System

Abstract

An object of a system according to an embodiment is to provide an environment in which a foreign language learner can repeat conversation with confidence.SOLUTION: A system according to an embodiment includes a reception unit, an analysis unit, and a proposal unit. The reception unit receives an utterance from a user. The analyzer analyzes the utterance received by the receiver and provides feedback. The suggestion unit suggests a topic on the basis of a result of analysis by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] With conventional technology, it was difficult to easily create a conversational environment with a native speaker when learning a foreign language, which could make learners feel nervous or afraid.

[0005] The system according to the embodiment aims to provide an environment in which foreign language learners can repeat conversations with confidence. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a suggestion unit. The reception unit receives utterances from users. The analysis unit analyzes the utterances received by the reception unit and provides feedback. The suggestion unit suggests topics based on the results of the analysis by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment in which foreign language learners can repeat conversations with confidence. [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 foreign language acquisition system according to an embodiment of the present invention utilizes a generative AI to allow users to easily practice foreign language conversation at home. The system supports users' foreign language acquisition by accepting user utterances, analyzing the utterances, providing appropriate feedback, and suggesting appropriate topics. For example, when a user asks the generative AI a question such as "Hello, what's the weather like today?", the generative AI understands the question and generates an appropriate response. The generative AI then analyzes the user's utterance, points out grammatical errors, and teaches correct expressions. Furthermore, the generative AI suggests appropriate topics based on the progress of the user's conversation. For example, if a user is talking about travel, the generative AI can continue the conversation by asking a question such as "Where do you want to go next?" This allows users to easily practice foreign language conversation at home. Using the generative AI eliminates the need to search for a conversation partner and is available at any time. Furthermore, because the conversation partner is not a human, users are less afraid or nervous about using a foreign language, allowing them to repeat the conversation with confidence. This allows users to efficiently acquire a foreign language.

[0029] A foreign language learning system according to an embodiment includes a reception unit, an analysis unit, and a suggestion unit. The reception unit receives utterances from a user. The user's utterances may include, but are not limited to, speech, text, and gestures. The reception unit converts the speech utterances into text using, for example, speech recognition technology. The reception unit can also directly receive text input. The reception unit can also analyze gesture utterances using gesture recognition technology. For example, the reception unit receives speech input using a microphone and converts it into text data using speech recognition technology. Text input can be directly input using a keyboard or a touchscreen. Gesture input is captured by a camera and analyzed using gesture recognition technology. The analysis unit analyzes the utterances received by the reception unit using a generation AI and provides feedback. The analysis is performed using, for example, natural language processing technology, but is not limited to, an example. For example, the generation AI analyzes the utterances using a text generation AI (e.g., LLM) to identify grammatical errors. The analysis unit can also identify pronunciation errors using speech analysis technology. The analysis unit can also estimate the user's emotions using sentiment analysis technology and reflect them in the feedback. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The voice analysis technology analyzes voice data and detects pronunciation errors. The sentiment analysis technology estimates the user's emotions from text and voice data and reflects them in the feedback. The suggestion unit uses the generation AI to suggest topics based on the results of the analysis by the analysis unit. Topics are selected based on, for example, learning content, interests, current affairs, etc., but are not limited to these examples. For example, the suggestion unit suggests appropriate topics based on the user's comments and past conversation history. The suggestion unit can also suggest topics based on the user's learning progress and areas of interest. The suggestion unit can also suggest topics based on the user's emotions. For example, if the user is talking about travel, the suggestion unit can ask a question such as, "Where do you want to go on your next trip?" Related topics are suggested based on the user's past conversation history.The system suggests topics of appropriate difficulty based on the user's learning progress. The system suggests relaxing topics based on the user's emotions. In this way, the foreign language acquisition system according to the embodiment can support foreign language acquisition by accepting and analyzing user utterances and suggesting appropriate topics.

[0030] The analysis unit can analyze the user's utterances, point out grammatical errors, and teach expressions. The analysis unit can analyze the user's utterances using, for example, natural language processing technology. For example, the analysis unit can detect grammatical errors such as subject-verb agreement and tense errors. The analysis unit can also point out grammatical errors in the user's utterances and teach correct expressions using a generative AI. For example, if a user utters, "I'm going yesterday," the analysis unit teaches the correct expression, "I went yesterday." The analysis unit can also analyze the user's utterances and teach the appropriate vocabulary. For example, if a user utters, "I want to buy a big house," the analysis unit teaches the appropriate vocabulary, "I want to buy a spacious house." This can improve the user's foreign language skills by pointing out grammatical errors and teaching correct expressions. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input a user's utterance into a generation AI and have the generation AI point out grammatical errors and generate correct expressions.

[0031] The suggestion unit can suggest topics based on the user's utterances and past conversation history. For example, the suggestion unit analyzes the user's utterances and suggests related topics. For example, if the user is talking about traveling, the suggestion unit poses a question such as, "Where do you want to go on your next trip?" The suggestion unit can also analyze the user's past conversation history and suggest related topics. For example, the suggestion unit poses a question related to topics in which the user has previously shown interest. The suggestion unit can also suggest topics based on the user's learning progress and areas of interest. For example, if the user is at a beginner's level, the suggestion unit suggests simple topics. If the user is at an intermediate level, the suggestion unit suggests more complex topics. The suggestion unit suggests topics related to the user's specific areas of interest (e.g., travel or cooking). This can help continue the conversation by suggesting appropriate topics based on the user's utterances and past conversation history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's utterances and past conversation history into the generation AI and have the generation AI suggest appropriate topics.

[0032] The reception unit can analyze the user's past utterance history and select a reception method. The reception unit, for example, stores and analyzes the user's past utterance history in a database. For example, the reception unit prioritizes receiving phrases that the user frequently used in the past. The reception unit can also prioritize receiving utterances that the user made incorrectly when retrying the utterance. The reception unit can also prioritize receiving utterances related to topics in which the user has shown interest in the past. For example, if the user has shown interest in the topic "travel" in the past, the reception unit prioritizes receiving utterances related to travel. This allows the user's past utterance history to be analyzed to select an optimal reception method and improve user convenience. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past utterance history into the generation AI and have the generation AI select an optimal reception method.

[0033] When receiving utterances, the reception unit can filter them based on the user's current learning progress and areas of interest. The reception unit, for example, evaluates the user's learning progress and filters the utterances. For example, if the user is at a beginner's level, the reception unit may preferentially receive simple phrases and words. Furthermore, if the user is at an intermediate level, the reception unit may also accept utterances containing more complex grammar and expressions. The reception unit can also filter utterances based on the user's areas of interest. For example, the reception unit may preferentially receive utterances related to a specific area of ​​interest (e.g., travel or cooking). By filtering utterances based on the user's learning progress and areas of interest, effective learning can be supported. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's learning progress data and area of ​​interest data into the generation AI and have the generation AI perform utterance filtering.

[0034] When receiving a utterance, the reception unit can select the reception means according to the user's input method. The reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.), for example. For example, if the user selects voice input, the reception unit can use voice recognition technology to accept the utterance. Furthermore, if the user selects text input, the reception unit can also use text analysis technology to accept the utterance. Furthermore, if the user selects image input, the reception unit can also use image analysis technology to accept the utterance. For example, the reception unit receives voice input via a microphone and converts it into text data using voice recognition technology. Text input can be directly input using a keyboard or touch screen. Image input is performed by capturing an image of the user with a camera and analyzing it using image analysis technology. This allows for selecting the optimal reception means according to the user's input method, thereby improving user convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal reception means.

[0035] When receiving a utterance, the reception unit can prioritize receiving the utterance while taking into account the user's geographical location information. The reception unit, for example, acquires and analyzes the user's geographical location information from GPS data or an IP address. For example, if the user is in a specific country, the reception unit can prioritize receiving utterances related to the culture and customs of that country. Furthermore, if the user is traveling, the reception unit can also prioritize receiving phrases and questions related to travel. Furthermore, if the user is in a specific region, the reception unit can prioritize receiving utterances related to the language and dialect of that region. For example, if the user is in Japan, the reception unit can prioritize receiving utterances related to Japanese culture and customs. If the user is traveling, the reception unit can prioritize receiving phrases and questions related to travel. If the user is in a specific region, the reception unit can prioritize receiving utterances related to the language and dialect of that region. In this way, by taking the user's geographical location information into account, it is possible to prioritize receiving highly relevant utterances. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI and have the generation AI determine the priority of highly relevant comments.

[0036] The reception unit can analyze the user's social media activity when receiving a comment and receive related comments. The reception unit, for example, analyzes the user's social media activity and prioritizes receiving related comments. For example, the reception unit prioritizes receiving comments related to content posted by the user on social media. The reception unit can also analyze content posted by the user's friends on social media and prioritize receiving related comments. The reception unit can also prioritize receiving comments related to places the user has checked into on social media. For example, if the user posts on social media related to the topic "travel," the reception unit prioritizes receiving travel-related comments. If the user's friend posts on social media related to the topic "cooking," the reception unit prioritizes receiving cooking-related comments. If the user checks into a specific location, the reception unit prioritizes receiving comments related to that location. In this way, by analyzing the user's social media activity, related comments can be prioritized. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI determine the priority of related comments.

[0037] When receiving a utterance, the reception unit can adjust the reception method by reflecting the user's past feedback. The reception unit, for example, stores and analyzes the user's past feedback in a database. For example, the reception unit prioritizes receiving utterance formats that the user previously preferred. The reception unit can also adjust the reception method by reflecting improvements previously pointed out by the user. The reception unit can also suggest an optimal reception method based on feedback previously provided by the user. For example, if the user previously preferred "voice input," the reception unit prioritizes receiving voice input. If the user previously preferred "text input," the reception unit prioritizes receiving text input. The reception method is adjusted by reflecting improvements previously pointed out by the user. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI suggest an optimal reception method.

[0038] During analysis, the analysis unit can adjust the content of the feedback based on the importance of the utterance. For example, the analysis unit evaluates the importance of the user's utterance and adjusts the content of the feedback. For example, the analysis unit provides detailed feedback for important utterances. The analysis unit can also provide concise feedback for general utterances. The analysis unit can also provide feedback including additional information for utterances that the user indicates a particular interest in. For example, if the user asks, "Is this grammar correct?", the analysis unit provides detailed feedback. If the user asks, "What did you do today?", the analysis unit provides concise feedback. If the user asks, "Please tell me the meaning of this word," the analysis unit provides feedback including additional information. In this way, by adjusting the level of detail of the feedback based on the importance of the utterance, it is possible to provide feedback that is appropriate for the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's utterance data into a generation AI and have the generation AI evaluate the importance of the utterance and adjust the content of the feedback.

[0039] During analysis, the analysis unit can apply an analysis algorithm depending on the category of the utterance. For example, the analysis unit classifies the category of the user's utterance and applies an appropriate analysis algorithm. For example, the analysis unit applies a grammar analysis algorithm to grammatical utterances. The analysis unit can also apply a lexical analysis algorithm to vocabulary-related utterances. The analysis unit can also apply a speech analysis algorithm to pronunciation-related utterances. For example, when a user asks, "Is this grammar correct?", the analysis unit applies a grammar analysis algorithm. When a user asks, "Please tell me the meaning of this word," the analysis unit applies a lexical analysis algorithm. When a user asks, "Is this pronunciation correct?" the analysis unit applies a speech analysis algorithm. This allows for more accurate feedback to be provided by applying different analysis algorithms depending on the category of the utterance. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's utterance data into a generation AI and have the generation AI classify the utterance category and apply an analysis algorithm.

[0040] During analysis, the analysis unit can improve the accuracy of feedback by referring to the user's past analysis results. The analysis unit, for example, stores the user's past analysis results in a database and analyzes them. For example, the analysis unit reanalyzes utterances that the user made incorrectly in the past to provide more detailed feedback. The analysis unit can also improve the accuracy of feedback by referring to utterances that the user made correctly in the past. The analysis unit can also provide individually customized feedback based on the user's past analysis results. For example, if the user previously asked, "Is this grammar correct?" and got it wrong, the analysis unit analyzes the same question again and provides more detailed feedback. If the user previously asked, "Please tell me the meaning of this word," and got it right, the analysis unit can improve the accuracy of feedback by referring to that result. Individually customized feedback is provided based on the user's past analysis results. In this way, the accuracy of feedback can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the feedback.

[0041] During analysis, the analysis unit can determine the order of feedback based on the time of submission of the comments. The analysis unit, for example, evaluates the time of submission of the user's comments and determines the order of feedback. For example, the analysis unit may provide feedback preferentially to recent comments. The analysis unit may also provide feedback later to older comments. The analysis unit may also provide feedback preferentially to comments submitted within a specific time period. For example, the analysis unit may provide feedback preferentially to comments recently submitted by the user. The analysis unit may provide feedback later to comments submitted by the user in the past. The analysis unit may provide feedback preferentially to comments submitted within a specific time period. In this way, by determining the priority of feedback based on the time of submission of the comments, it is possible to provide feedback appropriate to the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input the user's comment data into a generation AI and cause the generation AI to evaluate the submission time and determine the order of feedback.

[0042] During analysis, the analysis unit can adjust the order of feedback based on the relevance of the utterances. The analysis unit, for example, evaluates the relevance of the user's utterances and adjusts the order of feedback. For example, the analysis unit prioritizes providing feedback for important utterances. The analysis unit can also postpone providing feedback for general utterances. The analysis unit can also prioritize providing feedback for utterances that the user indicates a particular interest in. For example, when a user asks, "Is this grammar correct?", the analysis unit prioritizes providing feedback. When a user asks, "What did you do today?", the analysis unit postpones providing feedback. When a user asks, "Please tell me the meaning of this word," the analysis unit prioritizes providing feedback. In this way, by adjusting the order of feedback based on the relevance of the utterances, it is possible to provide feedback that is appropriate for the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's utterance data into a generation AI and have the generation AI evaluate the relevance and adjust the order of feedback.

[0043] During analysis, the analysis unit can adjust the terminology of the feedback according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise and adjusts the terminology of the feedback. For example, the analysis unit provides feedback in simple language to a beginner-level user. The analysis unit can also provide feedback containing appropriate technical terms to an intermediate-level user. The analysis unit can also provide feedback using a lot of technical terms to an advanced-level user. For example, when a beginner-level user is asked, "Is this grammar correct?", the analysis unit provides feedback in simple language. When an intermediate-level user is asked, "What is the meaning of this word?", the analysis unit provides feedback containing appropriate technical terms. When an advanced-level user is asked, "Is this pronunciation correct?", the analysis unit provides feedback using a lot of technical terms. In this way, by adjusting the use of technical terms in the feedback according to the user's level of expertise, it is possible to provide feedback appropriate for the user. Some or all of the above-described processing by the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the user's expertise level data to the generation AI and cause the generation AI to adjust the terms of the feedback.

[0044] The suggestion unit can adjust the content of the suggestion based on the importance of the utterance when making a suggestion. The suggestion unit, for example, evaluates the importance of the user's utterance and adjusts the content of the suggestion. For example, the suggestion unit provides detailed suggestions for important utterances. The suggestion unit can also provide concise suggestions for general utterances. The suggestion unit can also provide suggestions including additional information for utterances that the user indicates a particular interest in. For example, when the user asks, "Is this grammar correct?", the suggestion unit provides detailed suggestions. When the user asks, "What did you do today?", the suggestion unit provides concise suggestions. When the user asks, "Please tell me the meaning of this word," the suggestion unit provides suggestions including additional information. In this way, by adjusting the level of detail of the suggestion based on the importance of the utterance, it is possible to provide suggestions that are suitable for the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's utterance data into a generation AI and cause the generation AI to evaluate the importance of the utterance and adjust the content of the suggestion.

[0045] When making a proposal, the suggestion unit can apply a proposal algorithm depending on the category of the utterance. The suggestion unit, for example, classifies the category of the user's utterance and applies an appropriate proposal algorithm. For example, the suggestion unit applies a grammar proposal algorithm to a grammatical utterance. The suggestion unit can also apply a vocabulary proposal algorithm to a vocabulary-related utterance. The suggestion unit can also apply a speech proposal algorithm to a pronunciation-related utterance. For example, when a user asks, "Is this grammar correct?", the suggestion unit applies a grammar proposal algorithm. When a user asks, "Please tell me the meaning of this word," the suggestion unit applies a vocabulary proposal algorithm. When a user asks, "Is this pronunciation correct?" the suggestion unit applies a speech proposal algorithm. This allows for more accurate proposals to be provided by applying different proposal algorithms depending on the category of the utterance. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's utterance data into a generation AI and cause the generation AI to classify the utterance category and apply the proposal algorithm.

[0046] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, stores the user's past suggestion results in a database and analyzes them. For example, the suggestion unit makes similar suggestions by referring to suggestions the user has previously accepted. The suggestion unit can also make new suggestions by avoiding suggestions the user has previously rejected. The suggestion unit can also make individually customized suggestions based on the user's past suggestion results. For example, if the user has previously asked, "Is this grammar correct?" and accepted it, the suggestion unit makes a similar suggestion. If the user has previously asked, "Please tell me the meaning of this word," and rejected it, the suggestion unit makes a new suggestion while avoiding that suggestion. Individually customized suggestions are made based on the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0047] When making a proposal, the suggestion unit can determine the order of proposals based on the submission time of the utterances. The suggestion unit, for example, evaluates the submission time of the user's utterances and determines the order of proposals. For example, the suggestion unit prioritizes proposals for recent utterances. The suggestion unit can also postpone proposals for past utterances. The suggestion unit can also prioritize proposals for utterances submitted during a specific time period. For example, the suggestion unit prioritizes proposals for utterances recently submitted by the user. The suggestion unit postpones proposals for utterances submitted by the user in the past. The suggestion unit prioritizes proposals for utterances submitted by the user during a specific time period. In this way, by determining the priority of proposals based on the submission time of the utterances, it is possible to provide proposals suitable for the user. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's utterance data into the generation AI and cause the generation AI to evaluate the submission time and determine the order of proposals.

[0048] The suggestion unit can adjust the order of suggestions based on the relevance of the utterances when making suggestions. The suggestion unit, for example, evaluates the relevance of the user's utterances and adjusts the order of suggestions. For example, the suggestion unit prioritizes suggestions for important utterances. The suggestion unit can also postpone suggestions for general utterances. The suggestion unit can also prioritize suggestions for utterances that the user has indicated they particularly want to know about. For example, when the user asks, "Is this grammar correct?", the suggestion unit prioritizes suggestions. When the user asks, "What did you do today?", the suggestion unit postpones suggestions. When the user asks, "Please tell me the meaning of this word," the suggestion unit prioritizes suggestions. In this way, by adjusting the order of suggestions based on the relevance of the utterances, suggestions suitable for the user can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input user utterance data into a generation AI and cause the generation AI to evaluate the relevance and adjust the order of suggestions.

[0049] The suggestion unit can adjust the terminology of the suggestion according to the user's level of expertise when making a suggestion. The suggestion unit, for example, evaluates the user's level of expertise and adjusts the terminology of the suggestion. For example, the suggestion unit makes suggestions using simple language to a beginner-level user. The suggestion unit can also make suggestions including appropriate technical terms to an intermediate-level user. The suggestion unit can also make suggestions using a lot of technical terms to an advanced-level user. For example, when a beginner-level user asks, "Is this grammar correct?", the suggestion unit makes suggestions using simple language. When an intermediate-level user asks, "What does this word mean?", the suggestion unit makes suggestions including appropriate technical terms. When an advanced-level user asks, "Is this pronunciation correct?", the suggestion unit makes suggestions using a lot of technical terms. In this way, by adjusting the use of technical terms in the suggestion according to the user's level of expertise, it is possible to provide suggestions suitable for the user. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the terms of the suggestions.

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

[0051] When accepting a user's utterance, the reception unit can analyze the user's speaking speed and adjust the timing of appropriate feedback. For example, if the user speaks at a fast pace, the reception unit provides feedback quickly to match the user's rhythm. Conversely, if the user speaks slowly, the reception unit provides feedback with a slight delay to give the user time to think. Also, if the user pauses, the reception unit can provide feedback during that time, providing information before the user makes the next utterance. This makes it possible to provide a more natural conversation experience by adjusting the timing of feedback according to the user's speaking speed.

[0052] When analyzing a user's utterances, the analysis unit can provide feedback taking into account the background information of the user's utterances. For example, if a user makes a utterance related to a particular culture or region, the analysis unit can provide background information about that culture or region. Also, if a user makes a utterance related to a particular field of expertise, the analysis unit can provide specialized information about that field. Furthermore, if a user makes a utterance related to content that they have learned in the past, the analysis unit can also provide feedback to help them review that content. In this way, by providing feedback that takes into account the background information of the user's utterances, it is possible to improve learning effectiveness.

[0053] When accepting a user's utterance, the reception unit can provide feedback taking into account the context of the user's utterance. For example, if the user is continuing what was said in a previous conversation, the reception unit provides feedback taking into account the context. Also, if the user is talking about a specific topic, the reception unit can provide information related to that topic. Furthermore, if the user is talking about multiple topics alternately, the reception unit can provide appropriate feedback for each topic. As a result, providing feedback taking into account the context of the user's utterance can support more effective learning.

[0054] When suggesting topics based on the user's utterances and past conversation history, the suggestion unit can select topics taking the user's learning style into consideration. For example, if the user is a visual learner, the suggestion unit can suggest visual learning materials and topics. If the user is an auditory learner, the suggestion unit can suggest audio learning materials and topics related to listening. If the user is an experiential learner, the suggestion unit can also suggest topics based on actual experiences. This makes it possible to support more effective learning by suggesting topics that suit the user's learning style.

[0055] When accepting a user's utterance, the acceptance unit can provide feedback taking into account the frequency of the user's utterances. For example, if the user speaks frequently, the acceptance unit can provide feedback quickly to match the user's rhythm. Conversely, if the user does not speak often, the acceptance unit can provide feedback with a slight delay to give the user time to think. Also, if the user pauses, the acceptance unit can provide feedback during that time, providing information before the user makes the next utterance. This makes it possible to provide a more natural conversation experience by providing feedback according to the frequency of the user's utterances.

[0056] When suggesting topics based on the user's utterances and past conversation history, the suggestion unit can select topics taking the user's learning goals into consideration. For example, if the user's goal is to pass a specific exam, the suggestion unit can suggest topics related to that exam. If the user's goal is to acquire a specific skill, the suggestion unit can suggest topics related to that skill. If the user's goal is to learn about a specific culture or region, the suggestion unit can also suggest topics related to that culture or region. This makes it possible to support more effective learning by suggesting topics according to the user's learning goals.

[0057] The processing flow of the first embodiment will be briefly explained below.

[0058] Step 1: The reception unit receives user input. User input can include voice, text, gestures, etc. For example, voice input is received by a microphone and converted into text data using voice recognition technology. Text input can be entered directly using a keyboard or touch screen. Gesture input is captured by a camera and analyzed using gesture recognition technology. Step 2: The analysis unit uses the generation AI to analyze the utterances received by the reception unit and provide feedback. The analysis is performed using natural language processing technology. For example, a text generation AI (LLM) can be used to analyze the utterances and point out grammatical errors. It can also use speech analysis technology to point out pronunciation errors. Furthermore, it can use sentiment analysis technology to estimate the user's emotions and reflect them in the feedback. Step 3: The suggestion unit uses the generation AI to suggest topics based on the results of the analysis by the analysis unit. Topics are selected based on learning content, interests, current events, etc. For example, appropriate topics can be suggested based on the user's comments and past conversation history. Topics can also be suggested based on the user's learning progress, areas of interest, and emotions.

[0059] (Example 2) A foreign language acquisition system according to an embodiment of the present invention utilizes a generative AI to allow users to easily practice foreign language conversation at home. The system supports users' foreign language acquisition by accepting user utterances, analyzing the utterances, providing appropriate feedback, and suggesting appropriate topics. For example, when a user asks the generative AI a question such as "Hello, what's the weather like today?", the generative AI understands the question and generates an appropriate response. The generative AI then analyzes the user's utterance, points out grammatical errors, and teaches correct expressions. Furthermore, the generative AI suggests appropriate topics based on the progress of the user's conversation. For example, if a user is talking about travel, the generative AI can continue the conversation by asking a question such as "Where do you want to go next?" This allows users to easily practice foreign language conversation at home. Using the generative AI eliminates the need to search for a conversation partner and is available at any time. Furthermore, because the conversation partner is not a human, users are less afraid or nervous about using a foreign language, allowing them to repeat the conversation with confidence. This allows users to efficiently acquire a foreign language.

[0060] A foreign language learning system according to an embodiment includes a reception unit, an analysis unit, and a suggestion unit. The reception unit receives utterances from a user. The user's utterances may include, but are not limited to, speech, text, and gestures. The reception unit converts the speech utterances into text using, for example, speech recognition technology. The reception unit can also directly receive text input. The reception unit can also analyze gesture utterances using gesture recognition technology. For example, the reception unit receives speech input using a microphone and converts it into text data using speech recognition technology. Text input can be directly input using a keyboard or a touchscreen. Gesture input is captured by a camera and analyzed using gesture recognition technology. The analysis unit analyzes the utterances received by the reception unit using a generation AI and provides feedback. The analysis is performed using, for example, natural language processing technology, but is not limited to, an example. For example, the generation AI analyzes the utterances using a text generation AI (e.g., LLM) to identify grammatical errors. The analysis unit can also identify pronunciation errors using speech analysis technology. The analysis unit can also estimate the user's emotions using sentiment analysis technology and reflect them in the feedback. For example, the text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. The voice analysis technology analyzes voice data and detects pronunciation errors. The sentiment analysis technology estimates the user's emotions from text and voice data and reflects them in the feedback. The suggestion unit uses the generation AI to suggest topics based on the results of the analysis by the analysis unit. Topics are selected based on, for example, learning content, interests, current affairs, etc., but are not limited to these examples. For example, the suggestion unit suggests appropriate topics based on the user's comments and past conversation history. The suggestion unit can also suggest topics based on the user's learning progress and areas of interest. The suggestion unit can also suggest topics based on the user's emotions. For example, if the user is talking about travel, the suggestion unit can ask a question such as, "Where do you want to go on your next trip?" Related topics are suggested based on the user's past conversation history.The system suggests topics of appropriate difficulty based on the user's learning progress. The system suggests relaxing topics based on the user's emotions. In this way, the foreign language acquisition system according to the embodiment can support foreign language acquisition by accepting and analyzing user utterances and suggesting appropriate topics.

[0061] The analysis unit can analyze the user's utterances, point out grammatical errors, and teach expressions. The analysis unit can analyze the user's utterances using, for example, natural language processing technology. For example, the analysis unit can detect grammatical errors such as subject-verb agreement and tense errors. The analysis unit can also point out grammatical errors in the user's utterances and teach correct expressions using a generative AI. For example, if a user utters, "I'm going yesterday," the analysis unit teaches the correct expression, "I went yesterday." The analysis unit can also analyze the user's utterances and teach the appropriate vocabulary. For example, if a user utters, "I want to buy a big house," the analysis unit teaches the appropriate vocabulary, "I want to buy a spacious house." This can improve the user's foreign language skills by pointing out grammatical errors and teaching correct expressions. Some or all of the above-described processing in the analysis unit can be performed using, for example, a generative AI, or without a generative AI. For example, the analysis unit can input a user's utterance into a generation AI and have the generation AI point out grammatical errors and generate correct expressions.

[0062] The suggestion unit can suggest topics based on the user's utterances and past conversation history. For example, the suggestion unit analyzes the user's utterances and suggests related topics. For example, if the user is talking about traveling, the suggestion unit poses a question such as, "Where do you want to go on your next trip?" The suggestion unit can also analyze the user's past conversation history and suggest related topics. For example, the suggestion unit poses a question related to topics in which the user has previously shown interest. The suggestion unit can also suggest topics based on the user's learning progress and areas of interest. For example, if the user is at a beginner's level, the suggestion unit suggests simple topics. If the user is at an intermediate level, the suggestion unit suggests more complex topics. The suggestion unit suggests topics related to the user's specific areas of interest (e.g., travel or cooking). This can help continue the conversation by suggesting appropriate topics based on the user's utterances and past conversation history. Some or all of the above-mentioned processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's utterances and past conversation history into the generation AI and have the generation AI suggest appropriate topics.

[0063] The reception unit can estimate the user's emotions and adjust the timing of utterance acceptance based on the estimated user emotions. The reception unit, for example, analyzes the user's voice tone and facial expression to estimate the emotions. For example, the reception unit calculates an emotion score based on changes in voice tone and facial expression. The reception unit can also estimate the user's emotions using a generation AI and adjust the timing of utterance acceptance. For example, if the user is nervous, the reception unit may accept the user's utterance after a short pause, giving the user time to relax. If the user is relaxed, the reception unit may immediately accept the user's utterance, promoting a smooth conversation. If the user is excited, the reception unit may display a message encouraging the user to take a deep breath before accepting the utterance. This allows the user to relax and continue the conversation by adjusting the timing of utterance acceptance according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's voice data and image data into the generation AI and have the generation AI estimate emotions and adjust the timing of receiving comments.

[0064] The reception unit can analyze the user's past utterance history and select a reception method. The reception unit, for example, stores and analyzes the user's past utterance history in a database. For example, the reception unit prioritizes receiving phrases that the user frequently used in the past. The reception unit can also prioritize receiving utterances that the user made incorrectly when retrying the utterance. The reception unit can also prioritize receiving utterances related to topics in which the user has shown interest in the past. For example, if the user has shown interest in the topic "travel" in the past, the reception unit prioritizes receiving utterances related to travel. This allows the user's past utterance history to be analyzed to select an optimal reception method and improve user convenience. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past utterance history into the generation AI and have the generation AI select an optimal reception method.

[0065] When receiving utterances, the reception unit can filter them based on the user's current learning progress and areas of interest. The reception unit, for example, evaluates the user's learning progress and filters the utterances. For example, if the user is at a beginner's level, the reception unit may preferentially receive simple phrases and words. Furthermore, if the user is at an intermediate level, the reception unit may also accept utterances containing more complex grammar and expressions. The reception unit can also filter utterances based on the user's areas of interest. For example, the reception unit may preferentially receive utterances related to a specific area of ​​interest (e.g., travel or cooking). By filtering utterances based on the user's learning progress and areas of interest, effective learning can be supported. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit may input the user's learning progress data and area of ​​interest data into the generation AI and have the generation AI perform utterance filtering.

[0066] When receiving a utterance, the reception unit can select the reception means according to the user's input method. The reception unit selects the optimal reception means according to the user's input method (voice, text, image, etc.), for example. For example, if the user selects voice input, the reception unit can use voice recognition technology to accept the utterance. Furthermore, if the user selects text input, the reception unit can also use text analysis technology to accept the utterance. Furthermore, if the user selects image input, the reception unit can also use image analysis technology to accept the utterance. For example, the reception unit receives voice input via a microphone and converts it into text data using voice recognition technology. Text input can be directly input using a keyboard or touch screen. Image input is performed by capturing an image of the user with a camera and analyzing it using image analysis technology. This allows for selecting the optimal reception means according to the user's input method, thereby improving user convenience. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's input data into a generation AI and have the generation AI select the optimal reception means.

[0067] The reception unit can estimate the user's emotions and determine the order of utterances to be received based on the estimated user emotions. The reception unit, for example, analyzes the user's voice tone and facial expression to estimate the emotions. For example, the reception unit calculates an emotion score based on changes in voice tone and facial expression. The reception unit can also use a generation AI to estimate the user's emotions and determine the order of utterances to be received. For example, if the user is nervous, the reception unit prioritizes simple questions and phrases. If the user is relaxed, the reception unit prioritizes complex questions and phrases. If the user is excited, the reception unit prioritizes utterances related to topics that interest the user. This enables appropriate responses based on the user's emotions by determining the priority of utterances. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to these examples. Some or all of the above-described processing in the reception unit can be performed using, for example, the generation AI, or without the generation AI. For example, the reception unit can input the user's voice data and image data into the generation AI and have the generation AI estimate emotions and determine the order in which comments are received.

[0068] When receiving a utterance, the reception unit can prioritize receiving the utterance while taking into account the user's geographical location information. The reception unit, for example, acquires and analyzes the user's geographical location information from GPS data or an IP address. For example, if the user is in a specific country, the reception unit can prioritize receiving utterances related to the culture and customs of that country. Furthermore, if the user is traveling, the reception unit can also prioritize receiving phrases and questions related to travel. Furthermore, if the user is in a specific region, the reception unit can prioritize receiving utterances related to the language and dialect of that region. For example, if the user is in Japan, the reception unit can prioritize receiving utterances related to Japanese culture and customs. If the user is traveling, the reception unit can prioritize receiving phrases and questions related to travel. If the user is in a specific region, the reception unit can prioritize receiving utterances related to the language and dialect of that region. In this way, by taking the user's geographical location information into account, it is possible to prioritize receiving highly relevant utterances. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's geographical location information into the generation AI and have the generation AI determine the priority of highly relevant comments.

[0069] The reception unit can analyze the user's social media activity when receiving a comment and receive related comments. The reception unit, for example, analyzes the user's social media activity and prioritizes receiving related comments. For example, the reception unit prioritizes receiving comments related to content posted by the user on social media. The reception unit can also analyze content posted by the user's friends on social media and prioritize receiving related comments. The reception unit can also prioritize receiving comments related to places the user has checked into on social media. For example, if the user posts on social media related to the topic "travel," the reception unit prioritizes receiving travel-related comments. If the user's friend posts on social media related to the topic "cooking," the reception unit prioritizes receiving cooking-related comments. If the user checks into a specific location, the reception unit prioritizes receiving comments related to that location. In this way, by analyzing the user's social media activity, related comments can be prioritized. Some or all of the above-described processing in the reception unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the reception unit can input the user's social media activity data into the generation AI and have the generation AI determine the priority of related comments.

[0070] When receiving a utterance, the reception unit can adjust the reception method by reflecting the user's past feedback. The reception unit, for example, stores and analyzes the user's past feedback in a database. For example, the reception unit prioritizes receiving utterance formats that the user previously preferred. The reception unit can also adjust the reception method by reflecting improvements previously pointed out by the user. The reception unit can also suggest an optimal reception method based on feedback previously provided by the user. For example, if the user previously preferred "voice input," the reception unit prioritizes receiving voice input. If the user previously preferred "text input," the reception unit prioritizes receiving text input. The reception method is adjusted by reflecting improvements previously pointed out by the user. In this way, the optimal reception method can be provided by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can input the user's past feedback data into the generation AI and have the generation AI suggest an optimal reception method.

[0071] The analysis unit can estimate the user's emotions and adjust the feedback method based on the estimated user emotions. The analysis unit, for example, analyzes the user's voice tone and facial expression to estimate the emotions. For example, the analysis unit calculates an emotion score based on changes in voice tone and facial expression. The analysis unit can also estimate the user's emotions using a generation AI and adjust the feedback expression method. For example, if the user is nervous, the analysis unit provides feedback in a gentle tone. If the user is relaxed, the analysis unit provides detailed feedback. If the user is excited, the analysis unit provides feedback including encouraging words. In this way, by adjusting the feedback expression method based on the user's emotions, it is possible to provide feedback that is appropriate for the user. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the user's voice data and image data into the generation AI, and have the generation AI estimate emotions and adjust the way feedback is expressed.

[0072] During analysis, the analysis unit can adjust the content of the feedback based on the importance of the utterance. For example, the analysis unit evaluates the importance of the user's utterance and adjusts the content of the feedback. For example, the analysis unit provides detailed feedback for important utterances. The analysis unit can also provide concise feedback for general utterances. The analysis unit can also provide feedback including additional information for utterances that the user indicates a particular interest in. For example, if the user asks, "Is this grammar correct?", the analysis unit provides detailed feedback. If the user asks, "What did you do today?", the analysis unit provides concise feedback. If the user asks, "Please tell me the meaning of this word," the analysis unit provides feedback including additional information. In this way, by adjusting the level of detail of the feedback based on the importance of the utterance, it is possible to provide feedback that is appropriate for the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's utterance data into a generation AI and have the generation AI evaluate the importance of the utterance and adjust the content of the feedback.

[0073] During analysis, the analysis unit can apply an analysis algorithm depending on the category of the utterance. For example, the analysis unit classifies the category of the user's utterance and applies an appropriate analysis algorithm. For example, the analysis unit applies a grammar analysis algorithm to grammatical utterances. The analysis unit can also apply a lexical analysis algorithm to vocabulary-related utterances. The analysis unit can also apply a speech analysis algorithm to pronunciation-related utterances. For example, when a user asks, "Is this grammar correct?", the analysis unit applies a grammar analysis algorithm. When a user asks, "Please tell me the meaning of this word," the analysis unit applies a lexical analysis algorithm. When a user asks, "Is this pronunciation correct?" the analysis unit applies a speech analysis algorithm. This allows for more accurate feedback to be provided by applying different analysis algorithms depending on the category of the utterance. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can input the user's utterance data into a generation AI and have the generation AI classify the utterance category and apply an analysis algorithm.

[0074] During analysis, the analysis unit can improve the accuracy of feedback by referring to the user's past analysis results. The analysis unit, for example, stores the user's past analysis results in a database and analyzes them. For example, the analysis unit reanalyzes utterances that the user made incorrectly in the past to provide more detailed feedback. The analysis unit can also improve the accuracy of feedback by referring to utterances that the user made correctly in the past. The analysis unit can also provide individually customized feedback based on the user's past analysis results. For example, if the user previously asked, "Is this grammar correct?" and got it wrong, the analysis unit analyzes the same question again and provides more detailed feedback. If the user previously asked, "Please tell me the meaning of this word," and got it right, the analysis unit can improve the accuracy of feedback by referring to that result. Individually customized feedback is provided based on the user's past analysis results. In this way, the accuracy of feedback can be improved by referring to the user's past analysis results. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the user's past analysis result data into the generation AI and have the generation AI improve the accuracy of the feedback.

[0075] The analysis unit can estimate the user's emotions and adjust the amount of feedback based on the estimated user emotions. The analysis unit, for example, analyzes the user's voice tone and facial expressions to estimate emotions. For example, the analysis unit calculates an emotion score based on changes in voice tone and facial expressions. The analysis unit can also estimate the user's emotions using a generation AI and adjust the length of the feedback. For example, if the user is nervous, the analysis unit provides short and concise feedback. If the user is relaxed, the analysis unit provides detailed feedback. If the user is excited, the analysis unit provides feedback including encouraging words. This allows the length of the feedback to be adjusted based on the user's emotions, thereby providing appropriate feedback to the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or without a generation AI. For example, the analysis unit can input the user's voice data and image data into the generation AI and have the generation AI estimate emotions and adjust the length of feedback.

[0076] During analysis, the analysis unit can determine the order of feedback based on the time of submission of the comments. The analysis unit, for example, evaluates the time of submission of the user's comments and determines the order of feedback. For example, the analysis unit may provide feedback preferentially to recent comments. The analysis unit may also provide feedback later to older comments. The analysis unit may also provide feedback preferentially to comments submitted within a specific time period. For example, the analysis unit may provide feedback preferentially to comments recently submitted by the user. The analysis unit may provide feedback later to comments submitted by the user in the past. The analysis unit may provide feedback preferentially to comments submitted within a specific time period. In this way, by determining the priority of feedback based on the time of submission of the comments, it is possible to provide feedback appropriate to the user. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input the user's comment data into a generation AI and cause the generation AI to evaluate the submission time and determine the order of feedback.

[0077] During analysis, the analysis unit can adjust the order of feedback based on the relevance of the utterances. The analysis unit, for example, evaluates the relevance of the user's utterances and adjusts the order of feedback. For example, the analysis unit prioritizes providing feedback for important utterances. The analysis unit can also postpone providing feedback for general utterances. The analysis unit can also prioritize providing feedback for utterances that the user indicates a particular interest in. For example, when a user asks, "Is this grammar correct?", the analysis unit prioritizes providing feedback. When a user asks, "What did you do today?", the analysis unit postpones providing feedback. When a user asks, "Please tell me the meaning of this word," the analysis unit prioritizes providing feedback. In this way, by adjusting the order of feedback based on the relevance of the utterances, it is possible to provide feedback that is appropriate for the user. Some or all of the above-described processing in the analysis unit may be performed using, or without, a generation AI. For example, the analysis unit can input the user's utterance data into a generation AI and have the generation AI evaluate the relevance and adjust the order of feedback.

[0078] During analysis, the analysis unit can adjust the terminology of the feedback according to the user's level of expertise. The analysis unit, for example, evaluates the user's level of expertise and adjusts the terminology of the feedback. For example, the analysis unit provides feedback in simple language to a beginner-level user. The analysis unit can also provide feedback containing appropriate technical terms to an intermediate-level user. The analysis unit can also provide feedback using a lot of technical terms to an advanced-level user. For example, when a beginner-level user is asked, "Is this grammar correct?", the analysis unit provides feedback in simple language. When an intermediate-level user is asked, "What is the meaning of this word?", the analysis unit provides feedback containing appropriate technical terms. When an advanced-level user is asked, "Is this pronunciation correct?", the analysis unit provides feedback using a lot of technical terms. In this way, by adjusting the use of technical terms in the feedback according to the user's level of expertise, it is possible to provide feedback appropriate for the user. Some or all of the above-described processing by the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can input the user's expertise level data to the generation AI and cause the generation AI to adjust the terms of the feedback.

[0079] The suggestion unit can estimate the user's emotions and adjust the suggestion method based on the estimated user's emotions. The suggestion unit, for example, analyzes the user's voice tone and facial expression to estimate the emotions. For example, the suggestion unit calculates an emotion score based on changes in voice tone and facial expression. The suggestion unit can also estimate the user's emotions using a generation AI and adjust the way the suggestions are expressed. For example, if the user is nervous, the suggestion unit makes a suggestion in a gentle tone. If the user is relaxed, the suggestion unit makes a detailed suggestion. If the user is excited, the suggestion unit makes a suggestion including encouraging words. In this way, by adjusting the way the suggestions are expressed based on the user's emotions, it is possible to provide suggestions that are suitable for the user. Emotion estimation is realized using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's voice data and image data into the generation AI and have the generation AI estimate emotions and adjust the way the suggestions are expressed.

[0080] The suggestion unit can adjust the content of the suggestion based on the importance of the utterance when making a suggestion. The suggestion unit, for example, evaluates the importance of the user's utterance and adjusts the content of the suggestion. For example, the suggestion unit provides detailed suggestions for important utterances. The suggestion unit can also provide concise suggestions for general utterances. The suggestion unit can also provide suggestions including additional information for utterances that the user indicates a particular interest in. For example, when the user asks, "Is this grammar correct?", the suggestion unit provides detailed suggestions. When the user asks, "What did you do today?", the suggestion unit provides concise suggestions. When the user asks, "Please tell me the meaning of this word," the suggestion unit provides suggestions including additional information. In this way, by adjusting the level of detail of the suggestion based on the importance of the utterance, it is possible to provide suggestions that are suitable for the user. Some or all of the above-described processing in the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's utterance data into a generation AI and cause the generation AI to evaluate the importance of the utterance and adjust the content of the suggestion.

[0081] When making a proposal, the suggestion unit can apply a proposal algorithm depending on the category of the utterance. The suggestion unit, for example, classifies the category of the user's utterance and applies an appropriate proposal algorithm. For example, the suggestion unit applies a grammar proposal algorithm to a grammatical utterance. The suggestion unit can also apply a vocabulary proposal algorithm to a vocabulary-related utterance. The suggestion unit can also apply a speech proposal algorithm to a pronunciation-related utterance. For example, when a user asks, "Is this grammar correct?", the suggestion unit applies a grammar proposal algorithm. When a user asks, "Please tell me the meaning of this word," the suggestion unit applies a vocabulary proposal algorithm. When a user asks, "Is this pronunciation correct?" the suggestion unit applies a speech proposal algorithm. This allows for more accurate proposals to be provided by applying different proposal algorithms depending on the category of the utterance. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's utterance data into a generation AI and cause the generation AI to classify the utterance category and apply the proposal algorithm.

[0082] When making a suggestion, the suggestion unit can improve the accuracy of the suggestion by referring to the user's past suggestion results. The suggestion unit, for example, stores the user's past suggestion results in a database and analyzes them. For example, the suggestion unit makes similar suggestions by referring to suggestions the user has previously accepted. The suggestion unit can also make new suggestions by avoiding suggestions the user has previously rejected. The suggestion unit can also make individually customized suggestions based on the user's past suggestion results. For example, if the user has previously asked, "Is this grammar correct?" and accepted it, the suggestion unit makes a similar suggestion. If the user has previously asked, "Please tell me the meaning of this word," and rejected it, the suggestion unit makes a new suggestion while avoiding that suggestion. Individually customized suggestions are made based on the user's past suggestion results. In this way, the accuracy of the suggestion can be improved by referring to the user's past suggestion results. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's past suggestion result data into the generation AI and cause the generation AI to improve the accuracy of the suggestions.

[0083] The suggestion unit can estimate the user's emotions and adjust the amount of suggestions based on the estimated user emotions. The suggestion unit, for example, analyzes the user's voice tone and facial expression to estimate the emotions. For example, the suggestion unit calculates an emotion score based on changes in voice tone and facial expression. The suggestion unit can also estimate the user's emotions and adjust the length of suggestions using a generation AI. For example, if the user is nervous, the suggestion unit provides short and concise suggestions. If the user is relaxed, the suggestion unit provides detailed suggestions. If the user is excited, the suggestion unit provides suggestions including encouraging words. This allows the length of suggestions to be adjusted based on the user's emotions, thereby providing suggestions that are appropriate for the user. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the suggestion unit can be performed, for example, using the generation AI, or can be performed without using the generation AI. For example, the suggestion unit can input the user's voice data and image data into the generation AI and have the generation AI estimate emotions and adjust the length of suggestions.

[0084] When making a proposal, the suggestion unit can determine the order of proposals based on the submission time of the utterances. The suggestion unit, for example, evaluates the submission time of the user's utterances and determines the order of proposals. For example, the suggestion unit prioritizes proposals for recent utterances. The suggestion unit can also postpone proposals for past utterances. The suggestion unit can also prioritize proposals for utterances submitted during a specific time period. For example, the suggestion unit prioritizes proposals for utterances recently submitted by the user. The suggestion unit postpones proposals for utterances submitted by the user in the past. The suggestion unit prioritizes proposals for utterances submitted by the user during a specific time period. In this way, by determining the priority of proposals based on the submission time of the utterances, it is possible to provide proposals suitable for the user. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input the user's utterance data into the generation AI and cause the generation AI to evaluate the submission time and determine the order of proposals.

[0085] The suggestion unit can adjust the order of suggestions based on the relevance of the utterances when making suggestions. The suggestion unit, for example, evaluates the relevance of the user's utterances and adjusts the order of suggestions. For example, the suggestion unit prioritizes suggestions for important utterances. The suggestion unit can also postpone suggestions for general utterances. The suggestion unit can also prioritize suggestions for utterances that the user has indicated they particularly want to know about. For example, when the user asks, "Is this grammar correct?", the suggestion unit prioritizes suggestions. When the user asks, "What did you do today?", the suggestion unit postpones suggestions. When the user asks, "Please tell me the meaning of this word," the suggestion unit prioritizes suggestions. In this way, by adjusting the order of suggestions based on the relevance of the utterances, suggestions suitable for the user can be provided. Some or all of the above-described processing in the suggestion unit may be performed using, or without, a generation AI. For example, the suggestion unit can input user utterance data into a generation AI and cause the generation AI to evaluate the relevance and adjust the order of suggestions.

[0086] The suggestion unit can adjust the terminology of the suggestion according to the user's level of expertise when making a suggestion. The suggestion unit, for example, evaluates the user's level of expertise and adjusts the terminology of the suggestion. For example, the suggestion unit makes suggestions using simple language to a beginner-level user. The suggestion unit can also make suggestions including appropriate technical terms to an intermediate-level user. The suggestion unit can also make suggestions using a lot of technical terms to an advanced-level user. For example, when a beginner-level user asks, "Is this grammar correct?", the suggestion unit makes suggestions using simple language. When an intermediate-level user asks, "What does this word mean?", the suggestion unit makes suggestions including appropriate technical terms. When an advanced-level user asks, "Is this pronunciation correct?", the suggestion unit makes suggestions using a lot of technical terms. In this way, by adjusting the use of technical terms in the suggestion according to the user's level of expertise, it is possible to provide suggestions suitable for the user. Some or all of the above-mentioned processing by the suggestion unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the suggestion unit can input the user's expertise level data into the generation AI and cause the generation AI to adjust the terms of the suggestions. === Hard Collateral 1-1 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit receives a user's utterance using the microphone 38B or touch panel 38A of the smart device 14, and converts the speech into text using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the user's utterance using a generation AI, and provides feedback. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate topics based on the analysis results. === Hard Collateral 1-2 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and suggestion unit is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit receives a user's utterance using the microphone 238 of the smart glasses 214 and converts the speech into text using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the user's utterance using a generation AI, and provides feedback. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate topics based on the analysis results. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and suggestion unit is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit receives a user's utterance using the microphone 238 of the headset-type terminal 314 and converts the speech into text using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the user's utterance using a generation AI, and provides feedback. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate topics based on the analysis results. === Hard Collateral 1-4 === Each of the multiple elements including the above-mentioned reception unit, analysis unit, and suggestion unit is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit receives a user's utterance using the microphone 238 of the robot 414 and converts the speech into text using the control unit 46A. The analysis unit is realized, for example, by the specific processing unit 290 of the data processing device 12, analyzes the user's utterance using a generation AI, and provides feedback. The suggestion unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and suggests appropriate topics based on the analysis results.

[0087] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0088] When accepting a user's utterance, the reception unit can analyze the user's speaking speed and adjust the timing of appropriate feedback. For example, if the user speaks at a fast pace, the reception unit provides feedback quickly to match the user's rhythm. Conversely, if the user speaks slowly, the reception unit provides feedback with a slight delay to give the user time to think. Also, if the user pauses, the reception unit can provide feedback during that time, providing information before the user makes the next utterance. This makes it possible to provide a more natural conversation experience by adjusting the timing of feedback according to the user's speaking speed.

[0089] When analyzing a user's utterances, the analysis unit can provide feedback taking into account the background information of the user's utterances. For example, if a user makes a utterance related to a particular culture or region, the analysis unit can provide background information about that culture or region. Also, if a user makes a utterance related to a particular field of expertise, the analysis unit can provide specialized information about that field. Furthermore, if a user makes a utterance related to content that they have learned in the past, the analysis unit can also provide feedback to help them review that content. In this way, by providing feedback that takes into account the background information of the user's utterances, it is possible to improve learning effectiveness.

[0090] When suggesting topics based on the user's comments and past conversation history, the suggestion unit can select topics taking into consideration the user's current mood and physical condition. For example, if the user is tired, it can suggest relaxing topics. If the user is energetic, it can suggest challenging topics. If the user is stressed, it can also suggest topics that are useful for relieving stress. This makes it possible to support more effective learning by suggesting topics that suit the user's mood and physical condition.

[0091] When accepting a user's utterance, the reception unit can estimate the intention of the user's utterance and provide appropriate feedback. For example, if the user is asking a question, the reception unit can provide an answer to that question. If the user is expressing an opinion, the reception unit can provide feedback on that opinion. If the user is expressing an emotion, the reception unit can also provide words of empathy or encouragement for that emotion. In this way, by providing feedback that takes into account the intention of the user's utterance, a more natural conversation experience can be provided.

[0092] When accepting a user's utterance, the reception unit can provide feedback taking into account the context of the user's utterance. For example, if the user is continuing what was said in a previous conversation, the reception unit provides feedback taking into account the context. Also, if the user is talking about a specific topic, the reception unit can provide information related to that topic. Furthermore, if the user is talking about multiple topics alternately, the reception unit can provide appropriate feedback for each topic. As a result, providing feedback taking into account the context of the user's utterance can support more effective learning.

[0093] When analyzing a user's utterances, the analysis unit can provide feedback taking into account the emotional nuances of the user's utterances. For example, if the user is happy, the analysis unit can provide feedback that shares that joy. If the user is sad, the analysis unit can understand that sadness and provide words of encouragement. If the user is angry, the analysis unit can also accept the anger and provide advice on how to stay calm. This makes it possible to provide feedback that takes into account the user's emotional nuances, thereby supporting more effective communication.

[0094] When suggesting topics based on the user's utterances and past conversation history, the suggestion unit can select topics taking the user's learning style into consideration. For example, if the user is a visual learner, the suggestion unit can suggest visual learning materials and topics. If the user is an auditory learner, the suggestion unit can suggest audio learning materials and topics related to listening. If the user is an experiential learner, the suggestion unit can also suggest topics based on actual experiences. This makes it possible to support more effective learning by suggesting topics that suit the user's learning style.

[0095] When accepting a user's utterance, the acceptance unit can provide feedback taking into account the frequency of the user's utterances. For example, if the user speaks frequently, the acceptance unit can provide feedback quickly to match the user's rhythm. Conversely, if the user does not speak often, the acceptance unit can provide feedback with a slight delay to give the user time to think. Also, if the user pauses, the acceptance unit can provide feedback during that time, providing information before the user makes the next utterance. This makes it possible to provide a more natural conversation experience by providing feedback according to the frequency of the user's utterances.

[0096] When analyzing a user's speech, the analysis unit can provide feedback taking into account the tone of the user's speech. For example, if the user is speaking in a positive tone, the analysis unit provides feedback that maintains the positive tone. If the user is speaking in a negative tone, the analysis unit can provide feedback that softens the negative tone. If the user is speaking in a neutral tone, the analysis unit can also provide feedback that maintains the neutral tone. This makes it possible to support more effective communication by providing feedback that takes into account the tone of the user's speech.

[0097] When suggesting topics based on the user's utterances and past conversation history, the suggestion unit can select topics taking the user's learning goals into consideration. For example, if the user's goal is to pass a specific exam, the suggestion unit can suggest topics related to that exam. If the user's goal is to acquire a specific skill, the suggestion unit can suggest topics related to that skill. If the user's goal is to learn about a specific culture or region, the suggestion unit can also suggest topics related to that culture or region. This makes it possible to support more effective learning by suggesting topics according to the user's learning goals.

[0098] The processing flow of the second embodiment will be briefly explained below.

[0099] Step 1: The reception unit receives user input. User input can include voice, text, gestures, etc. For example, voice input is received by a microphone and converted into text data using voice recognition technology. Text input can be entered directly using a keyboard or touch screen. Gesture input is captured by a camera and analyzed using gesture recognition technology. Step 2: The analysis unit uses the generation AI to analyze the utterances received by the reception unit and provide feedback. The analysis is performed using natural language processing technology. For example, a text generation AI (LLM) can be used to analyze the utterances and point out grammatical errors. It can also use speech analysis technology to point out pronunciation errors. Furthermore, it can use sentiment analysis technology to estimate the user's emotions and reflect them in the feedback. Step 3: The suggestion unit uses the generation AI to suggest topics based on the results of the analysis by the analysis unit. Topics are selected based on learning content, interests, current events, etc. For example, appropriate topics can be suggested based on the user's comments and past conversation history. Topics can also be suggested based on the user's learning progress, areas of interest, and emotions.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0104] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0105] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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).

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0136] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0137] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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).

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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).

[0157] 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.

[0158] 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."

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] [Explanation of symbols]

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

Claims

1. a reception unit that receives comments from users; an analysis unit that analyzes the utterances received by the reception unit and provides feedback; a suggestion unit that proposes topics based on the results of the analysis by the analysis unit; Equipped with A system characterized by:

2. The analysis unit Analyzes user speech, points out grammatical errors, and teaches expressions 2. The system of claim 1.

3. The proposal unit Suggest topics based on what users say and their past conversation history 2. The system of claim 1.

4. The reception unit Estimates the user's emotions and adjusts the timing of speech acceptance based on the estimated user emotions.

2. The system of claim 1.

5. The reception unit Analyze the user's past speech history and select the reception method 2. The system of claim 1.

6. The reception unit Filter comments based on the user's current learning progress and interests 2. The system of claim 1.

7. The reception unit When accepting a speech, select the acceptance method according to the user's input method.

2. The system of claim 1.

8. The reception unit Estimate the user's emotions and determine the order of comments to be accepted based on the estimated user emotions.

2. The system of claim 1.

9. The reception unit When accepting comments, the system prioritizes the acceptance of comments by taking into account the user's geographical location information.

2. The system of claim 1.

10. The reception unit When accepting comments, analyze the user's social media activity and accept the comment.

2. The system of claim 1.

Images