system

Abstract

The system according to the embodiment aims to provide reference books and study methods suited to the user based on the content of the conversation. [Solution] A system according to an embodiment includes an analysis unit, an advice unit, and an evaluation unit. The analysis unit analyzes the content of the conversation. The advice unit advises the user on suitable study books or study methods based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the user's progress based on the advice provided by the advice unit.

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide individually appropriate reference books and study methods based on the content of conversations, and there is room for improvement.

[0005] The system according to the embodiment aims to provide reference books and study methods suited to the user based on the content of the conversation. [Means for solving the problem]

[0006] The system according to the embodiment includes an analysis unit, an advice unit, and an evaluation unit. The analysis unit analyzes the content of the conversation. The advice unit advises the user on suitable study books or study methods based on the analysis results obtained by the analysis unit. The evaluation unit evaluates the user's progress based on the advice provided by the advice unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide reference books and study methods suited to the user based on the content of the conversation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A system according to an embodiment of the present invention analyzes grammar, vocabulary, accent, and pronunciation through conversation, identifies issues, and suggests study methods and current levels. The system engages in conversations with users, analyzes the content, and identifies issues with grammar, vocabulary, accent, and pronunciation. The system then recommends appropriate study books and study methods. By registering the user, the system can provide advice on future levels and achievement. For example, when a user introduces themselves in English, the system inputs the content. The system then analyzes the conversation and identifies grammatical errors, vocabulary deficiencies, and accent and pronunciation problems. For example, if a user makes a grammatical error such as "I am an engineer" or if the user's pronunciation of "engineer" is incorrect, the system points out the error. The system then recommends appropriate study books and study methods. For example, if the user's grammar comprehension is low, the system recommends a grammar book. If the user's vocabulary is insufficient, the system recommends a study book to strengthen vocabulary. Furthermore, if the user has issues with accent or pronunciation, the system can recommend apps and materials for pronunciation practice. Furthermore, by registering the user, the system can provide advice on future levels and achievement. For example, by comparing the content of a previous conversation, the system evaluates whether grammatical errors have decreased or vocabulary has increased, and displays the level of achievement. This allows users to check their own progress as they study. This may make language classes unnecessary. Users can study at their own pace, improving their language skills efficiently. For example, being able to study without being tied to time or place is a major benefit for busy businessmen and students.

[0029] A language learning support system according to an embodiment includes an analysis unit, an advice unit, and an evaluation unit. The analysis unit analyzes the content of the conversation. For example, the analysis unit identifies grammatical errors, vocabulary deficiencies, and problems with accent and pronunciation. The analysis unit can also analyze the content of the conversation using natural language processing technology. For example, the analysis unit uses technologies such as text analysis, sentiment analysis, and keyword extraction. The advice unit advises the user on appropriate study books and study methods based on the analysis results obtained by the analysis unit. For example, if the user's level of grammar comprehension is low, the advice unit recommends a grammar book. If the user has a vocabulary deficiency, the advice unit recommends a study book to strengthen vocabulary. If the user has issues with accent or pronunciation, the advice unit can introduce apps or learning materials for pronunciation practice. The evaluation unit evaluates the user's progress based on the advice provided by the advice unit. For example, the evaluation unit evaluates whether the user's grammatical errors have decreased or vocabulary has increased compared to the previous conversation and presents the user's level of achievement. This allows the language learning support system according to an embodiment to analyze the content of the conversation, advise the user on appropriate study books and study methods, and evaluate the user's progress.

[0030] The language learning support system includes a speech recognition unit that uses speech recognition technology. The speech recognition unit accurately recognizes the content of a conversation. For example, the speech recognition unit can use speech recognition technology that uses deep learning. The speech recognition unit can also analyze speech using an acoustic model. For example, the speech recognition unit recognizes a user's speech in real time and converts it into text data. In this way, the speech recognition technology can accurately recognize the content of a conversation.

[0031] The language learning support system includes a natural language processing unit that uses natural language processing technology. The natural language processing unit accurately analyzes the content of a conversation. For example, the natural language processing unit can analyze the structure of a sentence using morphological analysis. The natural language processing unit can also identify grammatical errors using grammatical analysis. Furthermore, the natural language processing unit can understand the meaning of a sentence using semantic analysis. For example, the natural language processing unit analyzes the content of a user's conversation and identifies grammatical errors and vocabulary deficiencies. As a result, the content of a conversation can be accurately analyzed using natural language processing technology.

[0032] The language learning support system includes a data protection unit that protects user data. The data protection unit protects the user data. For example, the data protection unit can protect the data using encryption technology. The data protection unit can also restrict access to the data using access control. Furthermore, the data protection unit can protect the user's privacy using data anonymization technology. For example, the data protection unit encrypts the user's data and protects it from unauthorized access. In this way, privacy can be ensured by protecting the user data.

[0033] The language learning support system includes a feedback unit that reflects user feedback. The feedback unit reflects the user feedback. For example, the feedback unit can collect user opinions and reflect them in improving the system. The feedback unit can also analyze the user feedback and improve the accuracy of the system. Furthermore, the feedback unit can update the content of the next advice based on the user feedback. For example, the feedback unit collects user opinions and identifies areas for improvement in the system. In this way, the accuracy of the system can be improved by reflecting the user feedback.

[0034] The language learning support system includes an extension unit based on the scalability of the system. The extension unit takes into account the scalability of the system. For example, the extension unit may adopt a modularized design to make it easier to add new functions. The extension unit may also provide an API to facilitate linkage with other systems. Furthermore, the extension unit may take scalability into account and dynamically adjust resources according to the load on the system. For example, the extension unit may provide a module for adding new functions and extend the functions of the system. In this way, taking the scalability of the system into account makes it easier to add functions in the future.

[0035] When analyzing the content of a conversation, the analysis unit can optimize the analysis algorithm by referring to the user's past conversation history. For example, the analysis unit prioritizes analysis of grammar items that the user frequently made mistakes on in the past. The analysis unit can also analyze the frequency of use of specific vocabulary from the user's past conversation history to evaluate the level of comprehension. Furthermore, the analysis unit can refer to the user's past pronunciation problems and check whether the same problems have recurred. For example, the analysis unit retrieves the user's past conversation history from a database and optimizes the analysis algorithm. In this way, by referring to the past conversation history, the analysis algorithm can be optimized and accuracy can be improved.

[0036] When analyzing the content of a conversation, the analysis unit can apply different analysis methods depending on the user's language level. For example, the analysis unit may perform a basic grammar and vocabulary analysis on beginners, omitting detailed analysis of accent and pronunciation. The analysis unit may also perform a detailed grammar and vocabulary analysis on intermediate users, and point out problems with accent and pronunciation. Furthermore, the analysis unit may perform a detailed analysis of all elements on advanced users, and point out even minor errors. For example, the analysis unit may evaluate the user's language level and apply an analysis method according to that level. This allows for more appropriate analysis results to be provided by applying an analysis method according to the user's language level.

[0037] When analyzing the content of a conversation, the analysis unit can take into account the user's speaking speed and rhythm. For example, if the user speaks quickly, the analysis unit adjusts the accuracy of the analysis according to the speaking speed. Furthermore, if the user speaks slowly, the analysis unit can perform a detailed analysis and point out problems with the rhythm. Furthermore, if the user's speaking rhythm is irregular, the analysis unit can suggest areas for improvement in the rhythm. For example, the analysis unit measures the user's speaking speed and adjusts the analysis method based on that. In this way, by taking the user's speaking speed and rhythm into consideration, more accurate analysis results can be provided.

[0038] The analysis unit can perform analysis based on the user's geographical background when analyzing the content of a conversation. For example, if the user uses a dialect from a particular region, the analysis unit can analyze grammar and vocabulary based on that dialect. Furthermore, if the user comes from a different cultural region, the analysis unit can perform analysis taking into account expressions specific to that culture. Furthermore, the analysis unit can also identify specific accents or pronunciation issues based on the user's geographical background. For example, the analysis unit can obtain the user's geographical background from a database and adjust the analysis method based on that information. This allows the analysis unit to provide more appropriate analysis results by taking the user's geographical background into account.

[0039] When analyzing the conversation content, the analysis unit can evaluate the user's level of understanding of technical terms based on the user's field of expertise. For example, if the user is a medical expert, the analysis unit can evaluate the user's level of understanding of medical terms. If the user is a technical expert, the analysis unit can analyze the frequency of use and level of understanding of technical terms. Furthermore, if the user is a business expert, the analysis unit can evaluate the user's accurate use of business terms. For example, the analysis unit can evaluate the user's level of understanding of specific technical terms based on the user's field of expertise. This allows for more appropriate feedback to be provided by evaluating the user's level of understanding of technical terms based on the user's field of expertise.

[0040] When analyzing the content of a conversation, the analysis unit can perform an analysis based on the user's cultural background. For example, if the user is from a particular cultural region, the analysis unit analyzes grammar and vocabulary taking into account expressions unique to that culture. The analysis unit can also point out problems with specific accents or pronunciation based on the user's cultural background. Furthermore, the analysis unit can suggest appropriate study methods taking the user's cultural background into account. For example, the analysis unit can obtain the user's cultural background from a database and adjust the analysis method based on that information. This allows the analysis unit to provide more appropriate analysis results by taking the user's cultural background into account.

[0041] When providing advice, the advice unit can provide optimal advice by referring to the user's past study history. The advice unit can provide optimal advice based on, for example, reference books and learning materials used by the user in the past. The advice unit can also prioritize advice on specific problem areas based on the user's past study history. Furthermore, the advice unit can analyze the user's past study history and suggest effective study methods. For example, the advice unit can obtain the user's past study history from a database and optimize the advice content based on that. In this way, optimal advice can be provided by referring to the user's past study history.

[0042] When providing advice, the advice unit can apply different advice methods depending on the user's learning style. For example, if the user is a visual learner, the advice unit can provide advice using diagrams or illustrations. Furthermore, if the user is an auditory learner, the advice unit can provide advice using audio or video. Furthermore, if the user is an experiential learner, the advice unit can also suggest practical practice methods. For example, the advice unit evaluates the user's learning style and applies an advice method accordingly. In this way, more effective advice can be provided by applying an advice method according to the user's learning style.

[0043] When providing advice, the advice unit can customize the content of the advice based on the user's learning goals. For example, if the user wants to improve their TOEIC score, the advice unit can provide advice specialized for TOEIC preparation. Furthermore, if the user wants to improve their everyday conversation skills, the advice unit can provide advice useful for everyday conversation. Furthermore, if the user wants to learn business English, the advice unit can also provide advice specialized for business situations. For example, the advice unit evaluates the user's learning goals and customizes the content of the advice based on them. In this way, by customizing the content of the advice based on the user's learning goals, more effective advice can be provided.

[0044] When providing advice, the advice unit can suggest a study schedule based on the user's lifestyle rhythm. For example, if the user is busy, the advice unit can suggest a short and effective study method. Also, if the user has time, the advice unit can suggest a detailed study schedule. Furthermore, the advice unit can also suggest optimal study times that match the user's lifestyle rhythm. For example, the advice unit can evaluate the user's lifestyle rhythm and suggest a study schedule based on that. This allows for more effective study by suggesting a study schedule that matches the user's lifestyle rhythm.

[0045] When providing advice, the advice unit can recommend related learning resources based on the user's interests. For example, the advice unit can recommend reference books related to topics in which the user is interested. The advice unit can also recommend video and audio materials related to fields in which the user is interested. Furthermore, the advice unit can also suggest effective learning resources based on the user's interests. For example, the advice unit can evaluate the user's interests and recommend related learning resources based on the evaluation. This can increase the user's motivation to learn by recommending learning resources based on the user's interests.

[0046] When providing advice, the advice unit can update the advice content by reflecting user feedback. For example, if the user provides feedback in response to the provided advice, the advice unit updates the advice content based on that feedback. The advice unit can also analyze the user feedback and reflect it in the next advice. Furthermore, the advice unit can also improve the accuracy of the advice based on the user feedback. For example, the advice unit obtains the user feedback from a database and updates the advice content based on that. In this way, the accuracy of the advice can be improved by reflecting the user feedback.

[0047] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to the user's past learning outcomes. The evaluation unit, for example, adjusts the evaluation criteria based on the user's past learning outcomes. The evaluation unit can also prioritize evaluation of specific problem areas based on the user's past learning outcomes. Furthermore, the evaluation unit can analyze the user's past learning outcomes and optimize the evaluation algorithm. For example, the evaluation unit retrieves the user's past learning outcomes from a database and optimizes the evaluation algorithm based on them. In this way, by referring to the past learning outcomes, the evaluation algorithm can be optimized and accuracy can be improved.

[0048] The evaluation unit can apply different evaluation methods depending on the user's learning progress during evaluation. For example, the evaluation unit can apply a basic evaluation method to beginners and omit detailed evaluation. The evaluation unit can also apply a detailed evaluation method to intermediate students and focus on evaluating specific problem areas. Furthermore, the evaluation unit can evaluate all evaluation items in detail for advanced students and point out even minor errors. For example, the evaluation unit evaluates the user's learning progress and applies an evaluation method accordingly. In this way, by applying an evaluation method according to the user's learning progress, a more appropriate evaluation can be provided.

[0049] During evaluation, the evaluation unit can adjust the level of detail of the evaluation based on the user's learning goals. For example, if the user wants to improve their TOEIC score, the evaluation unit can perform a detailed evaluation based on the TOEIC evaluation criteria. Furthermore, if the user wants to improve their everyday conversation skills, the evaluation unit can perform an evaluation specialized for everyday conversation. Furthermore, if the user wants to learn business English, the evaluation unit can perform an evaluation specialized for business situations. For example, the evaluation unit evaluates the user's learning goals and adjusts the level of detail of the evaluation based on the evaluation. This allows for a more appropriate evaluation to be provided by adjusting the level of detail of the evaluation based on the user's learning goals.

[0050] The evaluation unit can adjust the frequency of evaluation based on the user's living situation when evaluating. For example, if the user is busy, the evaluation unit can reduce the frequency of evaluation and evaluate only the main evaluation items. Also, if the user has time, the evaluation unit can increase the frequency of evaluation and perform a detailed evaluation. Furthermore, the evaluation unit can also suggest an optimal frequency of evaluation depending on the user's living situation. For example, the evaluation unit can evaluate the user's living situation and adjust the frequency of evaluation based on that. In this way, by adjusting the frequency of evaluation according to the user's living situation, a more appropriate evaluation can be provided.

[0051] The evaluation unit can customize the evaluation method based on the user's learning environment during evaluation. For example, if the user is learning online, the evaluation unit can provide an evaluation method optimized for online learning. Furthermore, if the user is learning face-to-face, the evaluation unit can provide an evaluation method optimized for face-to-face learning. Furthermore, the evaluation unit can also propose an optimal evaluation method based on the user's learning environment. For example, the evaluation unit evaluates the user's learning environment and customizes the evaluation method based on the evaluation. This makes it possible to provide a more appropriate evaluation by providing an evaluation method based on the user's learning environment.

[0052] The evaluation unit can update the evaluation criteria by reflecting user feedback during evaluation. For example, if a user provides feedback on a provided evaluation, the evaluation unit updates the evaluation criteria based on that feedback. The evaluation unit can also analyze the user's feedback and reflect it in the next evaluation. Furthermore, the evaluation unit can also improve the accuracy of the evaluation criteria based on the user's feedback. For example, the evaluation unit obtains user feedback from a database and updates the evaluation criteria based on that. In this way, the accuracy of the evaluation criteria can be improved by reflecting user feedback.

[0053] During speech recognition, the speech recognition unit can optimize the recognition algorithm by referring to the user's speech patterns. The speech recognition unit optimizes the speech recognition algorithm based on, for example, speech patterns used by the user in the past. The speech recognition unit can also recognize specific accents or pronunciation problems from the user's speech patterns. Furthermore, the speech recognition unit can analyze the user's speech patterns and propose an optimal speech recognition algorithm. For example, the speech recognition unit retrieves the user's speech patterns from a database and optimizes the recognition algorithm based on them. In this way, the recognition algorithm can be optimized by referring to the user's speech patterns, thereby improving accuracy.

[0054] The speech recognition unit can apply a recognition method according to the user's speaking speed during speech recognition. For example, if the user speaks quickly, the speech recognition unit increases the speech recognition speed and recognizes the speech quickly. Also, if the user speaks slowly, the speech recognition unit can perform detailed speech recognition and reduce recognition errors. Furthermore, the speech recognition unit can also suggest an optimal speech recognition method according to the user's speaking speed. For example, the speech recognition unit measures the user's speaking speed and adjusts the recognition method based on that. This enables more accurate speech recognition by applying a recognition method according to the user's speaking speed.

[0055] The speech recognition unit can improve the recognition accuracy by taking into account the user's accent during speech recognition. For example, if the user has a specific accent, the speech recognition unit can improve the speech recognition accuracy based on that accent. The speech recognition unit can also analyze the user's accent and recognize specific pronunciation problems. Furthermore, the speech recognition unit can also suggest an optimal speech recognition algorithm based on the user's accent. For example, the speech recognition unit can obtain the user's accent from a database and improve the recognition accuracy based on that. In this way, the recognition accuracy can be improved by taking the user's accent into consideration.

[0056] The voice recognition unit can perform noise cancellation based on the user's speaking environment during voice recognition. For example, when the user is speaking in a noisy environment, the voice recognition unit performs noise cancellation to improve voice recognition accuracy. The voice recognition unit can also analyze the user's speaking environment and apply an optimal noise cancellation method. Furthermore, the voice recognition unit can adjust the strength of noise cancellation based on the user's speaking environment. For example, the voice recognition unit acquires the user's speaking environment from a database and performs noise cancellation based on that. In this way, noise cancellation based on the user's speaking environment can improve recognition accuracy.

[0057] During natural language processing, the natural language processing unit can optimize the processing algorithm by referring to the user's past conversation data. The natural language processing unit optimizes the natural language processing algorithm, for example, based on conversation data used by the user in the past. The natural language processing unit can also analyze the frequency of use of specific vocabulary and expressions from the user's past conversation data. Furthermore, the natural language processing unit can analyze the user's past conversation data and propose an optimal natural language processing algorithm. For example, the natural language processing unit obtains the user's past conversation data from a database and optimizes the processing algorithm based on that data. In this way, by referring to the past conversation data, the processing algorithm can be optimized and accuracy can be improved.

[0058] During natural language processing, the natural language processing unit can apply different processing methods depending on the user's language level. For example, the natural language processing unit applies basic natural language processing methods to beginners and omits detailed processing. The natural language processing unit can also apply detailed natural language processing methods to intermediate users and focus on processing specific issues. Furthermore, the natural language processing unit can process all processing items in detail for advanced users and point out even minor errors. For example, the natural language processing unit evaluates the user's language level and applies a processing method accordingly. In this way, more appropriate analysis results can be provided by applying a processing method according to the user's language level.

[0059] During natural language processing, the natural language processing unit can improve the accuracy of processing technical terms by taking into account the user's field of expertise. For example, if the user is a medical expert, the natural language processing unit can improve the accuracy of processing medical terms. Furthermore, if the user is a technical expert, the natural language processing unit can improve the accuracy of processing technical terms. Furthermore, if the user is a business expert, the natural language processing unit can also improve the accuracy of processing business terms. For example, the natural language processing unit acquires the user's field of expertise from a database and improves the accuracy of processing technical terms based on that. In this way, the accuracy of processing technical terms can be improved by taking the user's field of expertise into account.

[0060] The natural language processing unit can take into account the user's cultural background when processing natural language. For example, if the user is from a particular cultural region, the natural language processing unit performs natural language processing taking into account expressions specific to that culture. The natural language processing unit can also recognize problems with specific accents and pronunciation based on the user's cultural background. Furthermore, the natural language processing unit can also propose an appropriate natural language processing method taking into account the user's cultural background. For example, the natural language processing unit obtains the user's cultural background from a database and adjusts the processing method based on that information. This allows for more appropriate analysis results to be provided by taking the user's cultural background into account.

[0061] When protecting data, the data protection unit can optimize the protection algorithm by referring to the user's past data usage history. The data protection unit, for example, applies the optimal data protection algorithm based on the user's past data usage history. The data protection unit can also prioritize the protection of specific data based on the user's past data usage history. Furthermore, the data protection unit can analyze the user's past data usage history and propose the optimal data protection method. For example, the data protection unit obtains the user's past data usage history from a database and optimizes the protection algorithm based on that. In this way, the protection algorithm can be optimized and its accuracy improved by referring to the past data usage history.

[0062] When protecting data, the data protection unit can apply a protection method according to the user's data access frequency. For example, if the user accesses data frequently, the data protection unit performs data protection in real time. Also, if the user accesses data infrequently, the data protection unit can perform periodic data protection. Furthermore, the data protection unit can also propose an optimal data protection method according to the user's data access frequency. For example, the data protection unit obtains the user's data access frequency from a database and adjusts the protection method based on that. This enables more appropriate data protection by applying a protection method according to the user's data access frequency.

[0063] The data protection unit can adjust the protection level based on the user's geographical background when protecting data. For example, if the user is in a specific region, the data protection unit adjusts the protection level based on the data protection regulations of that region. The data protection unit can also analyze the user's geographical background and apply the optimal data protection method. Furthermore, the data protection unit can adjust the strength of data protection based on the user's geographical background. For example, the data protection unit obtains the user's geographical background from a database and adjusts the protection level based on that. This enables more appropriate data protection by taking the user's geographical background into consideration.

[0064] When protecting data, the data protection unit can customize the protection method by taking into account the user's device information. For example, if the user is using a smartphone, the data protection unit can provide a data protection method optimized for the smartphone. Also, if the user is using a tablet, the data protection unit can provide a data protection method optimized for the tablet. Furthermore, the data protection unit can also propose an optimal data protection method based on the user's device information. For example, the data protection unit obtains the user's device information from a database and adjusts the protection method based on that information. This enables more appropriate data protection by providing a protection method based on the user's device information.

[0065] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past feedback history. The feedback unit can provide optimal feedback, for example, based on the user's past feedback history. The feedback unit can also prioritize feedback on specific issues from the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and suggest effective feedback methods. For example, the feedback unit can obtain the user's past feedback history from a database and optimize the feedback content based on that. In this way, optimal feedback can be provided by referring to the past feedback history.

[0066] When providing feedback, the feedback unit can apply different feedback methods depending on the user's learning progress. For example, the feedback unit can apply a basic feedback method to beginners and omit detailed feedback. The feedback unit can also apply a detailed feedback method to intermediate learners and provide feedback that focuses on specific problem areas. Furthermore, the feedback unit can provide detailed feedback on all feedback items to advanced learners and point out even minor errors. For example, the feedback unit evaluates the user's learning progress and applies a feedback method accordingly. In this way, more appropriate feedback can be provided by applying a feedback method according to the user's learning progress.

[0067] When providing feedback, the feedback unit can adjust the frequency of feedback taking into account the user's lifestyle. For example, if the user is busy, the feedback unit can reduce the frequency of feedback and provide only major feedback items. Also, if the user has time, the feedback unit can increase the frequency of feedback and provide detailed feedback. Furthermore, the feedback unit can also suggest an optimal feedback frequency in accordance with the user's lifestyle. For example, the feedback unit can evaluate the user's lifestyle and adjust the feedback frequency based on that. This allows for more appropriate feedback to be provided by providing a feedback frequency that matches the user's lifestyle.

[0068] When providing feedback, the feedback unit can customize the feedback method based on the user's learning environment. For example, if the user is learning online, the feedback unit can provide a feedback method optimized for online learning. Also, if the user is learning face-to-face, the feedback unit can provide a feedback method optimized for face-to-face learning. Furthermore, the feedback unit can also suggest an optimal feedback method based on the user's learning environment. For example, the feedback unit evaluates the user's learning environment and customizes the feedback method based on the evaluation. This allows for more appropriate feedback to be provided by providing a feedback method based on the user's learning environment.

[0069] When expanding the system, the expansion unit can optimize the expansion algorithm by referring to the user's past usage history. The expansion unit, for example, applies the optimal expansion algorithm based on the user's past usage history. The expansion unit can also prioritize the expansion of specific functions based on the user's past usage history. Furthermore, the expansion unit can analyze the user's past usage history and propose the optimal expansion method. For example, the expansion unit obtains the user's past usage history from a database and optimizes the expansion algorithm based on that. In this way, the expansion algorithm can be optimized and its accuracy improved by referring to the past usage history.

[0070] The expansion unit can apply an expansion method according to the user's usage frequency when expanding the system. For example, if the user uses the system frequently, the expansion unit performs system expansion in real time. Also, if the user uses the system infrequently, the expansion unit can perform periodic system expansion. Furthermore, the expansion unit can also propose an optimal system expansion method according to the user's usage frequency. For example, the expansion unit obtains the user's usage frequency from a database and adjusts the expansion method based on that. This enables more appropriate system expansion by applying an expansion method according to the user's usage frequency.

[0071] The expansion unit can adjust the expansion method based on the user's geographical background when expanding the system. For example, if the user is in a specific area, the expansion unit adjusts the expansion method based on the needs of that area. The expansion unit can also analyze the user's geographical background and apply the optimal expansion method. Furthermore, the expansion unit can adjust the strength of the expansion based on the user's geographical background. For example, the expansion unit obtains the user's geographical background from a database and adjusts the expansion method based on the user's geographical background. This enables more appropriate system expansion by taking the user's geographical background into consideration.

[0072] The extension unit can customize the extension method by taking into account the user's device information when extending the system. For example, if the user is using a smartphone, the extension unit can provide an extension method optimized for the smartphone. Also, if the user is using a tablet, the extension unit can provide an extension method optimized for the tablet. Furthermore, the extension unit can also propose an optimal extension method based on the user's device information. For example, the extension unit obtains the user's device information from a database and adjusts the extension method based on that information. This enables more appropriate system extension by providing an extension method based on the user's device information.

[0073] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0074] When analyzing the content of a conversation, the analysis unit can optimize the analysis algorithm by referring to the user's past conversation history. For example, it can prioritize analysis of grammar items that the user frequently made mistakes on in the past. The analysis unit can also analyze the frequency of use of specific vocabulary from the user's past conversation history to evaluate the level of comprehension. Furthermore, the analysis unit can refer to the user's past pronunciation problems and check whether the same problems have recurred. For example, the analysis unit retrieves the user's past conversation history from a database and optimizes the analysis algorithm. By referring to the past conversation history, the analysis algorithm can be optimized and accuracy can be improved.

[0075] When providing advice, the advice unit can refer to the user's past study history to provide the most appropriate advice. For example, the advice unit can provide the most appropriate advice based on the reference books and learning materials the user has used in the past. The advice unit can also prioritize advice on specific problem areas based on the user's past study history. Furthermore, the advice unit can analyze the user's past study history and suggest effective study methods. For example, the advice unit can retrieve the user's past study history from a database and optimize the advice content based on that. In this way, the most appropriate advice can be provided by referring to the user's past study history.

[0076] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to the user's past learning outcomes. For example, the evaluation unit adjusts the evaluation criteria based on the user's past learning outcomes. The evaluation unit can also prioritize evaluation of specific problem areas based on the user's past learning outcomes. Furthermore, the evaluation unit can analyze the user's past learning outcomes and optimize the evaluation algorithm. For example, the evaluation unit retrieves the user's past learning outcomes from a database and optimizes the evaluation algorithm based on them. In this way, by referring to the past learning outcomes, the evaluation algorithm can be optimized and accuracy can be improved.

[0077] During speech recognition, the speech recognition unit can optimize the recognition algorithm by referring to the user's speech patterns. For example, the speech recognition algorithm is optimized based on the speech patterns used by the user in the past. The speech recognition unit can also recognize specific accents or pronunciation issues from the user's speech patterns. Furthermore, the speech recognition unit can analyze the user's speech patterns and propose an optimal speech recognition algorithm. For example, the speech recognition unit retrieves the user's speech patterns from a database and optimizes the recognition algorithm based on them. In this way, the recognition algorithm can be optimized by referring to the user's speech patterns, thereby improving accuracy.

[0078] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past feedback history. For example, optimal feedback can be provided based on the user's past feedback history. The feedback unit can also prioritize feedback on specific issues from the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and suggest effective feedback methods. For example, the feedback unit can obtain the user's past feedback history from a database and optimize the feedback content based on that. In this way, optimal feedback can be provided by referring to the past feedback history.

[0079] The processing flow of the first embodiment will be briefly explained below.

[0080] Step 1: The analysis unit analyzes the conversation content. For example, it identifies grammatical errors, vocabulary deficiencies, and problems with accent and pronunciation. It also uses natural language processing technology to perform text analysis, sentiment analysis, and keyword extraction. Step 2: The advice section advises the user on appropriate study books and methods based on the analysis results obtained by the analysis section. For example, if the user's level of grammar comprehension is low, it will recommend a grammar book, and if the user lacks vocabulary, it will recommend a study book to help improve vocabulary. Furthermore, if the user has issues with accent or pronunciation, it will introduce apps and study materials for pronunciation practice. Step 3: The evaluator evaluates the user's progress based on the advice provided by the advisor, for example, whether grammar errors have decreased or vocabulary has increased compared to the previous conversation, and presents the user's progress.

[0081] (Example 2) A system according to an embodiment of the present invention analyzes grammar, vocabulary, accent, and pronunciation through conversation, identifies issues, and suggests study methods and current levels. The system engages in conversations with users, analyzes the content, and identifies issues with grammar, vocabulary, accent, and pronunciation. The system then recommends appropriate study books and study methods. By registering the user, the system can provide advice on future levels and achievement. For example, when a user introduces themselves in English, the system inputs the content. The system then analyzes the conversation and identifies grammatical errors, vocabulary deficiencies, and accent and pronunciation problems. For example, if a user makes a grammatical error such as "I am an engineer" or if the user's pronunciation of "engineer" is incorrect, the system points out the error. The system then recommends appropriate study books and study methods. For example, if the user's grammar comprehension is low, the system recommends a grammar book. If the user's vocabulary is insufficient, the system recommends a study book to strengthen vocabulary. Furthermore, if the user has issues with accent or pronunciation, the system can recommend apps and materials for pronunciation practice. Furthermore, by registering the user, the system can provide advice on future levels and achievement. For example, by comparing the content of a previous conversation, the system evaluates whether grammatical errors have decreased or vocabulary has increased, and displays the level of achievement. This allows users to check their own progress as they study. This may make language classes unnecessary. Users can study at their own pace, improving their language skills efficiently. For example, being able to study without being tied to time or place is a major benefit for busy businessmen and students.

[0082] A language learning support system according to an embodiment includes an analysis unit, an advice unit, and an evaluation unit. The analysis unit analyzes the content of the conversation. For example, the analysis unit identifies grammatical errors, vocabulary deficiencies, and problems with accent and pronunciation. The analysis unit can also analyze the content of the conversation using natural language processing technology. For example, the analysis unit uses technologies such as text analysis, sentiment analysis, and keyword extraction. The advice unit advises the user on appropriate study books and study methods based on the analysis results obtained by the analysis unit. For example, if the user's level of grammar comprehension is low, the advice unit recommends a grammar book. If the user has a vocabulary deficiency, the advice unit recommends a study book to strengthen vocabulary. If the user has issues with accent or pronunciation, the advice unit can introduce apps or learning materials for pronunciation practice. The evaluation unit evaluates the user's progress based on the advice provided by the advice unit. For example, the evaluation unit evaluates whether the user's grammatical errors have decreased or vocabulary has increased compared to the previous conversation and presents the user's level of achievement. This allows the language learning support system according to an embodiment to analyze the content of the conversation, advise the user on appropriate study books and study methods, and evaluate the user's progress.

[0083] The language learning support system includes a speech recognition unit that uses speech recognition technology. The speech recognition unit accurately recognizes the content of a conversation. For example, the speech recognition unit can use speech recognition technology that uses deep learning. The speech recognition unit can also analyze speech using an acoustic model. For example, the speech recognition unit recognizes a user's speech in real time and converts it into text data. In this way, the speech recognition technology can accurately recognize the content of a conversation.

[0084] The language learning support system includes a natural language processing unit that uses natural language processing technology. The natural language processing unit accurately analyzes the content of a conversation. For example, the natural language processing unit can analyze the structure of a sentence using morphological analysis. The natural language processing unit can also identify grammatical errors using grammatical analysis. Furthermore, the natural language processing unit can understand the meaning of a sentence using semantic analysis. For example, the natural language processing unit analyzes the content of a user's conversation and identifies grammatical errors and vocabulary deficiencies. As a result, the content of a conversation can be accurately analyzed using natural language processing technology.

[0085] The language learning support system includes a data protection unit that protects user data. The data protection unit protects the user data. For example, the data protection unit can protect the data using encryption technology. The data protection unit can also restrict access to the data using access control. Furthermore, the data protection unit can protect the user's privacy using data anonymization technology. For example, the data protection unit encrypts the user's data and protects it from unauthorized access. In this way, privacy can be ensured by protecting the user data.

[0086] The language learning support system includes a feedback unit that reflects user feedback. The feedback unit reflects the user feedback. For example, the feedback unit can collect user opinions and reflect them in improving the system. The feedback unit can also analyze the user feedback and improve the accuracy of the system. Furthermore, the feedback unit can update the content of the next advice based on the user feedback. For example, the feedback unit collects user opinions and identifies areas for improvement in the system. In this way, the accuracy of the system can be improved by reflecting the user feedback.

[0087] The language learning support system includes an extension unit based on the scalability of the system. The extension unit takes into account the scalability of the system. For example, the extension unit may adopt a modularized design to make it easier to add new functions. The extension unit may also provide an API to facilitate linkage with other systems. Furthermore, the extension unit may take scalability into account and dynamically adjust resources according to the load on the system. For example, the extension unit may provide a module for adding new functions and extend the functions of the system. In this way, taking the scalability of the system into account makes it easier to add functions in the future.

[0088] The analysis unit can estimate the user's emotions and adjust the analysis accuracy of the conversation content based on the estimated user emotions. For example, if the user is nervous, the analysis unit can prioritize pointing out grammatical errors and reduce other elements during analysis. Furthermore, if the user is relaxed, the analysis unit can perform a detailed analysis of all grammar, vocabulary, accent, and pronunciation. Furthermore, if the user is in a hurry, the analysis unit can quickly analyze and present only the main issues. For example, the analysis unit can analyze the user's facial expressions to estimate emotions. This allows the analysis accuracy to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0089] When analyzing the content of a conversation, the analysis unit can optimize the analysis algorithm by referring to the user's past conversation history. For example, the analysis unit prioritizes analysis of grammar items that the user frequently made mistakes on in the past. The analysis unit can also analyze the frequency of use of specific vocabulary from the user's past conversation history to evaluate the level of comprehension. Furthermore, the analysis unit can refer to the user's past pronunciation problems and check whether the same problems have recurred. For example, the analysis unit retrieves the user's past conversation history from a database and optimizes the analysis algorithm. In this way, by referring to the past conversation history, the analysis algorithm can be optimized and accuracy can be improved.

[0090] When analyzing the content of a conversation, the analysis unit can apply different analysis methods depending on the user's language level. For example, the analysis unit may perform a basic grammar and vocabulary analysis on beginners, omitting detailed analysis of accent and pronunciation. The analysis unit may also perform a detailed grammar and vocabulary analysis on intermediate users, and point out problems with accent and pronunciation. Furthermore, the analysis unit may perform a detailed analysis of all elements on advanced users, and point out even minor errors. For example, the analysis unit may evaluate the user's language level and apply an analysis method according to that level. This allows for more appropriate analysis results to be provided by applying an analysis method according to the user's language level.

[0091] When analyzing the content of a conversation, the analysis unit can take into account the user's speaking speed and rhythm. For example, if the user speaks quickly, the analysis unit adjusts the accuracy of the analysis according to the speaking speed. Furthermore, if the user speaks slowly, the analysis unit can perform a detailed analysis and point out problems with the rhythm. Furthermore, if the user's speaking rhythm is irregular, the analysis unit can suggest areas for improvement in the rhythm. For example, the analysis unit measures the user's speaking speed and adjusts the analysis method based on that. In this way, by taking the user's speaking speed and rhythm into consideration, more accurate analysis results can be provided.

[0092] The analysis unit can estimate the user's emotions and adjust the display method of the analysis results based on the estimated user emotions. For example, if the user is nervous, the analysis unit can provide a simple, highly visible display method. If the user is relaxed, the analysis unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the analysis unit can also provide a display method that focuses on the main points. For example, the analysis unit can analyze the user's emotions and adjust the display method based on the analysis result. In this way, more appropriate feedback can be provided by adjusting the display method according to the user's emotions.

[0093] The analysis unit can perform analysis based on the user's geographical background when analyzing the content of a conversation. For example, if the user uses a dialect from a particular region, the analysis unit can analyze grammar and vocabulary based on that dialect. Furthermore, if the user comes from a different cultural region, the analysis unit can perform analysis taking into account expressions specific to that culture. Furthermore, the analysis unit can also identify specific accents or pronunciation issues based on the user's geographical background. For example, the analysis unit can obtain the user's geographical background from a database and adjust the analysis method based on that information. This allows the analysis unit to provide more appropriate analysis results by taking the user's geographical background into account.

[0094] When analyzing the conversation content, the analysis unit can evaluate the user's level of understanding of technical terms based on the user's field of expertise. For example, if the user is a medical expert, the analysis unit can evaluate the user's level of understanding of medical terms. If the user is a technical expert, the analysis unit can analyze the frequency of use and level of understanding of technical terms. Furthermore, if the user is a business expert, the analysis unit can evaluate the user's accurate use of business terms. For example, the analysis unit can evaluate the user's level of understanding of specific technical terms based on the user's field of expertise. This allows for more appropriate feedback to be provided by evaluating the user's level of understanding of technical terms based on the user's field of expertise.

[0095] When analyzing the content of a conversation, the analysis unit can perform an analysis based on the user's cultural background. For example, if the user is from a particular cultural region, the analysis unit analyzes grammar and vocabulary taking into account expressions unique to that culture. The analysis unit can also point out problems with specific accents or pronunciation based on the user's cultural background. Furthermore, the analysis unit can suggest appropriate study methods taking the user's cultural background into account. For example, the analysis unit can obtain the user's cultural background from a database and adjust the analysis method based on that information. This allows the analysis unit to provide more appropriate analysis results by taking the user's cultural background into account.

[0096] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is nervous, the advice unit can provide advice in gentle words. Furthermore, if the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is in a hurry, the advice unit can provide concise and to-the-point advice. For example, the advice unit analyzes the user's emotions and adjusts the way the advice is expressed based on the analysis. In this way, more appropriate advice can be provided by adjusting the way the advice is expressed according to the user's emotions.

[0097] When providing advice, the advice unit can provide optimal advice by referring to the user's past study history. The advice unit can provide optimal advice based on, for example, reference books and learning materials used by the user in the past. The advice unit can also prioritize advice on specific problem areas based on the user's past study history. Furthermore, the advice unit can analyze the user's past study history and suggest effective study methods. For example, the advice unit can obtain the user's past study history from a database and optimize the advice content based on that. In this way, optimal advice can be provided by referring to the user's past study history.

[0098] When providing advice, the advice unit can apply different advice methods depending on the user's learning style. For example, if the user is a visual learner, the advice unit can provide advice using diagrams or illustrations. Furthermore, if the user is an auditory learner, the advice unit can provide advice using audio or video. Furthermore, if the user is an experiential learner, the advice unit can also suggest practical practice methods. For example, the advice unit evaluates the user's learning style and applies an advice method accordingly. In this way, more effective advice can be provided by applying an advice method according to the user's learning style.

[0099] When providing advice, the advice unit can customize the content of the advice based on the user's learning goals. For example, if the user wants to improve their TOEIC score, the advice unit can provide advice specialized for TOEIC preparation. Furthermore, if the user wants to improve their everyday conversation skills, the advice unit can provide advice useful for everyday conversation. Furthermore, if the user wants to learn business English, the advice unit can also provide advice specialized for business situations. For example, the advice unit evaluates the user's learning goals and customizes the content of the advice based on them. In this way, by customizing the content of the advice based on the user's learning goals, more effective advice can be provided.

[0100] The advice unit can estimate the user's emotions and determine the priority of advice based on the estimated user's emotions. For example, if the user is nervous, the advice unit can prioritize advice on the most important issues. Furthermore, if the user is relaxed, the advice unit can provide detailed advice on all issues. Furthermore, if the user is in a hurry, the advice unit can quickly provide advice on only the main issues. For example, the advice unit analyzes the user's emotions and determines the priority of advice based on the analysis. In this way, more appropriate advice can be provided by determining the priority of advice according to the user's emotions.

[0101] When providing advice, the advice unit can suggest a study schedule based on the user's lifestyle rhythm. For example, if the user is busy, the advice unit can suggest a short and effective study method. Also, if the user has time, the advice unit can suggest a detailed study schedule. Furthermore, the advice unit can also suggest optimal study times that match the user's lifestyle rhythm. For example, the advice unit can evaluate the user's lifestyle rhythm and suggest a study schedule based on that. This allows for more effective study by suggesting a study schedule that matches the user's lifestyle rhythm.

[0102] When providing advice, the advice unit can recommend related learning resources based on the user's interests. For example, the advice unit can recommend reference books related to topics in which the user is interested. The advice unit can also recommend video and audio materials related to fields in which the user is interested. Furthermore, the advice unit can also suggest effective learning resources based on the user's interests. For example, the advice unit can evaluate the user's interests and recommend related learning resources based on the evaluation. This can increase the user's motivation to learn by recommending learning resources based on the user's interests.

[0103] When providing advice, the advice unit can update the advice content by reflecting user feedback. For example, if the user provides feedback in response to the provided advice, the advice unit updates the advice content based on that feedback. The advice unit can also analyze the user feedback and reflect it in the next advice. Furthermore, the advice unit can also improve the accuracy of the advice based on the user feedback. For example, the advice unit obtains the user feedback from a database and updates the advice content based on that. In this way, the accuracy of the advice can be improved by reflecting the user feedback.

[0104] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, if the user is nervous, the evaluation unit can relax the evaluation criteria and prioritize positive feedback. Also, if the user is relaxed, the evaluation unit can apply strict evaluation criteria. Furthermore, if the user is in a hurry, the evaluation unit can quickly evaluate only the main evaluation items. For example, the evaluation unit analyzes the user's emotions and adjusts the evaluation criteria based on the analysis. This allows for a more appropriate evaluation to be provided by adjusting the evaluation criteria according to the user's emotions.

[0105] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to the user's past learning outcomes. The evaluation unit, for example, adjusts the evaluation criteria based on the user's past learning outcomes. The evaluation unit can also prioritize evaluation of specific problem areas based on the user's past learning outcomes. Furthermore, the evaluation unit can analyze the user's past learning outcomes and optimize the evaluation algorithm. For example, the evaluation unit retrieves the user's past learning outcomes from a database and optimizes the evaluation algorithm based on them. In this way, by referring to the past learning outcomes, the evaluation algorithm can be optimized and accuracy can be improved.

[0106] The evaluation unit can apply different evaluation methods depending on the user's learning progress during evaluation. For example, the evaluation unit can apply a basic evaluation method to beginners and omit detailed evaluation. The evaluation unit can also apply a detailed evaluation method to intermediate students and focus on evaluating specific problem areas. Furthermore, the evaluation unit can evaluate all evaluation items in detail for advanced students and point out even minor errors. For example, the evaluation unit evaluates the user's learning progress and applies an evaluation method accordingly. In this way, by applying an evaluation method according to the user's learning progress, a more appropriate evaluation can be provided.

[0107] During evaluation, the evaluation unit can adjust the level of detail of the evaluation based on the user's learning goals. For example, if the user wants to improve their TOEIC score, the evaluation unit can perform a detailed evaluation based on the TOEIC evaluation criteria. Furthermore, if the user wants to improve their everyday conversation skills, the evaluation unit can perform an evaluation specialized for everyday conversation. Furthermore, if the user wants to learn business English, the evaluation unit can perform an evaluation specialized for business situations. For example, the evaluation unit evaluates the user's learning goals and adjusts the level of detail of the evaluation based on the evaluation. This allows for a more appropriate evaluation to be provided by adjusting the level of detail of the evaluation based on the user's learning goals.

[0108] The evaluation unit can estimate the user's emotions and adjust the display method of the evaluation results based on the estimated user emotions. For example, if the user is nervous, the evaluation unit can provide a simple, highly visible display method. If the user is relaxed, the evaluation unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the evaluation unit can also provide a display method that focuses on the main points. For example, the evaluation unit analyzes the user's emotions and adjusts the display method based on the analysis. This makes it possible to provide more appropriate feedback by adjusting the display method according to the user's emotions.

[0109] The evaluation unit can adjust the frequency of evaluation based on the user's living situation when evaluating. For example, if the user is busy, the evaluation unit can reduce the frequency of evaluation and evaluate only the main evaluation items. Also, if the user has time, the evaluation unit can increase the frequency of evaluation and perform a detailed evaluation. Furthermore, the evaluation unit can also suggest an optimal frequency of evaluation depending on the user's living situation. For example, the evaluation unit can evaluate the user's living situation and adjust the frequency of evaluation based on that. In this way, by adjusting the frequency of evaluation according to the user's living situation, a more appropriate evaluation can be provided.

[0110] The evaluation unit can customize the evaluation method based on the user's learning environment during evaluation. For example, if the user is learning online, the evaluation unit can provide an evaluation method optimized for online learning. Furthermore, if the user is learning face-to-face, the evaluation unit can provide an evaluation method optimized for face-to-face learning. Furthermore, the evaluation unit can also propose an optimal evaluation method based on the user's learning environment. For example, the evaluation unit evaluates the user's learning environment and customizes the evaluation method based on the evaluation. This makes it possible to provide a more appropriate evaluation by providing an evaluation method based on the user's learning environment.

[0111] The evaluation unit can update the evaluation criteria by reflecting user feedback during evaluation. For example, if a user provides feedback on a provided evaluation, the evaluation unit updates the evaluation criteria based on that feedback. The evaluation unit can also analyze the user's feedback and reflect it in the next evaluation. Furthermore, the evaluation unit can also improve the accuracy of the evaluation criteria based on the user's feedback. For example, the evaluation unit obtains user feedback from a database and updates the evaluation criteria based on that. In this way, the accuracy of the evaluation criteria can be improved by reflecting user feedback.

[0112] The voice recognition unit can estimate the user's emotions and adjust the accuracy of voice recognition based on the estimated user's emotions. For example, if the user is nervous, the voice recognition unit can increase the accuracy of voice recognition and reduce erroneous recognition. Furthermore, if the user is relaxed, the voice recognition unit can perform analysis with normal voice recognition accuracy. Furthermore, if the user is in a hurry, the voice recognition unit can quickly recognize voice and prioritize recognition of the main content. For example, the voice recognition unit can analyze the user's emotions and adjust the accuracy of voice recognition based on the analysis. In this way, more accurate voice recognition is possible by adjusting the accuracy of voice recognition according to the user's emotions.

[0113] During speech recognition, the speech recognition unit can optimize the recognition algorithm by referring to the user's speech patterns. The speech recognition unit optimizes the speech recognition algorithm based on, for example, speech patterns used by the user in the past. The speech recognition unit can also recognize specific accents or pronunciation problems from the user's speech patterns. Furthermore, the speech recognition unit can analyze the user's speech patterns and propose an optimal speech recognition algorithm. For example, the speech recognition unit retrieves the user's speech patterns from a database and optimizes the recognition algorithm based on them. In this way, the recognition algorithm can be optimized by referring to the user's speech patterns, thereby improving accuracy.

[0114] The speech recognition unit can apply a recognition method according to the user's speaking speed during speech recognition. For example, if the user speaks quickly, the speech recognition unit increases the speech recognition speed and recognizes the speech quickly. Also, if the user speaks slowly, the speech recognition unit can perform detailed speech recognition and reduce recognition errors. Furthermore, the speech recognition unit can also suggest an optimal speech recognition method according to the user's speaking speed. For example, the speech recognition unit measures the user's speaking speed and adjusts the recognition method based on that. This enables more accurate speech recognition by applying a recognition method according to the user's speaking speed.

[0115] The speech recognition unit can estimate the user's emotions and adjust the display method of the speech recognition results based on the estimated user's emotions. For example, if the user is nervous, the speech recognition unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the speech recognition unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the speech recognition unit can also provide a display method that focuses on the main points. For example, the speech recognition unit analyzes the user's emotions and adjusts the display method based on the analysis. In this way, more appropriate feedback can be provided by adjusting the display method according to the user's emotions.

[0116] The speech recognition unit can improve the recognition accuracy by taking into account the user's accent during speech recognition. For example, if the user has a specific accent, the speech recognition unit can improve the speech recognition accuracy based on that accent. The speech recognition unit can also analyze the user's accent and recognize specific pronunciation problems. Furthermore, the speech recognition unit can also suggest an optimal speech recognition algorithm based on the user's accent. For example, the speech recognition unit can obtain the user's accent from a database and improve the recognition accuracy based on that. In this way, the recognition accuracy can be improved by taking the user's accent into consideration.

[0117] The voice recognition unit can perform noise cancellation based on the user's speaking environment during voice recognition. For example, when the user is speaking in a noisy environment, the voice recognition unit performs noise cancellation to improve voice recognition accuracy. The voice recognition unit can also analyze the user's speaking environment and apply an optimal noise cancellation method. Furthermore, the voice recognition unit can adjust the strength of noise cancellation based on the user's speaking environment. For example, the voice recognition unit acquires the user's speaking environment from a database and performs noise cancellation based on that. In this way, noise cancellation based on the user's speaking environment can improve recognition accuracy.

[0118] The natural language processing unit can estimate the user's emotions and adjust the accuracy of the natural language processing based on the estimated user's emotions. For example, if the user is nervous, the natural language processing unit increases the accuracy of the natural language processing and reduces misrecognition. Furthermore, if the user is relaxed, the natural language processing unit can perform analysis with normal natural language processing accuracy. Furthermore, if the user is in a hurry, the natural language processing unit can quickly perform natural language processing and prioritize recognition of key content. For example, the natural language processing unit analyzes the user's emotions and adjusts the accuracy of the natural language processing based on the analysis. This allows for more accurate analysis by adjusting the accuracy of the natural language processing according to the user's emotions.

[0119] During natural language processing, the natural language processing unit can optimize the processing algorithm by referring to the user's past conversation data. The natural language processing unit optimizes the natural language processing algorithm, for example, based on conversation data used by the user in the past. The natural language processing unit can also analyze the frequency of use of specific vocabulary and expressions from the user's past conversation data. Furthermore, the natural language processing unit can analyze the user's past conversation data and propose an optimal natural language processing algorithm. For example, the natural language processing unit obtains the user's past conversation data from a database and optimizes the processing algorithm based on that data. In this way, by referring to the past conversation data, the processing algorithm can be optimized and accuracy can be improved.

[0120] During natural language processing, the natural language processing unit can apply different processing methods depending on the user's language level. For example, the natural language processing unit applies basic natural language processing methods to beginners and omits detailed processing. The natural language processing unit can also apply detailed natural language processing methods to intermediate users and focus on processing specific issues. Furthermore, the natural language processing unit can process all processing items in detail for advanced users and point out even minor errors. For example, the natural language processing unit evaluates the user's language level and applies a processing method accordingly. In this way, more appropriate analysis results can be provided by applying a processing method according to the user's language level.

[0121] The natural language processing unit can estimate the user's emotions and adjust the display method of the natural language processing results based on the estimated user's emotions. For example, if the user is nervous, the natural language processing unit can provide a simple, highly visible display method. Furthermore, if the user is relaxed, the natural language processing unit can provide a display method that includes detailed information. Furthermore, if the user is in a hurry, the natural language processing unit can also provide a display method that focuses on the main points. For example, the natural language processing unit analyzes the user's emotions and adjusts the display method based on the analysis. In this way, more appropriate feedback can be provided by adjusting the display method according to the user's emotions.

[0122] During natural language processing, the natural language processing unit can improve the accuracy of processing technical terms by taking into account the user's field of expertise. For example, if the user is a medical expert, the natural language processing unit can improve the accuracy of processing medical terms. Furthermore, if the user is a technical expert, the natural language processing unit can improve the accuracy of processing technical terms. Furthermore, if the user is a business expert, the natural language processing unit can also improve the accuracy of processing business terms. For example, the natural language processing unit acquires the user's field of expertise from a database and improves the accuracy of processing technical terms based on that. In this way, the accuracy of processing technical terms can be improved by taking the user's field of expertise into account.

[0123] The natural language processing unit can take into account the user's cultural background when processing natural language. For example, if the user is from a particular cultural region, the natural language processing unit performs natural language processing taking into account expressions specific to that culture. The natural language processing unit can also recognize problems with specific accents and pronunciation based on the user's cultural background. Furthermore, the natural language processing unit can also propose an appropriate natural language processing method taking into account the user's cultural background. For example, the natural language processing unit obtains the user's cultural background from a database and adjusts the processing method based on that information. This allows for more appropriate analysis results to be provided by taking the user's cultural background into account.

[0124] The data protection unit can estimate the user's emotions and adjust the level of data protection based on the estimated user's emotions. For example, if the user is nervous, the data protection unit can increase the level of data protection to provide a sense of security. Also, if the user is relaxed, the data protection unit can operate at a normal data protection level. Furthermore, if the user is in a hurry, the data protection unit can quickly protect data and prioritize the protection of important data. For example, the data protection unit can analyze the user's emotions and adjust the level of data protection based on the analysis. In this way, the level of data protection can be adjusted according to the user's emotions to provide a greater sense of security.

[0125] When protecting data, the data protection unit can optimize the protection algorithm by referring to the user's past data usage history. The data protection unit, for example, applies the optimal data protection algorithm based on the user's past data usage history. The data protection unit can also prioritize the protection of specific data based on the user's past data usage history. Furthermore, the data protection unit can analyze the user's past data usage history and propose the optimal data protection method. For example, the data protection unit obtains the user's past data usage history from a database and optimizes the protection algorithm based on that. In this way, the protection algorithm can be optimized and its accuracy improved by referring to the past data usage history.

[0126] When protecting data, the data protection unit can apply a protection method according to the user's data access frequency. For example, if the user accesses data frequently, the data protection unit performs data protection in real time. Also, if the user accesses data infrequently, the data protection unit can perform periodic data protection. Furthermore, the data protection unit can also propose an optimal data protection method according to the user's data access frequency. For example, the data protection unit obtains the user's data access frequency from a database and adjusts the protection method based on that. This enables more appropriate data protection by applying a protection method according to the user's data access frequency.

[0127] The data protection unit can estimate the user's emotions and determine the priority of data protection based on the estimated user's emotions. For example, if the user is nervous, the data protection unit prioritizes protection of the most important data. Also, if the user is relaxed, the data protection unit can protect all data equally. Furthermore, if the user is in a hurry, the data protection unit can quickly protect only the most important data. For example, the data protection unit analyzes the user's emotions and determines the priority of data protection based on the analysis. This enables more appropriate data protection by determining the priority of data protection according to the user's emotions.

[0128] The data protection unit can adjust the protection level based on the user's geographical background when protecting data. For example, if the user is in a specific region, the data protection unit adjusts the protection level based on the data protection regulations of that region. The data protection unit can also analyze the user's geographical background and apply the optimal data protection method. Furthermore, the data protection unit can adjust the strength of data protection based on the user's geographical background. For example, the data protection unit obtains the user's geographical background from a database and adjusts the protection level based on that. This enables more appropriate data protection by taking the user's geographical background into consideration.

[0129] When protecting data, the data protection unit can customize the protection method by taking into account the user's device information. For example, if the user is using a smartphone, the data protection unit can provide a data protection method optimized for the smartphone. Also, if the user is using a tablet, the data protection unit can provide a data protection method optimized for the tablet. Furthermore, the data protection unit can also propose an optimal data protection method based on the user's device information. For example, the data protection unit obtains the user's device information from a database and adjusts the protection method based on that information. This enables more appropriate data protection by providing a protection method based on the user's device information.

[0130] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide positive feedback preferentially. Also, if the user is relaxed, the feedback unit can provide detailed feedback. Furthermore, if the user is in a hurry, the feedback unit can quickly provide only key feedback. For example, the feedback unit analyzes the user's emotions and adjusts the content of the feedback based on the analysis. In this way, more appropriate feedback can be provided by adjusting the content of the feedback according to the user's emotions.

[0131] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past feedback history. The feedback unit can provide optimal feedback, for example, based on the user's past feedback history. The feedback unit can also prioritize feedback on specific issues from the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and suggest effective feedback methods. For example, the feedback unit can obtain the user's past feedback history from a database and optimize the feedback content based on that. In this way, optimal feedback can be provided by referring to the past feedback history.

[0132] When providing feedback, the feedback unit can apply different feedback methods depending on the user's learning progress. For example, the feedback unit can apply a basic feedback method to beginners and omit detailed feedback. The feedback unit can also apply a detailed feedback method to intermediate learners and provide feedback that focuses on specific problem areas. Furthermore, the feedback unit can provide detailed feedback on all feedback items to advanced learners and point out even minor errors. For example, the feedback unit evaluates the user's learning progress and applies a feedback method accordingly. In this way, more appropriate feedback can be provided by applying a feedback method according to the user's learning progress.

[0133] The feedback unit can estimate the user's emotions and determine the priority of feedback based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can prioritize feedback on the most important issues. Also, if the user is relaxed, the feedback unit can provide detailed feedback on all issues. Furthermore, if the user is in a hurry, the feedback unit can quickly provide feedback on only the main issues. For example, the feedback unit can analyze the user's emotions and determine the priority of feedback based on the analysis. In this way, more appropriate feedback can be provided by determining the priority of feedback according to the user's emotions.

[0134] When providing feedback, the feedback unit can adjust the frequency of feedback taking into account the user's lifestyle. For example, if the user is busy, the feedback unit can reduce the frequency of feedback and provide only major feedback items. Also, if the user has time, the feedback unit can increase the frequency of feedback and provide detailed feedback. Furthermore, the feedback unit can also suggest an optimal feedback frequency in accordance with the user's lifestyle. For example, the feedback unit can evaluate the user's lifestyle and adjust the feedback frequency based on that. This allows for more appropriate feedback to be provided by providing a feedback frequency that matches the user's lifestyle.

[0135] When providing feedback, the feedback unit can customize the feedback method based on the user's learning environment. For example, if the user is learning online, the feedback unit can provide a feedback method optimized for online learning. Also, if the user is learning face-to-face, the feedback unit can provide a feedback method optimized for face-to-face learning. Furthermore, the feedback unit can also suggest an optimal feedback method based on the user's learning environment. For example, the feedback unit evaluates the user's learning environment and customizes the feedback method based on the evaluation. This allows for more appropriate feedback to be provided by providing a feedback method based on the user's learning environment.

[0136] The extension unit can estimate the user's emotions and adjust the system extension method based on the estimated user emotions. For example, if the user is nervous, the extension unit can gradually extend the system to reduce the user's burden. Also, if the user is relaxed, the extension unit can extend the system all at once to provide all new functions. Furthermore, if the user is in a hurry, the extension unit can quickly provide only the main extension items. For example, the extension unit can analyze the user's emotions and adjust the extension method based on them. This allows for more appropriate extension by adjusting the system extension method according to the user's emotions.

[0137] When expanding the system, the expansion unit can optimize the expansion algorithm by referring to the user's past usage history. The expansion unit, for example, applies the optimal expansion algorithm based on the user's past usage history. The expansion unit can also prioritize the expansion of specific functions based on the user's past usage history. Furthermore, the expansion unit can analyze the user's past usage history and propose the optimal expansion method. For example, the expansion unit obtains the user's past usage history from a database and optimizes the expansion algorithm based on that. In this way, the expansion algorithm can be optimized and its accuracy improved by referring to the past usage history.

[0138] The expansion unit can apply an expansion method according to the user's usage frequency when expanding the system. For example, if the user uses the system frequently, the expansion unit performs system expansion in real time. Also, if the user uses the system infrequently, the expansion unit can perform periodic system expansion. Furthermore, the expansion unit can also propose an optimal system expansion method according to the user's usage frequency. For example, the expansion unit obtains the user's usage frequency from a database and adjusts the expansion method based on that. This enables more appropriate system expansion by applying an expansion method according to the user's usage frequency.

[0139] The extension unit can estimate the user's emotions and determine the priority of extensions based on the estimated user's emotions. For example, if the user is nervous, the extension unit can provide the most important extension items with priority. Also, if the user is relaxed, the extension unit can provide all extension items equally. Furthermore, if the user is in a hurry, the extension unit can quickly provide only the most important extension items. For example, the extension unit can analyze the user's emotions and determine the priority of extensions based on the analysis. This enables more appropriate extensions by determining the priority of extensions according to the user's emotions.

[0140] The expansion unit can adjust the expansion method based on the user's geographical background when expanding the system. For example, if the user is in a specific area, the expansion unit adjusts the expansion method based on the needs of that area. The expansion unit can also analyze the user's geographical background and apply the optimal expansion method. Furthermore, the expansion unit can adjust the strength of the expansion based on the user's geographical background. For example, the expansion unit obtains the user's geographical background from a database and adjusts the expansion method based on the user's geographical background. This enables more appropriate system expansion by taking the user's geographical background into consideration.

[0141] The extension unit can customize the extension method by taking into account the user's device information when extending the system. For example, if the user is using a smartphone, the extension unit can provide an extension method optimized for the smartphone. Also, if the user is using a tablet, the extension unit can provide an extension method optimized for the tablet. Furthermore, the extension unit can also propose an optimal extension method based on the user's device information. For example, the extension unit obtains the user's device information from a database and adjusts the extension method based on that information. This enables more appropriate system extension by providing an extension method based on the user's device information. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned analysis unit, advice unit, evaluation unit, voice recognition unit, natural language processing unit, data protection unit, feedback unit, and expansion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The advice unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The voice recognition unit is realized, for example, by the microphone 38B and control unit 46A of the smart device 14. The natural language processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The data protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the smart device 14 or the specific processing unit 290 of the data processing device 12. The extension unit is realized by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned analysis unit, advice unit, evaluation unit, voice recognition unit, natural language processing unit, data protection unit, feedback unit, and expansion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The advice unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The voice recognition unit is realized, for example, by the microphone 238 and control unit 46A of the smart glasses 214. The natural language processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The data protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214 or the specific processing unit 290 of the data processing device 12. The extension unit is realized by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned analysis unit, advice unit, evaluation unit, voice recognition unit, natural language processing unit, data protection unit, feedback unit, and expansion unit is realized, for example, by at least one of the headset type terminal 314 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The advice unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The voice recognition unit is realized, for example, by the microphone 238 and control unit 46A of the headset type terminal 314. The natural language processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The data protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the headset type terminal 314 or the specific processing unit 290 of the data processing device 12. The extension unit is realized by, for example, the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned analysis unit, advice unit, evaluation unit, voice recognition unit, natural language processing unit, data protection unit, feedback unit, and expansion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the analysis unit is realized by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The advice unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The evaluation unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The voice recognition unit is realized, for example, by the microphone 238 and control unit 46A of the robot 414. The natural language processing unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The data protection unit is realized, for example, by the specific processing unit 290 of the data processing device 12. The feedback unit is realized, for example, by the control unit 46A of the robot 414 or the specific processing unit 290 of the data processing device 12. The extension unit is realized by, for example, the specific processing unit 290 of the data processing device 12.

[0142] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0143] The analysis unit can estimate the user's emotions and adjust the analysis accuracy of the conversation content based on the estimated user emotions. For example, if the user is nervous, it can prioritize pointing out grammatical errors and reduce the analysis of other elements. The analysis unit can also perform a detailed analysis of all grammar, vocabulary, accent, and pronunciation if the user is relaxed. Furthermore, if the user is in a hurry, the analysis unit can quickly analyze and present only the main issues. For example, the analysis unit can analyze the user's facial expressions to estimate emotions. This allows the analysis accuracy to be adjusted according to the user's emotions, thereby providing more appropriate analysis results.

[0144] The advice unit can estimate the user's emotions and adjust the way the advice is expressed based on the estimated user's emotions. For example, if the user is nervous, the advice unit can provide advice in gentle words. Furthermore, if the user is relaxed, the advice unit can provide detailed advice. Furthermore, if the user is in a hurry, the advice unit can provide concise and to-the-point advice. For example, the advice unit can analyze the user's emotions and adjust the way the advice is expressed based on the analysis. In this way, more appropriate advice can be provided by adjusting the way the advice is expressed according to the user's emotions.

[0145] The evaluation unit can estimate the user's emotions and adjust the evaluation criteria based on the estimated user's emotions. For example, if the user is nervous, the evaluation unit can relax the evaluation criteria and prioritize positive feedback. The evaluation unit can also apply strict evaluation criteria when the user is relaxed. Furthermore, if the user is in a hurry, the evaluation unit can quickly evaluate only the main evaluation items. For example, the evaluation unit analyzes the user's emotions and adjusts the evaluation criteria based on the analysis. This allows the evaluation unit to provide a more appropriate evaluation by adjusting the evaluation criteria according to the user's emotions.

[0146] The voice recognition unit can estimate the user's emotions and adjust the accuracy of voice recognition based on the estimated user's emotions. For example, if the user is nervous, the voice recognition unit can increase the accuracy of voice recognition and reduce misrecognition. Also, if the user is relaxed, the voice recognition unit can perform analysis with normal voice recognition accuracy. Furthermore, if the user is in a hurry, the voice recognition unit can quickly recognize voice and prioritize recognition of the main content. For example, the voice recognition unit can analyze the user's emotions and adjust the accuracy of voice recognition based on that. This allows for more accurate voice recognition by adjusting the accuracy of voice recognition according to the user's emotions.

[0147] The feedback unit can estimate the user's emotions and adjust the content of the feedback based on the estimated user's emotions. For example, if the user is nervous, positive feedback can be provided preferentially. Furthermore, the feedback unit can provide detailed feedback if the user is relaxed. Furthermore, if the user is in a hurry, the feedback unit can quickly provide only key feedback. For example, the feedback unit analyzes the user's emotions and adjusts the content of the feedback based on the analysis. In this way, more appropriate feedback can be provided by adjusting the content of the feedback according to the user's emotions.

[0148] When analyzing the content of a conversation, the analysis unit can optimize the analysis algorithm by referring to the user's past conversation history. For example, it can prioritize analysis of grammar items that the user frequently made mistakes on in the past. The analysis unit can also analyze the frequency of use of specific vocabulary from the user's past conversation history to evaluate the level of comprehension. Furthermore, the analysis unit can refer to the user's past pronunciation problems and check whether the same problems have recurred. For example, the analysis unit retrieves the user's past conversation history from a database and optimizes the analysis algorithm. By referring to the past conversation history, the analysis algorithm can be optimized and accuracy can be improved.

[0149] When providing advice, the advice unit can refer to the user's past study history to provide the most appropriate advice. For example, the advice unit can provide the most appropriate advice based on the reference books and learning materials the user has used in the past. The advice unit can also prioritize advice on specific problem areas based on the user's past study history. Furthermore, the advice unit can analyze the user's past study history and suggest effective study methods. For example, the advice unit can retrieve the user's past study history from a database and optimize the advice content based on that. In this way, the most appropriate advice can be provided by referring to the user's past study history.

[0150] During evaluation, the evaluation unit can optimize the evaluation algorithm by referring to the user's past learning outcomes. For example, the evaluation unit adjusts the evaluation criteria based on the user's past learning outcomes. The evaluation unit can also prioritize evaluation of specific problem areas based on the user's past learning outcomes. Furthermore, the evaluation unit can analyze the user's past learning outcomes and optimize the evaluation algorithm. For example, the evaluation unit retrieves the user's past learning outcomes from a database and optimizes the evaluation algorithm based on them. In this way, by referring to the past learning outcomes, the evaluation algorithm can be optimized and accuracy can be improved.

[0151] During speech recognition, the speech recognition unit can optimize the recognition algorithm by referring to the user's speech patterns. For example, the speech recognition algorithm is optimized based on the speech patterns used by the user in the past. The speech recognition unit can also recognize specific accents or pronunciation issues from the user's speech patterns. Furthermore, the speech recognition unit can analyze the user's speech patterns and propose an optimal speech recognition algorithm. For example, the speech recognition unit retrieves the user's speech patterns from a database and optimizes the recognition algorithm based on them. In this way, the recognition algorithm can be optimized by referring to the user's speech patterns, thereby improving accuracy.

[0152] When providing feedback, the feedback unit can provide optimal feedback by referring to the user's past feedback history. For example, optimal feedback can be provided based on the user's past feedback history. The feedback unit can also prioritize feedback on specific issues from the user's past feedback history. Furthermore, the feedback unit can analyze the user's past feedback history and suggest effective feedback methods. For example, the feedback unit can obtain the user's past feedback history from a database and optimize the feedback content based on that. In this way, optimal feedback can be provided by referring to the past feedback history.

[0153] The processing flow of the second embodiment will be briefly explained below.

[0154] Step 1: The analysis unit analyzes the conversation content. For example, it identifies grammatical errors, vocabulary deficiencies, and problems with accent and pronunciation. It also uses natural language processing technology to perform text analysis, sentiment analysis, and keyword extraction. Step 2: The advice section advises the user on appropriate study books and methods based on the analysis results obtained by the analysis section. For example, if the user's level of grammar comprehension is low, it will recommend a grammar book, and if the user lacks vocabulary, it will recommend a study book to help improve vocabulary. Furthermore, if the user has issues with accent or pronunciation, it will introduce apps and study materials for pronunciation practice. Step 3: The evaluator evaluates the user's progress based on the advice provided by the advisor, for example, whether grammar errors have decreased or vocabulary has increased compared to the previous conversation, and presents the user's progress.

[0155] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0156] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0157] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0158] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0159] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0160] 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.

[0161] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0162] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0163] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0164] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0165] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0166] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

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

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

[0169] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0170] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0171] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0172] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0173] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0174] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0175] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0176] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0177] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0178] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0179] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0180] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0181] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0182] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0183] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0184] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0185] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0186] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0187] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0188] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0189] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0190] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0191] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0192] 7, a 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.

[0193] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0194] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0195] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0196] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0197] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0198] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0199] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0200] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0201] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0202] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0203] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0204] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0205] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0206] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0207] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0208] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0209] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0210] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0211] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0212] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0213] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0214] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0215] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0216] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0217] 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.

[0218] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0219] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0220] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0221] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0222] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0223] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0224] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0225] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference.

[0226] [Explanation of symbols]

[0227] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An analysis unit that analyzes the content of the conversation, an advice unit that advises the user on a reference book or study method suited to the user based on the analysis results obtained by the analysis unit; an evaluation unit that evaluates the progress of the user based on the advice provided by the advice unit; Equipped with A system characterized by:

2. Equipped with a voice recognition unit that uses voice recognition technology 2. The system of claim 1.

3. Equipped with a natural language processing unit that uses natural language processing technology 2. The system of claim 1.

4. Equipped with a data protection unit that protects user data 2. The system of claim 1.

5. Equipped with a feedback section that reflects user feedback 2. The system of claim 1.

6. Equipped with expansion parts based on the system's expandability 2. The system of claim 1.

7. The analysis unit Estimate the user's emotions and adjust the accuracy of conversation analysis based on the estimated user emotions.

2. The system of claim 1.

8. The analysis unit When analyzing conversation content, the analysis algorithm is optimized by referring to the user's past conversation history.

2. The system of claim 1.

9. The analysis unit When analyzing conversation content, apply different analysis methods depending on the user's language level.

2. The system of claim 1.

Images