System

Abstract

An object of the system according to the embodiment is to provide a speaking enhancement menu optimized for an individual and improve the accuracy of pronunciation and grammar.SOLUTION: A system includes a generation part, a pronunciation check part, a pronunciation sample provision part, a grammar check part, and a conversation simulation part. The generation unit generates a speaking enhancement menu optimized for a person using the generation AI. The pronunciation check unit checks the pronunciation of the user based on the speaking enhancement menu generated by the generation unit. The pronunciation sample providing unit provides a correct pronunciation sample based on the pronunciation checked by the pronunciation checking unit. A grammar check part analyzes the speaking contents of the user on the basis of the speaking enhancement menu generated by the generation part and checks the accuracy of grammar. The conversation simulator simulates a conversation between the user and the instructor based on the speaking enhancement menu generated by the generator.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to provide an optimized menu for individuals when learning to speak various foreign languages, and there was a particular problem of insufficient checking of pronunciation and grammar.

[0005] The system according to the embodiment aims to provide an individually optimized speaking enhancement menu to improve pronunciation and grammatical accuracy. [Means for solving the problem]

[0006] The system according to the embodiment includes a generation unit, a pronunciation check unit, a pronunciation sample providing unit, a grammar check unit, and a conversation simulation unit. The generation unit generates a speaking enhancement menu optimized for an individual using a generation AI. The pronunciation check unit checks the user's pronunciation based on the speaking enhancement menu generated by the generation unit. The pronunciation sample providing unit provides a sample of correct pronunciation based on the pronunciation checked by the pronunciation check unit. The grammar check unit analyzes the user's speaking content based on the speaking enhancement menu generated by the generation unit and checks the accuracy of grammar. The conversation simulation unit simulates a conversation between the user and a teacher based on the speaking enhancement menu generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide personalized speaking enhancement menus to improve pronunciation and grammar accuracy. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A speaking learning system according to an embodiment of the present invention uses a generation AI to generate and score a personalized speaking enhancement menu, thereby enabling the user to efficiently and effectively improve their speaking skills.

[0029] A speaking learning system according to an embodiment includes a generation unit, a pronunciation check unit, a pronunciation sample providing unit, a grammar check unit, and a conversation simulation unit. The generation unit uses a generation AI to generate a speaking enhancement menu optimized for each individual. For example, the generation AI generates a personalized speaking enhancement menu based on the user's learning progress and goals. The generation AI can also analyze the user's past conversation history, detect specific language patterns and habits, and generate a customized menu based on the results. The generation AI can also analyze the user's learning style (visual, auditory, tactile) and provide a speaking enhancement menu optimized for that style. For example, a speaking menu that makes extensive use of images and videos can be provided to a user who prefers visual learning. The pronunciation check unit checks the user's pronunciation based on the speaking enhancement menu generated by the generation unit. For example, the generation AI can analyze the user's pronunciation and provide feedback by visualizing the speech waveform. The generation AI can also check pronunciation for different accents and dialects to help the user become accustomed to diverse pronunciations. The generation AI can also use its emotion estimation function to evaluate a user's confidence in their pronunciation and provide feedback to build confidence. The pronunciation sample provider provides a sample of correct pronunciation based on the pronunciation checked by the pronunciation checker. For example, when a user pronounces "Bonjour," the generation AI analyzes the user's pronunciation and compares it with the correct pronunciation to provide feedback. It also provides an audio sample of correct pronunciation, which the user can listen to and practice. The grammar checker analyzes the user's speaking content based on the speaking enhancement menu generated by the generation unit and checks the accuracy of their grammar. For example, the generation AI analyzes the user's speaking content, analyzes patterns of grammatical errors, and provides practice menus to prevent specific errors from being repeated. The generation AI can also detect the user's grammatical errors in real time and provide feedback to correct them on the spot. The generation AI can also use its emotion estimation function to analyze the user's emotional reaction when they make a grammatical error and provide positive feedback.The conversation simulation unit simulates a conversation between the user and a lecturer based on the speaking enhancement menu generated by the generation unit. For example, the generation AI simulates a conversation between the user and a lecturer, analyzes the user's conversation style, and improves an algorithm to simulate more natural conversation. The generation AI can also provide a function to record the user's conversation content and allow the user to review it later. The generation AI can also use an emotion estimation function to monitor the user's emotional state during the conversation and provide appropriate feedback. This allows the speaking learning system according to the embodiment to efficiently and effectively improve the user's speaking skills. For example, the generation AI can analyze the user's learning environment and suggest optimal study times and locations. The generation AI can also analyze the user's personality traits and provide a learning menu based on the analysis. The generation AI can also use the emotion estimation function to monitor the user's emotional state during learning and provide appropriate feedback.

[0030] The generation unit can analyze the user's past conversation history, detect specific language patterns and habits, and generate a customized menu based on them. For example, the generation unit uses a generation AI to analyze the user's past conversation history and detect specific language patterns and habits. For example, it can identify pronunciation and grammar that the user frequently makes mistakes in and generate a practice menu based on that. This makes it possible to provide a customized menu that meets the user's individual needs.

[0031] The generation unit can analyze the user's learning style and provide a speaking enhancement menu optimized for that. For example, the generation unit uses a generation AI to analyze the user's learning style and provide a speaking menu that makes extensive use of images and videos to users who prefer visual learning. For example, it can provide a video that shows the mouth shape for pronunciation. This makes it possible to provide an optimal speaking enhancement menu that suits the user's learning style.

[0032] The generation unit can generate speaking menus that include cultural nuances for users with different cultural backgrounds. For example, the generation unit generates speaking menus that include cultural nuances by using a generation AI that takes into account the user's cultural background. For example, the generation unit provides a practice menu for greetings and etiquette in a specific culture. This makes it possible to provide speaking menus that include cultural nuances for users with different cultural backgrounds.

[0033] The generation unit generates speaking menus specialized for the user's occupation or field of expertise, and can provide skills directly related to practical work. For example, the generation unit uses a generation AI to consider the user's occupation or field of expertise and generate speaking menus specialized for that field. For example, a user in the medical field can be provided with a practice menu that includes medical terminology. This makes it possible to provide a speaking menu specialized for the user's occupation or field of expertise.

[0034] The pronunciation check unit can analyze the user's pronunciation and provide feedback by visualizing the audio waveform. For example, the pronunciation check unit uses a generation AI to analyze the user's pronunciation and provide feedback by visualizing the audio waveform. For example, it compares the waveform of the user's pronunciation with that of the correct pronunciation and indicates which parts differ. This makes it possible to provide feedback that makes it easy to understand the user's pronunciation visually.

[0035] The pronunciation check unit can check pronunciation corresponding to different accents and dialects, allowing the user to become accustomed to a variety of pronunciations. For example, the pronunciation check unit checks pronunciation corresponding to different accents and dialects by the generation AI, allowing the user to become accustomed to a variety of pronunciations. For example, the pronunciation check unit checks pronunciation of British English and American English. This allows the user to become accustomed to a variety of pronunciations.

[0036] The pronunciation check unit can provide a fun learning environment by having the user practice their pronunciation in sync with music and rhythm. The pronunciation check unit can provide a fun learning environment by, for example, having the generation AI practice the user's pronunciation in sync with music and rhythm. For example, pronunciation practice is performed in sync with rhythm. This allows the user to enjoy pronunciation practice.

[0037] The pronunciation check unit can compare the user's pronunciation with other users and provide feedback that stimulates a competitive spirit. For example, the pronunciation check unit uses a generation AI to compare the user's pronunciation with other users and provide feedback that stimulates a competitive spirit. For example, feedback is given by comparing the user's pronunciation with the pronunciation scores of other users. This stimulates the user's competitive spirit and increases their motivation to learn.

[0038] The grammar check unit can analyze the patterns of the user's grammatical mistakes and provide a practice menu to prevent the user from repeating specific mistakes. For example, the grammar check unit can analyze the patterns of the user's grammatical mistakes and provide a practice menu to prevent the user from repeating specific mistakes. For example, it can provide a menu that focuses on practicing grammar items that the user frequently makes mistakes on. This prevents the user from repeating specific grammatical mistakes.

[0039] The grammar checker can detect the user's grammatical errors in real time and provide feedback to correct them on the spot. For example, the generative AI can detect the user's grammatical errors in real time and provide feedback to correct them on the spot. For example, it can immediately point out the grammar mistakes the user has made and present the correct grammar. This allows the user to correct their grammar mistakes in real time.

[0040] The grammar check unit allows users to share their grammar mistakes with other users, forming a community for overcoming common mistakes. For example, the generation AI in the grammar check unit allows users to share their grammar mistakes with other users, forming a community for overcoming common mistakes. For example, users who make the same grammar mistakes exchange information with each other. This allows users to form a community for overcoming common grammar mistakes.

[0041] The grammar check unit can visually indicate the user's grammatical errors and provide easy-to-understand feedback. For example, the generation AI can visually indicate the user's grammatical errors and provide easy-to-understand feedback. For example, incorrect grammatical parts can be displayed in different colors. This allows the user to receive feedback that makes the grammatical errors visually easy to understand.

[0042] The conversation simulation unit can analyze the user's conversation style and improve the algorithm for simulating more natural conversation. For example, the conversation simulation unit uses a generation AI to analyze the user's conversation style and improve the algorithm for simulating more natural conversation. For example, it learns the user's speaking style and expression habits. This allows the user to simulate more natural conversation.

[0043] The conversation simulation unit can provide a function to record the content of a user's conversation and allow it to be reviewed later. The conversation simulation unit provides a function to, for example, allow the generation AI to record the content of a user's conversation and allow it to be reviewed later. For example, the conversation recording can be saved so that the user can listen to it again later. This allows the user to review the content of the conversation later.

[0044] The conversation simulation unit can provide conversation simulations based on different scenarios. For example, the generation AI provides conversation simulations based on different scenarios. For example, in a business scenario, the user practices meetings and presentations. This allows the user to perform conversation simulations based on different scenarios.

[0045] The conversation simulation unit can provide a function for sharing the content of a user's conversation with other users and for collaborative learning. For example, the conversation simulation unit provides a function for the generation AI to share the content of a user's conversation with other users and for collaborative learning. For example, group discussion practice is performed. This allows the user to learn collaboratively with other users.

[0046] The learning environment analysis unit can analyze the user's learning environment and suggest optimal study times and places. For example, the generation AI analyzes the user's learning environment and suggests optimal study times and places. For example, it suggests times when the user can easily concentrate or quiet places. This allows the user to find the optimal study time and place.

[0047] The learning environment analysis unit can analyze the user's personality traits and provide a learning menu based on them. For example, the learning environment analysis unit uses a generation AI to analyze the user's personality traits and provide a learning menu based on them. For example, an introverted user can be provided with a menu that allows them to study alone. This makes it possible to provide a learning menu that suits the user's personality traits.

[0048] The learning environment analysis unit can share a user's learning environment with other users and create a common learning environment. For example, the generation AI of the learning environment analysis unit can share a user's learning environment with other users and create a common learning environment. For example, an online learning group can be created with users who study at the same time. This allows users to create a common learning environment.

[0049] The learning environment analysis unit can visually show the user's learning progress and provide feedback that increases motivation. For example, the learning environment analysis unit uses a generation AI to visually show the user's learning progress and provide feedback that increases motivation. For example, the learning progress is displayed in a graph or chart. This allows the user to visually check their learning progress and increase their motivation.

[0050] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0051] The generator can also generate mini-games to strengthen specific speaking skills based on the user's learning history. For example, it can provide a game that tests pronunciation accuracy or a quiz-style game that tests grammar accuracy. It can also provide step-by-step guides for users to master specific skills. Furthermore, it can introduce a ranking system that allows users to compete with other learners, increasing motivation to learn.

[0052] The generator analyzes the user's past conversation history, detects specific language patterns and habits, and generates a customized menu based on them. For example, it can identify pronunciation and grammar mistakes that the user frequently makes and generate a practice menu based on them. This allows the system to provide a customized menu that meets the user's individual needs.

[0053] The generator can analyze the user's learning style and provide a speaking enhancement menu optimized for that style. For example, a speaking menu that makes extensive use of images and videos can be provided to a user who prefers visual learning. For example, a video showing the mouth shape for pronunciation can be provided. This allows the generator to provide a speaking enhancement menu optimized for the user's learning style.

[0054] The generation unit can generate speaking menus that include cultural nuances for users with different cultural backgrounds. For example, it can provide practice menus for greetings and etiquette in a specific culture. This allows users with different cultural backgrounds to receive speaking menus that include cultural nuances.

[0055] The generator generates speaking menus specialized for the user's occupation or field of expertise, providing skills directly relevant to the job. For example, a user in the medical field can be provided with a practice menu including medical terminology. This allows the generator to provide speaking menus specialized for the user's occupation or field of expertise.

[0056] The pronunciation checker can analyze the user's pronunciation and provide feedback by visualizing the speech waveform. For example, it can compare the waveform of the user's pronunciation with the correct pronunciation and indicate which parts differ. This allows for visual feedback of the user's pronunciation that is easy to understand.

[0057] The pronunciation checker can check pronunciation for different accents and dialects, allowing the user to become familiar with a variety of pronunciations, for example, British English and American English, allowing the user to become familiar with a variety of pronunciations.

[0058] The processing flow of the first embodiment will be briefly explained below.

[0059] Step 1: The generation unit uses generation AI to generate a speaking enhancement menu optimized for each individual. For example, the generation AI generates an individually optimized speaking enhancement menu based on the user's learning progress and goals. The generation AI can also analyze the user's past conversation history, detect specific language patterns and habits, and generate a customized menu based on them. The generation AI can also analyze the user's learning style (visual, auditory, tactile) and provide a speaking enhancement menu optimized for that. For example, a speaking menu that makes extensive use of images and videos can be provided to a user who prefers visual learning. Step 2: The pronunciation checker checks the user's pronunciation based on the speaking enhancement menu generated by the generator. For example, the generator AI analyzes the user's pronunciation and provides feedback by visualizing the speech waveform. The generator AI can also check pronunciation for different accents and dialects to help the user become familiar with various pronunciations. The generator AI can also use an emotion estimation function to evaluate the user's confidence in their pronunciation and provide feedback to build confidence. Step 3: The pronunciation sample provider provides a sample of the correct pronunciation based on the pronunciation checked by the pronunciation checker. For example, when a user pronounces "Bonjour," the generation AI analyzes the pronunciation, compares it with the correct pronunciation, and provides feedback. It also provides an audio sample of the correct pronunciation, which the user can listen to and practice. Step 4: The grammar checker analyzes the user's speaking content based on the speaking reinforcement menu generated by the generator and checks the accuracy of grammar. For example, the generator AI analyzes the user's speaking content, analyzes patterns of grammatical errors, and provides practice menus to prevent specific mistakes from being repeated. The generator AI can also detect the user's grammatical errors in real time and provide feedback to correct them on the spot. The generator AI can also use its emotion estimation function to analyze the user's emotional reactions when they make grammatical errors and provide positive feedback. Step 5: The conversation simulation unit simulates a conversation between the user and a lecturer based on the speaking enhancement menu generated by the generation unit. For example, the generation AI simulates a conversation between the user and a lecturer, analyzes the user's conversation style, and improves the algorithm to simulate a more natural conversation. The generation AI can also provide a function to record the user's conversation content and review it later. The generation AI can also use an emotion estimation function to monitor the user's emotional state during the conversation and provide appropriate feedback.

[0060] (Example 2) A speaking learning system according to an embodiment of the present invention uses a generation AI to generate and score a personalized speaking enhancement menu, thereby enabling the user to efficiently and effectively improve their speaking skills.

[0061] A speaking learning system according to an embodiment includes a generation unit, a pronunciation check unit, a pronunciation sample providing unit, a grammar check unit, and a conversation simulation unit. The generation unit uses a generation AI to generate a speaking enhancement menu optimized for each individual. For example, the generation AI generates a personalized speaking enhancement menu based on the user's learning progress and goals. The generation AI can also analyze the user's past conversation history, detect specific language patterns and habits, and generate a customized menu based on the results. The generation AI can also analyze the user's learning style (visual, auditory, tactile) and provide a speaking enhancement menu optimized for that style. For example, a speaking menu that makes extensive use of images and videos can be provided to a user who prefers visual learning. The pronunciation check unit checks the user's pronunciation based on the speaking enhancement menu generated by the generation unit. For example, the generation AI can analyze the user's pronunciation and provide feedback by visualizing the speech waveform. The generation AI can also check pronunciation for different accents and dialects to help the user become accustomed to diverse pronunciations. The generation AI can also use its emotion estimation function to evaluate a user's confidence in their pronunciation and provide feedback to build confidence. The pronunciation sample provider provides a sample of correct pronunciation based on the pronunciation checked by the pronunciation checker. For example, when a user pronounces "Bonjour," the generation AI analyzes the user's pronunciation and compares it with the correct pronunciation to provide feedback. It also provides an audio sample of correct pronunciation, which the user can listen to and practice. The grammar checker analyzes the user's speaking content based on the speaking enhancement menu generated by the generation unit and checks the accuracy of their grammar. For example, the generation AI analyzes the user's speaking content, analyzes patterns of grammatical errors, and provides practice menus to prevent specific errors from being repeated. The generation AI can also detect the user's grammatical errors in real time and provide feedback to correct them on the spot. The generation AI can also use its emotion estimation function to analyze the user's emotional reaction when they make a grammatical error and provide positive feedback.The conversation simulation unit simulates a conversation between the user and a lecturer based on the speaking enhancement menu generated by the generation unit. For example, the generation AI simulates a conversation between the user and a lecturer, analyzes the user's conversation style, and improves an algorithm to simulate more natural conversation. The generation AI can also provide a function to record the user's conversation content and allow the user to review it later. The generation AI can also use an emotion estimation function to monitor the user's emotional state during the conversation and provide appropriate feedback. This allows the speaking learning system according to the embodiment to efficiently and effectively improve the user's speaking skills. For example, the generation AI can analyze the user's learning environment and suggest optimal study times and locations. The generation AI can also analyze the user's personality traits and provide a learning menu based on the analysis. The generation AI can also use the emotion estimation function to monitor the user's emotional state during learning and provide appropriate feedback.

[0062] The generation unit can analyze the user's past conversation history, detect specific language patterns and habits, and generate a customized menu based on them. For example, the generation unit uses a generation AI to analyze the user's past conversation history and detect specific language patterns and habits. For example, it can identify pronunciation and grammar that the user frequently makes mistakes in and generate a practice menu based on that. This makes it possible to provide a customized menu that meets the user's individual needs.

[0063] The generation unit can analyze the user's learning style and provide a speaking enhancement menu optimized for that. For example, the generation unit uses a generation AI to analyze the user's learning style and provide a speaking menu that makes extensive use of images and videos to users who prefer visual learning. For example, it can provide a video that shows the mouth shape for pronunciation. This makes it possible to provide an optimal speaking enhancement menu that suits the user's learning style.

[0064] The generation unit can use the emotion estimation function to monitor the user's emotional state during learning in real time and generate a menu for reducing stress and anxiety. For example, the generation unit uses the emotion estimation function to monitor the user's emotional state during learning in real time. For example, if the user is feeling stressed, the generation unit provides a practice menu that helps the user relax. This makes it possible to provide a menu that reduces stress and anxiety according to the user's emotional state.

[0065] The generation unit can generate speaking menus that include cultural nuances for users with different cultural backgrounds. For example, the generation unit generates speaking menus that include cultural nuances by using a generation AI that takes into account the user's cultural background. For example, the generation unit provides a practice menu for greetings and etiquette in a specific culture. This makes it possible to provide speaking menus that include cultural nuances for users with different cultural backgrounds.

[0066] The generation unit generates speaking menus specialized for the user's occupation or field of expertise, and can provide skills directly related to practical work. For example, the generation unit uses a generation AI to consider the user's occupation or field of expertise and generate speaking menus specialized for that field. For example, a user in the medical field can be provided with a practice menu that includes medical terminology. This makes it possible to provide a speaking menu specialized for the user's occupation or field of expertise.

[0067] The generation unit uses the emotion estimation function to generate a speaking menu based on the topic that the user is most interested in, thereby increasing motivation to learn. For example, the generation unit uses the emotion estimation function to identify the topic that the user is most interested in and generate a speaking menu based on that. For example, if the user is interested in sports, a conversation practice menu related to sports is provided. This makes it possible to provide a speaking menu that piques the user's interest and increases motivation to learn.

[0068] The pronunciation check unit can analyze the user's pronunciation and provide feedback by visualizing the audio waveform. For example, the pronunciation check unit uses a generation AI to analyze the user's pronunciation and provide feedback by visualizing the audio waveform. For example, it compares the waveform of the user's pronunciation with that of the correct pronunciation and indicates which parts differ. This makes it possible to provide feedback that makes it easy to understand the user's pronunciation visually.

[0069] The pronunciation check unit can check pronunciation corresponding to different accents and dialects, allowing the user to become accustomed to a variety of pronunciations. For example, the pronunciation check unit checks pronunciation corresponding to different accents and dialects by the generation AI, allowing the user to become accustomed to a variety of pronunciations. For example, the pronunciation check unit checks pronunciation of British English and American English. This allows the user to become accustomed to a variety of pronunciations.

[0070] The pronunciation check unit can use the emotion estimation function to evaluate the user's confidence in their pronunciation and provide feedback that will help them to feel confident. The pronunciation check unit, for example, uses the emotion estimation function to evaluate the user's confidence in their pronunciation. For example, it analyzes whether the user is pronouncing with confidence and provides feedback that will help them to feel confident. This allows the user to pronounce with confidence.

[0071] The pronunciation check unit can provide a fun learning environment by having the user practice their pronunciation in sync with music and rhythm. The pronunciation check unit can provide a fun learning environment by, for example, having the generation AI practice the user's pronunciation in sync with music and rhythm. For example, pronunciation practice is performed in sync with rhythm. This allows the user to enjoy pronunciation practice.

[0072] The pronunciation check unit can compare the user's pronunciation with other users and provide feedback that stimulates a competitive spirit. For example, the pronunciation check unit uses a generation AI to compare the user's pronunciation with other users and provide feedback that stimulates a competitive spirit. For example, feedback is given by comparing the user's pronunciation with the pronunciation scores of other users. This stimulates the user's competitive spirit and increases their motivation to learn.

[0073] The pronunciation check unit can use the emotion estimation function to provide a relaxation menu for reducing stress felt by the user during pronunciation practice. The pronunciation check unit, for example, uses the emotion estimation function to provide a relaxation menu for reducing stress felt by the user during pronunciation practice. For example, relaxing music is played. This allows the user to relax and practice pronunciation.

[0074] The grammar check unit can analyze the patterns of the user's grammatical mistakes and provide a practice menu to prevent the user from repeating specific mistakes. For example, the grammar check unit can analyze the patterns of the user's grammatical mistakes and provide a practice menu to prevent the user from repeating specific mistakes. For example, it can provide a menu that focuses on practicing grammar items that the user frequently makes mistakes on. This prevents the user from repeating specific grammatical mistakes.

[0075] The grammar checker can detect the user's grammatical errors in real time and provide feedback to correct them on the spot. For example, the generative AI can detect the user's grammatical errors in real time and provide feedback to correct them on the spot. For example, it can immediately point out the grammar mistakes the user has made and present the correct grammar. This allows the user to correct their grammar mistakes in real time.

[0076] The grammar check unit can use the emotion estimation function to analyze the emotional reaction when the user makes a grammatical error and provide positive feedback. For example, the grammar check unit can use the emotion estimation function to analyze the emotional reaction when the user makes a grammatical error and provide positive feedback. For example, if the user is feeling down, an encouraging message can be displayed. This allows the user to receive positive feedback when they make a grammatical error.

[0077] The grammar check unit allows users to share their grammar mistakes with other users, forming a community for overcoming common mistakes. For example, the generation AI in the grammar check unit allows users to share their grammar mistakes with other users, forming a community for overcoming common mistakes. For example, users who make the same grammar mistakes exchange information with each other. This allows users to form a community for overcoming common grammar mistakes.

[0078] The grammar check unit can visually indicate the user's grammatical errors and provide easy-to-understand feedback. For example, the generation AI can visually indicate the user's grammatical errors and provide easy-to-understand feedback. For example, incorrect grammatical parts can be displayed in different colors. This allows the user to receive feedback that makes the grammatical errors visually easy to understand.

[0079] The grammar check unit can use the emotion estimation function to provide a relaxation menu for reducing stress when the user makes a grammatical error. For example, the grammar check unit can use the emotion estimation function to provide a relaxation menu for reducing stress when the user makes a grammatical error. For example, the grammar check unit can play relaxing music. In this way, the user can receive a relaxation menu for reducing stress when they make a grammatical error.

[0080] The conversation simulation unit can analyze the user's conversation style and improve the algorithm for simulating more natural conversation. For example, the conversation simulation unit uses a generation AI to analyze the user's conversation style and improve the algorithm for simulating more natural conversation. For example, it learns the user's speaking style and expression habits. This allows the user to simulate more natural conversation.

[0081] The conversation simulation unit can provide a function to record the content of a user's conversation and allow it to be reviewed later. The conversation simulation unit provides a function to, for example, allow the generation AI to record the content of a user's conversation and allow it to be reviewed later. For example, the conversation recording can be saved so that the user can listen to it again later. This allows the user to review the content of the conversation later.

[0082] The conversation simulation unit can use the emotion estimation function to monitor the emotional state of the user during conversation and provide appropriate feedback. For example, the conversation simulation unit uses the emotion estimation function to monitor the emotional state of the user during conversation and provide appropriate feedback. For example, if the user is nervous, feedback to relax is provided. This allows the user to receive feedback according to the emotional state during conversation.

[0083] The conversation simulation unit can provide conversation simulations based on different scenarios. For example, the generation AI provides conversation simulations based on different scenarios. For example, in a business scenario, the user practices meetings and presentations. This allows the user to perform conversation simulations based on different scenarios.

[0084] The conversation simulation unit can provide a function for sharing the content of a user's conversation with other users and for collaborative learning. For example, the conversation simulation unit provides a function for the generation AI to share the content of a user's conversation with other users and for collaborative learning. For example, group discussion practice is performed. This allows the user to learn collaboratively with other users.

[0085] The conversation simulation unit can use the emotion estimation function to provide a relaxation menu for reducing stress felt by the user during conversation. The conversation simulation unit, for example, uses the emotion estimation function to provide a relaxation menu for reducing stress felt by the user during conversation. For example, relaxing music is played. This can reduce the stress felt by the user during conversation.

[0086] The learning environment analysis unit can analyze the user's learning environment and suggest optimal study times and places. For example, the generation AI analyzes the user's learning environment and suggests optimal study times and places. For example, it suggests times when the user can easily concentrate or quiet places. This allows the user to find the optimal study time and place.

[0087] The learning environment analysis unit can analyze the user's personality traits and provide a learning menu based on them. For example, the learning environment analysis unit uses a generation AI to analyze the user's personality traits and provide a learning menu based on them. For example, an introverted user can be provided with a menu that allows them to study alone. This makes it possible to provide a learning menu that suits the user's personality traits.

[0088] The learning environment analysis unit can use the emotion estimation function to monitor the user's emotional state while studying and provide appropriate feedback. The learning environment analysis unit can, for example, use the emotion estimation function to monitor the user's emotional state while studying and provide appropriate feedback. For example, if the user is tired, the learning environment analysis unit can suggest taking a break. This makes it possible to provide feedback according to the user's emotional state while studying.

[0089] The learning environment analysis unit can share a user's learning environment with other users and create a common learning environment. For example, the generation AI of the learning environment analysis unit can share a user's learning environment with other users and create a common learning environment. For example, an online learning group can be created with users who study at the same time. This allows users to create a common learning environment.

[0090] The learning environment analysis unit can visually show the user's learning progress and provide feedback that increases motivation. For example, the learning environment analysis unit uses a generation AI to visually show the user's learning progress and provide feedback that increases motivation. For example, the learning progress is displayed in a graph or chart. This allows the user to visually check their learning progress and increase their motivation.

[0091] The learning environment analysis unit can use the emotion estimation function to provide a relaxation menu for reducing stress felt by the user while studying. The learning environment analysis unit can use the emotion estimation function to provide a relaxation menu for reducing stress felt by the user while studying. For example, the learning environment analysis unit can play relaxing music. This can reduce the stress felt by the user while studying.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The generator can also generate mini-games to strengthen specific speaking skills based on the user's learning history. For example, it can provide a game that tests pronunciation accuracy or a quiz-style game that tests grammar accuracy. It can also provide step-by-step guides for users to master specific skills. Furthermore, it can introduce a ranking system that allows users to compete with other learners, increasing motivation to learn.

[0094] The generator analyzes the user's past conversation history, detects specific language patterns and habits, and generates a customized menu based on them. For example, it can identify pronunciation and grammar mistakes that the user frequently makes and generate a practice menu based on them. This allows the system to provide a customized menu that meets the user's individual needs.

[0095] The generator can analyze the user's learning style and provide a speaking enhancement menu optimized for that style. For example, a speaking menu that makes extensive use of images and videos can be provided to a user who prefers visual learning. For example, a video showing the mouth shape for pronunciation can be provided. This allows the generator to provide a speaking enhancement menu optimized for the user's learning style.

[0096] The generation unit can use the emotion estimation function to monitor the user's emotional state during learning in real time and generate a menu to reduce stress and anxiety. For example, if the user is feeling stressed, it can provide a relaxation practice menu. This makes it possible to provide a menu that reduces stress and anxiety according to the user's emotional state.

[0097] The generation unit can generate speaking menus that include cultural nuances for users with different cultural backgrounds. For example, it can provide practice menus for greetings and etiquette in a specific culture. This allows users with different cultural backgrounds to receive speaking menus that include cultural nuances.

[0098] The generator generates speaking menus specialized for the user's occupation or field of expertise, providing skills directly relevant to the job. For example, a user in the medical field can be provided with a practice menu including medical terminology. This allows the generator to provide speaking menus specialized for the user's occupation or field of expertise.

[0099] The generation unit uses the emotion estimation function to generate speaking menus based on the topics that interest the user most, thereby increasing their motivation to learn. For example, if the user is interested in sports, a sports-related conversation practice menu is provided. This makes it possible to provide speaking menus that pique the user's interest and increase their motivation to learn.

[0100] The pronunciation checker can analyze the user's pronunciation and provide feedback by visualizing the speech waveform. For example, it can compare the waveform of the user's pronunciation with the correct pronunciation and indicate which parts differ. This allows for visual feedback of the user's pronunciation that is easy to understand.

[0101] The pronunciation checker can check pronunciation for different accents and dialects, allowing the user to become familiar with a variety of pronunciations, for example, British English and American English, allowing the user to become familiar with a variety of pronunciations.

[0102] The pronunciation check unit can use the emotion estimation function to evaluate the user's confidence in their pronunciation and provide feedback that will help them to feel more confident. For example, it can analyze whether the user is pronouncing with confidence and provide feedback that will help them to feel more confident. This allows the user to pronounce with confidence.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The generation unit uses generation AI to generate a speaking enhancement menu optimized for each individual. For example, the generation AI generates an individually optimized speaking enhancement menu based on the user's learning progress and goals. The generation AI can also analyze the user's past conversation history, detect specific language patterns and habits, and generate a customized menu based on them. The generation AI can also analyze the user's learning style (visual, auditory, tactile) and provide a speaking enhancement menu optimized for that. For example, a speaking menu that makes extensive use of images and videos can be provided to a user who prefers visual learning. Step 2: The pronunciation checker checks the user's pronunciation based on the speaking enhancement menu generated by the generator. For example, the generator AI analyzes the user's pronunciation and provides feedback by visualizing the speech waveform. The generator AI can also check pronunciation for different accents and dialects to help the user become familiar with various pronunciations. The generator AI can also use an emotion estimation function to evaluate the user's confidence in their pronunciation and provide feedback to build confidence. Step 3: The pronunciation sample provider provides a sample of the correct pronunciation based on the pronunciation checked by the pronunciation checker. For example, when a user pronounces "Bonjour," the generation AI analyzes the pronunciation, compares it with the correct pronunciation, and provides feedback. It also provides an audio sample of the correct pronunciation, which the user can listen to and practice. Step 4: The grammar checker analyzes the user's speaking content based on the speaking reinforcement menu generated by the generator and checks the accuracy of grammar. For example, the generator AI analyzes the user's speaking content, analyzes patterns of grammatical errors, and provides practice menus to prevent specific mistakes from being repeated. The generator AI can also detect the user's grammatical errors in real time and provide feedback to correct them on the spot. The generator AI can also use its emotion estimation function to analyze the user's emotional reactions when they make grammatical errors and provide positive feedback. Step 5: The conversation simulation unit simulates a conversation between the user and a lecturer based on the speaking enhancement menu generated by the generation unit. For example, the generation AI simulates a conversation between the user and a lecturer, analyzes the user's conversation style, and improves the algorithm to simulate a more natural conversation. The generation AI can also provide a function to record the user's conversation content and review it later. The generation AI can also use an emotion estimation function to monitor the user's emotional state during the conversation and provide appropriate feedback.

[0105] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0106] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0107] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0108] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0109] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0110] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0111] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0112] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0113] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0114] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0115] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0116] 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.

[0117] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0118] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0119] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0120] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0121] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0122] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0123] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0124] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0125] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0126] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0127] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0128] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0129] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0130] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0131] 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.

[0132] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0133] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0134] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0135] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0136] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0137] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0138] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0139] 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.

[0140] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0141] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0142] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0143] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0144] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0145] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0146] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0147] 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.

[0148] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0149] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0150] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0151] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0152] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0153] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0154] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0155] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0156] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0157] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0158] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0159] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0160] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0161] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0162] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0163] 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.

[0164] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0165] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0166] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0167] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0168] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0169] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0170] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0171] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0172] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A generation unit that uses generation AI to generate a speaking enhancement menu optimized for each individual; a pronunciation check unit that checks the user's pronunciation based on the speaking enhancement menu generated by the generation unit; a pronunciation sample providing unit that provides a sample of correct pronunciation based on the pronunciation checked by the pronunciation checking unit; a grammar check unit that analyzes the user's speaking content based on the speaking enhancement menu generated by the generation unit and checks the accuracy of grammar; a conversation simulation unit that simulates a conversation between a user and a lecturer based on the speaking reinforcement menu generated by the generation unit. A system characterized by:

2. The generation unit Analyzes a user's past conversation history, detects specific language patterns and habits, and generates customized menus based on them 2. The system of claim 1.

3. The pronunciation check unit Analyzing a user's pronunciation and providing feedback by visualizing the speech waveform of said pronunciation 2. The system of claim 1.

4. The grammar check unit Analyzes the user's grammar mistakes and provides practice menus to help prevent them from repeating specific mistakes.

2. The system of claim 1.

5. The conversation simulation unit Analyzing users' speaking styles and improving algorithms to simulate more natural conversations 2. The system of claim 1.

6. The generation unit Monitors the user's emotional state in real time while learning and generates a menu to reduce stress and anxiety 2. The system of claim 1.

7. The pronunciation check unit Evaluate users' confidence in their pronunciation and provide feedback to build confidence 2. The system of claim 1.

8. The conversation simulation unit Monitor the user's emotional state during conversation and provide appropriate feedback 2. The system of claim 1.

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