system

Abstract

The system according to this embodiment aims to analyze the user's speech, identify grammatical and pronunciation errors, and provide appropriate improvement suggestions. [Solution] The system according to the embodiment comprises an analysis unit, a feedback unit, a suggestion unit, a provision unit, and a generation unit. The analysis unit analyzes the user's utterances. The feedback unit points out grammatical and pronunciation errors in the utterances analyzed by the analysis unit. The suggestion unit makes suggestions for improvement regarding the errors pointed out by the feedback unit. The provision unit provides everyday conversation simulations and role-playing. The generation unit generates custom lessons and quizzes tailored to the user's learning progress.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a method for controlling a persona chatbot, which is performed by at least one processor, and includes steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the conventional technology, grammar and pronunciation errors in a user's utterance have not been sufficiently pointed out efficiently, and appropriate improvement proposals have not been made, leaving room for improvement.

[0005] The system according to the embodiment aims to analyze a user's utterance, point out grammar and pronunciation errors, and make appropriate improvement proposals.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a feedback unit, a suggestion unit, a provision unit, and a generation unit. The analysis unit analyzes the user's utterances. The feedback unit points out grammatical and pronunciation errors in the utterances analyzed by the analysis unit. The suggestion unit provides suggestions for improvement to the errors pointed out by the feedback unit. The provision unit provides everyday conversation simulations and role-playing exercises. The generation unit generates custom lessons and quizzes tailored to the user's learning progress. [Effects of the Invention]

[0007] The system according to this embodiment can analyze the user's speech, point out grammatical and pronunciation errors, and provide appropriate improvement suggestions. [Brief explanation of the drawing]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction with the specific processing program 56 by the data processing system 10. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

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

[0028] (Example of form 1) The interactive AI coach AI assistant according to an embodiment of the present invention is a system that analyzes the user's utterances and provides feedback and suggestions for improvement regarding grammar and pronunciation. This system analyzes the user's utterances, identifies grammatical and pronunciation errors, and suggests improvements. Furthermore, it provides everyday conversation simulations and role-playing exercises, creating an environment where the user can practice in real conversations. Finally, it generates custom lessons and quizzes tailored to the user's learning progress to enhance learning effectiveness. For example, the interactive AI coach AI assistant converts the user's utterances into text and detects grammatical and pronunciation errors. For instance, if the user says "I went to the store," the system detects that "goed" is incorrect and suggests correcting it to "went." Next, the system shows the user the correct grammar and pronunciation and explains specifically how to correct it. For example, it explains why "goed" should be corrected to "went" and demonstrates the correct pronunciation. Furthermore, the system provides everyday conversation simulations and role-playing exercises. The user can practice real conversations while interacting with the system. For example, by simulating situations commonly used in daily life, such as ordering at a restaurant or asking for directions, users can acquire practical skills. Finally, the system generates custom lessons and quizzes tailored to the user's learning progress. The system analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, it provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation. In this way, the conversational AI coach / AI assistant not only analyzes the user's speech and points out and suggests improvements to grammar and pronunciation, but also supports effective language learning by providing everyday conversation simulations and custom lessons. As a result, the conversational AI coach / AI assistant can efficiently analyze the user's speech, point out grammatical and pronunciation errors, and suggest improvements.

[0029] The interactive AI coach AI assistant according to this embodiment comprises an analysis unit, a feedback unit, a suggestion unit, a provision unit, and a generation unit. The analysis unit analyzes the user's utterances. The analysis unit converts the user's utterances into text using, for example, speech recognition technology and detects grammatical and pronunciation errors. For example, if the user utters "I went to the store," the analysis unit detects that "goed" is an error and suggests correcting it to "went." The feedback unit points out grammatical and pronunciation errors in the utterances analyzed by the analysis unit. The feedback unit, for example, shows the correct grammar and pronunciation for the detected errors and specifically explains how to correct them. For example, the feedback unit explains the reason for correcting "goed" to "went" and shows the correct pronunciation. The suggestion unit makes improvement suggestions for the errors pointed out by the feedback unit. The suggestion unit, for example, shows the user the correct grammar and pronunciation and specifically explains how to correct them. For example, the suggestion unit explains the reason for correcting "goed" to "went" and shows the correct pronunciation. The provisioning unit provides everyday conversation simulations and role-playing. The provisioning unit simulates situations commonly used in daily life, such as ordering at a restaurant or asking for directions. For example, the provisioning unit simulates a situation where the user orders at a restaurant, providing an environment for practicing actual conversation. The generation unit generates custom lessons and quizzes tailored to the user's learning progress. For example, the generation unit analyzes the user's learning history and progress and provides lessons and quizzes tailored to individual needs. For example, the generation unit provides basic lessons to users who want to learn the basics of grammar, and quizzes specifically for pronunciation to users who want to improve their pronunciation. As a result, the interactive AI coach AI assistant according to this embodiment can efficiently analyze the user's speech, point out grammatical and pronunciation errors, and suggest improvements.

[0030] The analysis department analyzes user utterances. Specifically, it uses speech recognition technology to convert user utterances into text and then analyzes that text in detail. The speech recognition technology utilizes a deep learning model to convert utterances into text with high accuracy. For example, if a user says "I went to the store," the speech recognition technology converts the utterance into text and then uses natural language processing (NLP) technology to detect grammatical and pronunciation errors. NLP technology performs grammatical and morphological analysis to clarify the roles and relationships of words in the sentence. This allows it to detect that "goed" is inappropriate as a past tense and suggests correcting it to the correct form, "went." The analysis department also detects errors in the user's pronunciation. For example, it analyzes the speech waveform and compares it to standard pronunciation to identify deviations in pronunciation. This allows it to provide specific feedback to help the user acquire correct pronunciation. Furthermore, the analysis department also evaluates the fluency and naturalness of the user's utterances. For example, it analyzes the speed, rhythm, and intonation of the utterances and provides advice to make the conversation sound more natural. This allows the analysis department to analyze user utterances from multiple perspectives and support comprehensive improvements.

[0031] The feedback unit points out grammatical and pronunciation errors in speech analyzed by the analysis unit. Specifically, it shows the correct grammar and pronunciation for the detected errors and explains how to correct them. For example, if a user says "I went to the store," the feedback unit points out that "goed" is incorrect and explains why it should be corrected to "went." Furthermore, the feedback unit provides audio samples to demonstrate the correct pronunciation, allowing the user to hear and learn the correct pronunciation. The feedback unit also provides visual feedback to help users understand the errors. For example, it highlights incorrect words and phrases in red and displays correct expressions in green, making errors visually easier to recognize. The feedback unit also provides practice exercises and example sentences to help users correct their errors, giving them opportunities to actually try correcting them. This allows users not only to correct errors but also to acquire the skills to avoid repeating similar mistakes. In addition, the feedback unit tracks the user's progress and analyzes trends in previously pointed-out errors to provide personalized feedback to each user. This allows the feedback unit to maximize the user's learning effectiveness and support efficient learning.

[0032] The suggestion team provides improvement suggestions for errors pointed out by the feedback team. Specifically, it shows users the correct grammar and pronunciation and explains in detail how to correct them. For example, if a user says "I went to the store," the suggestion team explains why "goed" should be corrected to "went" and demonstrates the correct pronunciation. The suggestion team provides users with specific steps to correct their errors and gives them opportunities to actually try correcting them. For example, the suggestion team provides users with practice exercises and example sentences to correct "goed" to "went" and gives them opportunities to actually try correcting them. Furthermore, the suggestion team tracks users' progress and analyzes trends in previously pointed-out errors to provide personalized feedback to each user. This allows the suggestion team to maximize user learning effectiveness and support efficient learning.

[0033] The service provider offers everyday conversation simulations and role-playing. Specifically, it simulates situations commonly used in daily life, such as ordering food at a restaurant or asking for directions. For example, the service provider simulates a situation where a user orders food at a restaurant, providing an environment for practicing actual conversation. The service provider provides scenarios for users to practice actual conversations, allowing them to practice at their own pace. For example, the service provider simulates a situation where a user orders food at a restaurant, providing an environment for practicing actual conversation. The service provider provides scenarios for users to practice actual conversations, allowing them to practice at their own pace. Furthermore, the service provider records the conversations the user has practiced, allowing them to play them back later for self-evaluation. This allows users to objectively evaluate their own speech and find areas for improvement. The service provider also has a function where AI evaluates the conversations the user has practiced and provides feedback. This allows users to objectively evaluate their own speech and find areas for improvement. Furthermore, the service provider also has a function where AI evaluates the conversations the user has practiced and provides feedback. This allows users to objectively evaluate their own speech and identify areas for improvement.

[0034] The generation unit generates custom lessons and quizzes tailored to the user's learning progress. Specifically, it analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, the generation unit provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation. The generation unit analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, the generation unit provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation. Furthermore, the generation unit analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, the generation unit provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation. Furthermore, the generation unit analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, the generation unit provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation.

[0035] The analysis unit can convert user utterances into text and detect grammatical and pronunciation errors. For example, the analysis unit converts user utterances into text using speech recognition technology. For instance, if a user utters "I went to the store," the analysis unit detects that "goed" is an error and suggests correcting it to "went." This allows for accurate identification of errors by converting user utterances into text and detecting grammatical and pronunciation errors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can convert user utterances into text using speech recognition technology, input that text data into a generating AI, and have the generating AI perform grammatical and pronunciation error detection.

[0036] The feedback unit can provide the correct grammar and pronunciation for detected errors and explain how to correct them. For example, the feedback unit can provide the correct grammar and pronunciation for detected errors and explain how to correct them. For example, the feedback unit can explain why "goed" should be corrected to "went" and provide the correct pronunciation. This facilitates user understanding by providing the correct grammar and pronunciation and explaining how to correct them. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the detected error data into a generating AI and have the generating AI provide the correct grammar and pronunciation and explain how to correct them.

[0037] The service provider can simulate situations commonly encountered in daily life, including ordering at a restaurant and asking for directions. For example, the service provider simulates situations where a user orders at a restaurant, providing an environment for practicing actual conversation. This allows users to acquire practical skills by simulating situations commonly encountered in daily life. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input simulation scenarios into a generating AI and have the generating AI generate and provide the scenarios.

[0038] The generation unit can analyze the user's learning history and progress and provide lessons and quizzes tailored to their individual needs. For example, the generation unit can provide basic lessons to users who want to learn the basics of grammar, and pronunciation-focused quizzes to users who want to improve their pronunciation. By analyzing the user's learning history and progress and providing lessons and quizzes tailored to their individual needs, the learning effect can be enhanced. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's learning history and progress into a generation AI and have the generation AI generate lessons and quizzes.

[0039] The analysis unit can analyze the user's past speech history and select the optimal analysis algorithm. For example, the analysis unit can identify grammatical patterns that the user has frequently misused in the past and perform analysis specifically on those patterns. The analysis unit can also analyze the user's pronunciation tendencies and focus on pointing out errors related to specific phonemes. Furthermore, the analysis unit can consider the user's past speaking speed and perform analysis at an appropriate speed. This allows for the selection of the optimal analysis algorithm and highly accurate analysis by analyzing the user's past speech history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past speech history data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0040] The analysis unit can filter utterances based on the user's current learning status and areas of interest. For example, the analysis unit can prioritize identifying relevant errors based on the grammatical items the user is currently learning. The analysis unit can also focus on analyzing the pronunciation of words and phrases related to the user's areas of interest. Furthermore, the analysis unit can identify errors of appropriate difficulty levels according to the user's learning progress. This allows for more relevant feedback by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's current learning status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0041] The analysis unit can prioritize the analysis of highly relevant utterances by considering the user's geographical location information during utterance analysis. For example, if the user is in a specific region, the analysis unit can prioritize the analysis of phrases and words commonly used in that region. Furthermore, if the user is traveling, the analysis unit can focus on analyzing the pronunciation of phrases and words useful in their travel destination. Additionally, if the user is in a specific country, the analysis unit can prioritize the analysis of utterances related to the culture and customs of that country. This allows for the prioritization of highly relevant utterances by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform a priority analysis of highly relevant utterances.

[0042] The analysis unit can analyze a user's social media activity and analyze relevant utterances when analyzing utterances. For example, the analysis unit can prioritize the analysis of phrases and words that the user frequently uses on social media. The analysis unit can also analyze relevant utterances based on the content of the user's social media posts. Furthermore, the analysis unit can analyze relevant utterances based on the content of accounts that the user follows on social media. This allows for the priority analysis of relevant utterances by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the analysis of relevant utterances.

[0043] The feedback function can adjust the level of detail in its feedback based on the importance of the utterance. For example, it can provide detailed explanations for significant grammatical errors to facilitate user understanding. It can also provide concise feedback for minor pronunciation errors to reduce the user's burden. Furthermore, it can provide detailed explanations and demonstrate correct pronunciation for significant pronunciation errors. By adjusting the level of detail in the feedback based on the importance of the utterance, it can provide optimal feedback to the user. Some or all of the above processing in the feedback function may be performed using AI, for example, or without AI. For example, the feedback function can input utterance importance data into a generating AI and have the generating AI adjust the level of detail in the feedback.

[0044] The feedback unit can apply different feedback algorithms depending on the category of the utterance when providing feedback. For example, it can provide feedback based on grammatical rules for grammatical errors. It can also provide feedback based on speech analysis for pronunciation errors. Furthermore, it can suggest appropriate word choices for vocabulary errors. By applying different feedback algorithms depending on the category of the utterance, it can provide more accurate feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input utterance category data into a generating AI and have the generating AI perform the application of the feedback algorithm.

[0045] The feedback unit can prioritize feedback based on when the utterance was submitted. For example, it can prioritize feedback on recently submitted utterances to enhance the user's learning effect. It can also postpone feedback on utterances submitted in the past. Furthermore, it can provide feedback relevant to utterances submitted during a specific time period. This allows for optimal feedback for the user by prioritizing feedback based on the utterance submission time. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input utterance submission time data into a generating AI and have the generating AI determine the priority of feedback.

[0046] The feedback unit can adjust the order of feedback based on the relevance of the utterances. For example, it can prioritize pointing out important grammatical errors to facilitate user understanding. It can also postpone pointing out minor pronunciation errors. Furthermore, it can prioritize pointing out important pronunciation errors and demonstrate the correct pronunciation. By adjusting the order of feedback based on the relevance of the utterances, it can provide the user with optimal feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input utterance relevance data into a generating AI and have the generating AI adjust the order of feedback.

[0047] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the utterance. For example, it can provide detailed explanations for significant grammatical errors to facilitate user understanding. It can also provide concise suggestions for minor pronunciation errors to reduce the user's burden. Furthermore, it can provide detailed explanations and demonstrate correct pronunciation for significant pronunciation errors. By adjusting the level of detail of suggestions based on the importance of the utterance, the suggestion unit can provide optimal feedback to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input utterance importance data into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0048] The suggestion unit can apply different suggestion algorithms depending on the category of the utterance during the suggestion process. For example, the suggestion unit can make suggestions based on grammatical rules for grammatical errors. It can also make suggestions based on speech analysis for pronunciation errors. Furthermore, it can suggest appropriate word selections for lexical errors. By applying different suggestion algorithms depending on the category of the utterance, more accurate feedback can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input utterance category data into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0049] The suggestion unit can prioritize suggestions based on when the utterance was submitted. For example, it can prioritize suggestions for recently submitted utterances to enhance the user's learning effect. It can also postpone suggestions for previously submitted utterances. Furthermore, it can provide suggestions relevant to utterances submitted during a specific time period. This allows for optimal feedback for the user by prioritizing suggestions based on the utterance submission time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input utterance submission time data into a generating AI and have the generating AI determine the priority of suggestions.

[0050] The suggestion unit can adjust the order of suggestions based on the relevance of the utterances. For example, the suggestion unit can prioritize suggestions for important grammatical errors to facilitate user understanding. It can also postpone suggestions for minor pronunciation errors. Furthermore, it can prioritize suggestions for important pronunciation errors and demonstrate correct pronunciation. By adjusting the order of suggestions based on the relevance of the utterances, the suggestion unit can provide optimal feedback to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input utterance relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0051] The service provider can select the optimal scenario during simulation by referring to the user's past simulation history. For example, the service provider can provide similar scenarios based on the results of simulations the user has performed in the past. The service provider can also analyze the user's response to a specific scenario from their past simulation history and select the optimal scenario. Furthermore, the service provider can prioritize providing simulations that the user has preferred to perform in the past. In this way, the service provider can provide the optimal scenario by referring to the user's past simulation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past simulation history data into a generating AI and have the generating AI perform the selection of the optimal scenario.

[0052] The service provider can customize scenarios during simulation based on the user's current learning status. For example, the service provider can provide relevant scenarios based on the grammar items the user is currently learning. The service provider can also provide scenarios of appropriate difficulty according to the user's learning progress. Furthermore, the service provider can provide scenarios related to the user's areas of interest. This allows for a more effective learning environment by customizing scenarios based on the user's current learning status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current learning status data into a generating AI and have the generating AI perform the scenario customization.

[0053] The service provider can select the optimal scenario during simulation by considering the user's geographical location. For example, if the user is in a specific region, the service provider can provide a scenario that includes phrases and words commonly used in that region. Furthermore, if the user is traveling, the service provider can provide a scenario that includes phrases and words useful in their travel destination. Additionally, if the user is in a specific country, the service provider can provide a scenario related to the culture and customs of that country. This allows the service provider to provide the optimal scenario by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal scenario.

[0054] The service provider can analyze the user's social media activity during simulation and propose scenarios. For example, the service provider can provide scenarios that include phrases and words that the user frequently uses on social media. The service provider can also provide relevant scenarios based on the content of the user's social media posts. Furthermore, the service provider can provide relevant scenarios based on the content of accounts that the user follows on social media. This allows the service provider to provide highly relevant scenarios by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute the scenario proposal.

[0055] The generation unit can select the most suitable content by referring to the user's past learning history when generating lessons and quizzes. For example, the generation unit can provide relevant lessons and quizzes based on what the user has learned in the past. The generation unit can also analyze the user's level of understanding of specific items from their past learning history and select the most suitable content. Furthermore, the generation unit can prioritize providing content that the user has enjoyed learning in the past. In this way, the generation unit can provide the most suitable lessons and quizzes by referring to the user's past learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past learning history data into a generation AI and have the generation AI select the most suitable lesson and quiz content.

[0056] The generation unit can customize the content of lessons and quizzes based on the user's current learning status. For example, the generation unit can provide relevant lessons and quizzes based on the grammar items the user is currently studying. The generation unit can also provide lessons and quizzes of appropriate difficulty according to the user's learning progress. Furthermore, the generation unit can provide lessons and quizzes related to the user's areas of interest. This allows for a more effective learning environment by customizing the content based on the user's current learning status. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current learning status data into a generation AI and have the generation AI customize the content of lessons and quizzes.

[0057] The generation unit can select the most suitable content for lessons and quizzes by considering the user's geographical location. For example, if the user is in a specific region, the generation unit can provide lessons and quizzes that include phrases and words commonly used in that region. Furthermore, if the user is traveling, the generation unit can provide lessons and quizzes that include phrases and words useful in their travel destination. Additionally, if the user is in a specific country, the generation unit can provide lessons and quizzes related to the culture and customs of that country. This allows the generation unit to provide optimal lessons and quizzes by considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI select the most suitable lesson and quiz content.

[0058] The generation unit can analyze the user's social media activity and suggest content when generating lessons and quizzes. For example, the generation unit can provide lessons and quizzes that include phrases and words that the user frequently uses on social media. The generation unit can also provide relevant lessons and quizzes based on the content of the user's social media posts. Furthermore, the generation unit can provide relevant lessons and quizzes based on the content of accounts that the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant lessons and quizzes. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI suggest lesson and quiz content.

[0059] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0060] The interactive AI coach / AI assistant analyzes user speech, referencing the user's past learning history to focus on specific grammatical and pronunciation errors. For example, if a user frequently made the mistake of saying "I went to the store" in the past, the system will prioritize pointing out this error and emphasize the correct expression, "I went to the store." It can also focus on providing feedback related to pronunciation difficulties if the user struggles with a particular sound. Furthermore, it can provide more advanced grammatical and pronunciation feedback as the user progresses. This allows for more effective feedback by leveraging the user's past learning history.

[0061] The conversational AI coach / AI assistant can analyze user speech and provide highly relevant feedback by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize feedback on phrases and words commonly used in that area. If the user is traveling, it can focus on highlighting useful phrases and word pronunciations for their destination. Furthermore, if the user is in a specific country, it can provide feedback related to the culture and customs of that country. This allows for the provision of appropriate feedback that takes the user's geographical location into account.

[0062] The conversational AI coach / AI assistant can analyze the user's social media activity while analyzing their speech and provide relevant feedback. For example, it can prioritize feedback on phrases and words that the user frequently uses on social media. It can also provide relevant feedback based on the content of the user's social media posts. Furthermore, it can provide relevant feedback based on the content of accounts the user follows on social media. This allows it to provide appropriate feedback that takes the user's social media activity into consideration.

[0063] The conversational AI coach / AI assistant can analyze user speech and customize feedback based on the user's current learning status and areas of interest. For example, it can prioritize pointing out relevant errors based on the grammar items the user is currently studying. It can also focus on analyzing the pronunciation of words and phrases related to the user's areas of interest. Furthermore, it can provide feedback of appropriate difficulty level according to the user's learning progress. This allows for the provision of appropriate feedback based on the user's current learning status and areas of interest.

[0064] The conversational AI coach / AI assistant can analyze the user's past speech history to select the optimal analysis algorithm when analyzing the user's utterances. For example, it can identify grammatical patterns that the user has frequently made mistakes with in the past and perform analysis specifically on those patterns. It can also analyze the user's pronunciation tendencies and focus on pointing out errors related to specific phonemes. Furthermore, it can consider the user's past speaking speed and perform analysis at an appropriate speed. By analyzing the user's past speech history, it can select the optimal analysis algorithm and enable highly accurate analysis.

[0065] The conversational AI coach / AI assistant can analyze user speech and filter it based on the user's current learning status and areas of interest. For example, it can prioritize pointing out relevant errors based on the grammar points the user is currently learning. It can also focus on analyzing the pronunciation of words and phrases related to the user's areas of interest. Furthermore, it can point out errors of appropriate difficulty level according to the user's learning progress. This allows for more relevant feedback by filtering based on the user's current learning status and areas of interest.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The analysis unit analyzes the user's speech. The analysis unit converts the user's speech into text using, for example, speech recognition technology, and detects grammatical and pronunciation errors. For example, if the user says "I went to the store," the analysis unit detects that "goed" is incorrect and suggests correcting it to "went." Step 2: The feedback section points out grammatical and pronunciation errors in the utterances analyzed by the analysis section. For example, the feedback section provides the correct grammar and pronunciation for the detected errors and explains how to correct them. For example, the feedback section explains why "goed" should be corrected to "went" and provides the correct pronunciation. Step 3: The suggestion team makes improvement suggestions for the errors pointed out by the feedback team. For example, the suggestion team will show the user the correct grammar and pronunciation and explain specifically how to make the corrections. For example, the suggestion team will explain why "goed" should be changed to "went" and show the correct pronunciation. Step 4: The provider offers everyday conversation simulations and role-playing. The provider simulates situations commonly used in daily life, such as ordering at a restaurant or asking for directions. For example, the provider simulates a situation where the user orders at a restaurant, providing an environment to practice actual conversation. Step 5: The generator creates custom lessons and quizzes tailored to the user's learning progress. For example, the generator analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For instance, it might provide basic lessons to users who want to learn the fundamentals of grammar, or pronunciation-focused quizzes to users who want to improve their pronunciation.

[0068] (Example of form 2) The interactive AI coach AI assistant according to an embodiment of the present invention is a system that analyzes the user's utterances and provides feedback and suggestions for improvement regarding grammar and pronunciation. This system analyzes the user's utterances, identifies grammatical and pronunciation errors, and suggests improvements. Furthermore, it provides everyday conversation simulations and role-playing exercises, creating an environment where the user can practice in real conversations. Finally, it generates custom lessons and quizzes tailored to the user's learning progress to enhance learning effectiveness. For example, the interactive AI coach AI assistant converts the user's utterances into text and detects grammatical and pronunciation errors. For instance, if the user says "I went to the store," the system detects that "goed" is incorrect and suggests correcting it to "went." Next, the system shows the user the correct grammar and pronunciation and explains specifically how to correct it. For example, it explains why "goed" should be corrected to "went" and demonstrates the correct pronunciation. Furthermore, the system provides everyday conversation simulations and role-playing exercises. The user can practice real conversations while interacting with the system. For example, by simulating situations commonly used in daily life, such as ordering at a restaurant or asking for directions, users can acquire practical skills. Finally, the system generates custom lessons and quizzes tailored to the user's learning progress. The system analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, it provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation. In this way, the conversational AI coach / AI assistant not only analyzes the user's speech and points out and suggests improvements to grammar and pronunciation, but also supports effective language learning by providing everyday conversation simulations and custom lessons. As a result, the conversational AI coach / AI assistant can efficiently analyze the user's speech, point out grammatical and pronunciation errors, and suggest improvements.

[0069] The interactive AI coach AI assistant according to this embodiment comprises an analysis unit, a feedback unit, a suggestion unit, a provision unit, and a generation unit. The analysis unit analyzes the user's utterances. The analysis unit converts the user's utterances into text using, for example, speech recognition technology and detects grammatical and pronunciation errors. For example, if the user utters "I went to the store," the analysis unit detects that "goed" is an error and suggests correcting it to "went." The feedback unit points out grammatical and pronunciation errors in the utterances analyzed by the analysis unit. The feedback unit, for example, shows the correct grammar and pronunciation for the detected errors and specifically explains how to correct them. For example, the feedback unit explains the reason for correcting "goed" to "went" and shows the correct pronunciation. The suggestion unit makes improvement suggestions for the errors pointed out by the feedback unit. The suggestion unit, for example, shows the user the correct grammar and pronunciation and specifically explains how to correct them. For example, the suggestion unit explains the reason for correcting "goed" to "went" and shows the correct pronunciation. The provisioning unit provides everyday conversation simulations and role-playing. The provisioning unit simulates situations commonly used in daily life, such as ordering at a restaurant or asking for directions. For example, the provisioning unit simulates a situation where the user orders at a restaurant, providing an environment for practicing actual conversation. The generation unit generates custom lessons and quizzes tailored to the user's learning progress. For example, the generation unit analyzes the user's learning history and progress and provides lessons and quizzes tailored to individual needs. For example, the generation unit provides basic lessons to users who want to learn the basics of grammar, and quizzes specifically for pronunciation to users who want to improve their pronunciation. As a result, the interactive AI coach AI assistant according to this embodiment can efficiently analyze the user's speech, point out grammatical and pronunciation errors, and suggest improvements.

[0070] The analysis department analyzes user utterances. Specifically, it uses speech recognition technology to convert user utterances into text and then analyzes that text in detail. The speech recognition technology utilizes a deep learning model to convert utterances into text with high accuracy. For example, if a user says "I went to the store," the speech recognition technology converts the utterance into text and then uses natural language processing (NLP) technology to detect grammatical and pronunciation errors. NLP technology performs grammatical and morphological analysis to clarify the roles and relationships of words in the sentence. This allows it to detect that "goed" is inappropriate as a past tense and suggests correcting it to the correct form, "went." The analysis department also detects errors in the user's pronunciation. For example, it analyzes the speech waveform and compares it to standard pronunciation to identify deviations in pronunciation. This allows it to provide specific feedback to help the user acquire correct pronunciation. Furthermore, the analysis department also evaluates the fluency and naturalness of the user's utterances. For example, it analyzes the speed, rhythm, and intonation of the utterances and provides advice to make the conversation sound more natural. This allows the analysis department to analyze user utterances from multiple perspectives and support comprehensive improvements.

[0071] The feedback unit points out grammatical and pronunciation errors in speech analyzed by the analysis unit. Specifically, it shows the correct grammar and pronunciation for the detected errors and explains how to correct them. For example, if a user says "I went to the store," the feedback unit points out that "goed" is incorrect and explains why it should be corrected to "went." Furthermore, the feedback unit provides audio samples to demonstrate the correct pronunciation, allowing the user to hear and learn the correct pronunciation. The feedback unit also provides visual feedback to help users understand the errors. For example, it highlights incorrect words and phrases in red and displays correct expressions in green, making errors visually easier to recognize. The feedback unit also provides practice exercises and example sentences to help users correct their errors, giving them opportunities to actually try correcting them. This allows users not only to correct errors but also to acquire the skills to avoid repeating similar mistakes. In addition, the feedback unit tracks the user's progress and analyzes trends in previously pointed-out errors to provide personalized feedback to each user. This allows the feedback unit to maximize the user's learning effectiveness and support efficient learning.

[0072] The suggestion team provides improvement suggestions for errors pointed out by the feedback team. Specifically, it shows users the correct grammar and pronunciation and explains in detail how to correct them. For example, if a user says "I went to the store," the suggestion team explains why "goed" should be corrected to "went" and demonstrates the correct pronunciation. The suggestion team provides users with specific steps to correct their errors and gives them opportunities to actually try correcting them. For example, the suggestion team provides users with practice exercises and example sentences to correct "goed" to "went" and gives them opportunities to actually try correcting them. Furthermore, the suggestion team tracks users' progress and analyzes trends in previously pointed-out errors to provide personalized feedback to each user. This allows the suggestion team to maximize user learning effectiveness and support efficient learning.

[0073] The service provider offers everyday conversation simulations and role-playing. Specifically, it simulates situations commonly used in daily life, such as ordering food at a restaurant or asking for directions. For example, the service provider simulates a situation where a user orders food at a restaurant, providing an environment for practicing actual conversation. The service provider provides scenarios for users to practice actual conversations, allowing them to practice at their own pace. For example, the service provider simulates a situation where a user orders food at a restaurant, providing an environment for practicing actual conversation. The service provider provides scenarios for users to practice actual conversations, allowing them to practice at their own pace. Furthermore, the service provider records the conversations the user has practiced, allowing them to play them back later for self-evaluation. This allows users to objectively evaluate their own speech and find areas for improvement. The service provider also has a function where AI evaluates the conversations the user has practiced and provides feedback. This allows users to objectively evaluate their own speech and find areas for improvement. Furthermore, the service provider also has a function where AI evaluates the conversations the user has practiced and provides feedback. This allows users to objectively evaluate their own speech and identify areas for improvement.

[0074] The generation unit generates custom lessons and quizzes tailored to the user's learning progress. Specifically, it analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, the generation unit provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation. The generation unit analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, the generation unit provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation. Furthermore, the generation unit analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, the generation unit provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation. Furthermore, the generation unit analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For example, the generation unit provides basic lessons for users who want to learn the basics of grammar, and pronunciation-focused quizzes for users who want to improve their pronunciation.

[0075] The analysis unit can convert user utterances into text and detect grammatical and pronunciation errors. For example, the analysis unit converts user utterances into text using speech recognition technology. For instance, if a user utters "I went to the store," the analysis unit detects that "goed" is an error and suggests correcting it to "went." This allows for accurate identification of errors by converting user utterances into text and detecting grammatical and pronunciation errors. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can convert user utterances into text using speech recognition technology, input that text data into a generating AI, and have the generating AI perform grammatical and pronunciation error detection.

[0076] The feedback unit can provide the correct grammar and pronunciation for detected errors and explain how to correct them. For example, the feedback unit can provide the correct grammar and pronunciation for detected errors and explain how to correct them. For example, the feedback unit can explain why "goed" should be corrected to "went" and provide the correct pronunciation. This facilitates user understanding by providing the correct grammar and pronunciation and explaining how to correct them. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input the detected error data into a generating AI and have the generating AI provide the correct grammar and pronunciation and explain how to correct them.

[0077] The service provider can simulate situations commonly encountered in daily life, including ordering at a restaurant and asking for directions. For example, the service provider simulates situations where a user orders at a restaurant, providing an environment for practicing actual conversation. This allows users to acquire practical skills by simulating situations commonly encountered in daily life. Some or all of the above-described processes in the service provider may be performed using AI, or not. For example, the service provider can input simulation scenarios into a generating AI and have the generating AI generate and provide the scenarios.

[0078] The generation unit can analyze the user's learning history and progress and provide lessons and quizzes tailored to their individual needs. For example, the generation unit can provide basic lessons to users who want to learn the basics of grammar, and pronunciation-focused quizzes to users who want to improve their pronunciation. By analyzing the user's learning history and progress and providing lessons and quizzes tailored to their individual needs, the learning effect can be enhanced. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input data on the user's learning history and progress into a generation AI and have the generation AI generate lessons and quizzes.

[0079] The analysis unit can estimate the user's emotions and adjust the speech analysis method based on the estimated emotions. For example, if the user is nervous, the analysis unit can perform a gentler speech analysis to help the user relax. If the user is relaxed, the analysis unit can perform a more detailed analysis and provide more feedback. Furthermore, if the user is in a hurry, the analysis unit can quickly identify major errors and aim for improvement in a short time. This allows for more appropriate feedback by adjusting the speech analysis method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the speech analysis method.

[0080] The analysis unit can analyze the user's past speech history and select the optimal analysis algorithm. For example, the analysis unit can identify grammatical patterns that the user has frequently misused in the past and perform analysis specifically on those patterns. The analysis unit can also analyze the user's pronunciation tendencies and focus on pointing out errors related to specific phonemes. Furthermore, the analysis unit can consider the user's past speaking speed and perform analysis at an appropriate speed. This allows for the selection of the optimal analysis algorithm and highly accurate analysis by analyzing the user's past speech history. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's past speech history data into a generating AI and have the generating AI select the optimal analysis algorithm.

[0081] The analysis unit can filter utterances based on the user's current learning status and areas of interest. For example, the analysis unit can prioritize identifying relevant errors based on the grammatical items the user is currently learning. The analysis unit can also focus on analyzing the pronunciation of words and phrases related to the user's areas of interest. Furthermore, the analysis unit can identify errors of appropriate difficulty levels according to the user's learning progress. This allows for more relevant feedback by filtering based on the user's current learning status and areas of interest. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input data on the user's current learning status and areas of interest into a generating AI and have the generating AI perform the filtering.

[0082] The analysis unit can estimate the user's emotions and prioritize the analysis results based on the estimated emotions. For example, if the user is stressed, the analysis unit will prioritize pointing out only the most significant errors. If the user is relaxed, the analysis unit can provide detailed analysis results and point out all errors. Furthermore, if the user is in a hurry, the analysis unit can prioritize pointing out major errors and aim for quick improvement. This allows for the provision of optimal feedback to the user by prioritizing the analysis results according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI or not. For example, the analysis unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and prioritize the analysis results.

[0083] The analysis unit can prioritize the analysis of highly relevant utterances by considering the user's geographical location information during utterance analysis. For example, if the user is in a specific region, the analysis unit can prioritize the analysis of phrases and words commonly used in that region. Furthermore, if the user is traveling, the analysis unit can focus on analyzing the pronunciation of phrases and words useful in their travel destination. Additionally, if the user is in a specific country, the analysis unit can prioritize the analysis of utterances related to the culture and customs of that country. This allows for the prioritization of highly relevant utterances by considering the user's geographical location information. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's geographical location data into a generating AI and have the generating AI perform a priority analysis of highly relevant utterances.

[0084] The analysis unit can analyze a user's social media activity and analyze relevant utterances when analyzing utterances. For example, the analysis unit can prioritize the analysis of phrases and words that the user frequently uses on social media. The analysis unit can also analyze relevant utterances based on the content of the user's social media posts. Furthermore, the analysis unit can analyze relevant utterances based on the content of accounts that the user follows on social media. This allows for the priority analysis of relevant utterances by analyzing the user's social media activity. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the user's social media activity data into a generating AI and have the generating AI perform the analysis of relevant utterances.

[0085] The feedback unit can estimate the user's emotions and adjust the way it expresses its feedback based on those emotions. For example, if the user is tense, the feedback unit can offer gentle feedback to help the user relax. If the user is relaxed, the feedback unit can offer detailed feedback to enhance learning effectiveness. Furthermore, if the user is in a hurry, the feedback unit can offer concise and quick feedback to aim for improvement in a short amount of time. This allows for more effective feedback by adjusting the way feedback is expressed according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback unit may be performed using AI or not. For example, the feedback unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjust the way feedback is expressed.

[0086] The feedback function can adjust the level of detail in its feedback based on the importance of the utterance. For example, it can provide detailed explanations for significant grammatical errors to facilitate user understanding. It can also provide concise feedback for minor pronunciation errors to reduce the user's burden. Furthermore, it can provide detailed explanations and demonstrate correct pronunciation for significant pronunciation errors. By adjusting the level of detail in the feedback based on the importance of the utterance, it can provide optimal feedback to the user. Some or all of the above processing in the feedback function may be performed using AI, for example, or without AI. For example, the feedback function can input utterance importance data into a generating AI and have the generating AI adjust the level of detail in the feedback.

[0087] The feedback unit can apply different feedback algorithms depending on the category of the utterance when providing feedback. For example, it can provide feedback based on grammatical rules for grammatical errors. It can also provide feedback based on speech analysis for pronunciation errors. Furthermore, it can suggest appropriate word choices for vocabulary errors. By applying different feedback algorithms depending on the category of the utterance, it can provide more accurate feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input utterance category data into a generating AI and have the generating AI perform the application of the feedback algorithm.

[0088] The feedback function can estimate the user's emotions and adjust the length of the feedback based on the estimated emotions. For example, if the user is nervous, the feedback function can provide short, concise feedback to reduce the user's burden. If the user is relaxed, the feedback function can provide longer feedback with detailed explanations to enhance the learning effect. Furthermore, if the user is in a hurry, the feedback function can provide short, to-the-point feedback to aim for improvement in a short time. In this way, by adjusting the length of feedback according to the user's emotions, the feedback function can provide the user with optimal feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the feedback function may be performed using AI or not using AI. For example, the feedback function can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the length of the feedback.

[0089] The feedback unit can prioritize feedback based on when the utterance was submitted. For example, it can prioritize feedback on recently submitted utterances to enhance the user's learning effect. It can also postpone feedback on utterances submitted in the past. Furthermore, it can provide feedback relevant to utterances submitted during a specific time period. This allows for optimal feedback for the user by prioritizing feedback based on the utterance submission time. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input utterance submission time data into a generating AI and have the generating AI determine the priority of feedback.

[0090] The feedback unit can adjust the order of feedback based on the relevance of the utterances. For example, it can prioritize pointing out important grammatical errors to facilitate user understanding. It can also postpone pointing out minor pronunciation errors. Furthermore, it can prioritize pointing out important pronunciation errors and demonstrate the correct pronunciation. By adjusting the order of feedback based on the relevance of the utterances, it can provide the user with optimal feedback. Some or all of the above processing in the feedback unit may be performed using AI, for example, or without AI. For example, the feedback unit can input utterance relevance data into a generating AI and have the generating AI adjust the order of feedback.

[0091] The suggestion unit can estimate the user's emotions and adjust the way suggestions are presented based on those emotions. For example, if the user is nervous, the suggestion unit can make suggestions in gentle language to help the user relax. If the user is relaxed, the suggestion unit can also make suggestions with detailed explanations to enhance the learning effect. Furthermore, if the user is in a hurry, the suggestion unit can make concise and quick suggestions to aim for improvement in a short amount of time. In this way, by adjusting the way suggestions are presented according to the user's emotions, more effective feedback can be provided. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the way suggestions are presented.

[0092] The suggestion unit can adjust the level of detail of its suggestions based on the importance of the utterance. For example, it can provide detailed explanations for significant grammatical errors to facilitate user understanding. It can also provide concise suggestions for minor pronunciation errors to reduce the user's burden. Furthermore, it can provide detailed explanations and demonstrate correct pronunciation for significant pronunciation errors. By adjusting the level of detail of suggestions based on the importance of the utterance, the suggestion unit can provide optimal feedback to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input utterance importance data into a generating AI and have the generating AI adjust the level of detail of the suggestions.

[0093] The suggestion unit can apply different suggestion algorithms depending on the category of the utterance during the suggestion process. For example, the suggestion unit can make suggestions based on grammatical rules for grammatical errors. It can also make suggestions based on speech analysis for pronunciation errors. Furthermore, it can suggest appropriate word selections for lexical errors. By applying different suggestion algorithms depending on the category of the utterance, more accurate feedback can be provided. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input utterance category data into a generating AI and have the generating AI perform the application of the suggestion algorithm.

[0094] The suggestion unit can estimate the user's emotions and adjust the length of the suggestions based on the estimated emotions. For example, if the user is nervous, the suggestion unit can provide short, concise suggestions to reduce the user's burden. If the user is relaxed, the suggestion unit can provide longer suggestions with detailed explanations to enhance the learning effect. Furthermore, if the user is in a hurry, the suggestion unit can provide short, to-the-point suggestions to aim for improvement in a short amount of time. In this way, by adjusting the length of suggestions according to the user's emotions, the system can provide the user with optimal feedback. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the suggestion unit may be performed using AI or not using AI. For example, the suggestion unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and suggestion length adjustment.

[0095] The suggestion unit can prioritize suggestions based on when the utterance was submitted. For example, it can prioritize suggestions for recently submitted utterances to enhance the user's learning effect. It can also postpone suggestions for previously submitted utterances. Furthermore, it can provide suggestions relevant to utterances submitted during a specific time period. This allows for optimal feedback for the user by prioritizing suggestions based on the utterance submission time. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input utterance submission time data into a generating AI and have the generating AI determine the priority of suggestions.

[0096] The suggestion unit can adjust the order of suggestions based on the relevance of the utterances. For example, the suggestion unit can prioritize suggestions for important grammatical errors to facilitate user understanding. It can also postpone suggestions for minor pronunciation errors. Furthermore, it can prioritize suggestions for important pronunciation errors and demonstrate correct pronunciation. By adjusting the order of suggestions based on the relevance of the utterances, the suggestion unit can provide optimal feedback to the user. Some or all of the above processing in the suggestion unit may be performed using AI, for example, or without AI. For example, the suggestion unit can input utterance relevance data into a generating AI and have the generating AI adjust the order of suggestions.

[0097] The service provider can estimate the user's emotions and adjust the simulation scenario based on the estimated emotions. For example, if the user is nervous, the service provider can provide a simple and relaxing scenario. If the user is relaxed, the service provider can also provide a detailed and complex scenario. Furthermore, if the user is in a hurry, the service provider can provide a scenario that can be completed in a short time. This allows for a more effective learning environment by adjusting the simulation scenario according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and adjustment of the simulation scenario.

[0098] The service provider can select the optimal scenario during simulation by referring to the user's past simulation history. For example, the service provider can provide similar scenarios based on the results of simulations the user has performed in the past. The service provider can also analyze the user's response to a specific scenario from their past simulation history and select the optimal scenario. Furthermore, the service provider can prioritize providing simulations that the user has preferred to perform in the past. In this way, the service provider can provide the optimal scenario by referring to the user's past simulation history. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's past simulation history data into a generating AI and have the generating AI perform the selection of the optimal scenario.

[0099] The service provider can customize scenarios during simulation based on the user's current learning status. For example, the service provider can provide relevant scenarios based on the grammar items the user is currently learning. The service provider can also provide scenarios of appropriate difficulty according to the user's learning progress. Furthermore, the service provider can provide scenarios related to the user's areas of interest. This allows for a more effective learning environment by customizing scenarios based on the user's current learning status. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's current learning status data into a generating AI and have the generating AI perform the scenario customization.

[0100] The service provider can estimate the user's emotions and determine the priority of simulations based on the estimated emotions. For example, if the user is stressed, the service provider will prioritize relaxing scenarios. If the user is relaxed, the service provider may also prioritize detailed and complex scenarios. Furthermore, if the user is in a hurry, the service provider may prioritize scenarios that can be completed quickly. This allows the service provider to provide the user with an optimal learning environment by prioritizing simulations according to their emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI or not using AI. For example, the service provider can input user emotion data into a generative AI and have the generative AI perform emotion estimation and simulation priority determination.

[0101] The service provider can select the optimal scenario during simulation by considering the user's geographical location. For example, if the user is in a specific region, the service provider can provide a scenario that includes phrases and words commonly used in that region. Furthermore, if the user is traveling, the service provider can provide a scenario that includes phrases and words useful in their travel destination. Additionally, if the user is in a specific country, the service provider can provide a scenario related to the culture and customs of that country. This allows the service provider to provide the optimal scenario by considering the user's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's geographical location data into a generating AI and have the generating AI select the optimal scenario.

[0102] The service provider can analyze the user's social media activity during simulation and propose scenarios. For example, the service provider can provide scenarios that include phrases and words that the user frequently uses on social media. The service provider can also provide relevant scenarios based on the content of the user's social media posts. Furthermore, the service provider can provide relevant scenarios based on the content of accounts that the user follows on social media. This allows the service provider to provide highly relevant scenarios by analyzing the user's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the user's social media activity data into a generating AI and have the generating AI execute the scenario proposal.

[0103] The generation unit can estimate the user's emotions and adjust the content of lessons and quizzes based on the estimated emotions. For example, if the user is nervous, the generation unit can provide simple and relaxing lessons and quizzes. If the user is relaxed, the generation unit can also provide detailed and complex lessons and quizzes. Furthermore, if the user is in a hurry, the generation unit can provide lessons and quizzes that can be completed in a short time. In this way, a more effective learning environment can be provided by adjusting the content of lessons and quizzes according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input user emotion data into the generative AI and have the generative AI perform emotion estimation and adjustment of lesson and quiz content.

[0104] The generation unit can select the most suitable content by referring to the user's past learning history when generating lessons and quizzes. For example, the generation unit can provide relevant lessons and quizzes based on what the user has learned in the past. The generation unit can also analyze the user's level of understanding of specific items from their past learning history and select the most suitable content. Furthermore, the generation unit can prioritize providing content that the user has enjoyed learning in the past. In this way, the generation unit can provide the most suitable lessons and quizzes by referring to the user's past learning history. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's past learning history data into a generation AI and have the generation AI select the most suitable lesson and quiz content.

[0105] The generation unit can customize the content of lessons and quizzes based on the user's current learning status. For example, the generation unit can provide relevant lessons and quizzes based on the grammar items the user is currently studying. The generation unit can also provide lessons and quizzes of appropriate difficulty according to the user's learning progress. Furthermore, the generation unit can provide lessons and quizzes related to the user's areas of interest. This allows for a more effective learning environment by customizing the content based on the user's current learning status. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's current learning status data into a generation AI and have the generation AI customize the content of lessons and quizzes.

[0106] The generation unit can estimate the user's emotions and prioritize lessons and quizzes based on the estimated emotions. For example, if the user is stressed, the generation unit will prioritize providing relaxing lessons and quizzes. If the user is relaxed, the generation unit can also prioritize providing detailed and complex lessons and quizzes. Furthermore, if the user is in a hurry, the generation unit can prioritize providing lessons and quizzes that can be completed quickly. This allows for the provision of an optimal learning environment for the user by prioritizing lessons and quizzes according to their emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generative AI. The generative AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, or not. For example, the generation unit can input user emotion data into a generative AI and have the generative AI perform emotion estimation and lesson and quiz prioritization.

[0107] The generation unit can select the most suitable content for lessons and quizzes by considering the user's geographical location. For example, if the user is in a specific region, the generation unit can provide lessons and quizzes that include phrases and words commonly used in that region. Furthermore, if the user is traveling, the generation unit can provide lessons and quizzes that include phrases and words useful in their travel destination. Additionally, if the user is in a specific country, the generation unit can provide lessons and quizzes related to the culture and customs of that country. This allows the generation unit to provide optimal lessons and quizzes by considering the user's geographical location. Some or all of the above processing in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the user's geographical location data into a generation AI and have the generation AI select the most suitable lesson and quiz content.

[0108] The generation unit can analyze the user's social media activity and suggest content when generating lessons and quizzes. For example, the generation unit can provide lessons and quizzes that include phrases and words that the user frequently uses on social media. The generation unit can also provide relevant lessons and quizzes based on the content of the user's social media posts. Furthermore, the generation unit can provide relevant lessons and quizzes based on the content of accounts that the user follows on social media. In this way, by analyzing the user's social media activity, it is possible to provide highly relevant lessons and quizzes. Some or all of the above processing in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input the user's social media activity data into a generation AI and have the generation AI suggest lesson and quiz content.

[0109] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0110] The interactive AI coach / AI assistant analyzes user speech, referencing the user's past learning history to focus on specific grammatical and pronunciation errors. For example, if a user frequently made the mistake of saying "I went to the store" in the past, the system will prioritize pointing out this error and emphasize the correct expression, "I went to the store." It can also focus on providing feedback related to pronunciation difficulties if the user struggles with a particular sound. Furthermore, it can provide more advanced grammatical and pronunciation feedback as the user progresses. This allows for more effective feedback by leveraging the user's past learning history.

[0111] The conversational AI coach / AI assistant can analyze user speech to estimate the user's emotions and adjust the content and method of feedback based on those emotions. For example, if the user is nervous, the system can provide feedback in gentle language to help them relax. If the user is relaxed, it can provide detailed feedback to enhance learning effectiveness. Furthermore, if the user is in a hurry, it can provide concise and quick feedback to aim for improvement in a short amount of time. This allows for the provision of appropriate feedback tailored to the user's emotions.

[0112] The conversational AI coach / AI assistant can analyze user speech and provide highly relevant feedback by considering the user's geographical location. For example, if the user is in a specific region, it can prioritize feedback on phrases and words commonly used in that area. If the user is traveling, it can focus on highlighting useful phrases and word pronunciations for their destination. Furthermore, if the user is in a specific country, it can provide feedback related to the culture and customs of that country. This allows for the provision of appropriate feedback that takes the user's geographical location into account.

[0113] The conversational AI coach / AI assistant can analyze the user's social media activity while analyzing their speech and provide relevant feedback. For example, it can prioritize feedback on phrases and words that the user frequently uses on social media. It can also provide relevant feedback based on the content of the user's social media posts. Furthermore, it can provide relevant feedback based on the content of accounts the user follows on social media. This allows it to provide appropriate feedback that takes the user's social media activity into consideration.

[0114] The conversational AI coach / AI assistant can analyze user speech and customize feedback based on the user's current learning status and areas of interest. For example, it can prioritize pointing out relevant errors based on the grammar items the user is currently studying. It can also focus on analyzing the pronunciation of words and phrases related to the user's areas of interest. Furthermore, it can provide feedback of appropriate difficulty level according to the user's learning progress. This allows for the provision of appropriate feedback based on the user's current learning status and areas of interest.

[0115] The conversational AI coach / AI assistant can analyze user speech to estimate the user's emotions and prioritize feedback based on those emotions. For example, if the user is stressed, it will prioritize pointing out only the most significant errors. If the user is relaxed, it can provide detailed feedback and point out all errors. Furthermore, if the user is in a hurry, it can prioritize pointing out major errors to aim for quick improvement. This allows for the provision of appropriate feedback tailored to the user's emotions.

[0116] The conversational AI coach / AI assistant can analyze the user's past speech history to select the optimal analysis algorithm when analyzing the user's utterances. For example, it can identify grammatical patterns that the user has frequently made mistakes with in the past and perform analysis specifically on those patterns. It can also analyze the user's pronunciation tendencies and focus on pointing out errors related to specific phonemes. Furthermore, it can consider the user's past speaking speed and perform analysis at an appropriate speed. By analyzing the user's past speech history, it can select the optimal analysis algorithm and enable highly accurate analysis.

[0117] The conversational AI coach / AI assistant can analyze user speech to estimate the user's emotions and adjust the way feedback is delivered based on those emotions. For example, if the user is nervous, it can provide feedback in gentle language to help them relax. If the user is relaxed, it can provide feedback with detailed explanations to enhance learning effectiveness. Furthermore, if the user is in a hurry, it can provide concise and quick feedback to aim for improvement in a short amount of time. This allows for the provision of appropriate feedback tailored to the user's emotions.

[0118] The conversational AI coach / AI assistant can analyze user speech and filter it based on the user's current learning status and areas of interest. For example, it can prioritize pointing out relevant errors based on the grammar points the user is currently learning. It can also focus on analyzing the pronunciation of words and phrases related to the user's areas of interest. Furthermore, it can point out errors of appropriate difficulty level according to the user's learning progress. This allows for more relevant feedback by filtering based on the user's current learning status and areas of interest.

[0119] The conversational AI coach / AI assistant can analyze user speech to estimate the user's emotions and adjust the length of feedback based on that estimation. For example, if the user is nervous, it can provide short, concise feedback to reduce the user's burden. If the user is relaxed, it can provide longer feedback with more detailed explanations to enhance learning effectiveness. Furthermore, if the user is in a hurry, it can provide short, to-the-point feedback to aim for improvement in a short amount of time. This allows for the provision of appropriate feedback tailored to the user's emotions.

[0120] The following briefly describes the processing flow for example form 2.

[0121] Step 1: The analysis unit analyzes the user's speech. The analysis unit converts the user's speech into text using, for example, speech recognition technology, and detects grammatical and pronunciation errors. For example, if the user says "I went to the store," the analysis unit detects that "goed" is incorrect and suggests correcting it to "went." Step 2: The feedback section points out grammatical and pronunciation errors in the utterances analyzed by the analysis section. For example, the feedback section provides the correct grammar and pronunciation for the detected errors and explains how to correct them. For example, the feedback section explains why "goed" should be corrected to "went" and provides the correct pronunciation. Step 3: The suggestion team makes improvement suggestions for the errors pointed out by the feedback team. For example, the suggestion team will show the user the correct grammar and pronunciation and explain specifically how to make the corrections. For example, the suggestion team will explain why "goed" should be changed to "went" and show the correct pronunciation. Step 4: The provider offers everyday conversation simulations and role-playing. The provider simulates situations commonly used in daily life, such as ordering at a restaurant or asking for directions. For example, the provider simulates a situation where the user orders at a restaurant, providing an environment to practice actual conversation. Step 5: The generator creates custom lessons and quizzes tailored to the user's learning progress. For example, the generator analyzes the user's learning history and progress to provide lessons and quizzes that meet individual needs. For instance, it might provide basic lessons to users who want to learn the fundamentals of grammar, or pronunciation-focused quizzes to users who want to improve their pronunciation.

[0122] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0123] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0124] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0125] For example, the analysis unit is implemented by the processor 46 of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the identification unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the proposal unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0126] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0127] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0128] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0129] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0130] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0131] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0132] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0133] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0134] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0135] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0136] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0137] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0138] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0139] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0140] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0141] For example, the analysis unit is implemented by the processor 46 of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the identification unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0142] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0143] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0144] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0145] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0146] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0147] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0148] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0149] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0150] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0151] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0152] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0153] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0154] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0155] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0156] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0157] For example, the analysis unit is implemented by the processor 46 of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the identification unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the proposal unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the headset terminal 314 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0158] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0159] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0160] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0161] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0162] The microphone 238 receives voice signals from the user and accepts instructions from the user. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0163] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0164] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0165] The controlled object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0166] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0167] The processor 28 reads a specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0168] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0169] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Furthermore, other devices besides the data processing device 12 may also have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0171] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of a data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0173] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0174] For example, the analysis unit is implemented by the processor 46 of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the identification unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the suggestion unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the provision unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. For example, the generation unit is implemented by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The correspondence between each unit and the device or control unit is not limited to the examples described above, and various modifications are possible.

[0175] Furthermore, the emotion identification model 59, acting as an emotion engine, may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0176] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0177] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0178] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0179] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0180] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0181] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0182] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0183] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0184] 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.

[0185] Furthermore, it is not necessary to store the entirety of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0186] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0187] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0188] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0189] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0190] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0191] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0192] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0193] (Note 1) The analysis department analyzes user utterances, The analysis unit identifies grammatical and pronunciation errors in the utterances analyzed by the aforementioned analysis unit, A proposal unit that makes suggestions for improvement regarding the errors pointed out by the aforementioned identification unit, The service provider offers everyday conversation simulations and role-playing, It comprises a generation unit that generates custom lessons and quizzes tailored to the learning progress. A system characterized by the following features. (Note 2) The aforementioned analysis unit is It converts user speech into text and detects grammatical and pronunciation errors. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned point is, The system will show the correct grammar and pronunciation for detected errors and provide specific instructions on how to correct them. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, This simulation covers common everyday situations, including ordering at a restaurant and asking for directions. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is We analyze users' learning history and progress to provide lessons and quizzes tailored to their individual needs. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit is It estimates the user's emotions and adjusts the speech analysis method based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit is The system analyzes the user's past speech history and selects the optimal analysis algorithm. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit is When analyzing speech, filtering is performed based on the user's current learning status and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit is It estimates the user's emotions and prioritizes the analysis results based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned analysis unit is When analyzing speech, the system prioritizes analyzing highly relevant utterances by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit is When analyzing utterances, we analyze the user's social media activity and analyze related utterances. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned point is, It estimates the user's emotions and adjusts the way criticisms are expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned point is, When giving feedback, adjust the level of detail based on the importance of the statement. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned point is, When making a comment, different commenting algorithms are applied depending on the category of the utterance. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned point is, It estimates the user's emotions and adjusts the length of the comment based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned point is, When providing feedback, prioritize the feedback based on when the utterance was submitted. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned point is, When pointing out issues, adjust the order of the points based on the relevance of the utterances. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned proposal section is, It estimates the user's emotions and adjusts the way suggestions are presented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned proposal section is, When making a proposal, adjust the level of detail based on the importance of the utterance. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned proposal section is, When making a proposal, different proposal algorithms are applied depending on the category of the utterance. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned proposal section is, It estimates the user's emotions and adjusts the length of the suggestion based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned proposal section is, When making a proposal, prioritize the proposals based on when they were submitted. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned proposal section is, When making suggestions, adjust the order of suggestions based on the relevance of the utterances. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, It estimates the user's emotions and adjusts the simulation scenario based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned supply unit is, During simulation, the system selects the optimal scenario by referring to the user's past simulation history. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned supply unit is, During simulation, the scenario is customized based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned supply unit is, It estimates the user's emotions and determines the priority of simulations based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned supply unit is, During the simulation, the optimal scenario is selected by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned supply unit is, During simulations, the system analyzes users' social media activity and proposes scenarios. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is The system estimates the user's emotions and adjusts the content of lessons and quizzes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 31) The generating unit is When generating lessons and quizzes, the system selects the most suitable content by referring to the user's past learning history. The system described in Appendix 1, characterized by the features described herein. (Note 32) The generating unit is When generating lessons and quizzes, customize the content based on the user's current learning progress. The system described in Appendix 1, characterized by the features described herein. (Note 33) The generating unit is It estimates the user's emotions and prioritizes lessons and quizzes based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 34) The generating unit is When generating lessons and quizzes, the system selects the most suitable content by considering the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 35) The generating unit is When generating lessons and quizzes, we analyze users' social media activity to suggest content. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0194] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. The analysis department analyzes user utterances, The analysis unit identifies grammatical and pronunciation errors in the utterances analyzed by the aforementioned analysis unit, A proposal unit that makes suggestions for improvement regarding the errors pointed out by the aforementioned identification unit, The service provider offers everyday conversation simulations and role-playing, It comprises a generation unit that generates custom lessons and quizzes tailored to the learning progress. A system characterized by the following features.

2. The aforementioned analysis unit is It converts user speech into text and detects grammatical and pronunciation errors. The system according to feature 1.

3. The aforementioned point is, The system will show the correct grammar and pronunciation for detected errors and provide specific instructions on how to correct them. The system according to feature 1.

4. The aforementioned supply unit is, This simulation covers common everyday situations, including ordering at a restaurant and asking for directions. The system according to feature 1.

5. The generating unit is We analyze users' learning history and progress to provide lessons and quizzes tailored to their individual needs. The system according to feature 1.

6. The aforementioned analysis unit is It estimates the user's emotions and adjusts the speech analysis method based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit is The system analyzes the user's past speech history and selects the optimal analysis algorithm. The system according to feature 1.

8. The aforementioned analysis unit is When analyzing speech, filtering is performed based on the user's current learning status and areas of interest. The system according to feature 1.

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