system

Abstract

The system according to this embodiment aims to identify the user's weaknesses and areas of difficulty in language learning, and to create and update a curriculum based on these. [Solution] The system according to the embodiment comprises an analysis unit, a generation unit, and an update unit. The analysis unit analyzes the user's speech and identifies grammatical and pronunciation errors. The generation unit creates a language learning curriculum based on the weaknesses and areas of difficulty identified by the analysis unit. The update unit monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, the method including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a character of the chatbot, 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] Conventional technologies have a problem that it is difficult to efficiently identify the weaknesses and areas of difficulty in language learning of individual users and create a curriculum based on them.

[0005] The system according to the embodiment aims to identify the weaknesses and areas of difficulty in language learning of a user and create / update a curriculum based on them.

Means for Solving the Problems

[0006] The system according to this embodiment comprises an analysis unit, a generation unit, and an update unit. The analysis unit analyzes the user's speech and identifies grammatical and pronunciation errors. The generation unit creates a language learning curriculum based on the weaknesses and areas of difficulty identified by the analysis unit. The update unit monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed. [Effects of the Invention]

[0007] The system according to this embodiment can identify the user's weaknesses and areas of difficulty in language learning, and create and update a curriculum based on them. [Brief explanation of the drawing]

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

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

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

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

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

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

[0014] In the following embodiments, the numbered communication I / F (Interface) is an interface including a communication processor, an antenna, and the like. The communication I / F manages communication between a plurality of computers. Examples of communication standards applicable to the communication I / F include wireless communication standards including 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 language learning system according to an embodiment of the present invention is a system that uses a generative AI to identify an individual's weaknesses and areas of difficulty in language learning, and creates a personalized language learning curriculum (materials) based on that information. In the language learning system, the user communicates with the generative AI for 30 minutes. During this time, the generative AI analyzes the user's statements, pronunciation, grammatical errors, etc., to identify the individual's weaknesses and areas of difficulty. Next, the generative AI creates an individualized language learning curriculum based on the identified weaknesses and areas of difficulty. This curriculum is designed to focus on covering the user's weaknesses, allowing for efficient improvement of language skills. For example, the user communicates with the generative AI for 30 minutes. During this time, the generative AI analyzes the user's statements in detail and identifies grammatical errors and pronunciation problems. For example, if the user says "I went to the park yesterday," the generative AI will point out that "goed" is incorrect and "went" is correct. It will also identify issues with pronunciation, such as incorrect pronunciation of "r" or improper pronunciation of "th." Next, the generating AI creates an individualized language learning curriculum based on identified weaknesses and areas of difficulty. For example, if a user makes many grammatical errors, it creates materials covering grammar from basic to advanced levels. If there are problems with pronunciation, it creates materials for pronunciation practice. In this way, a curriculum that focuses on addressing the user's weaknesses is created. Furthermore, the generating AI monitors the user's progress and updates the curriculum as needed. For example, if a user has mastered basic grammar, it provides a curriculum for learning advanced grammar as the next step. If pronunciation practice progresses, it provides a curriculum for more advanced pronunciation practice. In this way, an optimal curriculum tailored to the user's learning progress is provided. This allows users to efficiently overcome their weaknesses and improve their language skills. Existing materials are inefficient because they include information already learned, but this invention provides a curriculum specifically tailored to individual weaknesses, allowing for efficient learning. Also, since the generating AI can generate an unlimited number of curricula and example exercises, users can always use the latest materials.This allows language learning systems to efficiently improve users' language skills.

[0029] The language learning system according to this embodiment comprises an analysis unit, a generation unit, and an update unit. The analysis unit analyzes the user's speech and identifies grammatical and pronunciation errors. For example, the analysis unit acquires the user's speech as audio data and converts it into text data using speech recognition technology. The analysis unit identifies grammatical errors based on the text data. For example, the analysis unit detects grammatical errors based on grammatical rules. The analysis unit also analyzes the audio data using phoneme analysis technology to identify pronunciation errors. For example, the analysis unit identifies pronunciation errors using phoneme analysis technology. The generation unit creates a language learning curriculum based on the identified weaknesses and areas of difficulty. For example, the generation unit creates a curriculum that focuses on covering the user's grammatical errors. The generation unit can also create a curriculum that focuses on covering pronunciation errors. The generation unit creates teaching materials to cover the user's weaknesses. For example, the generation unit creates teaching materials that cover grammar from basic to advanced levels. The generation unit can also create teaching materials for pronunciation practice. The generation unit can generate an unlimited number of curricula and example problems. The update unit monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed. For example, the update unit monitors the user's learning progress in real time. The update unit updates the curriculum according to the user's learning progress. For example, if the user has mastered the basics of grammar, the update unit provides a curriculum for learning advanced grammar as the next step. If the user has progressed in pronunciation practice, the update unit can also provide a curriculum for more advanced pronunciation practice. In this way, the language learning system according to the embodiment can efficiently improve the user's language skills. Some or all of the above processing in the analysis unit, generation unit, and update unit is performed using a generation AI. For example, the analysis unit inputs the user's utterances into the generation AI, which identifies grammatical and pronunciation errors. The generation unit uses the generation AI to create a curriculum based on the identified weaknesses and areas of difficulty. The update unit uses the generation AI to monitor the user's progress and updates the curriculum as needed.

[0030] The analysis unit analyzes the user's utterances and identifies grammatical and pronunciation errors. For example, the analysis unit acquires the user's utterances as audio data and converts them into text data using speech recognition technology. Specifically, the speech recognition technology utilizes a deep learning model to accurately transcribe the user's utterances into text. This text data is input into the grammar analysis engine, where grammatical errors are detected based on grammatical rules. The grammar analysis engine uses natural language processing technology to analyze the sentence structure and grammatical rules in detail and identify errors. The analysis unit also analyzes the audio data using phoneme analysis technology to identify pronunciation errors. Phoneme analysis technology breaks down the audio data into small phoneme units and evaluates whether each phoneme is pronounced correctly. For example, phoneme analysis technology compares the user's pronunciation with standard pronunciation to identify which phonemes are incorrect. Furthermore, the analysis unit analyzes the user's utterances using generative AI. The generative AI is based on a large-scale language model and can identify grammatical and pronunciation errors with high accuracy. The generation AI receives user utterances as input, automatically detects grammatical and pronunciation errors, and provides detailed feedback. This allows the analysis unit to comprehensively analyze user utterances and accurately identify grammatical and pronunciation errors.

[0031] The generation unit creates language learning curricula based on identified weaknesses and areas of difficulty. For example, the generation unit creates a curriculum that focuses on addressing the user's grammatical errors. Specifically, the generation unit analyzes the user's grammatical errors and designs a curriculum to correct them. The generation unit creates materials that cover grammar from basic to advanced levels, allowing the user to progress through the learning process step by step. The generation unit can also create a curriculum that focuses on addressing pronunciation errors. The materials for pronunciation practice include audio samples and pronunciation guides, designed to help the user acquire correct pronunciation. Furthermore, the generation unit creates curricula using a generation AI. The generation AI analyzes the user's weaknesses and areas of difficulty and automatically generates the optimal curriculum based on that analysis. The generation AI is based on a large amount of learning data and can generate an unlimited number of curricula tailored to the user's learning needs. For example, the generation AI generates example sentences and practice exercises to correct the user's grammatical errors, enabling the user to learn efficiently. The generation AI also generates audio samples and pronunciation guides for the user's pronunciation practice, supporting the user in acquiring correct pronunciation. This allows the generation unit to create a curriculum that effectively addresses the user's weaknesses and improve their language skills.

[0032] The update unit monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed. For example, the update unit monitors the user's learning progress in real time. Specifically, the update unit collects the user's learning data and evaluates their learning progress. It analyzes in detail how much progress the user has made and in which areas they are struggling. The update unit updates the curriculum according to the user's learning progress. For example, if the user has mastered the basics of grammar, it provides a curriculum for learning advanced grammar as the next step. If the user has progressed in pronunciation practice, the update unit can also provide a curriculum for more advanced pronunciation practice. Furthermore, the update unit monitors the user's progress using a generation AI and updates the curriculum as needed. The generation AI analyzes the user's learning data in real time and evaluates the user's progress. The generation AI automatically updates the curriculum according to the user's learning needs and provides the optimal learning plan. For example, if the generation AI is struggling with a particular grammar item, it generates a curriculum that focuses on that item. The generation AI also evaluates the user's progress in pronunciation practice and provides new pronunciation practice materials as needed. This allows the update unit to continuously monitor the user's learning progress and provide an optimal curriculum, thereby efficiently improving the user's language skills.

[0033] The analysis unit can analyze the user's utterances in detail and identify grammatical errors and pronunciation problems. For example, the analysis unit acquires the user's utterances as audio data and converts them into text data using speech recognition technology. The analysis unit identifies grammatical errors based on the text data. For example, the analysis unit detects grammatical errors based on grammatical rules. The analysis unit also analyzes the audio data using phoneme analysis technology to identify pronunciation errors. For example, the analysis unit identifies pronunciation errors using phoneme analysis technology. As a result, the analysis unit can analyze the user's utterances in detail and accurately identify grammatical errors and pronunciation problems. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the user's utterances into the generation AI, which then identifies grammatical and pronunciation errors.

[0034] The generation unit can create individualized language learning curricula based on identified weaknesses and areas of difficulty. For example, the generation unit can create a curriculum that focuses on addressing the user's grammatical errors. It can also create a curriculum that focuses on addressing pronunciation errors. The generation unit creates learning materials to address the user's weaknesses. For example, it can create materials that cover grammar from basic to advanced levels. It can also create materials for pronunciation practice. In this way, the generation unit can efficiently improve language skills by creating curricula based on identified weaknesses and areas of difficulty. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit uses a generation AI to create a curriculum based on identified weaknesses and areas of difficulty.

[0035] The update unit can monitor the user's progress and update the curriculum as needed. For example, the update unit monitors the user's learning progress in real time. The update unit updates the curriculum according to the user's learning progress. For example, if the user has mastered the basics of grammar, the update unit will provide a curriculum for learning advanced grammar as the next step. If the user has progressed in pronunciation practice, the update unit can also provide a curriculum for more advanced pronunciation practice. In this way, the update unit can always provide the most optimal learning content by updating the curriculum according to the user's progress. Some or all of the above processes in the update unit are performed using generative AI. For example, the update unit uses generative AI to monitor the user's progress and update the curriculum as needed.

[0036] The generation unit can generate an infinite number of curricula and example exercises. For example, the generation unit can generate an infinite number of curricula that focus on covering the user's grammatical errors. The generation unit can also generate an infinite number of curricula that focus on covering pronunciation errors. The generation unit generates an infinite number of learning materials to cover the user's weaknesses. For example, the generation unit can generate an infinite number of learning materials that cover grammar from basic to advanced levels. The generation unit can also generate an infinite number of learning materials for pronunciation practice. As a result, by generating an infinite number of curricula and example exercises, the user always has access to new materials. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate an infinite number of curricula and example exercises.

[0037] The analysis unit can analyze the user's past speech history and extract specific patterns to improve the accuracy of the analysis. For example, if the user has frequently used incorrect grammar in the past, the analysis unit can extract that pattern to improve the accuracy of the analysis. The analysis unit can also extract specific pronunciation errors from the user's past speech history to improve the accuracy of the analysis. The analysis unit analyzes the user's past speech history and extracts specific grammatical patterns to improve the accuracy of the analysis. In this way, the analysis unit can extract specific patterns by analyzing past speech history and improve the accuracy of the analysis. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's past speech history into the generative AI, and the generative AI extracts specific patterns to improve the accuracy of the analysis.

[0038] The analysis unit can identify errors by considering the user's cultural background and native language when analyzing the content of speech. For example, if the user's native language is English, the analysis unit will consider errors specific to English. The analysis unit will consider the user's cultural background and identify errors related to a specific culture. If the user's native language is different, the analysis unit will consider errors specific to that language. As a result, the analysis unit can identify errors more accurately by considering the user's cultural background and native language. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's cultural background and native language into the generative AI, which then identifies errors.

[0039] The analysis unit can identify region-specific errors by considering the user's geographical location information when analyzing spoken content. For example, if the user lives in a specific region, the analysis unit can identify region-specific pronunciation errors. The analysis unit identifies region-specific grammatical errors based on the user's geographical location information. The analysis unit analyzes region-specific language patterns, taking the user's geographical location information into consideration. As a result, the analysis unit can accurately identify region-specific errors by considering the user's geographical location information. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs the user's geographical location information into the generation AI, which then identifies region-specific errors.

[0040] The analysis unit can analyze the user's social media activity and identify relevant errors when analyzing the content of a statement. For example, the analysis unit can analyze the user's social media posts and identify frequently occurring errors. The analysis unit can extract specific grammatical errors from the user's social media activity. The analysis unit can identify specific pronunciation errors based on the user's social media activity. In this way, the analysis unit can identify relevant errors by analyzing the user's social media activity and improve the accuracy of the analysis. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs the user's social media activity into the generative AI, which then identifies relevant errors.

[0041] The generation unit can select the format of learning materials based on the user's learning style and preferences when creating a curriculum. For example, if the user is a visual learner, the generation AI will select visual learning materials. If the user is an auditory learner, the generation AI will select audio learning materials. If the user is a tactile learner, the generation AI will select interactive learning materials. In this way, the generation unit can enhance learning effectiveness by providing learning materials that match the user's learning style and preferences. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's learning style and preferences into the generation AI, and the generation AI selects the format of the learning materials.

[0042] The generation unit can determine the optimal learning order by referring to the user's learning history when creating a curriculum. For example, the generation unit determines the optimal learning order based on what the user has learned in the past. The generation unit proposes an effective learning order from the user's learning history. The generation unit analyzes the user's learning history and determines the most efficient learning order. In this way, the generation unit can provide the optimal learning order by referring to the user's learning history and improve learning effectiveness. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's learning history into the generation AI, and the generation AI determines the optimal learning order.

[0043] The generation unit can propose a learning schedule based on the user's lifestyle when creating a curriculum. For example, if the user is a morning person, the generation AI will propose a morning learning schedule. If the user is a night owl, the generation AI will propose an evening learning schedule. The generation unit considers the user's lifestyle and proposes the optimal learning schedule. In this way, the generation unit can enhance learning effectiveness by providing a learning schedule based on the user's lifestyle. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's lifestyle into the generation AI, and the generation AI proposes a learning schedule.

[0044] The generation unit can include relevant topics based on the user's interests when creating the curriculum. For example, if the user is interested in sports, the generation AI will include sports-related topics. If the user is interested in music, the generation AI will include music-related topics. The generation unit considers the user's interests and includes relevant topics in the curriculum. This allows the generation unit to increase the user's motivation to learn by including topics based on their interests. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's interests into the generation AI, and the generation AI includes relevant topics.

[0045] The update unit can analyze the user's learning progress in detail during updates and optimize the next step. For example, the update unit optimizes the next step based on the user's learning progress. The update unit analyzes the user's learning progress in detail and suggests an effective next step. The update unit considers the user's learning progress and determines the optimal next step. In this way, the update unit can optimize the next step and improve learning effectiveness by analyzing the user's learning progress in detail. Some or all of the above processing in the update unit is performed using generative AI. For example, the update unit inputs the user's learning progress into the generative AI, and the generative AI optimizes the next step.

[0046] The update unit can improve the curriculum by incorporating user feedback during updates. For example, the update unit improves the curriculum based on user feedback. The update unit creates an effective curriculum by incorporating user feedback. The update unit optimizes the curriculum by considering user feedback. As a result, the update unit can provide a more effective curriculum by incorporating user feedback. Some or all of the above processes in the update unit are performed using generative AI. For example, the update unit inputs user feedback into the generative AI, and the generative AI improves the curriculum.

[0047] The update unit can add region-specific learning content during updates, taking into account the user's geographical location. For example, if the user lives in a specific region, the update unit will add region-specific learning content. Based on the user's geographical location, the update unit will add region-specific language patterns to the learning content. The update unit will add learning content related to region-specific culture, taking the user's geographical location into consideration. In this way, the update unit can provide region-specific learning content by taking the user's geographical location into consideration. Some or all of the above processing in the update unit is performed using a generative AI. For example, the update unit inputs the user's geographical location information into the generative AI, and the generative AI adds region-specific learning content.

[0048] The update unit can analyze the user's social media activity and add relevant learning content during updates. For example, the update unit can analyze the user's social media posts and add relevant learning content. The update unit can add specific language patterns to the learning content from the user's social media activity. The update unit adds relevant learning content based on the user's social media activity. In this way, the update unit can provide relevant learning content by analyzing the user's social media activity. Some or all of the above processing in the update unit is performed using generative AI. For example, the update unit inputs the user's social media activity into the generative AI, and the generative AI adds relevant learning content.

[0049] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0050] The analysis unit can improve the accuracy of its analysis by referring to the user's learning history when analyzing the user's utterances. For example, it can analyze the user's current utterances based on what the user has learned in the past. If the user has frequently used incorrect grammar in the past, the analysis unit will take that pattern into consideration during the analysis. Furthermore, the analysis unit can refer to the user's past pronunciation errors to identify current pronunciation errors. In this way, the analysis unit can provide more accurate analysis results by utilizing the user's learning history.

[0051] The update unit can update the curriculum based on the user's learning style and preferences when monitoring the user's progress. For example, if the user is a visual learner, the generative AI will provide a curriculum with a lot of visual materials. If the user is an auditory learner, the update unit can also provide a curriculum with a lot of audio materials. Furthermore, if the user is a haptic learner, it can provide a curriculum with a lot of interactive materials. In this way, the update unit can provide an optimal curriculum tailored to the user's learning style and preferences.

[0052] The analysis unit can identify errors by considering the user's cultural background and native language when analyzing user utterances. For example, if the user's native language is English, the analysis will take into account errors specific to English. The analysis unit can also identify errors related to a particular culture by considering the user's cultural background. Furthermore, if the user's native language is different, the analysis can also take into account errors specific to that language. As a result, the analysis unit can identify errors more accurately by considering the user's cultural background and native language.

[0053] The update unit can suggest a learning schedule based on the user's lifestyle when monitoring the user's progress. For example, if the user is a morning person, the generating AI will suggest a morning learning schedule. If the user is a night owl, the generating AI can also suggest an evening learning schedule. Furthermore, it can even suggest an optimal learning schedule considering the user's lifestyle. In this way, the update unit can enhance learning effectiveness by providing a learning schedule tailored to the user's lifestyle.

[0054] The generation unit can include relevant topics based on the user's interests when creating the curriculum. For example, if the user is interested in sports, the generation AI will include sports-related topics. If the user is interested in music, the generation AI can also include music-related topics. Furthermore, it is possible to include relevant topics in the curriculum while considering the user's interests. This allows the generation unit to increase the user's motivation to learn by including topics based on their interests.

[0055] The update unit can improve the curriculum by incorporating user feedback during updates. For example, it can improve the curriculum based on user feedback. The update unit can also create an effective curriculum by incorporating user feedback. Furthermore, it can optimize the curriculum by considering user feedback. In this way, the update unit can provide a more effective curriculum by incorporating user feedback.

[0056] The following briefly describes the processing flow for example form 1.

[0057] Step 1: The analysis unit analyzes the user's speech and identifies grammatical and pronunciation errors. The analysis unit acquires the user's speech as audio data and converts it into text data using speech recognition technology. Furthermore, the analysis unit detects grammatical errors based on grammatical rules and identifies pronunciation errors using phoneme analysis technology. Step 2: The generation unit creates a language learning curriculum based on the weaknesses and areas of difficulty identified by the analysis unit. The generation unit creates a curriculum that focuses on addressing the user's grammatical and pronunciation errors, and creates learning materials to address the user's weaknesses. Step 3: The update unit monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed. The update unit monitors the user's learning progress in real time and updates the curriculum according to the user's progress.

[0058] (Example of form 2) The language learning system according to an embodiment of the present invention is a system that uses a generative AI to identify an individual's weaknesses and areas of difficulty in language learning, and creates a personalized language learning curriculum (materials) based on that information. In the language learning system, the user communicates with the generative AI for 30 minutes. During this time, the generative AI analyzes the user's statements, pronunciation, grammatical errors, etc., to identify the individual's weaknesses and areas of difficulty. Next, the generative AI creates an individualized language learning curriculum based on the identified weaknesses and areas of difficulty. This curriculum is designed to focus on covering the user's weaknesses, allowing for efficient improvement of language skills. For example, the user communicates with the generative AI for 30 minutes. During this time, the generative AI analyzes the user's statements in detail and identifies grammatical errors and pronunciation problems. For example, if the user says "I went to the park yesterday," the generative AI will point out that "goed" is incorrect and "went" is correct. It will also identify issues with pronunciation, such as incorrect pronunciation of "r" or improper pronunciation of "th." Next, the generating AI creates an individualized language learning curriculum based on identified weaknesses and areas of difficulty. For example, if a user makes many grammatical errors, it creates materials covering grammar from basic to advanced levels. If there are problems with pronunciation, it creates materials for pronunciation practice. In this way, a curriculum that focuses on addressing the user's weaknesses is created. Furthermore, the generating AI monitors the user's progress and updates the curriculum as needed. For example, if a user has mastered basic grammar, it provides a curriculum for learning advanced grammar as the next step. If pronunciation practice progresses, it provides a curriculum for more advanced pronunciation practice. In this way, an optimal curriculum tailored to the user's learning progress is provided. This allows users to efficiently overcome their weaknesses and improve their language skills. Existing materials are inefficient because they include information already learned, but this invention provides a curriculum specifically tailored to individual weaknesses, allowing for efficient learning. Also, since the generating AI can generate an unlimited number of curricula and example exercises, users can always use the latest materials.This allows language learning systems to efficiently improve users' language skills.

[0059] The language learning system according to this embodiment comprises an analysis unit, a generation unit, and an update unit. The analysis unit analyzes the user's speech and identifies grammatical and pronunciation errors. For example, the analysis unit acquires the user's speech as audio data and converts it into text data using speech recognition technology. The analysis unit identifies grammatical errors based on the text data. For example, the analysis unit detects grammatical errors based on grammatical rules. The analysis unit also analyzes the audio data using phoneme analysis technology to identify pronunciation errors. For example, the analysis unit identifies pronunciation errors using phoneme analysis technology. The generation unit creates a language learning curriculum based on the identified weaknesses and areas of difficulty. For example, the generation unit creates a curriculum that focuses on covering the user's grammatical errors. The generation unit can also create a curriculum that focuses on covering pronunciation errors. The generation unit creates teaching materials to cover the user's weaknesses. For example, the generation unit creates teaching materials that cover grammar from basic to advanced levels. The generation unit can also create teaching materials for pronunciation practice. The generation unit can generate an unlimited number of curricula and example problems. The update unit monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed. For example, the update unit monitors the user's learning progress in real time. The update unit updates the curriculum according to the user's learning progress. For example, if the user has mastered the basics of grammar, the update unit provides a curriculum for learning advanced grammar as the next step. If the user has progressed in pronunciation practice, the update unit can also provide a curriculum for more advanced pronunciation practice. In this way, the language learning system according to the embodiment can efficiently improve the user's language skills. Some or all of the above processing in the analysis unit, generation unit, and update unit is performed using a generation AI. For example, the analysis unit inputs the user's utterances into the generation AI, which identifies grammatical and pronunciation errors. The generation unit uses the generation AI to create a curriculum based on the identified weaknesses and areas of difficulty. The update unit uses the generation AI to monitor the user's progress and updates the curriculum as needed.

[0060] The analysis unit analyzes the user's utterances and identifies grammatical and pronunciation errors. For example, the analysis unit acquires the user's utterances as audio data and converts them into text data using speech recognition technology. Specifically, the speech recognition technology utilizes a deep learning model to accurately transcribe the user's utterances into text. This text data is input into the grammar analysis engine, where grammatical errors are detected based on grammatical rules. The grammar analysis engine uses natural language processing technology to analyze the sentence structure and grammatical rules in detail and identify errors. The analysis unit also analyzes the audio data using phoneme analysis technology to identify pronunciation errors. Phoneme analysis technology breaks down the audio data into small phoneme units and evaluates whether each phoneme is pronounced correctly. For example, phoneme analysis technology compares the user's pronunciation with standard pronunciation to identify which phonemes are incorrect. Furthermore, the analysis unit analyzes the user's utterances using generative AI. The generative AI is based on a large-scale language model and can identify grammatical and pronunciation errors with high accuracy. The generation AI receives the user's utterances as input, automatically detects grammatical and pronunciation errors, and provides detailed feedback. This allows the analysis unit to comprehensively analyze the user's utterances and accurately identify grammatical and pronunciation errors.

[0061] The generation unit creates language learning curricula based on identified weaknesses and areas of difficulty. For example, the generation unit creates a curriculum that focuses on addressing the user's grammatical errors. Specifically, the generation unit analyzes the user's grammatical errors and designs a curriculum to correct them. The generation unit creates materials that cover grammar from basic to advanced levels, allowing the user to progress through the learning process step by step. The generation unit can also create a curriculum that focuses on addressing pronunciation errors. The materials for pronunciation practice include audio samples and pronunciation guides, designed to help the user acquire correct pronunciation. Furthermore, the generation unit creates curricula using a generation AI. The generation AI analyzes the user's weaknesses and areas of difficulty and automatically generates the optimal curriculum based on that analysis. The generation AI is based on a large amount of learning data and can generate an unlimited number of curricula tailored to the user's learning needs. For example, the generation AI generates example sentences and practice exercises to correct the user's grammatical errors, enabling the user to learn efficiently. The generation AI also generates audio samples and pronunciation guides for the user's pronunciation practice, supporting the user in acquiring correct pronunciation. This allows the generation unit to create a curriculum that effectively addresses the user's weaknesses and improve their language skills.

[0062] The update unit monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed. For example, the update unit monitors the user's learning progress in real time. Specifically, the update unit collects the user's learning data and evaluates their learning progress. It analyzes in detail how much progress the user has made and in which areas they are struggling. The update unit updates the curriculum according to the user's learning progress. For example, if the user has mastered the basics of grammar, it provides a curriculum for learning advanced grammar as the next step. If the user has progressed in pronunciation practice, the update unit can also provide a curriculum for more advanced pronunciation practice. Furthermore, the update unit monitors the user's progress using a generation AI and updates the curriculum as needed. The generation AI analyzes the user's learning data in real time and evaluates the user's progress. The generation AI automatically updates the curriculum according to the user's learning needs and provides the optimal learning plan. For example, if the generation AI is struggling with a particular grammar item, it generates a curriculum that focuses on that item. The generation AI also evaluates the user's progress in pronunciation practice and provides new pronunciation practice materials as needed. This allows the update unit to continuously monitor the user's learning progress and provide an optimal curriculum, thereby efficiently improving the user's language skills.

[0063] The analysis unit can analyze the user's utterances in detail and identify grammatical errors and pronunciation problems. For example, the analysis unit acquires the user's utterances as audio data and converts them into text data using speech recognition technology. The analysis unit identifies grammatical errors based on the text data. For example, the analysis unit detects grammatical errors based on grammatical rules. The analysis unit also analyzes the audio data using phoneme analysis technology to identify pronunciation errors. For example, the analysis unit identifies pronunciation errors using phoneme analysis technology. As a result, the analysis unit can analyze the user's utterances in detail and accurately identify grammatical errors and pronunciation problems. Some or all of the above processing in the analysis unit is performed using a generation AI. For example, the analysis unit inputs the user's utterances into the generation AI, which then identifies grammatical and pronunciation errors.

[0064] The generation unit can create individualized language learning curricula based on identified weaknesses and areas of difficulty. For example, the generation unit can create a curriculum that focuses on addressing the user's grammatical errors. It can also create a curriculum that focuses on addressing pronunciation errors. The generation unit creates learning materials to address the user's weaknesses. For example, it can create materials that cover grammar from basic to advanced levels. It can also create materials for pronunciation practice. In this way, the generation unit can efficiently improve language skills by creating curricula based on identified weaknesses and areas of difficulty. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit uses a generation AI to create a curriculum based on identified weaknesses and areas of difficulty.

[0065] The update unit can monitor the user's progress and update the curriculum as needed. For example, the update unit monitors the user's learning progress in real time. The update unit updates the curriculum according to the user's learning progress. For example, if the user has mastered the basics of grammar, the update unit will provide a curriculum for learning advanced grammar as the next step. If the user has progressed in pronunciation practice, the update unit can also provide a curriculum for more advanced pronunciation practice. In this way, the update unit can always provide the most optimal learning content by updating the curriculum according to the user's progress. Some or all of the above processes in the update unit are performed using generative AI. For example, the update unit uses generative AI to monitor the user's progress and update the curriculum as needed.

[0066] The generation unit can generate an infinite number of curricula and example exercises. For example, the generation unit can generate an infinite number of curricula that focus on covering the user's grammatical errors. The generation unit can also generate an infinite number of curricula that focus on covering pronunciation errors. The generation unit generates an infinite number of learning materials to cover the user's weaknesses. For example, the generation unit can generate an infinite number of learning materials that cover grammar from basic to advanced levels. The generation unit can also generate an infinite number of learning materials for pronunciation practice. As a result, by generating an infinite number of curricula and example exercises, the user always has access to new materials. Some or all of the above processing in the generation unit is performed using a generation AI. For example, the generation unit uses a generation AI to generate an infinite number of curricula and example exercises.

[0067] The analysis unit can estimate the user's emotions and adjust the accuracy of the analysis based on the estimated emotions. For example, if the user is tense, the generating AI will loosen the accuracy of the analysis to help the user relax. If the user is relaxed, the generating AI will increase the accuracy of the analysis to identify detailed errors. If the user is excited, the generating AI will adjust the accuracy of the analysis to more accurately analyze the user's statements. In this way, the analysis unit can obtain more appropriate analysis results by adjusting the accuracy of the analysis according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the analysis unit is performed using the generating AI. For example, the analysis unit inputs the user's emotions into the generating AI, and the generating AI adjusts the accuracy of the analysis.

[0068] The analysis unit can analyze the user's past speech history and extract specific patterns to improve the accuracy of the analysis. For example, if the user has frequently used incorrect grammar in the past, the analysis unit can extract that pattern to improve the accuracy of the analysis. The analysis unit can also extract specific pronunciation errors from the user's past speech history to improve the accuracy of the analysis. The analysis unit analyzes the user's past speech history and extracts specific grammatical patterns to improve the accuracy of the analysis. In this way, the analysis unit can extract specific patterns by analyzing past speech history and improve the accuracy of the analysis. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's past speech history into the generative AI, and the generative AI extracts specific patterns to improve the accuracy of the analysis.

[0069] The analysis unit can identify errors by considering the user's cultural background and native language when analyzing the content of speech. For example, if the user's native language is English, the analysis unit will consider errors specific to English. The analysis unit will consider the user's cultural background and identify errors related to a specific culture. If the user's native language is different, the analysis unit will consider errors specific to that language. As a result, the analysis unit can identify errors more accurately by considering the user's cultural background and native language. Some or all of the above processing in the analysis unit is performed using a generative AI. For example, the analysis unit inputs the user's cultural background and native language into the generative AI, which then identifies errors.

[0070] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is tense, the generating AI will provide feedback in gentle words. If the user is relaxed, the analysis unit will provide detailed feedback. If the user is excited, the generating AI will provide quick and concise feedback. In this way, the analysis unit can provide more appropriate feedback by adjusting the feedback method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating 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 analysis unit is performed using the generating AI. For example, the analysis unit inputs the user's emotions into the generating AI, and the generating AI adjusts the feedback method.

[0071] The analysis unit can identify region-specific errors by considering the user's geographical location information when analyzing spoken content. For example, if the user lives in a specific region, the analysis unit can identify region-specific pronunciation errors. The analysis unit identifies region-specific grammatical errors based on the user's geographical location information. The analysis unit analyzes region-specific language patterns, taking the user's geographical location information into consideration. As a result, the analysis unit can accurately identify region-specific errors by considering the user's geographical location information. Some or all of the above processes in the analysis unit are performed using a generation AI. For example, the analysis unit inputs the user's geographical location information into the generation AI, which then identifies region-specific errors.

[0072] The analysis unit can analyze the user's social media activity and identify relevant errors when analyzing the content of a statement. For example, the analysis unit can analyze the user's social media posts and identify frequently occurring errors. The analysis unit can extract specific grammatical errors from the user's social media activity. The analysis unit can identify specific pronunciation errors based on the user's social media activity. In this way, the analysis unit can identify relevant errors by analyzing the user's social media activity and improve the accuracy of the analysis. Some or all of the above processes in the analysis unit are performed using a generative AI. For example, the analysis unit inputs the user's social media activity into the generative AI, which then identifies relevant errors.

[0073] The generation unit can estimate the user's emotions and adjust the curriculum content based on the estimated emotions. For example, if the user is tense, the generation AI will create a curriculum with relaxing content. If the user is relaxed, the generation AI will create a curriculum with detailed content. If the user is excited, the generation AI will create a curriculum with stimulating content. This allows the generation unit to adjust the curriculum content according to the user's emotions, enabling more effective learning. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotions into the generation AI, and the generation AI adjusts the curriculum content.

[0074] The generation unit can select the format of learning materials based on the user's learning style and preferences when creating a curriculum. For example, if the user is a visual learner, the generation AI will select visual learning materials. If the user is an auditory learner, the generation AI will select audio learning materials. If the user is a tactile learner, the generation AI will select interactive learning materials. In this way, the generation unit can enhance learning effectiveness by providing learning materials that match the user's learning style and preferences. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's learning style and preferences into the generation AI, and the generation AI selects the format of the learning materials.

[0075] The generation unit can determine the optimal learning order by referring to the user's learning history when creating a curriculum. For example, the generation unit determines the optimal learning order based on what the user has learned in the past. The generation unit proposes an effective learning order from the user's learning history. The generation unit analyzes the user's learning history and determines the most efficient learning order. In this way, the generation unit can provide the optimal learning order by referring to the user's learning history and improve learning effectiveness. Some or all of the above processes in the generation unit are performed using a generation AI. For example, the generation unit inputs the user's learning history into the generation AI, and the generation AI determines the optimal learning order.

[0076] The generation unit can estimate the user's emotions and adjust the difficulty level of the curriculum based on the estimated emotions. For example, if the user is nervous, the generation AI will create a curriculum with a lower difficulty level. If the user is relaxed, the generation AI will create a curriculum with a higher difficulty level. If the user is excited, the generation AI will create a curriculum with an adjusted difficulty level. This allows the generation unit to enable more effective learning by adjusting the curriculum difficulty level according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation 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 generation unit is performed using the generation AI. For example, the generation unit inputs the user's emotions into the generation AI, and the generation AI adjusts the difficulty level of the curriculum.

[0077] The generation unit can propose a learning schedule based on the user's lifestyle when creating a curriculum. For example, if the user is a morning person, the generation AI will propose a morning learning schedule. If the user is a night owl, the generation AI will propose an evening learning schedule. The generation unit considers the user's lifestyle and proposes the optimal learning schedule. In this way, the generation unit can enhance learning effectiveness by providing a learning schedule based on the user's lifestyle. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's lifestyle into the generation AI, and the generation AI proposes a learning schedule.

[0078] The generation unit can include relevant topics based on the user's interests when creating the curriculum. For example, if the user is interested in sports, the generation AI will include sports-related topics. If the user is interested in music, the generation AI will include music-related topics. The generation unit considers the user's interests and includes relevant topics in the curriculum. This allows the generation unit to increase the user's motivation to learn by including topics based on their interests. Some or all of the above processing in the generation unit is performed using the generation AI. For example, the generation unit inputs the user's interests into the generation AI, and the generation AI includes relevant topics.

[0079] The update unit can estimate the user's emotions and adjust the curriculum update frequency based on the estimated emotions. For example, if the user is nervous, the generating AI will set a lower update frequency. If the user is relaxed, the generating AI will set a higher update frequency. If the user is excited, the generating AI will adjust the update frequency. This allows the update unit to update the curriculum at a more appropriate time by adjusting the update frequency according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating 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 update unit is performed using the generating AI. For example, the update unit inputs the user's emotions into the generating AI, and the generating AI adjusts the update frequency.

[0080] The update unit can analyze the user's learning progress in detail during updates and optimize the next step. For example, the update unit optimizes the next step based on the user's learning progress. The update unit analyzes the user's learning progress in detail and suggests an effective next step. The update unit considers the user's learning progress and determines the optimal next step. In this way, the update unit can optimize the next step and improve learning effectiveness by analyzing the user's learning progress in detail. Some or all of the above processing in the update unit is performed using generative AI. For example, the update unit inputs the user's learning progress into the generative AI, and the generative AI optimizes the next step.

[0081] The update unit can improve the curriculum by incorporating user feedback during updates. For example, the update unit improves the curriculum based on user feedback. The update unit creates an effective curriculum by incorporating user feedback. The update unit optimizes the curriculum by considering user feedback. As a result, the update unit can provide a more effective curriculum by incorporating user feedback. Some or all of the above processes in the update unit are performed using generative AI. For example, the update unit inputs user feedback into the generative AI, and the generative AI improves the curriculum.

[0082] The update unit can estimate the user's emotions and adjust the notification method for updates based on the estimated emotions. For example, if the user is tense, the generating AI will notify them in gentle language. If the user is relaxed, the update unit will provide a detailed notification. If the user is excited, the update unit will provide a quick and concise notification. In this way, the update unit can provide more appropriate notifications by adjusting the notification method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generating AI. The generating AI is a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above processing in the update unit is performed using the generating AI. For example, the update unit inputs the user's emotions into the generating AI, and the generating AI adjusts the notification method.

[0083] The update unit can add region-specific learning content during updates, taking into account the user's geographical location. For example, if the user lives in a specific region, the update unit will add region-specific learning content. Based on the user's geographical location, the update unit will add region-specific language patterns to the learning content. The update unit will add learning content related to region-specific culture, taking the user's geographical location into consideration. In this way, the update unit can provide region-specific learning content by taking the user's geographical location into consideration. Some or all of the above processing in the update unit is performed using a generative AI. For example, the update unit inputs the user's geographical location information into the generative AI, and the generative AI adds region-specific learning content.

[0084] The update unit can analyze the user's social media activity and add relevant learning content during updates. For example, the update unit can analyze the user's social media posts and add relevant learning content. The update unit can add specific language patterns to the learning content from the user's social media activity. The update unit adds relevant learning content based on the user's social media activity. In this way, the update unit can provide relevant learning content by analyzing the user's social media activity. Some or all of the above processing in the update unit is performed using generative AI. For example, the update unit inputs the user's social media activity into the generative AI, and the generative AI adds relevant learning content.

[0085] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0086] The analysis unit can improve the accuracy of its analysis by referring to the user's learning history when analyzing the user's utterances. For example, it can analyze the user's current utterances based on what the user has learned in the past. If the user has frequently used incorrect grammar in the past, the analysis unit will take that pattern into consideration during the analysis. Furthermore, the analysis unit can refer to the user's past pronunciation errors to identify current pronunciation errors. In this way, the analysis unit can provide more accurate analysis results by utilizing the user's learning history.

[0087] The generation unit can estimate the user's emotions and adjust the curriculum content based on those emotions. For example, if the user is nervous, the generation AI will create a curriculum with relaxing content. If the user is relaxed, the generation AI will create a curriculum with detailed content. Furthermore, if the user is excited, the generation AI can also create a curriculum with stimulating content. In this way, the generation unit can adjust the curriculum content according to the user's emotions, enabling more effective learning.

[0088] The update unit can update the curriculum based on the user's learning style and preferences when monitoring the user's progress. For example, if the user is a visual learner, the generative AI will provide a curriculum with a lot of visual materials. If the user is an auditory learner, the update unit can also provide a curriculum with a lot of audio materials. Furthermore, if the user is a haptic learner, it can provide a curriculum with a lot of interactive materials. In this way, the update unit can provide an optimal curriculum tailored to the user's learning style and preferences.

[0089] The analysis unit can identify errors by considering the user's cultural background and native language when analyzing user utterances. For example, if the user's native language is English, the analysis will take into account errors specific to English. The analysis unit can also identify errors related to a particular culture by considering the user's cultural background. Furthermore, if the user's native language is different, the analysis can also take into account errors specific to that language. As a result, the analysis unit can identify errors more accurately by considering the user's cultural background and native language.

[0090] The generation unit can estimate the user's emotions and adjust the difficulty level of the curriculum based on those emotions. For example, if the user is nervous, the generation AI will create a curriculum with a lower difficulty level. If the user is relaxed, the generation AI will create a curriculum with a higher difficulty level. Furthermore, if the user is excited, the generation AI can also create a curriculum with an adjusted difficulty level. In this way, the generation unit can adjust the difficulty level of the curriculum according to the user's emotions, enabling more effective learning.

[0091] The update unit can suggest a learning schedule based on the user's lifestyle when monitoring the user's progress. For example, if the user is a morning person, the generating AI will suggest a morning learning schedule. If the user is a night owl, the generating AI can also suggest an evening learning schedule. Furthermore, it can even suggest an optimal learning schedule considering the user's lifestyle. In this way, the update unit can enhance learning effectiveness by providing a learning schedule tailored to the user's lifestyle.

[0092] The analysis unit can estimate the user's emotions and adjust the feedback method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the generating AI will provide feedback in gentle words. If the user is relaxed, the analysis unit will provide detailed feedback. Furthermore, if the user is excited, the generating AI can also provide quick and concise feedback. In this way, the analysis unit can provide more appropriate feedback by adjusting the feedback method according to the user's emotions.

[0093] The generation unit can include relevant topics based on the user's interests when creating the curriculum. For example, if the user is interested in sports, the generation AI will include sports-related topics. If the user is interested in music, the generation AI can also include music-related topics. Furthermore, it is possible to include relevant topics in the curriculum while considering the user's interests. This allows the generation unit to increase the user's motivation to learn by including topics based on their interests.

[0094] The update unit can estimate the user's emotions and adjust the curriculum update frequency based on those emotions. For example, if the user is stressed, the generating AI will set a lower update frequency. If the user is relaxed, the generating AI will set a higher update frequency. Furthermore, if the user is excited, the generating AI can also adjust the update frequency. This allows the update unit to update the curriculum at a more appropriate time by adjusting the update frequency according to the user's emotions.

[0095] The update unit can improve the curriculum by incorporating user feedback during updates. For example, it can improve the curriculum based on user feedback. The update unit can also create an effective curriculum by incorporating user feedback. Furthermore, it can optimize the curriculum by considering user feedback. In this way, the update unit can provide a more effective curriculum by incorporating user feedback.

[0096] The following briefly describes the processing flow for example form 2.

[0097] Step 1: The analysis unit analyzes the user's speech and identifies grammatical and pronunciation errors. The analysis unit acquires the user's speech as audio data and converts it into text data using speech recognition technology. Furthermore, the analysis unit detects grammatical errors based on grammatical rules and identifies pronunciation errors using phoneme analysis technology. Step 2: The generation unit creates a language learning curriculum based on the weaknesses and areas of difficulty identified by the analysis unit. The generation unit creates a curriculum that focuses on addressing the user's grammatical and pronunciation errors, and creates learning materials to address the user's weaknesses. Step 3: The update unit monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed. The update unit monitors the user's learning progress in real time and updates the curriculum according to the user's progress.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] Each of the multiple elements described above, including the analysis unit, generation unit, and update unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 38B of the smart device 14 to acquire the user's speech and the control unit 46A identifies grammatical and pronunciation errors. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and creates a language learning curriculum based on the identified weaknesses and areas of difficulty. The update unit is implemented in the identification processing unit 290 of the data processing unit 12 and monitors the user's progress and updates the curriculum as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0102] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] Each of the multiple elements described above, including the analysis unit, generation unit, and update unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the smart glasses 214 to acquire the user's speech and the control unit 46A identifies grammatical and pronunciation errors. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12 and creates a language learning curriculum based on the identified weaknesses and areas of difficulty. The update unit is implemented in the identification processing unit 290 of the data processing unit 12 and monitors the user's progress and updates the curriculum as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0118] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.).

[0130] 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.

[0131] 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.

[0132] 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.

[0133] Each of the multiple elements, including the analysis unit, generation unit, and update unit described above, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the headset terminal 314 to acquire the user's spoken content, and the control unit 46A identifies grammatical and pronunciation errors. The generation unit is implemented in the identification processing unit 290 of the data processing unit 12, and creates a language learning curriculum based on the identified weaknesses and areas of difficulty. The update unit is implemented in the identification processing unit 290 of the data processing unit 12, and monitors the user's progress and updates the curriculum as needed. The correspondence between each unit and the device or control unit is not limited to the example described above, and various changes are possible.

[0134] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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).

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] Each of the multiple elements described above, including the analysis unit, generation unit, and update unit, is implemented in, for example, at least one of the robot 414 and the data processing unit 12. For example, the analysis unit uses the camera 42 and microphone 238 of the robot 414 to acquire the user's speech and the control unit 46A identifies grammatical and pronunciation errors. The generation unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12 and creates a language learning curriculum based on the identified weaknesses and areas of difficulty. The update unit is implemented in, for example, the identification processing unit 290 of the data processing unit 12 and monitors the user's progress and updates the curriculum as needed. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] (Note 1) An analysis unit analyzes the user's speech to identify grammatical and pronunciation errors, A generation unit creates a language learning curriculum based on the weaknesses and areas of difficulty identified by the analysis unit, The system includes an update unit that monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed. A system characterized by the following features. (Note 2) The aforementioned analysis unit, The system analyzes the user's speech in detail to identify grammatical errors and pronunciation problems. The system described in Appendix 1, characterized by the features described herein. (Note 3) The generating unit is Based on identified weaknesses and areas of difficulty, we create a personalized language learning curriculum. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned update unit is, Monitor user progress and update the curriculum as needed. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate an infinite number of curricula and example problems. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned analysis unit, By analyzing the user's past statements and extracting specific patterns, we improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned analysis unit, When analyzing the content of a statement, errors are identified by considering the user's cultural background and native language. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned analysis unit, It estimates the user's emotions and adjusts the feedback method of 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, When analyzing the content of a user's speech, the system takes into account the user's geographical location to identify region-specific errors. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned analysis unit, When analyzing the content of a statement, the system analyzes the user's social media activity and identifies related errors. The system described in Appendix 1, characterized by the features described herein. (Note 12) The generating unit is The system estimates the user's emotions and adjusts the curriculum content based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 13) The generating unit is When creating a curriculum, select the format of the learning materials based on the user's learning style and preferences. The system described in Appendix 1, characterized by the features described herein. (Note 14) The generating unit is When creating a curriculum, the optimal learning sequence is determined by referring to the user's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 15) The generating unit is The system estimates the user's emotions and adjusts the difficulty level of the curriculum based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 16) The generating unit is When creating a curriculum, we propose a learning schedule based on the user's daily routine. The system described in Appendix 1, characterized by the features described herein. (Note 17) The generating unit is When creating the curriculum, include relevant topics based on the user's interests and concerns. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned update unit is The system estimates user sentiment and adjusts the frequency of curriculum updates based on the estimated user sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned update unit is During updates, the system analyzes the user's learning progress in detail and optimizes the next steps. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned update unit is When updating, we improve the curriculum by incorporating user feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned update unit is We estimate the user's sentiment and adjust how update notifications are sent based on that estimated sentiment. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned update unit is During updates, region-specific learning content will be added, taking into account the user's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned update unit is During updates, we analyze users' social media activity and add relevant learning content. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0170] 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. An analysis unit analyzes the user's speech to identify grammatical and pronunciation errors, A generation unit creates a language learning curriculum based on the weaknesses and areas of difficulty identified by the analysis unit, The system includes an update unit that monitors the user's progress based on the curriculum created by the generation unit and updates the curriculum as needed. A system characterized by the following features.

2. The aforementioned analysis unit, The system analyzes the user's speech in detail to identify grammatical errors and pronunciation problems. The system according to feature 1.

3. The generating unit is Based on identified weaknesses and areas of difficulty, we create a personalized language learning curriculum. The system according to feature 1.

4. The aforementioned update unit is, Monitor user progress and update the curriculum as needed. The system according to feature 1.

5. The generating unit is Generate an infinite number of curricula and example problems. The system according to feature 1.

6. The aforementioned analysis unit, It estimates the user's emotions and adjusts the accuracy of the analysis based on the estimated user emotions. The system according to feature 1.

7. The aforementioned analysis unit, By analyzing the user's past statements and extracting specific patterns, we improve the accuracy of the analysis. The system according to feature 1.

8. The aforementioned analysis unit, When analyzing the content of a statement, errors are identified by considering the user's cultural background and native language. The system according to feature 1.

9. The aforementioned analysis unit, It estimates the user's emotions and adjusts the feedback method of the analysis results based on the estimated user emotions. The system according to feature 1.

10. The aforementioned analysis unit, When analyzing the content of a user's speech, the system takes into account the user's geographical location to identify region-specific errors. The system according to feature 1.

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