology.

[0394] "Natural language processing means for analyzing converted symbol sequences and detecting logical errors" refers to processing technology for analyzing strings obtained through speech recognition and detecting semantic errors and grammatical inconsistencies.

[0395] An "evaluation method for evaluating a response and calculating a numerical value" is a method for objectively evaluating the content and structure of an analyzed response and expressing the results as a numerical value.

[0396] A "feedback generation method for generating information for improvement" is a technique that generates advice and suggestions for improvement based on evaluation results to enhance the user's speech.

[0397] "A means of displaying generated information to the user" refers to a method for conveying feedback information to the user in an easy-to-understand manner, and can be used for screen displays, audio notifications, etc.

[0398] "An emotion analysis method that recognizes emotional states from voice" is a method that identifies emotional states and changes based on the user's voice data.

[0399] This invention is a system that provides comprehensive feedback to users when they practice for interviews by combining voice input and emotion analysis. This is realized by a system that includes: an information processing device that accepts voice input; a voice recognition means for converting it into a sequence of symbols; a natural language processing means for analyzing the converted sequence of symbols and detecting logical errors; an evaluation means for evaluating the response and calculating a numerical value; a feedback generation means for generating information for improvement; a display means for notifying the user of the generated information; and an emotion analysis means for recognizing emotional states from voice.

[0400] An information processing device that accepts voice input acts as a terminal, acquiring the user's spoken content as data. This data is converted into a sequence of symbols via a cloud server using speech recognition software (e.g., Google Speech-to-Text API). The converted sequence of symbols is then analyzed using natural language processing techniques (e.g., Python's NLTK toolkit) to detect logical errors in the response.

[0401] Furthermore, the server performs a numerical evaluation of the analyzed response using an evaluation tool. Based on this evaluation, a feedback generation tool generates information for improvement. The feedback also takes emotional states into consideration, and for sentiment analysis, for example, the Azure Cognitive Services Emotion API can be used. The generated information is notified to the user's terminal by a display tool.

[0402] For example, if a user responds nervously to the question, "Please introduce yourself," the system can provide feedback such as, "Relax more and try talking about your hobbies."

[0403] An example of a prompt for the generative AI model is, "Analyze the user's current emotions from this audio data and provide feedback in a cheerful tone." This is used to recognize the user's emotional state and provide appropriate feedback accordingly.

[0404] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0405] Step 1:

[0406] The user begins voice input. The terminal receives voice data from the user. This input voice data is used for subsequent processing.

[0407] Step 2:

[0408] The device sends voice input to the server. The server uses speech recognition technology to convert the voice data into a sequence of symbols. This process uses the Google Speech-to-Text API to obtain the voice data as text. The output text is used in the next parsing step.

[0409] Step 3:

[0410] The server analyzes the converted text using natural language processing techniques. It uses the Python natural language processing toolkit NLTK to detect grammatical and logical errors in the text. The analysis results are then fed into a subsequent evaluation step.

[0411] Step 4:

[0412] Based on the analysis results, the server evaluates the response using evaluation tools and quantifies it. The evaluation results output numerical values ​​for the accuracy and expressiveness of the response. This allows for a quantitative evaluation of the quality of the user's speech.

[0413] Step 5:

[0414] The server recognizes the user's emotional state from the voice data. Using the Azure Cognitive Services Emotion API, it identifies emotions from voice tone and speed. This analysis includes the type and intensity of emotion, which is then used to generate subsequent feedback.

[0415] Step 6:

[0416] The server generates feedback that includes areas for improvement based on accurate response evaluation and sentiment analysis results. The feedback generation mechanism uses a generation AI model to create appropriate prompt sentences and provide advice such as relaxation.

[0417] Step 7:

[0418] The generated feedback information is notified to the terminal via a display device. The user reviews this feedback and works to improve their speech. In this way, interview practice progresses more effectively.

[0419] 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.

[0420] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0421] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the smart glasses 214.

[0422] [Third Embodiment]

[0423] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0424] 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.

[0425] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0426] 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.

[0427] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0428] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0429] 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.

[0430] 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.

[0431] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0432] The 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.

[0433] In the headset terminal 314, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0434] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the headset terminal 314 will be referred to as the "terminal".

[0435] This invention provides a system that utilizes voice input to offer users high-quality interview practice. This system supports interview practice through the cooperation of the user, terminal, and server. Specific embodiments are described below.

[0436] The user first logs into the system via a terminal and selects the type of interview they want to practice. The terminal sends a request to the server, which generates interview questions based on information in its database. The generated questions are sent to the terminal and presented to the user.

[0437] The user answers the presented questions using the device's microphone. The device records the audio and sends it to the server in real time. The server converts this audio into text using speech recognition and analyzes the answers using natural language processing. During the analysis process, the server detects inconsistencies and errors in the answers and calculates an evaluation score for each answer using a scoring system.

[0438] Next, the server uses a feedback generation mechanism to generate feedback based on the evaluation results and detected areas for improvement. The feedback includes suggestions to improve the specificity and logical consistency of the responses. The generated feedback is sent to the terminal and notified to the user.

[0439] Based on this feedback, users can improve their answers. By repeating this process, users can efficiently improve their interview skills. This embodiment allows users to receive high-quality evaluations and feedback, preparing them for actual interviews.

[0440] The following describes the processing flow.

[0441] Step 1:

[0442] The user operates a device to log in to the interview practice system and selects the type of interview they want to practice. The device then sends this selection information to the server.

[0443] Step 2:

[0444] The server receives the selection information and searches the database for the corresponding question data. The server uses a question generation algorithm to generate a specific question and sends it to the terminal.

[0445] Step 3:

[0446] The terminal presents the user with a question received from the server, either verbally or in text. The user then answers the presented question verbally.

[0447] Step 4:

[0448] The terminal records the user's voice input in real time and transfers the audio data to the server.

[0449] Step 5:

[0450] The server converts the received audio data into text using speech recognition technology. The converted text is then analyzed using natural language processing technology to detect inconsistencies and errors.

[0451] Step 6:

[0452] Based on the analysis results, the server uses a scoring algorithm to evaluate the responses and calculate a score.

[0453] Step 7:

[0454] The server generates feedback based on the evaluation results and analyzed areas for improvement. This feedback includes specific improvement suggestions.

[0455] Step 8:

[0456] The device receives feedback from the server and displays it to the user. After reviewing the feedback, the user can improve their answers and start a new practice session.

[0457] (Example 1)

[0458] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0459] In interview practice, there is a problem in that it is difficult for users to objectively evaluate and improve their own answers. In particular, there is a lack of systems that provide effective feedback for skill improvement using voice input. Solving this problem will allow users to prepare for interviews more efficiently.

[0460] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0461] In this invention, the server includes a speech conversion means, a natural language processing means, and a solution generation means. This enables the conversion of the user's voice input into text, as well as detailed response analysis and feedback provision.

[0462] "Voice input" is a method in which users provide data to an information processing device using their voice.

[0463] An "information processing device" refers to an electronic device used to process voice and text input and perform judgments and evaluations.

[0464] "Speech conversion means" refers to technologies and devices for converting speech data into text data.

[0465] "Natural language processing methods" are technologies used to analyze text data and understand its content and meaning.

[0466] "Evaluation methods" refer to the process of quantifying and ranking user responses based on analyzed data.

[0467] The "solution generation method" is a function that creates suggestions and advice to improve the user's response based on the evaluation results.

[0468] "Presentation means" refers to the means of displaying or notifying the user of the generated feedback.

[0469] "Data transmission means" refers to the functions and technologies for sending and receiving data between an information processing device and a computing device.

[0470] A "dialogue mechanism" is a system that allows users to try again or make corrections based on feedback.

[0471] This invention is an interview practice support system that utilizes voice input. The system primarily operates through the cooperation of the user, terminal, and server. A specific embodiment of this system is described below.

[0472] First, the user logs in to the terminal, which is an information processing device. Logging in is done using an information processing device such as a PC or smartphone, and accessing it via an internet connection. The user then selects the type of interview they want to practice from the displayed selection menu. For example, they can choose "job interview for experienced professionals" or "new graduate interview for entry-level positions."

[0473] Subsequently, the terminal transmits the user's selection information to the server using a communication method as data for question generation. The server retrieves appropriate questions from its database and uses a generation AI model to create interview questions. The questions are returned to the terminal and presented visually to the user. When the user answers the presented questions verbally, they record their answers using the terminal's voice input function. For example, a question like "Please tell me what you learned from your recent project" might be used.

[0474] The audio data is immediately sent to the server, which uses speech recognition software as a speech-to-text conversion tool to convert the audio into text. Then, natural language processing tools are used to analyze the text and identify inconsistencies and areas for improvement in the user's response. Once the analysis is complete, the server calculates a score for the response using an evaluation tool, and based on this result, a solution generation tool creates feedback.

[0475] The feedback is notified to the device and presented to the user as specific areas for improvement. For example, suggestions such as "Please add specific examples to your answer" may be made. Based on this feedback, the user can reconsider their answer and revise it if necessary.

[0476] This system functions as a practical tool to help users efficiently improve their interview skills and effectively support interview preparation. By using the prompt "Please give specific examples that demonstrate your understanding of the question," the AI ​​model generates detailed feedback.

[0477] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0478] Step 1:

[0479] The user logs into the system using their terminal. The login screen prompts the user to enter their user ID and password. The entered information is sent to the server, which verifies the user information against its database and performs authentication. Upon successful authentication, a menu for selecting the interview type is displayed on the terminal.

[0480] Step 2:

[0481] The user selects the interview type on their device. The selected information is sent to the server as necessary information for using the AI ​​model generated from the device. Based on the received data, the server retrieves relevant interview question data from its database.

[0482] Step 3:

[0483] The server uses a generative AI model to generate interview questions based on the acquired data. This AI model constructs the questions using prompts. The generated questions are sent from the server to the terminal and presented to the user in text format.

[0484] Step 4:

[0485] The user answers the presented questions using the device's microphone. The device records the voice input and sends it to the server. In this process, the device samples the voice data in digital format and sends it to the server as a file.

[0486] Step 5:

[0487] The server uses speech recognition software to convert audio data into text data. This conversion involves analyzing the audio waveform data and converting it into a string of characters. The converted text is then analyzed by natural language processing tools.

[0488] Step 6:

[0489] The server uses natural language processing to analyze the text data. During this process, it identifies inconsistencies and logical inconsistencies within the responses. The analysis results are then input into the next evaluation step.

[0490] Step 7:

[0491] The server calculates a score for the response based on the analysis results using an evaluation tool. This evaluation assigns points based on criteria such as logic, specificity, and fluency. The calculated score is then used to generate subsequent feedback.

[0492] Step 8:

[0493] The server uses a solution generation mechanism to generate feedback based on the evaluation score and text analysis results. This feedback includes specific suggestions for improvement. The generated feedback is then sent from the server to the terminal.

[0494] Step 9:

[0495] The terminal notifies and displays feedback received from the server to the user. The user then uses the provided feedback to improve their answers. By analyzing this feedback and re-responding as needed, the goal is to improve interview skills.

[0496] (Application Example 1)

[0497] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0498] In today's technologically advanced world, it is crucial to provide individual users with opportunities to improve their communication skills. However, traditional interview practice methods are repetitive and lack the immediate, specific feedback needed. Therefore, there is a need to develop systems that enable efficient and high-quality interview practice at home.

[0499] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0500] In this invention, the server includes a device for acquiring audio information, a speech conversion means for converting the acquired audio information to generate text information, and a natural language processing means for analyzing the generated text information and detecting inconsistencies or omissions in the arguments. This allows users to receive real-time feedback and effectively practice interviews at home.

[0501] A "device for acquiring voice information" is a device that receives voice input from a user and records that information.

[0502] "Voice conversion means" refers to a device or software that has the function of processing acquired voice information and converting it into text information.

[0503] "Natural language analysis means" refers to techniques or methods for analyzing textual information and detecting illogical or inconsistent content.

[0504] The "evaluation method" is a mechanism that numerically evaluates user responses based on information obtained through natural language processing.

[0505] A "guideline generation method" is a system that generates advice and suggestions to improve the user's response based on the results calculated by the evaluation method.

[0506] A "display device" is a device used to visually present generated guidelines or other information to the user.

[0507] A "human-machine interaction device" is an interface technology that enables two-way information exchange between a user and a machine.

[0508] A "communication device" is a communication infrastructure used to send or receive data from a terminal to a remote server.

[0509] The system of this invention is designed to support a user's interview practice based on voice information. The system consists of a device for acquiring voice information, a voice conversion means, a natural language processing means, an evaluation means, a guideline generation means, a display device, and a human-machine interaction device.

[0510] The server receives audio information from the user and converts it into text using a speech-to-text conversion tool. Existing speech recognition software, such as Google Speech-to-Text, is used for this conversion. After the audio is converted into text, the content is analyzed by a natural language processing tool. This analysis utilizes natural language processing tools such as spaCy or NLTK. The analysis detects illogical inconsistencies and contradictions in the response, and the results are numerically evaluated by an evaluation tool.

[0511] Furthermore, the terminal uses a guideline generation mechanism to generate suggestions for improving the user's answers based on the evaluation results. These suggestions are provided to the user via a display device, allowing the user to practice their answers again based on these suggestions. For example, prompts such as "Please tell me specific ways to solve difficult situations in the workplace" are used. This system enables users to experience effective and high-quality interview practice from the comfort of their homes.

[0512] This invention allows users to improve their interview skills at their own pace, anytime, anywhere.

[0513] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0514] Step 1:

[0515] The user accesses the terminal, logs into the system, and selects the type of interview they want to practice. This selection information is entered, and the terminal sends a request to the server. The server generates questions based on the received information and sends them to the terminal.

[0516] Step 2:

[0517] The terminal displays a question sent from the server to the user. The user answers the presented question verbally. This voice input is acquired by the terminal and sent to the server as an audio file.

[0518] Step 3:

[0519] The server uses a speech conversion device to convert the audio file received from the terminal into text information. The converted text information is output and used as data for the next analysis process.

[0520] Step 4:

[0521] The server analyzes the textual information using natural language processing tools and evaluates the logic and consistency of the response. Inconsistencies and deficiencies are identified as a result of the analysis, and this information is passed to the evaluation tool.

[0522] Step 5:

[0523] The server numerically evaluates the user's responses based on the analysis results using an evaluation method. The output here is represented as an evaluation score and becomes data for generating feedback.

[0524] Step 6:

[0525] The server uses a guidance generation mechanism with the evaluation score as input to generate specific suggestions for improving the user's response. These suggestions are sent to the terminal via a display device for the user to receive.

[0526] Step 7:

[0527] Users can practice again based on the displayed suggestions and repeat the same process as needed. This allows for continuous improvement.

[0528] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0529] This invention combines a voice input system with an emotion engine to provide users with more comprehensive interview practice. This system analyzes voice to recognize not only the content of the user's responses but also their emotional state, thereby supporting improved expressiveness during interviews. Specific embodiments are described below.

[0530] First, the user accesses the system through a terminal and selects the type of interview they want to practice. The terminal sends the user's selection to the server, which then generates questions. These questions are sent to the terminal and presented to the user either verbally or as text.

[0531] When a user responds verbally, the device sends the audio data to the server. The server converts this data into text using speech recognition and analyzes it using natural language processing. The server also uses an emotion engine to recognize the user's emotional state from the audio. This emotion analysis allows the system to evaluate how the emotional expression during practice affects the interviewer.

[0532] Based on the analysis results, the server evaluates the responses using a scoring system and generates feedback using a feedback generation system. This feedback includes not only areas for improvement in the responses themselves, but also advice on how to express emotions. For example, if tension is detected, specific suggestions such as "Relax and showcase your strengths more" will be given.

[0533] Finally, the generated feedback is notified to the user via their device. Based on this feedback, the user can improve both the content and emotional expression of their interview responses. By repeating this process, the user can develop richer and more expressive interview skills.

[0534] The following describes the processing flow.

[0535] Step 1:

[0536] The user accesses the system using a device and selects the type of interview they want to practice. The device then sends the selected information to the server.

[0537] Step 2:

[0538] Based on the information received by the server, it searches the database for question data. The server generates a question and sends it to the terminal.

[0539] Step 3:

[0540] The device presents the received question to the user via voice or text. The user then answers the question via voice.

[0541] Step 4:

[0542] The device records the user's voice response and prepares to send it to the server in real time.

[0543] Step 5:

[0544] The server converts the received audio data into text using speech recognition technology. Simultaneously, it analyzes the user's emotional state from the audio using an emotion engine.

[0545] Step 6:

[0546] The server analyzes the text obtained through speech recognition using natural language processing tools to detect inconsistencies and errors in the content.

[0547] Step 7:

[0548] The server evaluates the responses using a scoring method based on the results of text analysis and sentiment analysis, and calculates a score.

[0549] Step 8:

[0550] Based on the analysis results and scores, the server generates feedback using a feedback generation mechanism. This feedback includes suggestions for improving the answers and advice on expressing emotions.

[0551] Step 9:

[0552] The device receives the generated feedback from the server and displays it to the user. The user can review the feedback and practice again based on the areas for improvement.

[0553] (Example 2)

[0554] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0555] In modern communication and evaluation methods, providing comprehensive feedback that includes not only information retrieval from voice but also recognition of emotional states is a challenging task. Especially when practicing for interviews or improving communication skills, not only the content of the user's responses but also their emotional expression are crucial elements. However, existing technologies struggle to accurately recognize a user's emotional state and provide feedback based on that understanding.

[0556] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0557] In this invention, the server includes an information terminal means for receiving voice input, an emotion analysis means for evaluating emotions and recognizing emotional states based on analysis results, and a feedback creation method means for generating feedback for improvement based on the analysis results and emotional states. This enables comprehensive interview practice support by combining information obtained from voice with the emotional state at that time.

[0558] A "voice input-enabled information terminal" is a device that accepts voice information emitted by a user and processes that information as digital data.

[0559] A "speech recognition device for converting speech to text" is a device that analyzes received speech data and converts it into textual information.

[0560] A "natural language processing device" is a processing device that analyzes converted text data and understands its meaning and context.

[0561] "Emotion analysis means" refers to a technical means that analyzes data obtained from the user's voice to determine their emotional state.

[0562] An "evaluation tool" is a device that quantitatively scores the user's responses and evaluates their quality based on the analyzed data and emotional state.

[0563] A "feedback generation method" is a technical technique that generates responses indicating areas for improvement based on analysis results and evaluations.

[0564] An "information display means" is a device that visually presents information in order to notify the user of the generated feedback.

[0565] "Information and communication means" refers to communication technologies for sending and receiving data between information terminals and central processing units.

[0566] An "interaction device" is a technological device that requires effective interaction when the user refers to feedback and practices again.

[0567] This invention is a system that improves users' communication skills through voice input. Specifically, it has functions to receive voice input, convert it to text, perform sentiment analysis, and generate feedback.

[0568] First, the user inputs voice using a device. The device acts as a hub for sending the voice data to the server. The server then uses a speech recognition device to convert the voice data into text. A language model is used to improve accuracy during this process. The converted text data is analyzed by natural language processing tools to detect meaning and errors.

[0569] Next, the server uses emotion analysis tools to analyze the emotion data extracted from the voice. This analysis allows the server to understand the user's emotional state and determine, for example, whether they are tense or relaxed.

[0570] Based on these analysis results, the server quantitatively evaluates the user's responses using evaluation tools. This evaluation is based on the quality of the content and the expression of emotion. Then, feedback is generated that encourages the user to improve, according to the feedback generation method. An example of such feedback is, "Relax and showcase your strengths more."

[0571] Finally, the generated feedback is sent to the terminal via an information display device and notified to the user. The user can then use this feedback to improve their communication skills. An example of a prompt might be, "Generate feedback for a user who is nervous when asked 'Please introduce yourself' in a sales job interview."

[0572] This system is designed to support the improvement of comprehensive communication skills and will serve as a valuable practice tool for users.

[0573] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0574] Step 1:

[0575] The user accesses the interview practice system through their device. The user selects the type of interview they wish to practice and enters this selection into their device. The device then sends this information to a server, requesting that appropriate questions be generated based on the selected interview type. The input is the interview type, and the output is the request data sent to the server. Specific actions include the user tapping on options on the screen to enter their selection data into the fields.

[0576] Step 2:

[0577] The server uses information received from the terminal to generate relevant questions using a generative AI model. The server constructs a set of questions that match the interview format selected by the user and sends the generated questions to the terminal. The input is the interview type information received from the terminal, and the output is the generated question data. Specifically, the AI ​​model performs text generation and format conversion for requests.

[0578] Step 3:

[0579] The terminal receives questions sent from the server and presents them to the user. When the user answers the interview questions by voice, the terminal acquires the audio data. In this step, the input is the question data from the server and the user's voice, and the output is the recorded audio data. Specifically, the terminal's microphone function is used to capture the audio.

[0580] Step 4:

[0581] The terminal compresses the recorded audio data and sends it to the server. The server receives this audio data and converts it to text using a speech recognition device. The input is the user's audio data, and the output is text data generated by speech recognition. Specifically, the process involves digital processing of the audio data on the server and conversion by the recognition engine.

[0582] Step 5:

[0583] The server processes the converted text data using natural language processing (NLP) to analyze the user's responses. In addition, it analyzes emotional information extracted from the speech using sentiment analysis tools. The input is text data, and the output is the resulting understanding of the content and emotional information. Specific operations include grammar checking and emotional pattern identification by the analysis engine.

[0584] Step 6:

[0585] The server quantitatively evaluates the user's responses using an evaluation tool based on the analyzed data. Using the evaluation results, it generates feedback for the user using a feedback creation method. The input is the analysis results and emotional state data, and the output is the generated feedback. Specifically, it performs numerical scoring based on evaluation criteria and creates feedback statements.

[0586] Step 7:

[0587] The server sends the generated feedback to the terminal, which then notifies the user. The user reviews the feedback and aims to improve their interview skills based on its content. The input is the generated feedback data, and the output is the display of the feedback. Specifically, the process involves displaying the feedback text on the terminal's display for the user to review.

[0588] (Application Example 2)

[0589] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server," and the headset-type terminal 314 will be referred to as the "terminal."

[0590] In modern interview practice, it is a challenging task for users to receive comprehensive feedback on not only the content of their statements but also their emotional expression and attitude. Traditional systems lack emotional analysis, making it difficult to adequately assess the impact a user's demeanor and emotional state have on the interviewer. This makes it difficult for users to achieve the desired results in actual interviews.

[0591] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0592] In this invention, the server includes emotion analysis means for recognizing emotional states from voice, feedback generation means for generating information for improvement based on the analysis results and emotion analysis, and display means for notifying the user of the generated information. This enables accurate evaluation of the user's voice and the associated emotional state, allowing for more improved interview practice.

[0593] A "voice input receiving information processing device" is a device that acquires the content spoken by a user as data and processes it within the system.

[0594] "A speech recognition method for converting into a sequence of symbols" is a method for converting received speech data into a sequence of characters or symbols, and is implemented using speech recognition technology.

[0595] "Natural language processing means for analyzing converted symbol sequences and detecting logical errors" refers to processing technology for analyzing strings obtained through speech recognition and detecting semantic errors and grammatical inconsistencies.

[0596] An "evaluation method for evaluating a response and calculating a numerical value" is a method for objectively evaluating the content and structure of an analyzed response and expressing the results as a numerical value.

[0597] A "feedback generation method for generating information for improvement" is a technique that generates advice and suggestions for improvement based on evaluation results to enhance the user's speech.

[0598] "A means of displaying generated information to the user" refers to a method for conveying feedback information to the user in an easy-to-understand manner, and can be used for screen displays, audio notifications, etc.

[0599] "An emotion analysis method that recognizes emotional states from voice" is a method that identifies emotional states and changes based on the user's voice data.

[0600] This invention is a system that provides comprehensive feedback to users when they practice for interviews by combining voice input and emotion analysis. This is realized by a system that includes: an information processing device that accepts voice input; a voice recognition means for converting it into a sequence of symbols; a natural language processing means for analyzing the converted sequence of symbols and detecting logical errors; an evaluation means for evaluating the response and calculating a numerical value; a feedback generation means for generating information for improvement; a display means for notifying the user of the generated information; and an emotion analysis means for recognizing emotional states from voice.

[0601] An information processing device that accepts voice input acts as a terminal, acquiring the user's spoken content as data. This data is converted into a sequence of symbols via a cloud server using speech recognition software (e.g., Google Speech-to-Text API). The converted sequence of symbols is then analyzed using natural language processing techniques (e.g., Python's NLTK toolkit) to detect logical errors in the response.

[0602] Furthermore, the server performs a numerical evaluation of the analyzed response using an evaluation tool. Based on this evaluation, a feedback generation tool generates information for improvement. The feedback also takes emotional states into consideration, and for sentiment analysis, for example, the Azure Cognitive Services Emotion API can be used. The generated information is notified to the user's terminal by a display tool.

[0603] For example, if a user responds nervously to the question, "Please introduce yourself," the system can provide feedback such as, "Relax more and try talking about your hobbies."

[0604] An example of a prompt for the generative AI model is, "Analyze the user's current emotions from this audio data and provide feedback in a cheerful tone." This is used to recognize the user's emotional state and provide appropriate feedback accordingly.

[0605] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0606] Step 1:

[0607] The user begins voice input. The terminal receives voice data from the user. This input voice data is used for subsequent processing.

[0608] Step 2:

[0609] The device sends voice input to the server. The server uses speech recognition technology to convert the voice data into a sequence of symbols. This process uses the Google Speech-to-Text API to obtain the voice data as text. The output text is used in the next parsing step.

[0610] Step 3:

[0611] The server analyzes the converted text using natural language processing techniques. It uses the Python natural language processing toolkit NLTK to detect grammatical and logical errors in the text. The analysis results are then fed into a subsequent evaluation step.

[0612] Step 4:

[0613] Based on the analysis results, the server evaluates the response using evaluation tools and quantifies it. The evaluation results output numerical values ​​for the accuracy and expressiveness of the response. This allows for a quantitative evaluation of the quality of the user's speech.

[0614] Step 5:

[0615] The server recognizes the user's emotional state from the voice data. Using the Azure Cognitive Services Emotion API, it identifies emotions from voice tone and speed. This analysis includes the type and intensity of emotion, which is then used to generate subsequent feedback.

[0616] Step 6:

[0617] The server generates feedback that includes areas for improvement based on accurate response evaluation and sentiment analysis results. The feedback generation mechanism uses a generation AI model to create appropriate prompt sentences and provide advice such as relaxation.

[0618] Step 7:

[0619] The generated feedback information is notified to the terminal via a display device. The user reviews this feedback and works to improve their speech. In this way, interview practice progresses more effectively.

[0620] 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.

[0621] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0622] In the above embodiment, an example was given in which specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and specific processing may also be performed by the headset terminal 314.

[0623] [Fourth Embodiment]

[0624] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0625] 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.

[0626] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 is an example of a "computer" related to the technology of this disclosure. 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 (Wide Area Network) and / or a LAN (Local Area Network).

[0627] 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.

[0628] The microphone 238 receives voice signals from the user 20 and receives instructions from the user 20. The microphone 238 captures the voice signals from the user 20, 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.

[0629] 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, and captures images of the area around the user 20 (for example, an imaging range defined by a field of view equivalent to the width of a typical healthy person's field of vision).

[0630] 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.

[0631] 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. Furthermore, the robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0632] 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.

[0633] The specific processing program 56 is an example of a "program" relating 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 in accordance with the specific processing program 56 executed on the RAM 30.

[0634] The 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.

[0635] In robot 414, the processor 46 performs the reception output processing. The storage 50 stores the reception output program 60. The processor 46 reads the reception output program 60 from the storage 50 and executes the read reception output program 60 on the RAM 48. The reception output processing is realized by the processor 46 operating as a control unit 46A according to the reception output program 60 executed on the RAM 48.

[0636] Next, the specific processing performed by the specific processing unit 290 of the data processing device 12 will be described. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0637] This invention provides a system that utilizes voice input to offer users high-quality interview practice. This system supports interview practice through the cooperation of the user, terminal, and server. Specific embodiments are described below.

[0638] The user first logs into the system via a terminal and selects the type of interview they want to practice. The terminal sends a request to the server, which generates interview questions based on information in its database. The generated questions are sent to the terminal and presented to the user.

[0639] The user answers the presented questions using the device's microphone. The device records the audio and sends it to the server in real time. The server converts this audio into text using speech recognition and analyzes the answers using natural language processing. During the analysis process, the server detects inconsistencies and errors in the answers and calculates an evaluation score for each answer using a scoring system.

[0640] Next, the server uses a feedback generation mechanism to generate feedback based on the evaluation results and detected areas for improvement. The feedback includes suggestions to improve the specificity and logical consistency of the responses. The generated feedback is sent to the terminal and notified to the user.

[0641] Based on this feedback, users can improve their answers. By repeating this process, users can efficiently improve their interview skills. This embodiment allows users to receive high-quality evaluations and feedback, preparing them for actual interviews.

[0642] The following describes the processing flow.

[0643] Step 1:

[0644] The user operates a device to log in to the interview practice system and selects the type of interview they want to practice. The device then sends this selection information to the server.

[0645] Step 2:

[0646] The server receives the selection information and searches the database for the corresponding question data. The server uses a question generation algorithm to generate a specific question and sends it to the terminal.

[0647] Step 3:

[0648] The terminal presents the user with a question received from the server, either verbally or in text. The user then answers the presented question verbally.

[0649] Step 4:

[0650] The terminal records the user's voice input in real time and transfers the audio data to the server.

[0651] Step 5:

[0652] The server converts the received audio data into text using speech recognition technology. The converted text is then analyzed using natural language processing technology to detect inconsistencies and errors.

[0653] Step 6:

[0654] Based on the analysis results, the server uses a scoring algorithm to evaluate the responses and calculate a score.

[0655] Step 7:

[0656] The server generates feedback based on the evaluation results and analyzed areas for improvement. This feedback includes specific improvement suggestions.

[0657] Step 8:

[0658] The device receives feedback from the server and displays it to the user. After reviewing the feedback, the user can improve their answers and start a new practice session.

[0659] (Example 1)

[0660] Next, we will describe Example 1. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0661] In interview practice, there is a problem in that it is difficult for users to objectively evaluate and improve their own answers. In particular, there is a lack of systems that provide effective feedback for skill improvement using voice input. Solving this problem will allow users to prepare for interviews more efficiently.

[0662] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 1 is realized by the following means.

[0663] In this invention, the server includes a speech conversion means, a natural language processing means, and a solution generation means. This enables the conversion of the user's voice input into text, as well as detailed response analysis and feedback provision.

[0664] "Voice input" is a method in which users provide data to an information processing device using their voice.

[0665] An "information processing device" refers to an electronic device used to process voice and text input and perform judgments and evaluations.

[0666] "Speech conversion means" refers to technologies and devices for converting speech data into text data.

[0667] "Natural language processing methods" are technologies used to analyze text data and understand its content and meaning.

[0668] "Evaluation methods" refer to the process of quantifying and ranking user responses based on analyzed data.

[0669] The "solution generation method" is a function that creates suggestions and advice to improve the user's response based on the evaluation results.

[0670] "Presentation means" refers to the means of displaying or notifying the user of the generated feedback.

[0671] "Data transmission means" refers to the functions and technologies for sending and receiving data between an information processing device and a computing device.

[0672] A "dialogue mechanism" is a system that allows users to try again or make corrections based on feedback.

[0673] This invention is an interview practice support system that utilizes voice input. The system primarily operates through the cooperation of the user, terminal, and server. A specific embodiment of this system is described below.

[0674] First, the user logs in to the terminal, which is an information processing device. Logging in is done using an information processing device such as a PC or smartphone, and accessing it via an internet connection. The user then selects the type of interview they want to practice from the displayed selection menu. For example, they can choose "job interview for experienced professionals" or "new graduate interview for entry-level positions."

[0675] Subsequently, the terminal transmits the user's selection information to the server using a communication method as data for question generation. The server retrieves appropriate questions from its database and uses a generation AI model to create interview questions. The questions are returned to the terminal and presented visually to the user. When the user answers the presented questions verbally, they record their answers using the terminal's voice input function. For example, a question like "Please tell me what you learned from your recent project" might be used.

[0676] The audio data is immediately sent to the server, which uses speech recognition software as a speech-to-text conversion tool to convert the audio into text. Then, natural language processing tools are used to analyze the text and identify inconsistencies and areas for improvement in the user's response. Once the analysis is complete, the server calculates a score for the response using an evaluation tool, and based on this result, a solution generation tool creates feedback.

[0677] The feedback is notified to the device and presented to the user as specific areas for improvement. For example, suggestions such as "Please add specific examples to your answer" may be made. Based on this feedback, the user can reconsider their answer and revise it if necessary.

[0678] This system functions as a practical tool to help users efficiently improve their interview skills and effectively support interview preparation. By using the prompt "Please give specific examples that demonstrate your understanding of the question," the AI ​​model generates detailed feedback.

[0679] The flow of the specific processing in Example 1 will be explained using Figure 11.

[0680] Step 1:

[0681] The user logs into the system using their terminal. The login screen prompts the user to enter their user ID and password. The entered information is sent to the server, which verifies the user information against its database and performs authentication. Upon successful authentication, a menu for selecting the interview type is displayed on the terminal.

[0682] Step 2:

[0683] The user selects the interview type on their device. The selected information is sent to the server as necessary information for using the AI ​​model generated from the device. Based on the received data, the server retrieves relevant interview question data from its database.

[0684] Step 3:

[0685] The server uses a generative AI model to generate interview questions based on the acquired data. This AI model constructs the questions using prompts. The generated questions are sent from the server to the terminal and presented to the user in text format.

[0686] Step 4:

[0687] The user answers the presented questions using the device's microphone. The device records the voice input and sends it to the server. In this process, the device samples the voice data in digital format and sends it to the server as a file.

[0688] Step 5:

[0689] The server uses speech recognition software to convert audio data into text data. This conversion involves analyzing the audio waveform data and converting it into a string of characters. The converted text is then analyzed by natural language processing tools.

[0690] Step 6:

[0691] The server uses natural language processing to analyze the text data. During this process, it identifies inconsistencies and logical inconsistencies within the responses. The analysis results are then input into the next evaluation step.

[0692] Step 7:

[0693] The server calculates a score for the response based on the analysis results using an evaluation tool. This evaluation assigns points based on criteria such as logic, specificity, and fluency. The calculated score is then used to generate subsequent feedback.

[0694] Step 8:

[0695] The server uses a solution generation mechanism to generate feedback based on the evaluation score and text analysis results. This feedback includes specific suggestions for improvement. The generated feedback is then sent from the server to the terminal.

[0696] Step 9:

[0697] The terminal notifies and displays feedback received from the server to the user. The user then uses the provided feedback to improve their answers. By analyzing this feedback and re-responding as needed, the goal is to improve interview skills.

[0698] (Application Example 1)

[0699] Next, we will explain Application Example 1. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0700] In today's technologically advanced world, it is crucial to provide individual users with opportunities to improve their communication skills. However, traditional interview practice methods are repetitive and lack the immediate, specific feedback needed. Therefore, there is a need to develop systems that enable efficient and high-quality interview practice at home.

[0701] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 1 is realized by the following means.

[0702] In this invention, the server includes a device for acquiring audio information, a speech conversion means for converting the acquired audio information to generate text information, and a natural language processing means for analyzing the generated text information and detecting inconsistencies or omissions in the arguments. This allows users to receive real-time feedback and effectively practice interviews at home.

[0703] A "device for acquiring voice information" is a device that receives voice input from a user and records that information.

[0704] "Voice conversion means" refers to a device or software that has the function of processing acquired voice information and converting it into text information.

[0705] "Natural language analysis means" refers to techniques or methods for analyzing textual information and detecting illogical or inconsistent content.

[0706] The "evaluation method" is a mechanism that numerically evaluates user responses based on information obtained through natural language processing.

[0707] A "guideline generation method" is a system that generates advice and suggestions to improve the user's response based on the results calculated by the evaluation method.

[0708] A "display device" is a device used to visually present generated guidelines or other information to the user.

[0709] A "human-machine interaction device" is an interface technology that enables two-way information exchange between a user and a machine.

[0710] A "communication device" is a communication infrastructure used to send or receive data from a terminal to a remote server.

[0711] The system of this invention is designed to support a user's interview practice based on voice information. The system consists of a device for acquiring voice information, a voice conversion means, a natural language processing means, an evaluation means, a guideline generation means, a display device, and a human-machine interaction device.

[0712] The server receives audio information from the user and converts it into text using a speech-to-text conversion tool. Existing speech recognition software, such as Google Speech-to-Text, is used for this conversion. After the audio is converted into text, the content is analyzed by a natural language processing tool. This analysis utilizes natural language processing tools such as spaCy or NLTK. The analysis detects illogical inconsistencies and contradictions in the response, and the results are numerically evaluated by an evaluation tool.

[0713] Furthermore, the terminal uses a guideline generation mechanism to generate suggestions for improving the user's answers based on the evaluation results. These suggestions are provided to the user via a display device, allowing the user to practice their answers again based on these suggestions. For example, prompts such as "Please tell me specific ways to solve difficult situations in the workplace" are used. This system enables users to experience effective and high-quality interview practice from the comfort of their homes.

[0714] This invention allows users to improve their interview skills at their own pace, anytime, anywhere.

[0715] The flow of a specific process in Application Example 1 will be explained using Figure 12.

[0716] Step 1:

[0717] The user accesses the terminal, logs into the system, and selects the type of interview they want to practice. This selection information is entered, and the terminal sends a request to the server. The server generates questions based on the received information and sends them to the terminal.

[0718] Step 2:

[0719] The terminal displays a question sent from the server to the user. The user answers the presented question verbally. This voice input is acquired by the terminal and sent to the server as an audio file.

[0720] Step 3:

[0721] The server uses a speech conversion device to convert the audio file received from the terminal into text information. The converted text information is output and used as data for the next analysis process.

[0722] Step 4:

[0723] The server analyzes the textual information using natural language processing tools and evaluates the logic and consistency of the response. Inconsistencies and deficiencies are identified as a result of the analysis, and this information is passed to the evaluation tool.

[0724] Step 5:

[0725] The server numerically evaluates the user's responses based on the analysis results using an evaluation method. The output here is represented as an evaluation score and becomes data for generating feedback.

[0726] Step 6:

[0727] The server uses a guidance generation mechanism with the evaluation score as input to generate specific suggestions for improving the user's response. These suggestions are sent to the terminal via a display device for the user to receive.

[0728] Step 7:

[0729] Users can practice again based on the displayed suggestions and repeat the same process as needed. This allows for continuous improvement.

[0730] Furthermore, an emotion engine that estimates the user's emotions may be incorporated. That is, the identification processing unit 290 may use the emotion identification model 59 to estimate the user's emotions and perform identification processing using the user's emotions.

[0731] This invention combines a voice input system with an emotion engine to provide users with more comprehensive interview practice. This system analyzes voice to recognize not only the content of the user's responses but also their emotional state, thereby supporting improved expressiveness during interviews. Specific embodiments are described below.

[0732] First, the user accesses the system through a terminal and selects the type of interview they want to practice. The terminal sends the user's selection to the server, which then generates questions. These questions are sent to the terminal and presented to the user either verbally or as text.

[0733] When a user responds verbally, the device sends the audio data to the server. The server converts this data into text using speech recognition and analyzes it using natural language processing. The server also uses an emotion engine to recognize the user's emotional state from the audio. This emotion analysis allows the system to evaluate how the emotional expression during practice affects the interviewer.

[0734] Based on the analysis results, the server evaluates the responses using a scoring system and generates feedback using a feedback generation system. This feedback includes not only areas for improvement in the responses themselves, but also advice on how to express emotions. For example, if tension is detected, specific suggestions such as "Relax and showcase your strengths more" will be given.

[0735] Finally, the generated feedback is notified to the user via their device. Based on this feedback, the user can improve both the content and emotional expression of their interview responses. By repeating this process, the user can develop richer and more expressive interview skills.

[0736] The following describes the processing flow.

[0737] Step 1:

[0738] The user accesses the system using a device and selects the type of interview they want to practice. The device then sends the selected information to the server.

[0739] Step 2:

[0740] Based on the information received by the server, it searches the database for question data. The server generates a question and sends it to the terminal.

[0741] Step 3:

[0742] The device presents the received question to the user via voice or text. The user then answers the question via voice.

[0743] Step 4:

[0744] The device records the user's voice response and prepares to send it to the server in real time.

[0745] Step 5:

[0746] The server converts the received audio data into text using speech recognition technology. Simultaneously, it analyzes the user's emotional state from the audio using an emotion engine.

[0747] Step 6:

[0748] The server analyzes the text obtained through speech recognition using natural language processing tools to detect inconsistencies and errors in the content.

[0749] Step 7:

[0750] The server evaluates the responses using a scoring method based on the results of text analysis and sentiment analysis, and calculates a score.

[0751] Step 8:

[0752] Based on the analysis results and scores, the server generates feedback using a feedback generation mechanism. This feedback includes suggestions for improving the answers and advice on expressing emotions.

[0753] Step 9:

[0754] The device receives the generated feedback from the server and displays it to the user. The user can review the feedback and practice again based on the areas for improvement.

[0755] (Example 2)

[0756] Next, we will describe Example 2. In the following description, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0757] In modern communication and evaluation methods, providing comprehensive feedback that includes not only information retrieval from voice but also recognition of emotional states is a challenging task. Especially when practicing for interviews or improving communication skills, not only the content of the user's responses but also their emotional expression are crucial elements. However, existing technologies struggle to accurately recognize a user's emotional state and provide feedback based on that understanding.

[0758] The identification process performed by the identification processing unit 290 of the data processing device 12 in Example 2 is realized by the following means.

[0759] In this invention, the server includes an information terminal means for receiving voice input, an emotion analysis means for evaluating emotions and recognizing emotional states based on analysis results, and a feedback creation method means for generating feedback for improvement based on the analysis results and emotional states. This enables comprehensive interview practice support by combining information obtained from voice with the emotional state at that time.

[0760] A "voice input-enabled information terminal" is a device that accepts voice information emitted by a user and processes that information as digital data.

[0761] A "speech recognition device for converting speech to text" is a device that analyzes received speech data and converts it into textual information.

[0762] A "natural language processing device" is a processing device that analyzes converted text data and understands its meaning and context.

[0763] "Emotion analysis means" refers to a technical means that analyzes data obtained from the user's voice to determine their emotional state.

[0764] An "evaluation tool" is a device that quantitatively scores the user's responses and evaluates their quality based on the analyzed data and emotional state.

[0765] A "feedback generation method" is a technical technique that generates responses indicating areas for improvement based on analysis results and evaluations.

[0766] An "information display means" is a device that visually presents information in order to notify the user of the generated feedback.

[0767] "Information and communication means" refers to communication technologies for sending and receiving data between information terminals and central processing units.

[0768] An "interaction device" is a technological device that requires effective interaction when the user refers to feedback and practices again.

[0769] This invention is a system that improves users' communication skills through voice input. Specifically, it has functions to receive voice input, convert it to text, perform sentiment analysis, and generate feedback.

[0770] First, the user inputs voice using a device. The device acts as a hub for sending the voice data to the server. The server then uses a speech recognition device to convert the voice data into text. A language model is used to improve accuracy during this process. The converted text data is analyzed by natural language processing tools to detect meaning and errors.

[0771] Next, the server uses emotion analysis tools to analyze the emotion data extracted from the voice. This analysis allows the server to understand the user's emotional state and determine, for example, whether they are tense or relaxed.

[0772] Based on these analysis results, the server quantitatively evaluates the user's responses using evaluation tools. This evaluation is based on the quality of the content and the expression of emotion. Then, feedback is generated that encourages the user to improve, according to the feedback generation method. An example of such feedback is, "Relax and showcase your strengths more."

[0773] Finally, the generated feedback is sent to the terminal via an information display device and notified to the user. The user can then use this feedback to improve their communication skills. An example of a prompt might be, "Generate feedback for a user who is nervous when asked 'Please introduce yourself' in a sales job interview."

[0774] This system is designed to support the improvement of comprehensive communication skills and will serve as a valuable practice tool for users.

[0775] The flow of the specific processing in Example 2 will be explained using Figure 13.

[0776] Step 1:

[0777] The user accesses the interview practice system through their device. The user selects the type of interview they wish to practice and enters this selection into their device. The device then sends this information to a server, requesting that appropriate questions be generated based on the selected interview type. The input is the interview type, and the output is the request data sent to the server. Specific actions include the user tapping on options on the screen to enter their selection data into the fields.

[0778] Step 2:

[0779] The server uses information received from the terminal to generate relevant questions using a generative AI model. The server constructs a set of questions that match the interview format selected by the user and sends the generated questions to the terminal. The input is the interview type information received from the terminal, and the output is the generated question data. Specifically, the AI ​​model performs text generation and format conversion for requests.

[0780] Step 3:

[0781] The terminal receives questions sent from the server and presents them to the user. When the user answers the interview questions by voice, the terminal acquires the audio data. In this step, the input is the question data from the server and the user's voice, and the output is the recorded audio data. Specifically, the terminal's microphone function is used to capture the audio.

[0782] Step 4:

[0783] The terminal compresses the recorded audio data and sends it to the server. The server receives this audio data and converts it to text using a speech recognition device. The input is the user's audio data, and the output is text data generated by speech recognition. Specifically, the process involves digital processing of the audio data on the server and conversion by the recognition engine.

[0784] Step 5:

[0785] The server processes the converted text data using natural language processing (NLP) to analyze the user's responses. In addition, it analyzes emotional information extracted from the speech using sentiment analysis tools. The input is text data, and the output is the resulting understanding of the content and emotional information. Specific operations include grammar checking and emotional pattern identification by the analysis engine.

[0786] Step 6:

[0787] The server quantitatively evaluates the user's responses using an evaluation tool based on the analyzed data. Using the evaluation results, it generates feedback for the user using a feedback creation method. The input is the analysis results and emotional state data, and the output is the generated feedback. Specifically, it performs numerical scoring based on evaluation criteria and creates feedback statements.

[0788] Step 7:

[0789] The server sends the generated feedback to the terminal, which then notifies the user. The user reviews the feedback and aims to improve their interview skills based on its content. The input is the generated feedback data, and the output is the display of the feedback. Specifically, the process involves displaying the feedback text on the terminal's display for the user to review.

[0790] (Application Example 2)

[0791] Next, we will explain application example 2. In the following explanation, the data processing device 12 will be referred to as the "server" and the robot 414 as the "terminal".

[0792] In modern interview practice, it is a challenging task for users to receive comprehensive feedback on not only the content of their statements but also their emotional expression and attitude. Traditional systems lack emotional analysis, making it difficult to adequately assess the impact a user's demeanor and emotional state have on the interviewer. This makes it difficult for users to achieve the desired results in actual interviews.

[0793] The specific processing performed by the specific processing unit 290 of the data processing device 12 in Application Example 2 is realized by the following means.

[0794] In this invention, the server includes emotion analysis means for recognizing emotional states from voice, feedback generation means for generating information for improvement based on the analysis results and emotion analysis, and display means for notifying the user of the generated information. This enables accurate evaluation of the user's voice and the associated emotional state, allowing for more improved interview practice.

[0795] A "voice input receiving information processing device" is a device that acquires the content spoken by a user as data and processes it within the system.

[0796] "A speech recognition method for converting into a sequence of symbols" is a method for converting received speech data into a sequence of characters or symbols, and is implemented using speech recognition technology.

[0797] "Natural language processing means for analyzing converted symbol sequences and detecting logical errors" refers to processing technology for analyzing strings obtained through speech recognition and detecting semantic errors and grammatical inconsistencies.

[0798] An "evaluation method for evaluating a response and calculating a numerical value" is a method for objectively evaluating the content and structure of an analyzed response and expressing the results as a numerical value.

[0799] A "feedback generation method for generating information for improvement" is a technique that generates advice and suggestions for improvement based on evaluation results to enhance the user's speech.

[0800] "A means of displaying generated information to the user" refers to a method for conveying feedback information to the user in an easy-to-understand manner, and can be used for screen displays, audio notifications, etc.

[0801] "An emotion analysis method that recognizes emotional states from voice" is a method that identifies emotional states and changes based on the user's voice data.

[0802] This invention is a system that provides comprehensive feedback to users when they practice for interviews by combining voice input and emotion analysis. This is realized by a system that includes: an information processing device that accepts voice input; a voice recognition means for converting it into a sequence of symbols; a natural language processing means for analyzing the converted sequence of symbols and detecting logical errors; an evaluation means for evaluating the response and calculating a numerical value; a feedback generation means for generating information for improvement; a display means for notifying the user of the generated information; and an emotion analysis means for recognizing emotional states from voice.

[0803] An information processing device that accepts voice input acts as a terminal, acquiring the user's spoken content as data. This data is converted into a sequence of symbols via a cloud server using speech recognition software (e.g., Google Speech-to-Text API). The converted sequence of symbols is then analyzed using natural language processing techniques (e.g., Python's NLTK toolkit) to detect logical errors in the response.

[0804] Furthermore, the server performs a numerical evaluation of the analyzed response using an evaluation tool. Based on this evaluation, a feedback generation tool generates information for improvement. The feedback also takes emotional states into consideration, and for sentiment analysis, for example, the Azure Cognitive Services Emotion API can be used. The generated information is notified to the user's terminal by a display tool.

[0805] For example, if a user responds nervously to the question, "Please introduce yourself," the system can provide feedback such as, "Relax more and try talking about your hobbies."

[0806] An example of a prompt for the generative AI model is, "Analyze the user's current emotions from this audio data and provide feedback in a cheerful tone." This is used to recognize the user's emotional state and provide appropriate feedback accordingly.

[0807] The flow of a specific process in Application Example 2 will be explained using Figure 14.

[0808] Step 1:

[0809] The user begins voice input. The terminal receives voice data from the user. This input voice data is used for subsequent processing.

[0810] Step 2:

[0811] The device sends voice input to the server. The server uses speech recognition technology to convert the voice data into a sequence of symbols. This process uses the Google Speech-to-Text API to obtain the voice data as text. The output text is used in the next parsing step.

[0812] Step 3:

[0813] The server analyzes the converted text using natural language processing techniques. It uses the Python natural language processing toolkit NLTK to detect grammatical and logical errors in the text. The analysis results are then fed into a subsequent evaluation step.

[0814] Step 4:

[0815] Based on the analysis results, the server evaluates the response using evaluation tools and quantifies it. The evaluation results output numerical values ​​for the accuracy and expressiveness of the response. This allows for a quantitative evaluation of the quality of the user's speech.

[0816] Step 5:

[0817] The server recognizes the user's emotional state from the voice data. Using the Azure Cognitive Services Emotion API, it identifies emotions from voice tone and speed. This analysis includes the type and intensity of emotion, which is then used to generate subsequent feedback.

[0818] Step 6:

[0819] The server generates feedback that includes areas for improvement based on accurate response evaluation and sentiment analysis results. The feedback generation mechanism uses a generation AI model to create appropriate prompt sentences and provide advice such as relaxation.

[0820] Step 7:

[0821] The generated feedback information is notified to the terminal via a display device. The user reviews this feedback and works to improve their speech. In this way, interview practice progresses more effectively.

[0822] 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.

[0823] Data generation model 58 is a type of so-called generative AI (Artificial Intelligence). One example of data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ), Gemini (Internet search) <url: https: gemini.google.com ?hl="ja">Examples of generative AI include the following. 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. The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference results in data formats such as audio data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0824] In the above embodiment, an example was given in which the specific processing is performed by the data processing device 12, but the technology of this disclosure is not limited thereto, and the specific processing may also be performed by the robot 414.

[0825] 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.

[0826] Figure 9 shows an 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.

[0827] 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.

[0828] 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.

[0829] 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, motorcycles, etc., 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, for example, based 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.

[0830] 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."

[0831] 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.

[0832] The above description primarily focuses on the functions of the data processing device 12 in relation to this disclosure. However, the system related to this disclosure is not necessarily implemented on a server. The system related to this disclosure may be implemented as a general information processing system. This disclosure may be implemented, for example, as a software program that runs on a personal computer or as an application that runs on a smartphone. The method related to this disclosure may be provided to users in SaaS (Software as a Service) format.

[0833] 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 of the specific process may be performed by multiple computers, including computer 22. For example, a data generation model 58 may be provided in an external device of the data processing device 12, and the external device may generate data according to the input data.

[0834] 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.

[0835] 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.

[0836] 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.

[0837] 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.

[0838] 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.

[0839] 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.

[0840] 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.

[0841] 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 the like 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.

[0842] 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.

[0843] The following is further disclosed regarding the embodiments described above.

[0844] (Claim 1)

[0845] A terminal device that accepts voice input,

[0846] A speech recognition means for converting received audio into text,

[0847] A natural language processing method that analyzes the converted text and detects inconsistencies and errors,

[0848] A scoring method that grades the responses and calculates a score based on the analysis results,

[0849] A feedback generation means that generates feedback for improvement based on the analysis results and scores,

[0850] A display means for notifying the user of the generated feedback,

[0851] A system that includes this.

[0852] (Claim 2)

[0853] The system according to claim 1, further comprising a communication means for transmitting information for generating a question from a terminal to a server.

[0854] (Claim 3)

[0855] The system according to claim 1, further comprising interaction means for the user to repractice in response to feedback.

[0856] "Example 1"

[0857] (Claim 1)

[0858] Information processing device means for receiving voice input,

[0859] A speech conversion means that converts received audio into text,

[0860] A natural language processing method that analyzes the converted text and detects inconsistencies and errors,

[0861] An evaluation method that scores responses and calculates an evaluation based on the analysis results,

[0862] A solution generation method that generates feedback for improvement based on analysis results and evaluation,

[0863] A means of notifying the user of the generated feedback,

[0864] A system that includes this.

[0865] (Claim 2)

[0866] The system according to claim 1, further comprising data transmission means for transmitting information for generating a question from an information processing device to a computing device.

[0867] (Claim 3)

[0868] The system according to claim 1, further comprising a means of dialogue that enables the user to repractice in response to feedback.

[0869] "Application Example 1"

[0870] (Claim 1)

[0871] A device for acquiring audio information,

[0872] A speech conversion means for converting acquired speech information to generate text information,

[0873] A natural language processing method for analyzing generated textual information and detecting inconsistencies or omissions in arguments,

[0874] An evaluation method that evaluates responses and calculates a score based on the analysis results,

[0875] A guideline generation means for generating guidelines for improving responses based on the analysis results and scores,

[0876] A display device for reporting the generated guidelines to the user,

[0877] A human-machine interaction device means for receiving voice input from a user,

[0878] An automated practice support system including [specific feature].

[0879] (Claim 2)

[0880] The automated practice support system according to claim 1, further comprising a communication device that transmits information for generating questions from the device to a remote device.

[0881] (Claim 3)

[0882] The automated training support system according to claim 1, further comprising a human-machine interaction device for the user to retrain in accordance with guidelines.

[0883] "Example 2 of combining an emotion engine"

[0884] (Claim 1)

[0885] An information terminal that accepts voice input,

[0886] A speech recognition device means for converting received audio into text,

[0887] A natural language processing means that analyzes the converted text and processes the information,

[0888] An emotion analysis means that evaluates emotions and recognizes emotional states based on the analysis results,

[0889] An evaluation method that scores and evaluates responses based on analysis results and emotional state,

[0890] A method and means for generating feedback for improvement based on analysis results and evaluations,

[0891] Information display means for notifying the user of the generated feedback,

[0892] A system that includes this.

[0893] (Claim 2)

[0894] The system according to claim 1, further comprising information communication means for transmitting information for generating a question from an information terminal to a central processing unit.

[0895] (Claim 3)

[0896] The system according to claim 1, further comprising interaction means for the user to repeatedly practice based on feedback.

[0897] "Application example 2 when combining with an emotional engine"

[0898] (Claim 1)

[0899] Information processing device means for receiving voice input,

[0900] A speech recognition means for converting received audio into a sequence of symbols,

[0901] A natural language processing means that analyzes the converted symbol sequence and detects logical errors,

[0902] An evaluation means that evaluates the response and calculates a numerical value based on the analysis results,

[0903] A feedback generation means that generates information for improvement based on evaluation results and sentiment analysis,

[0904] A display means for notifying the user of the generated information,

[0905] An emotion analysis method that recognizes emotional states from speech,

[0906] A system that includes this.

[0907] (Claim 2)

[0908] The system according to claim 1, further comprising a transmission means for transmitting information for generating dialogue from an information processing device to a data processing device.

[0909] (Claim 3)

[0910] The system according to claim 1, further comprising means for communication for the user to repractice in response to improvement information. [Explanation of Symbols]

[0911] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots< / url:> < / url:> < / url:> < / url:>

Claims

1. A terminal device that accepts voice input, A speech recognition means for converting received audio into text, A natural language processing method that analyzes the converted text and detects inconsistencies and errors, A scoring method that grades the responses and calculates a score based on the analysis results, A feedback generation means that generates feedback for improvement based on the analysis results and scores, A display means for notifying the user of the generated feedback, A system that includes this.

2. The system according to claim 1, further comprising a communication means for transmitting information for generating a question from a terminal to a server.

3. The system according to claim 1, further comprising interaction means for the user to repractice in response to feedback.

Images