System

Abstract

To support effective language learning by performing feedback and correction in real time.SOLUTION: A system according to an embodiment includes a reception unit, a generation unit, a feedback unit, a recording unit, and a plan providing unit. The reception unit receives a user's utterance as a voice input. The generation unit analyzes the voice accepted by the acceptance unit and generates a response. The feedback unit performs feedback or correction in real time based on the response generated by the generation unit. The recording unit records the feedback and the correction performed by the feedback unit. The plan providing unit provides a learning plan tailored to each user based on the learning progress recorded by the recording 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] Conventional technologies do not provide sufficient real-time feedback or correction in language learning, leaving room for improvement in improving learning effectiveness.

[0005] The system according to the embodiment aims to provide real-time feedback and correction to support effective language learning. [Means for solving the problem]

[0006] The system according to the embodiment includes a receiving unit, a generating unit, a feedback unit, a recording unit, and a plan providing unit. The receiving unit receives a user's utterance as a voice input. The generating unit analyzes the voice received by the receiving unit and generates a response. The feedback unit provides feedback and corrections in real time based on the response generated by the generating unit. The recording unit records the feedback and corrections made by the feedback unit. The plan providing unit provides a learning plan tailored to each individual user based on the learning progress recorded by the recording unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide real-time feedback and correction to support effective language learning. [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 language learning system according to an embodiment of the present invention allows users to learn a foreign language and improve their conversational skills through a natural dialogue. The system accepts user speech as voice input, analyzes it using a generation AI, and generates an appropriate response. Furthermore, the system provides real-time feedback and corrections to support effective learning. For example, when a user speaks to an app in a foreign language, the speech is input into the system. The generation AI then analyzes the input speech and generates an appropriate response. The generation AI understands the user's speech and generates a natural-sounding response that matches the context. Furthermore, the system provides real-time feedback and corrections to the user's speech. For example, if a user makes a pronunciation error, the system provides feedback such as, "That pronunciation is slightly off. The correct pronunciation is as follows." Similarly, if a user makes a grammatical error, the system provides correction such as, "That grammar is incorrect. The correct grammar is as follows." This allows users to learn a foreign language through a natural dialogue while receiving real-time feedback and corrections, effectively improving their conversational skills. The language learning system allows users to learn a foreign language through a natural dialogue and improve their conversational skills. For example, when a user practices everyday conversation, the system generates appropriate responses and provides feedback and corrections to the user's utterances, providing a learning experience that is closer to real conversation. The system can also record the user's learning progress and provide a learning plan tailored to each individual user. For example, if a user has difficulty with a particular grammar or pronunciation, the system can suggest a plan to focus on that area. This allows users to learn a foreign language effectively at their own pace.

[0029] A language learning system according to an embodiment includes a reception unit, a generation unit, a feedback unit, a recording unit, and a plan providing unit. The reception unit receives a user's speech as a voice input. The user's speech may include, but is not limited to, everyday conversation, business conversation, and academic conversation. The reception unit receives speech using, for example, a microphone. The reception unit can also convert the speech into text data using speech recognition technology. The generation unit analyzes the speech received by the reception unit and generates an appropriate response using a generation AI. The generation AI generates a response using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a natural response according to the context using the generation AI. For example, the generation AI understands the content of the user's speech and generates an appropriate response. The feedback unit provides feedback and corrections to the user's speech based on the response generated by the generation unit. For example, the feedback unit points out errors in the user's pronunciation or grammar and suggests correct pronunciation and grammar. The feedback unit can also provide feedback in real time. The recording unit records the feedback and corrections made by the feedback unit. The recording unit, for example, stores the feedback and corrections in a database. The recording unit can also record the user's learning progress. The plan providing unit provides a learning plan tailored to each individual user based on the learning progress recorded by the recording unit. The plan providing unit, for example, proposes a plan that focuses on the user's weak areas. The plan providing unit can also provide a curriculum based on the user's learning goals. This allows the language learning system according to the embodiment to enable the user to learn a foreign language in a natural, interactive format and improve their conversational ability. For example, when the user practices everyday conversation, the system generates appropriate responses and provides feedback and corrections to the user's utterances, providing a learning experience that is closer to actual conversation. The system can also record the user's learning progress and provide a learning plan tailored to each individual user. For example, if the user is concerned about weaknesses in specific grammar or pronunciation, the system can propose a plan that focuses on those areas.This allows users to effectively learn a foreign language at their own pace.

[0030] The reception unit can analyze the user's past utterance history and select a voice input method. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has preferred gesture input in the past, the reception unit can preferentially suggest gesture input. In this way, the optimal voice input method can be selected by analyzing the user's past utterance history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past utterance history data to the generation AI and cause the generation AI to select the optimal voice input method.

[0031] The reception unit can filter speech input based on the user's current learning status and areas of interest. For example, the reception unit can only accept utterances related to the grammar the user is currently studying. The reception unit can also accept only utterances related to topics in which the user is interested. The reception unit can also accept only utterances of an appropriate level of difficulty according to the user's learning progress. This makes it possible to accept appropriate speech input by filtering based on the user's current learning status and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's learning status data to a generation AI and have the generation AI perform filtering.

[0032] The reception unit can select a reception means according to the user's input method when receiving voice input. For example, if the user selects voice input, the reception unit can use voice recognition technology to receive the input. Furthermore, if the user selects text input, the reception unit can also use keyboard input to receive the input. Furthermore, if the user selects gesture input, the reception unit can also use gesture recognition technology to receive the input. This allows for more appropriate voice input to be received by selecting the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal reception means.

[0033] When generating a response, the generation unit can adjust the level of detail of the response based on the importance of the utterance. For example, the generation unit generates a detailed response for an important utterance. The generation unit can also generate a concise response for a general utterance. The generation unit can also generate a focused response for a utterance related to the user's learning goal. In this way, by adjusting the level of detail of the response based on the importance of the utterance, a more appropriate response can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's utterance data into the generation AI and cause the generation AI to adjust the level of detail of the response.

[0034] When generating a response, the generation unit can apply different response algorithms depending on the category of the utterance. For example, the generation unit can apply a casual response algorithm to everyday conversation. The generation unit can also apply a formal response algorithm to business conversation. The generation unit can also apply a specialized response algorithm to academic conversation. In this way, by applying different response algorithms depending on the category of the utterance, more appropriate responses can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user utterance category data into the generation AI and cause the generation AI to apply the response algorithm.

[0035] When generating a response, the generation unit can improve the accuracy of the response by referring to the user's past response results. The generation unit generates a response by referring to, for example, response patterns that the user has preferred in the past. The generation unit can also avoid response patterns that the user has been dissatisfied with in the past. The generation unit can also generate an optimal response based on the user's past response history. In this way, the accuracy of the response can be improved by referring to the user's past response results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past response result data into the generation AI and cause the generation AI to improve the accuracy of the response.

[0036] The feedback unit can adjust the level of detail of the feedback based on the importance of the utterance when providing feedback. For example, the feedback unit can provide detailed feedback for important utterances. The feedback unit can also provide concise feedback for general utterances. The feedback unit can also provide focused feedback for utterances related to the user's learning goals. In this way, by adjusting the level of detail of the feedback based on the importance of the utterance, more appropriate feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's utterance data into a generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0037] The feedback unit can apply different feedback algorithms depending on the category of the utterance when providing feedback. For example, the feedback unit can apply a casual feedback algorithm to everyday conversation. The feedback unit can also apply a formal feedback algorithm to business conversation. The feedback unit can also apply a specialized feedback algorithm to academic conversation. In this way, by applying different feedback algorithms depending on the category of the utterance, more appropriate feedback can be provided. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the user's utterance category data into the generation AI and cause the generation AI to apply the feedback algorithm.

[0038] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results. The feedback unit, for example, provides feedback by referring to feedback patterns that the user has preferred in the past. The feedback unit can also avoid feedback patterns that the user has been dissatisfied with in the past. The feedback unit can also provide optimal feedback based on the user's past feedback history. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback result data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0039] The recording unit can optimize the recording algorithm by referring to previously recorded data during recording. The recording unit, for example, selects an optimal recording algorithm based on the user's previously recorded data. The recording unit can also analyze the user's previously recorded data and improve the recording algorithm. The recording unit can also improve recording accuracy by referring to the user's previously recorded data. In this way, the recording algorithm can be optimized by referring to the previously recorded data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the previously recorded data into a generation AI and cause the generation AI to optimize the recording algorithm.

[0040] The recording unit can update the recorded data by reflecting user feedback during recording. The recording unit updates the recorded data based on, for example, feedback provided by the user. The recording unit can also improve the accuracy of the recorded data by reflecting the user feedback. The recording unit can also improve the content of the recorded data by referring to the user feedback. In this way, the recorded data can be updated by reflecting the user feedback. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user feedback data to the generation AI and cause the generation AI to update the recorded data.

[0041] When providing a study plan, the plan providing unit can provide an optimal plan by referring to the user's past study history. The plan providing unit provides an optimal study plan based on, for example, the user's past study history. The plan providing unit can also analyze the user's past study history and improve the study plan. The plan providing unit can also improve the accuracy of the study plan by referring to the user's past study history. In this way, an optimal study plan can be provided by referring to the user's past study history. Some or all of the above-mentioned processing in the plan providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan providing unit can input the user's past study history data into the generation AI and cause the generation AI to provide an optimal study plan.

[0042] When providing a study plan, the plan providing unit can customize the content of the plan based on the user's current study situation. The plan providing unit, for example, provides an optimal study plan based on the user's current study situation. The plan providing unit can also analyze the user's current study situation and improve the study plan. The plan providing unit can also improve the accuracy of the study plan by referring to the user's current study situation. This makes it possible to provide a more appropriate study plan by customizing the content of the plan based on the user's current study situation. Some or all of the above-mentioned processing in the plan providing unit may be performed using AI, for example, or may be performed without using AI. For example, the plan providing unit can input the user's current study situation data into the generation AI and have the generation AI customize the content of the plan.

[0043] When providing a study plan, the plan providing unit can improve the content of the plan by reflecting user feedback. The plan providing unit, for example, improves the study plan based on user feedback. The plan providing unit can also improve the accuracy of the study plan by reflecting user feedback. The plan providing unit can also improve the content of the study plan by referring to user feedback. In this way, the content of the study plan can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the plan providing unit may be performed using AI, for example, or may be performed without using AI. For example, the plan providing unit can input user feedback data into a generation AI and cause the generation AI to improve the content of the plan.

[0044] When providing a study plan, the plan providing unit can provide an optimal plan by taking into account the user's geographical location information. For example, if the user is in a specific area, the plan providing unit can provide a study plan related to that area. Furthermore, if the user is traveling, the plan providing unit can provide a study plan related to the travel destination. Furthermore, if the user is at home, the plan providing unit can provide a study plan related to the user's home. In this way, an optimal study plan can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the plan providing unit may be performed using AI, for example, or may be performed without using AI. For example, the plan providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide an optimal study plan.

[0045] When providing a study plan, the plan providing unit can analyze the user's social media activity and suggest plan contents. For example, the plan providing unit can provide a study plan related to topics the user is talking about on social media. The plan providing unit can also provide a study plan related to the activities of the user's friends on social media. The plan providing unit can also provide a study plan related to the content of the user's social media posts. This makes it possible to provide a more appropriate study plan by analyzing the user's social media activity. Some or all of the above-described processing by the plan providing unit can be performed using AI, for example, or without AI. For example, the plan providing unit can input the user's social media activity data into a generation AI and have the generation AI suggest plan contents.

[0046] When providing a study plan, the plan providing unit can customize the content of the plan by reflecting the user's past feedback. The plan providing unit customizes the study plan based on, for example, the user's past feedback. The plan providing unit can also improve the accuracy of the study plan by reflecting the user's past feedback. The plan providing unit can also customize the content of the study plan by referring to the user's past feedback. In this way, the content of the study plan can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing by the plan providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the content of the plan.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] When accepting a user's speech as voice input, the reception unit can analyze the user's speech rate and encourage the user to speak at an appropriate rate. For example, if the user is speaking very quickly, the reception unit can provide feedback such as "Please speak a little more slowly." Also, if the user is speaking very slowly, the reception unit can provide feedback such as "Try speaking a little faster." Furthermore, it is possible to adjust the response speed of the entire system according to the user's speech rate. This allows the user to receive appropriate feedback according to their speech rate, improving learning effectiveness.

[0049] The feedback unit can estimate the intention of the user's statement based on the content of the statement and provide appropriate feedback. For example, if the user makes a statement in the form of a question, the feedback unit can provide an answer to the question. Also, if the user expresses an opinion, the feedback unit can provide support or counterargument to the opinion. Furthermore, if the user expresses gratitude, the feedback unit can provide a response such as "You're welcome." This makes it possible to provide appropriate feedback according to the user's intention of the statement and improve the quality of the dialogue.

[0050] When recording the user's learning progress, the recording unit can analyze the user's learning style and select the optimal recording method. For example, if the user prefers visual learning, the recording unit can record the learning progress using graphs or charts. If the user prefers auditory learning, the recording unit can also record the learning progress using voice memos. Furthermore, if the user prefers tactile learning, the recording unit can record the learning progress using interactive tools. This makes it possible to provide the optimal recording method according to the user's learning style and improve learning effectiveness.

[0051] The reception unit can analyze the content of the user's utterance and generate an appropriate response based on the topic of the utterance. For example, if the user is talking about travel, the reception unit can provide travel-related information. If the user is talking about work, the reception unit can also provide work-related advice. Furthermore, if the user is talking about a hobby, the reception unit can provide topics related to that hobby. This makes it possible to provide an appropriate response according to the user's utterance topic and improve the quality of the dialogue.

[0052] The feedback unit can evaluate the accuracy of the user's utterance based on the content of the user's utterance and provide appropriate feedback. For example, if the user utters with correct grammar, the feedback unit can provide positive feedback such as "That is correct grammar." Also, if the user utters with incorrect grammar, the feedback unit can provide corrective feedback such as "That grammar is incorrect. The correct grammar is as follows." Furthermore, if the user makes a pronunciation error, the feedback unit can provide feedback such as "That pronunciation is slightly off. The correct pronunciation is as follows." This allows the user to receive appropriate feedback according to the accuracy of their utterance, thereby improving their learning effectiveness.

[0053] When recording the user's learning progress, the recording unit can customize the recording content based on the user's learning goals. For example, if the user's goal is to master a specific grammar, the recording unit can focus on recording learning progress related to that grammar. Also, if the user's goal is to improve pronunciation, the recording unit can focus on recording learning progress related to pronunciation. Furthermore, if the user's goal is to improve conversational ability, the recording unit can focus on recording learning progress related to conversation. This makes it possible to provide appropriate recording content according to the user's learning goals and improve learning effectiveness.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The reception unit receives the user's speech as voice input. The user's speech may be everyday conversation, business conversation, academic conversation, etc. The reception unit receives the speech using a microphone and can convert the speech into text data using speech recognition technology. Step 2: The generation unit analyzes the voice received by the reception unit and generates an appropriate response. The generation unit uses a generation AI (e.g., a text generation AI) to generate a natural response according to the context. The generation AI understands the content of the user's speech and generates an appropriate response. Step 3: The feedback unit provides feedback and corrections to the user's utterance based on the response generated by the generation unit. The feedback unit points out the user's pronunciation and grammar errors and suggests correct pronunciation and grammar. The feedback unit provides feedback in real time. Step 4: The recording unit records the feedback and corrections made by the feedback unit. The recording unit saves the feedback and corrections in a database and records the user's learning progress. Step 5: The plan provider provides a learning plan tailored to each individual user based on the learning progress recorded by the recorder. The plan provider proposes a plan that focuses on the user's weak areas and provides a curriculum that meets the user's learning goals.

[0056] (Example 2) A language learning system according to an embodiment of the present invention allows users to learn a foreign language and improve their conversational skills through a natural dialogue. The system accepts user speech as voice input, analyzes it using a generation AI, and generates an appropriate response. Furthermore, the system provides real-time feedback and corrections to support effective learning. For example, when a user speaks to an app in a foreign language, the speech is input into the system. The generation AI then analyzes the input speech and generates an appropriate response. The generation AI understands the user's speech and generates a natural-sounding response that matches the context. Furthermore, the system provides real-time feedback and corrections to the user's speech. For example, if a user makes a pronunciation error, the system provides feedback such as, "That pronunciation is slightly off. The correct pronunciation is as follows." Similarly, if a user makes a grammatical error, the system provides correction such as, "That grammar is incorrect. The correct grammar is as follows." This allows users to learn a foreign language through a natural dialogue while receiving real-time feedback and corrections, effectively improving their conversational skills. The language learning system allows users to learn a foreign language through a natural dialogue and improve their conversational skills. For example, when a user practices everyday conversation, the system generates appropriate responses and provides feedback and corrections to the user's utterances, providing a learning experience that is closer to real conversation. The system can also record the user's learning progress and provide a learning plan tailored to each individual user. For example, if a user has difficulty with a particular grammar or pronunciation, the system can suggest a plan to focus on that area. This allows users to learn a foreign language effectively at their own pace.

[0057] A language learning system according to an embodiment includes a reception unit, a generation unit, a feedback unit, a recording unit, and a plan providing unit. The reception unit receives a user's speech as a voice input. The user's speech may include, but is not limited to, everyday conversation, business conversation, and academic conversation. The reception unit receives speech using, for example, a microphone. The reception unit can also convert the speech into text data using speech recognition technology. The generation unit analyzes the speech received by the reception unit and generates an appropriate response using a generation AI. The generation AI generates a response using, for example, a text generation AI (e.g., LLM). The generation unit can also generate a natural response according to the context using the generation AI. For example, the generation AI understands the content of the user's speech and generates an appropriate response. The feedback unit provides feedback and corrections to the user's speech based on the response generated by the generation unit. For example, the feedback unit points out errors in the user's pronunciation or grammar and suggests correct pronunciation and grammar. The feedback unit can also provide feedback in real time. The recording unit records the feedback and corrections made by the feedback unit. The recording unit, for example, stores the feedback and corrections in a database. The recording unit can also record the user's learning progress. The plan providing unit provides a learning plan tailored to each individual user based on the learning progress recorded by the recording unit. The plan providing unit, for example, proposes a plan that focuses on the user's weak areas. The plan providing unit can also provide a curriculum based on the user's learning goals. This allows the language learning system according to the embodiment to enable the user to learn a foreign language in a natural, interactive format and improve their conversational ability. For example, when the user practices everyday conversation, the system generates appropriate responses and provides feedback and corrections to the user's utterances, providing a learning experience that is closer to actual conversation. The system can also record the user's learning progress and provide a learning plan tailored to each individual user. For example, if the user is concerned about weaknesses in specific grammar or pronunciation, the system can propose a plan that focuses on those areas.This allows users to effectively learn a foreign language at their own pace.

[0058] The reception unit can estimate the user's emotions and adjust the timing of receiving the voice input based on the estimated user emotions. For example, if the user is nervous, the reception unit waits until the user relaxes before accepting the voice input. Furthermore, if the user is excited, the reception unit can immediately accept the voice input and start a dialogue. Furthermore, if the user is tired, the reception unit can wait until the user has taken a short break before accepting the voice input. This allows the voice input to be accepted at a more appropriate timing by adjusting the timing of accepting the voice input according to the user's emotions. The emotion estimation is realized using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit can input the user's voice data to the generation AI and have the generation AI estimate the user's emotions.

[0059] The reception unit can analyze the user's past utterance history and select a voice input method. For example, if the user has preferred voice input in the past, the reception unit can preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit can preferentially suggest text input. Furthermore, if the user has preferred gesture input in the past, the reception unit can preferentially suggest gesture input. In this way, the optimal voice input method can be selected by analyzing the user's past utterance history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's past utterance history data to the generation AI and cause the generation AI to select the optimal voice input method.

[0060] The reception unit can filter speech input based on the user's current learning status and areas of interest. For example, the reception unit can only accept utterances related to the grammar the user is currently studying. The reception unit can also accept only utterances related to topics in which the user is interested. The reception unit can also accept only utterances of an appropriate level of difficulty according to the user's learning progress. This makes it possible to accept appropriate speech input by filtering based on the user's current learning status and areas of interest. Some or all of the above-described processing in the reception unit can be performed using, for example, AI, or can be performed without using AI. For example, the reception unit can input the user's learning status data to a generation AI and have the generation AI perform filtering.

[0061] The reception unit can select a reception means according to the user's input method when receiving voice input. For example, if the user selects voice input, the reception unit can use voice recognition technology to receive the input. Furthermore, if the user selects text input, the reception unit can also use keyboard input to receive the input. Furthermore, if the user selects gesture input, the reception unit can also use gesture recognition technology to receive the input. This allows for more appropriate voice input to be received by selecting the optimal reception means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to a generation AI and have the generation AI select the optimal reception means.

[0062] The generation unit can estimate the user's emotions and adjust the way a response is expressed based on the estimated user's emotions. For example, if the user is nervous, the generation unit can generate a response in a gentle tone. If the user is relaxed, the generation unit can also generate a response in a friendly tone. If the user is excited, the generation unit can also generate a response in an energetic tone. This allows for adjusting the way a response is expressed according to the user's emotions, thereby generating a more appropriate response. 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-described processing in the generation unit can be performed using, for example, AI, or without AI. For example, the generation unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way a response is expressed.

[0063] When generating a response, the generation unit can adjust the level of detail of the response based on the importance of the utterance. For example, the generation unit generates a detailed response for an important utterance. The generation unit can also generate a concise response for a general utterance. The generation unit can also generate a focused response for a utterance related to the user's learning goal. In this way, by adjusting the level of detail of the response based on the importance of the utterance, a more appropriate response can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's utterance data into the generation AI and cause the generation AI to adjust the level of detail of the response.

[0064] When generating a response, the generation unit can apply different response algorithms depending on the category of the utterance. For example, the generation unit can apply a casual response algorithm to everyday conversation. The generation unit can also apply a formal response algorithm to business conversation. The generation unit can also apply a specialized response algorithm to academic conversation. In this way, by applying different response algorithms depending on the category of the utterance, more appropriate responses can be generated. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input user utterance category data into the generation AI and cause the generation AI to apply the response algorithm.

[0065] When generating a response, the generation unit can improve the accuracy of the response by referring to the user's past response results. The generation unit generates a response by referring to, for example, response patterns that the user has preferred in the past. The generation unit can also avoid response patterns that the user has been dissatisfied with in the past. The generation unit can also generate an optimal response based on the user's past response history. In this way, the accuracy of the response can be improved by referring to the user's past response results. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, AI, or may be performed without using AI. For example, the generation unit can input the user's past response result data into the generation AI and cause the generation AI to improve the accuracy of the response.

[0066] The feedback unit can estimate the user's emotions and adjust the way the feedback is expressed based on the estimated user's emotions. For example, if the user is nervous, the feedback unit can provide feedback in a gentle tone. If the user is relaxed, the feedback unit can also provide feedback in a friendly tone. If the user is excited, the feedback unit can also provide feedback in an energetic tone. This allows the feedback expression method to be adjusted according to the user's emotions, thereby providing more appropriate feedback. Emotion estimation is achieved 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-described processing in the feedback unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the feedback unit can input the user's emotion data into the generation AI and cause the generation AI to adjust the way the feedback is expressed.

[0067] The feedback unit can adjust the level of detail of the feedback based on the importance of the utterance when providing feedback. For example, the feedback unit can provide detailed feedback for important utterances. The feedback unit can also provide concise feedback for general utterances. The feedback unit can also provide focused feedback for utterances related to the user's learning goals. In this way, by adjusting the level of detail of the feedback based on the importance of the utterance, more appropriate feedback can be provided. Some or all of the above-described processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's utterance data into a generation AI and cause the generation AI to adjust the level of detail of the feedback.

[0068] The feedback unit can apply different feedback algorithms depending on the category of the utterance when providing feedback. For example, the feedback unit can apply a casual feedback algorithm to everyday conversation. The feedback unit can also apply a formal feedback algorithm to business conversation. The feedback unit can also apply a specialized feedback algorithm to academic conversation. In this way, by applying different feedback algorithms depending on the category of the utterance, more appropriate feedback can be provided. Some or all of the above-mentioned processing in the feedback unit can be performed using, for example, AI, or can be performed without using AI. For example, the feedback unit can input the user's utterance category data into the generation AI and cause the generation AI to apply the feedback algorithm.

[0069] When providing feedback, the feedback unit can improve the accuracy of the feedback by referring to the user's past feedback results. The feedback unit, for example, provides feedback by referring to feedback patterns that the user has preferred in the past. The feedback unit can also avoid feedback patterns that the user has been dissatisfied with in the past. The feedback unit can also provide optimal feedback based on the user's past feedback history. In this way, the accuracy of the feedback can be improved by referring to the user's past feedback results. Some or all of the above-mentioned processing in the feedback unit may be performed using, for example, AI, or may be performed without using AI. For example, the feedback unit can input the user's past feedback result data into the generation AI and cause the generation AI to improve the accuracy of the feedback.

[0070] The recording unit can estimate the user's emotions and select data to record based on the estimated user emotions. For example, if the user is nervous, the recording unit records only important data. Furthermore, if the user is relaxed, the recording unit can also record detailed data. Furthermore, if the user is excited, the recording unit can also record energetic data. By selecting data to record based on the user's emotions, more appropriate data can be recorded. The emotion estimation is realized using an emotion estimation function, for example, with 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-described processing in the recording unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the recording unit can input the user's emotion data into the generation AI and have the generation AI select the data to record.

[0071] The recording unit can optimize the recording algorithm by referring to previously recorded data during recording. The recording unit, for example, selects an optimal recording algorithm based on the user's previously recorded data. The recording unit can also analyze the user's previously recorded data and improve the recording algorithm. The recording unit can also improve recording accuracy by referring to the user's previously recorded data. In this way, the recording algorithm can be optimized by referring to the previously recorded data. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the previously recorded data into a generation AI and cause the generation AI to optimize the recording algorithm.

[0072] The recording unit can update the recorded data by reflecting user feedback during recording. The recording unit updates the recorded data based on, for example, feedback provided by the user. The recording unit can also improve the accuracy of the recorded data by reflecting the user feedback. The recording unit can also improve the content of the recorded data by referring to the user feedback. In this way, the recorded data can be updated by reflecting the user feedback. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input the user feedback data to the generation AI and cause the generation AI to update the recorded data.

[0073] The plan providing unit can estimate the user's emotions and adjust the content of the study plan based on the estimated user's emotions. For example, if the user is nervous, the plan providing unit can provide a study plan with relaxing content. Furthermore, if the user is relaxed, the plan providing unit can provide a study plan with challenging content. Furthermore, if the user is excited, the plan providing unit can provide a study plan with energetic content. By adjusting the content of the study plan based on the user's emotions, a more appropriate study plan can be provided. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the plan providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the plan providing unit can input the user's emotion data into the generation AI and have the generation AI adjust the content of the study plan.

[0074] When providing a study plan, the plan providing unit can provide an optimal plan by referring to the user's past study history. The plan providing unit provides an optimal study plan based on, for example, the user's past study history. The plan providing unit can also analyze the user's past study history and improve the study plan. The plan providing unit can also improve the accuracy of the study plan by referring to the user's past study history. In this way, an optimal study plan can be provided by referring to the user's past study history. Some or all of the above-mentioned processing in the plan providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan providing unit can input the user's past study history data into the generation AI and cause the generation AI to provide an optimal study plan.

[0075] When providing a study plan, the plan providing unit can customize the content of the plan based on the user's current study situation. The plan providing unit, for example, provides an optimal study plan based on the user's current study situation. The plan providing unit can also analyze the user's current study situation and improve the study plan. The plan providing unit can also improve the accuracy of the study plan by referring to the user's current study situation. This makes it possible to provide a more appropriate study plan by customizing the content of the plan based on the user's current study situation. Some or all of the above-mentioned processing in the plan providing unit may be performed using AI, for example, or may be performed without using AI. For example, the plan providing unit can input the user's current study situation data into the generation AI and have the generation AI customize the content of the plan.

[0076] When providing a study plan, the plan providing unit can improve the content of the plan by reflecting user feedback. The plan providing unit, for example, improves the study plan based on user feedback. The plan providing unit can also improve the accuracy of the study plan by reflecting user feedback. The plan providing unit can also improve the content of the study plan by referring to user feedback. In this way, the content of the study plan can be improved by reflecting user feedback. Some or all of the above-mentioned processing in the plan providing unit may be performed using AI, for example, or may be performed without using AI. For example, the plan providing unit can input user feedback data into a generation AI and cause the generation AI to improve the content of the plan.

[0077] The plan providing unit can estimate the user's emotions and prioritize study plans based on the estimated user emotions. For example, if the user is nervous, the plan providing unit can prioritize study plans with relaxing content. Furthermore, if the user is relaxed, the plan providing unit can prioritize study plans with challenging content. Furthermore, if the user is excited, the plan providing unit can prioritize study plans with energetic content. This allows for more appropriate study plans to be provided by prioritizing study plans based on the user's emotions. The emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the plan providing unit can be performed using, for example, an AI, or can be performed without using an AI. For example, the plan providing unit can input the user's emotion data into the generation AI and have the generation AI determine the priority of the study plans.

[0078] When providing a study plan, the plan providing unit can provide an optimal plan by taking into account the user's geographical location information. For example, if the user is in a specific area, the plan providing unit can provide a study plan related to that area. Furthermore, if the user is traveling, the plan providing unit can provide a study plan related to the travel destination. Furthermore, if the user is at home, the plan providing unit can provide a study plan related to the user's home. In this way, an optimal study plan can be provided by taking into account the user's geographical location information. Some or all of the above-described processing in the plan providing unit may be performed using AI, for example, or may be performed without using AI. For example, the plan providing unit can input the user's geographical location information data into the generation AI and cause the generation AI to provide an optimal study plan.

[0079] When providing a study plan, the plan providing unit can analyze the user's social media activity and suggest plan contents. For example, the plan providing unit can provide a study plan related to topics the user is talking about on social media. The plan providing unit can also provide a study plan related to the activities of the user's friends on social media. The plan providing unit can also provide a study plan related to the content of the user's social media posts. This makes it possible to provide a more appropriate study plan by analyzing the user's social media activity. Some or all of the above-described processing by the plan providing unit can be performed using AI, for example, or without AI. For example, the plan providing unit can input the user's social media activity data into a generation AI and have the generation AI suggest plan contents.

[0080] When providing a study plan, the plan providing unit can customize the content of the plan by reflecting the user's past feedback. The plan providing unit customizes the study plan based on, for example, the user's past feedback. The plan providing unit can also improve the accuracy of the study plan by reflecting the user's past feedback. The plan providing unit can also customize the content of the study plan by referring to the user's past feedback. In this way, the content of the study plan can be customized by reflecting the user's past feedback. Some or all of the above-mentioned processing by the plan providing unit may be performed using, for example, AI, or may be performed without using AI. For example, the plan providing unit can input the user's past feedback data into the generation AI and cause the generation AI to customize the content of the plan. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, generation unit, feedback unit, recording unit, and plan providing unit, is implemented, 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 speech as voice input using the microphone 38B of the smart device 14. The generation unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using a generation AI. The feedback unit is implemented, for example, by the control unit 46A of the smart device 14 and points out the user's pronunciation and grammar errors and suggests correct pronunciation and grammar. The recording unit stores the feedback and corrections in the database 24 of the data processing device 12, for example. The plan providing unit is implemented, for example, by the specific processing unit 290 of the data processing device 12 and provides a study plan based on the user's learning progress. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, feedback unit, recording unit, and plan providing 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 the user's speech as voice input using the microphone 238 of the smart glasses 214. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using a generation AI. The feedback unit is realized, for example, by the control unit 46A of the smart glasses 214 and points out the user's pronunciation and grammar errors and suggests correct pronunciation and grammar. The recording unit stores the feedback and corrections in, for example, the database 24 of the data processing device 12. The plan providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a study plan based on the user's study progress. === Hard Collateral 1-3 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, feedback unit, recording unit, and plan providing 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 the user's speech as voice input using the microphone 238 of the headset-type terminal 314. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using a generation AI. The feedback unit is realized, for example, by the control unit 46A of the headset-type terminal 314 and points out the user's pronunciation and grammar errors and suggests correct pronunciation and grammar. The recording unit stores the feedback and corrections in the database 24 of the data processing device 12, for example. The plan providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a study plan based on the user's learning progress. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, generation unit, feedback unit, recording unit, and plan providing 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 the user's utterance as voice input using the microphone 238 of the robot 414. The generation unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and generates an appropriate response using a generation AI. The feedback unit is realized, for example, by the control unit 46A of the robot 414 and points out the user's pronunciation and grammar errors and suggests correct pronunciation and grammar. The recording unit stores the feedback and corrections in, for example, the database 24 of the data processing device 12. The plan providing unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and provides a study plan based on the user's learning progress.

[0081] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0082] When accepting a user's speech as voice input, the reception unit can analyze the user's speech rate and encourage the user to speak at an appropriate rate. For example, if the user is speaking very quickly, the reception unit can provide feedback such as "Please speak a little more slowly." Also, if the user is speaking very slowly, the reception unit can provide feedback such as "Try speaking a little faster." Furthermore, it is possible to adjust the response speed of the entire system according to the user's speech rate. This allows the user to receive appropriate feedback according to their speech rate, improving learning effectiveness.

[0083] The generation unit can estimate the user's emotions and adjust the content of the response based on the estimated user emotions. For example, if the user is sad, the generation unit can generate a response including encouraging words. If the user is happy, the generation unit can also generate a response including empathetic words. Furthermore, if the user is angry, the generation unit can generate a response in a calm tone to calm the user. This allows for the generation of appropriate responses according to the user's emotions and realizes more natural dialogue.

[0084] The feedback unit can estimate the intention of the user's statement based on the content of the statement and provide appropriate feedback. For example, if the user makes a statement in the form of a question, the feedback unit can provide an answer to the question. Also, if the user expresses an opinion, the feedback unit can provide support or counterargument to the opinion. Furthermore, if the user expresses gratitude, the feedback unit can provide a response such as "You're welcome." This makes it possible to provide appropriate feedback according to the user's intention of the statement and improve the quality of the dialogue.

[0085] When recording the user's learning progress, the recording unit can analyze the user's learning style and select the optimal recording method. For example, if the user prefers visual learning, the recording unit can record the learning progress using graphs or charts. If the user prefers auditory learning, the recording unit can also record the learning progress using voice memos. Furthermore, if the user prefers tactile learning, the recording unit can record the learning progress using interactive tools. This makes it possible to provide the optimal recording method according to the user's learning style and improve learning effectiveness.

[0086] The plan providing unit can estimate the user's emotions and adjust the progress rate of the study plan based on the estimated user emotions. For example, if the user is feeling stressed, the plan providing unit can slow down the progress rate of the study plan to allow the user to relax. If the user is motivated, the plan providing unit can speed up the progress rate of the study plan to maintain the user's motivation to study. Furthermore, if the user is tired, the plan providing unit can provide a study plan that includes short breaks. This allows the user to receive an appropriate study plan based on their emotions, improving their learning effectiveness.

[0087] The reception unit can analyze the content of the user's utterance and generate an appropriate response based on the topic of the utterance. For example, if the user is talking about travel, the reception unit can provide travel-related information. If the user is talking about work, the reception unit can also provide work-related advice. Furthermore, if the user is talking about a hobby, the reception unit can provide topics related to that hobby. This makes it possible to provide an appropriate response according to the user's utterance topic and improve the quality of the dialogue.

[0088] The generation unit can estimate the user's emotions and adjust the length of the response based on the estimated user's emotions. For example, if the user is impatient, the generation unit can generate a short and concise response. If the user is relaxed, the generation unit can also generate a detailed response. Furthermore, if the user is excited, the generation unit can generate an energetic response. This makes it possible to provide an appropriate response length according to the user's emotions and improve the quality of the dialogue.

[0089] The feedback unit can evaluate the accuracy of the user's utterance based on the content of the user's utterance and provide appropriate feedback. For example, if the user utters with correct grammar, the feedback unit can provide positive feedback such as "That is correct grammar." Also, if the user utters with incorrect grammar, the feedback unit can provide corrective feedback such as "That grammar is incorrect. The correct grammar is as follows." Furthermore, if the user makes a pronunciation error, the feedback unit can provide feedback such as "That pronunciation is slightly off. The correct pronunciation is as follows." This allows the user to receive appropriate feedback according to the accuracy of their utterance, thereby improving their learning effectiveness.

[0090] When recording the user's learning progress, the recording unit can customize the recording content based on the user's learning goals. For example, if the user's goal is to master a specific grammar, the recording unit can focus on recording learning progress related to that grammar. Also, if the user's goal is to improve pronunciation, the recording unit can focus on recording learning progress related to pronunciation. Furthermore, if the user's goal is to improve conversational ability, the recording unit can focus on recording learning progress related to conversation. This makes it possible to provide appropriate recording content according to the user's learning goals and improve learning effectiveness.

[0091] The plan providing unit can estimate the user's emotions and adjust the content of the study plan based on the estimated user's emotions. For example, if the user is nervous, a study plan with relaxing content can be provided. Also, if the user is relaxed, a study plan with challenging content can be provided. Also, if the user is excited, a study plan with energetic content can be provided. In this way, by adjusting the content of the study plan based on the user's emotions, a more appropriate study plan can be provided.

[0092] The processing flow of the second embodiment will be briefly explained below.

[0093] Step 1: The reception unit receives the user's speech as voice input. The user's speech may be everyday conversation, business conversation, academic conversation, etc. The reception unit receives the speech using a microphone and can convert the speech into text data using speech recognition technology. Step 2: The generation unit analyzes the voice received by the reception unit and generates an appropriate response. The generation unit uses a generation AI (e.g., a text generation AI) to generate a natural response according to the context. The generation AI understands the content of the user's speech and generates an appropriate response. Step 3: The feedback unit provides feedback and corrections to the user's utterance based on the response generated by the generation unit. The feedback unit points out the user's pronunciation and grammar errors and suggests correct pronunciation and grammar. The feedback unit provides feedback in real time. Step 4: The recording unit records the feedback and corrections made by the feedback unit. The recording unit saves the feedback and corrections in a database and records the user's learning progress. Step 5: The plan provider provides a learning plan tailored to each individual user based on the learning progress recorded by the recorder. The plan provider proposes a plan that focuses on the user's weak areas and provides a curriculum that meets the user's learning goals.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0114] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0130] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0131] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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).

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] [Explanation of symbols]

[0166] 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 receiving unit that receives a user's speech as a voice input; a generation unit that analyzes the voice received by the reception unit and generates a response; a feedback unit that provides feedback and correction in real time based on the response generated by the generation unit; a recording unit for recording the feedback and corrections made by the feedback unit; a plan providing unit that provides a learning plan tailored to each individual user based on the learning progress recorded by the recording unit; Equipped with A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of voice input acceptance based on the estimated user emotions.

2. The system of claim 1.

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

4. The reception unit When you speak, it filters based on the user's current learning status and interests.

2. The system of claim 1.

5. The reception unit When inputting voice, select the reception method according to the user's input method 2. The system of claim 1.

6. The generation unit Inferring user emotions and adjusting the way responses are expressed based on the estimated user emotions 2. The system of claim 1.

7. The generation unit When generating a response, adjust the detail of the response based on the importance of the utterance 2. The system of claim 1.

8. The generation unit When generating responses, apply different response algorithms depending on the category of the utterance.

2. The system of claim 1.

9. The generation unit When generating a response, the accuracy of the response is improved by referring to the user's past response results.

2. The system of claim 1.

10. The feedback unit Inferring user emotions and adjusting feedback presentation based on the estimated user emotions 2. The system of claim 1.

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