system

Abstract

The system according to the embodiment aims to allow a user to select a teacher according to the user's level and preferences and manage the progress of learning. [Solution] A system according to an embodiment includes a reception unit, a conversation unit, and a progress management unit. The reception unit receives an instructor selection from a user. The conversation unit conducts a conversation based on the instructor selected by the reception unit. The progress management unit records a history of the conversation conducted by the conversation unit and manages the progress of learning.

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 techniques have had the problem that it is difficult for users to select a teacher according to their level and preferences and to manage their learning progress.

[0005] The system according to the embodiment aims to allow a user to select a teacher according to the user's level and preferences and manage the progress of learning. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, a conversation unit, and a progress management unit. The reception unit receives an instructor selection from a user. The conversation unit conducts a conversation based on the instructor selected by the reception unit. The progress management unit records a history of the conversation conducted by the conversation unit and manages the progress of learning. [Effects of the Invention]

[0007] The system according to the embodiment allows a user to select a teacher according to his / her level and preferences and manage the progress of his / her 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) An English conversation service according to an embodiment of the present invention is a system in which a user selects a teacher based on their level and preferences, converses with them via voice or text, and manages their learning progress. The English conversation service allows a user to access the service using a smartphone app or web browser and select a teacher from a variety of pre-prepared characters based on their level and preferences. Next, the user selects the conversation method (voice or text). This allows the service to be used even while traveling, such as on a train. For example, in an English conversation service, a user accesses the service and selects a teacher. All teachers are AI, and various characters are available. For example, there are characters for beginners and characters specialized in business English. The user can select a teacher based on their English level and preferences. Next, the user selects the conversation method. If the user selects voice conversation, the user converses with the AI ​​teacher using a microphone. If the user selects text conversation, the user converses with the AI ​​teacher using a keyboard in chat format. This allows the user to select the conversation method best suited to their situation. Furthermore, the English conversation service records the user's usage history and manages their learning progress. For example, the service records information such as which character the user spoke to, what kind of conversation the user had, and their level of understanding. This allows users to check their learning progress and receive advice on how to move on to the next step. This allows the English conversation service to provide an English conversation service in which users can choose a teacher according to their level and preferences and select either audio or text as the conversation method. This allows users to practice English conversation anytime, anywhere. This allows the English conversation service to provide an English conversation service in which users can choose a teacher according to their level and preferences and select either audio or text as the conversation method. For example, users can practice English conversation on the train during their commute or during their breaks. In addition, AI teachers are available 24 hours a day, so users can study at their own convenience.

[0029] An English conversation service according to an embodiment includes a reception unit, a conversation unit, and a progress management unit. The reception unit accepts a user's selection of an instructor. The user can access the service using, for example, a smartphone app or a web browser and select an instructor from a variety of pre-prepared characters based on their level and preferences. For example, the reception unit provides characters for beginners and characters specialized in business English. The conversation unit conducts a conversation based on the instructor selected by the reception unit. For example, when the conversation unit conducts an audio conversation, the user uses a microphone to converse with the AI ​​instructor. Alternatively, when the conversation unit conducts a text conversation, the user can also use a keyboard to chat with the AI ​​instructor. Furthermore, the conversation unit provides characters based on the user's level and preferences. For example, the conversation unit can provide characters for beginners and characters specialized in business English. The progress management unit records a history of conversations conducted by the conversation unit and manages the user's learning progress. For example, the progress management unit records information such as the type of conversation the user had with which character and the user's level of understanding. This allows the user to check their learning progress and receive advice on how to proceed to the next step. As a result, the English conversation service according to the embodiment allows the user to select a teacher according to their level and preferences, engage in conversations, and manage their learning progress.

[0030] The reception unit can accept a user's instructor selection using a smartphone app or a web browser. Examples of smartphone apps include, but are not limited to, iOS apps and Android apps. For example, the reception unit allows a user to select an instructor using the smartphone app. The reception unit can also accept a user's instructor selection using a web browser. For example, the reception unit can select an instructor using a web browser such as Chrome, Firefox, or Safari. This allows a user to select an instructor via a smartphone or web browser. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's selection history into AI and recommend the most suitable instructor.

[0031] The conversation unit can conduct a conversation by voice or text. Voice conversation includes, but is not limited to, voice recognition technology and voice synthesis technology, for example. The conversation unit can, for example, allow a user to have a voice conversation with an AI instructor using a microphone. When conducting a text conversation, the conversation unit can use chatbot technology or natural language processing technology. For example, the conversation unit can allow a user to have a chat-style conversation with an AI instructor using a keyboard. This allows the user to have a conversation by voice or text. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input voice recognition technology into AI and convert voice data into text data.

[0032] The progress management unit can record the user's usage history and manage the user's learning progress. The usage history includes, but is not limited to, for example, the content of conversations, usage time, and learning progress. The progress management unit records information such as which characters the user had conversations with and the level of understanding the user has. This allows the user to check their learning progress and receive advice on how to proceed to the next step. Some or all of the above-described processing in the progress management unit can be performed using, for example, AI, or can be performed without using AI. For example, the progress management unit can input the user's usage history into AI to evaluate the user's learning progress.

[0033] The conversation unit can prepare characters according to the user's level and preferences. Examples of characters include, but are not limited to, avatars and virtual characters. The conversation unit can provide, for example, characters for beginners or characters specialized in business English. This allows the user to select a character according to their level and preferences. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input characters according to the user's level and preferences into AI and recommend the most suitable character.

[0034] The conversation unit can be available 24 hours a day. 24-hour availability includes, but is not limited to, a shift system or automatic response by AI. For example, the conversation unit has an AI instructor available 24 hours a day, allowing users to progress with their studies according to their own convenience. This allows users to have conversations at any time. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, or may be performed without using AI. For example, the conversation unit can provide 24-hour availability by deploying AI instructors on a shift system.

[0035] The reception unit can analyze the user's past instructor selection history and automatically suggest the most suitable instructor. For example, the reception unit can analyze the tendency of instructors selected by the user in the past and suggest similar instructors. For example, the reception unit can also preferentially suggest instructors that the user has given high ratings to in the past. For example, the reception unit can also suggest instructors of an appropriate level based on the level of instructors selected by the user in the past. This makes it possible to suggest the most suitable instructor based on the user's past selection history. Some or all of the above-mentioned processing by 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 instructor selection history into AI and automatically suggest the most suitable instructor.

[0036] The reception unit can filter instructors based on the user's current learning goals. For example, if the user wants to learn business English, the reception unit can suggest an instructor specializing in business English. For example, if the user wants to learn everyday conversation, the reception unit can also suggest an instructor specializing in everyday conversation. For example, if the user wants to prepare for an exam, the reception unit can also suggest an instructor specializing in exam preparation. This makes it possible to suggest an instructor according to the user's learning goals. Some or all of the above-mentioned 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 learning goals into AI and filter out the most suitable instructors.

[0037] The reception unit can provide an optimal instructor selection means according to the user's input method. For example, when a user uses voice input, the reception unit provides an interface that allows the user to select an instructor by voice. For example, when a user uses text input, the reception unit can also provide an interface that allows the user to select an instructor by text. For example, when a user uses touch operation, the reception unit can also provide an interface that allows the user to select an instructor by touch. This makes it possible to provide an instructor selection means according to the user's input method. Some or all of the above-mentioned 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 into AI and provide an optimal instructor selection means.

[0038] The reception unit can prioritize displaying highly relevant instructors by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying instructors related to that area. For example, if the user is traveling, the reception unit can also prioritize displaying instructors who are familiar with the culture and language of the travel destination. For example, if the user is in a specific city, the reception unit can also prioritize displaying instructors who can provide topics related to that city. This makes it possible to display highly relevant instructors based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI and prioritize displaying highly relevant instructors.

[0039] The reception unit can analyze the user's social media activity and suggest relevant instructors. For example, the reception unit can suggest instructors related to topics in which the user has shown interest on social media. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant instructors. For example, the reception unit can also suggest relevant instructors by referring to the activities of the user's friends on social media. In this way, relevant instructors can be suggested based on the user's social media activity. Some or all of the above-mentioned 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 social media activity into AI and suggest relevant instructors.

[0040] The reception unit can customize the instructor selection method by reflecting the user's past feedback. For example, the reception unit can preferentially display instructors who the user has previously given high ratings. For example, the reception unit can also prevent the display of instructors who the user has previously given low ratings. For example, the reception unit can also customize the instructor selection interface based on the user's past feedback. This allows the instructor selection method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI to customize the instructor selection method.

[0041] The conversation unit can evaluate the user's level of understanding in real time during the conversation and provide appropriate feedback. For example, the conversation unit can add a brief explanation if the user does not understand. For example, the conversation unit can proceed to the next step if the user understands. For example, the conversation unit can provide supplementary explanation if the user only partially understands. This makes it possible to provide appropriate feedback according to the user's level of understanding. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's level of understanding into AI, evaluate it in real time, and provide appropriate feedback.

[0042] The conversation unit can automatically detect the user's pronunciation and grammar errors during conversation and make correction suggestions. For example, if the user makes a pronunciation error, the conversation unit can suggest the correct pronunciation. For example, if the user makes a grammatical error, the conversation unit can also suggest the correct grammar. For example, if the user makes a word incorrectly, the conversation unit can also suggest the correct word. In this way, by automatically detecting the user's pronunciation and grammatical errors and making correction suggestions, the learning effect is improved. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's pronunciation and grammatical errors into AI and make correction suggestions.

[0043] The conversation unit can select a topic based on the user's interests during a conversation. For example, the conversation unit selects a topic based on topics that the user is interested in. For example, the conversation unit can also select a related topic based on the user's past conversation history. For example, the conversation unit can also select a related topic by analyzing the user's social media activity. In this way, selecting a topic based on the user's interests makes the conversation more interesting. Some or all of the above-described processing in the conversation unit can be performed using, or without, AI. For example, the conversation unit can input the user's interests into AI and select the most appropriate topic.

[0044] The conversation unit can provide relevant topics during a conversation, taking into account the user's geographical location information. For example, if the user is in a specific area, the conversation unit can provide topics related to that area. For example, if the user is traveling, the conversation unit can also provide topics related to the culture and language of the travel destination. For example, if the user is in a specific city, the conversation unit can also provide topics related to that city. This makes it possible to provide relevant topics based on the user's geographical location information. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's geographical location information into AI and provide relevant topics.

[0045] The conversation unit can analyze the user's social media activity during the conversation and provide related topics. The conversation unit can, for example, provide topics based on topics in which the user has shown interest on social media. The conversation unit can also, for example, analyze the content of the user's social media posts and provide related topics. The conversation unit can also, for example, provide related topics by referring to the activities of the user's friends on social media. In this way, related topics can be provided based on the user's social media activity. Some or all of the above-described processing in the conversation unit can be performed, for example, using AI or without AI. For example, the conversation unit can input the user's social media activity into AI and provide related topics.

[0046] The conversation unit can customize the conversation content by reflecting the user's past feedback during the conversation. For example, the conversation unit can preferentially provide topics that the user has previously rated highly. For example, the conversation unit can also avoid providing topics that the user has previously rated poorly. For example, the conversation unit can also customize the conversation content based on the user's past feedback. This makes it possible to customize the conversation content based on the user's past feedback. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's past feedback into AI to customize the conversation content.

[0047] The progress management unit can monitor the learning progress in real time and provide appropriate advice. For example, if the user has achieved progress, the progress management unit can provide advice to proceed to the next step. For example, if the user has not achieved progress, the progress management unit can also provide advice on supplementary learning. For example, if the user has only partially achieved progress, the progress management unit can also provide supplementary explanation. This makes it possible to monitor the learning progress in real time and provide appropriate advice. Some or all of the above-mentioned processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the user's learning data into AI, monitor it in real time, and provide appropriate advice.

[0048] The progress management unit can analyze the user's past learning history and propose an optimal learning plan. The progress management unit can, for example, propose an optimal learning plan based on the user's past learning history. The progress management unit can also, for example, propose an effective learning method based on the user's past learning history. The progress management unit can, for example, analyze the user's past learning history and propose the most efficient learning plan. This makes it possible to propose an optimal learning plan based on the user's past learning history. Some or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's past learning history into AI and propose an optimal learning plan.

[0049] The progress management unit can suggest specific actions to take to advance to the next step according to the progress of learning. For example, if the user has achieved progress, the progress management unit suggests specific actions to take to advance to the next step. For example, if the user has not achieved progress, the progress management unit can also suggest specific actions for supplementary learning. For example, if the user has only partially achieved progress, the progress management unit can also provide supplementary explanations. This makes it possible to suggest specific actions to take to advance to the next step according to the progress of learning. Some or all of the above-described processing in the progress management unit may be performed using AI, for example, or may be performed without using AI. For example, the progress management unit can input the user's learning data into AI and suggest specific actions to take to advance to the next step.

[0050] The progress management unit can associate the learning progress with geographical location information and analyze learning trends by region. For example, if the user is in a specific region, the progress management unit can analyze learning trends in that region. For example, if the user is traveling, the progress management unit can also analyze learning trends at the travel destination. For example, if the user is in a specific city, the progress management unit can analyze learning trends in that city. This makes it possible to associate the learning progress with geographical location information and analyze learning trends by region. Some or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's geographical location information into AI and analyze learning trends by region.

[0051] The progress management unit can improve the user's motivation to learn by associating the user's learning progress with social media activity. The progress management unit can improve motivation by, for example, having the user share their learning content on social media. The progress management unit can also improve the user's motivation to learn by, for example, referring to the activities of the user's friends on social media. The progress management unit can also improve the user's motivation to learn by, for example, analyzing the content of the user's posts on social media. In this way, the user's motivation to learn is improved by associating the user's learning progress with social media activity. Some or all of the above-described processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's social media activity into AI to improve the user's motivation to learn.

[0052] The progress management unit can provide a customized study plan that reflects the user's past feedback based on the user's learning progress. For example, the progress management unit can prioritize providing study plans that the user has previously rated highly. For example, the progress management unit can also avoid providing study plans that the user has previously rated poorly. For example, the progress management unit can also provide a customized study plan based on the user's past feedback. This makes it possible to provide a customized study plan that reflects the user's past feedback based on the user's learning progress. Some or all of the above-described processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the user's past feedback into AI and provide a customized study plan.

[0053] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0054] The reception unit can propose an optimal study plan based on the user's past learning history. For example, if the user has demonstrated a high level of understanding of a particular topic in the past, the reception unit can propose a study plan that includes more advanced content related to that topic. In addition, for topics that the user has struggled with in the past, the reception unit can propose a plan that includes supplementary learning resources. Furthermore, it can also suggest activities to accelerate progress according to the user's learning pace. This allows the user to receive an optimal study plan based on their learning history.

[0055] The conversation unit can detect the user's pronunciation and grammar errors in real time and immediately suggest corrections. For example, if the user makes a pronunciation error, the correct pronunciation can be provided via audio. Also, if there is a grammatical error, the correct grammar can be presented in text. Furthermore, if the user uses a particular word incorrectly, the correct usage of that word can be provided along with an example sentence. This allows the user to receive feedback in real time and make corrections immediately.

[0056] The progress management unit can associate a user's learning progress with geographical location information to analyze learning trends by region. For example, it can aggregate the learning progress of users in a specific region to understand the learning trends in that region. It can also provide users who are traveling with learning resources related to the culture and language of their destination. Furthermore, it can propose learning plans that include topics related to that city to users in a specific city. This makes it possible to understand learning trends by region and provide users with the most suitable learning resources.

[0057] The conversation unit can select topics based on the user's interests. For example, it can provide related topics based on topics in which the user has shown interest in the past. It can also analyze the user's social media activity and provide topics related to topics of interest. It can also select related topics based on the user's past conversation history. This allows it to provide topics based on the user's interests and make the conversation more interesting.

[0058] The progress management unit can suggest specific actions to take to advance to the next step based on the user's learning progress. For example, if the user achieves a specific learning goal, it can suggest specific actions to take to advance to the next step. It can also suggest specific actions for supplementary learning if the user has not achieved the desired progress. Furthermore, if the user has only partially achieved the desired progress, it can provide supplementary explanations. This allows the user to receive specific actions according to their learning progress and advance to the next step.

[0059] The processing flow of the first embodiment will be briefly explained below.

[0060] Step 1: The reception desk accepts the user's selection of a teacher. Users can access the service using a smartphone app or web browser and choose a teacher from a variety of pre-prepared characters based on their level and preferences. For example, the reception desk may offer characters for beginners or characters specialized in business English. Step 2: The conversation unit conducts a conversation based on the instructor selected by the reception unit. When the conversation unit conducts a voice conversation, the user uses a microphone to converse with the AI ​​instructor. When the conversation unit conducts a text conversation, the user can also use a keyboard to chat with the AI ​​instructor. Furthermore, the conversation unit prepares characters according to the user's level and preferences. For example, the conversation unit can provide characters for beginners or characters specialized in business English. Step 3: The progress management unit records the history of conversations conducted by the conversation unit and manages the learning progress. The progress management unit records information such as what conversations the user had with which characters and the level of understanding. This allows the user to check their learning progress and receive advice on how to proceed to the next step.

[0061] (Example 2) An English conversation service according to an embodiment of the present invention is a system in which a user selects a teacher based on their level and preferences, converses with them via voice or text, and manages their learning progress. The English conversation service allows a user to access the service using a smartphone app or web browser and select a teacher from a variety of pre-prepared characters based on their level and preferences. Next, the user selects the conversation method (voice or text). This allows the service to be used even while traveling, such as on a train. For example, in an English conversation service, a user accesses the service and selects a teacher. All teachers are AI, and various characters are available. For example, there are characters for beginners and characters specialized in business English. The user can select a teacher based on their English level and preferences. Next, the user selects the conversation method. If the user selects voice conversation, the user converses with the AI ​​teacher using a microphone. If the user selects text conversation, the user converses with the AI ​​teacher using a keyboard in chat format. This allows the user to select the conversation method best suited to their situation. Furthermore, the English conversation service records the user's usage history and manages their learning progress. For example, the service records information such as which character the user spoke to, what kind of conversation the user had, and their level of understanding. This allows users to check their learning progress and receive advice on how to move on to the next step. This allows the English conversation service to provide an English conversation service in which users can choose a teacher according to their level and preferences and select either audio or text as the conversation method. This allows users to practice English conversation anytime, anywhere. This allows the English conversation service to provide an English conversation service in which users can choose a teacher according to their level and preferences and select either audio or text as the conversation method. For example, users can practice English conversation on the train during their commute or during their breaks. In addition, AI teachers are available 24 hours a day, so users can study at their own convenience.

[0062] An English conversation service according to an embodiment includes a reception unit, a conversation unit, and a progress management unit. The reception unit accepts a user's selection of an instructor. The user can access the service using, for example, a smartphone app or a web browser and select an instructor from a variety of pre-prepared characters based on their level and preferences. For example, the reception unit provides characters for beginners and characters specialized in business English. The conversation unit conducts a conversation based on the instructor selected by the reception unit. For example, when the conversation unit conducts an audio conversation, the user uses a microphone to converse with the AI ​​instructor. Alternatively, when the conversation unit conducts a text conversation, the user can also use a keyboard to chat with the AI ​​instructor. Furthermore, the conversation unit provides characters based on the user's level and preferences. For example, the conversation unit can provide characters for beginners and characters specialized in business English. The progress management unit records a history of conversations conducted by the conversation unit and manages the user's learning progress. For example, the progress management unit records information such as the type of conversation the user had with which character and the user's level of understanding. This allows the user to check their learning progress and receive advice on how to proceed to the next step. As a result, the English conversation service according to the embodiment allows the user to select a teacher according to their level and preferences, engage in conversations, and manage their learning progress.

[0063] The reception unit can accept a user's instructor selection using a smartphone app or a web browser. Examples of smartphone apps include, but are not limited to, iOS apps and Android apps. For example, the reception unit allows a user to select an instructor using the smartphone app. The reception unit can also accept a user's instructor selection using a web browser. For example, the reception unit can select an instructor using a web browser such as Chrome, Firefox, or Safari. This allows a user to select an instructor via a smartphone or web browser. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without AI. For example, the reception unit can input the user's selection history into AI and recommend the most suitable instructor.

[0064] The conversation unit can conduct a conversation by voice or text. Voice conversation includes, but is not limited to, voice recognition technology and voice synthesis technology, for example. The conversation unit can, for example, allow a user to have a voice conversation with an AI instructor using a microphone. When conducting a text conversation, the conversation unit can use chatbot technology or natural language processing technology. For example, the conversation unit can allow a user to have a chat-style conversation with an AI instructor using a keyboard. This allows the user to have a conversation by voice or text. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input voice recognition technology into AI and convert voice data into text data.

[0065] The progress management unit can record the user's usage history and manage the user's learning progress. The usage history includes, but is not limited to, for example, the content of conversations, usage time, and learning progress. The progress management unit records information such as which characters the user had conversations with and the level of understanding the user has. This allows the user to check their learning progress and receive advice on how to proceed to the next step. Some or all of the above-described processing in the progress management unit can be performed using, for example, AI, or can be performed without using AI. For example, the progress management unit can input the user's usage history into AI to evaluate the user's learning progress.

[0066] The conversation unit can prepare characters according to the user's level and preferences. Examples of characters include, but are not limited to, avatars and virtual characters. The conversation unit can provide, for example, characters for beginners or characters specialized in business English. This allows the user to select a character according to their level and preferences. Some or all of the above-described processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input characters according to the user's level and preferences into AI and recommend the most suitable character.

[0067] The conversation unit can be available 24 hours a day. 24-hour availability includes, but is not limited to, a shift system or automatic response by AI. For example, the conversation unit has an AI instructor available 24 hours a day, allowing users to progress with their studies according to their own convenience. This allows users to have conversations at any time. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, or may be performed without using AI. For example, the conversation unit can provide 24-hour availability by deploying AI instructors on a shift system.

[0068] The reception unit can estimate the user's emotions and recommend the most suitable instructor based on the estimated user emotions. For example, if the user is nervous, the reception unit can recommend an instructor with a gentle tone to help the user relax. For example, if the user is excited, the reception unit can also recommend an energetic instructor. For example, if the user is tired, the reception unit can also recommend an instructor with a calm tone. This makes it possible to recommend the most suitable instructor based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the reception unit may be performed using AI, or may be performed without AI. For example, the reception unit can input the user's emotion data into the generation AI to recommend the most suitable instructor.

[0069] The reception unit can analyze the user's past instructor selection history and automatically suggest the most suitable instructor. For example, the reception unit can analyze the tendency of instructors selected by the user in the past and suggest similar instructors. For example, the reception unit can also preferentially suggest instructors that the user has given high ratings to in the past. For example, the reception unit can also suggest instructors of an appropriate level based on the level of instructors selected by the user in the past. This makes it possible to suggest the most suitable instructor based on the user's past selection history. Some or all of the above-mentioned processing by 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 instructor selection history into AI and automatically suggest the most suitable instructor.

[0070] The reception unit can filter instructors based on the user's current learning goals. For example, if the user wants to learn business English, the reception unit can suggest an instructor specializing in business English. For example, if the user wants to learn everyday conversation, the reception unit can also suggest an instructor specializing in everyday conversation. For example, if the user wants to prepare for an exam, the reception unit can also suggest an instructor specializing in exam preparation. This makes it possible to suggest an instructor according to the user's learning goals. Some or all of the above-mentioned 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 learning goals into AI and filter out the most suitable instructors.

[0071] The reception unit can provide an optimal instructor selection means according to the user's input method. For example, when a user uses voice input, the reception unit provides an interface that allows the user to select an instructor by voice. For example, when a user uses text input, the reception unit can also provide an interface that allows the user to select an instructor by text. For example, when a user uses touch operation, the reception unit can also provide an interface that allows the user to select an instructor by touch. This makes it possible to provide an instructor selection means according to the user's input method. Some or all of the above-mentioned 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 into AI and provide an optimal instructor selection means.

[0072] The reception unit can estimate the user's emotions and determine the priority of instructor selection based on the estimated user emotions. For example, if the user is nervous, the reception unit can preferentially display instructors with a gentle tone to help the user relax. For example, if the user is excited, the reception unit can preferentially display energetic instructors. For example, if the user is tired, the reception unit can preferentially display instructors with a calm tone. This allows the priority of instructor selection to be determined according to the user's emotions. 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 reception unit can be performed using AI, for example, or without AI. For example, the reception unit can input the user's emotion data into the generation AI to determine the priority of instructor selection.

[0073] The reception unit can prioritize displaying highly relevant instructors by taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit can prioritize displaying instructors related to that area. For example, if the user is traveling, the reception unit can also prioritize displaying instructors who are familiar with the culture and language of the travel destination. For example, if the user is in a specific city, the reception unit can also prioritize displaying instructors who can provide topics related to that city. This makes it possible to display highly relevant instructors based on the user's geographical location information. Some or all of the above-mentioned processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information into AI and prioritize displaying highly relevant instructors.

[0074] The reception unit can analyze the user's social media activity and suggest relevant instructors. For example, the reception unit can suggest instructors related to topics in which the user has shown interest on social media. For example, the reception unit can analyze the content of the user's social media posts and suggest relevant instructors. For example, the reception unit can also suggest relevant instructors by referring to the activities of the user's friends on social media. In this way, relevant instructors can be suggested based on the user's social media activity. Some or all of the above-mentioned 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 social media activity into AI and suggest relevant instructors.

[0075] The reception unit can customize the instructor selection method by reflecting the user's past feedback. For example, the reception unit can preferentially display instructors who the user has previously given high ratings. For example, the reception unit can also prevent the display of instructors who the user has previously given low ratings. For example, the reception unit can also customize the instructor selection interface based on the user's past feedback. This allows the instructor selection method to be customized based on the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's past feedback into AI to customize the instructor selection method.

[0076] The conversation unit can estimate the user's emotions and adjust the tone and content of the conversation based on the estimated user's emotions. For example, if the user is nervous, the conversation unit can use a gentle tone to relax the user. For example, if the user is excited, the conversation unit can use an energetic tone to promote the conversation. For example, if the user is tired, the conversation unit can use a calm tone to promote the conversation. This allows the tone and content of the conversation to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversation unit may be performed using AI, or may be performed without AI. For example, the conversation unit can input the user's emotion data into the generation AI and adjust the tone and content of the conversation.

[0077] The conversation unit can evaluate the user's level of understanding in real time during the conversation and provide appropriate feedback. For example, the conversation unit can add a brief explanation if the user does not understand. For example, the conversation unit can proceed to the next step if the user understands. For example, the conversation unit can provide supplementary explanation if the user only partially understands. This makes it possible to provide appropriate feedback according to the user's level of understanding. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's level of understanding into AI, evaluate it in real time, and provide appropriate feedback.

[0078] The conversation unit can automatically detect the user's pronunciation and grammar errors during conversation and make correction suggestions. For example, if the user makes a pronunciation error, the conversation unit can suggest the correct pronunciation. For example, if the user makes a grammatical error, the conversation unit can also suggest the correct grammar. For example, if the user makes a word incorrectly, the conversation unit can also suggest the correct word. In this way, by automatically detecting the user's pronunciation and grammatical errors and making correction suggestions, the learning effect is improved. Some or all of the above-mentioned processing in the conversation unit may be performed using, for example, AI, or may be performed without using AI. For example, the conversation unit can input the user's pronunciation and grammatical errors into AI and make correction suggestions.

[0079] The conversation unit can select a topic based on the user's interests during a conversation. For example, the conversation unit selects a topic based on topics that the user is interested in. For example, the conversation unit can also select a related topic based on the user's past conversation history. For example, the conversation unit can also select a related topic by analyzing the user's social media activity. In this way, selecting a topic based on the user's interests makes the conversation more interesting. Some or all of the above-described processing in the conversation unit can be performed using, or without, AI. For example, the conversation unit can input the user's interests into AI and select the most appropriate topic.

[0080] The conversation unit can estimate the user's emotions and adjust the conversation speed based on the estimated user's emotions. For example, if the user is nervous, the conversation unit can proceed the conversation at a slower speed. For example, if the user is excited, the conversation unit can also proceed the conversation at a faster speed. For example, if the user is tired, the conversation unit can also proceed the conversation at a slower speed. This allows the conversation speed to be adjusted according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the conversation unit can be performed using, for example, AI, or can be performed without using AI. For example, the conversation unit can input the user's emotion data into the generation AI and adjust the conversation speed.

[0081] The conversation unit can provide relevant topics during a conversation, taking into account the user's geographical location information. For example, if the user is in a specific area, the conversation unit can provide topics related to that area. For example, if the user is traveling, the conversation unit can also provide topics related to the culture and language of the travel destination. For example, if the user is in a specific city, the conversation unit can also provide topics related to that city. This makes it possible to provide relevant topics based on the user's geographical location information. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's geographical location information into AI and provide relevant topics.

[0082] The conversation unit can analyze the user's social media activity during the conversation and provide related topics. The conversation unit can, for example, provide topics based on topics in which the user has shown interest on social media. The conversation unit can also, for example, analyze the content of the user's social media posts and provide related topics. The conversation unit can also, for example, provide related topics by referring to the activities of the user's friends on social media. In this way, related topics can be provided based on the user's social media activity. Some or all of the above-described processing in the conversation unit can be performed, for example, using AI or without AI. For example, the conversation unit can input the user's social media activity into AI and provide related topics.

[0083] The conversation unit can customize the conversation content by reflecting the user's past feedback during the conversation. For example, the conversation unit can preferentially provide topics that the user has previously rated highly. For example, the conversation unit can also avoid providing topics that the user has previously rated poorly. For example, the conversation unit can also customize the conversation content based on the user's past feedback. This makes it possible to customize the conversation content based on the user's past feedback. Some or all of the above-described processing in the conversation unit may be performed using AI, for example, or may be performed without using AI. For example, the conversation unit can input the user's past feedback into AI to customize the conversation content.

[0084] The progress management unit can estimate the user's emotions and evaluate the learning progress based on the estimated user emotions. For example, if the user is tense, the progress management unit can ease the progress evaluation to relax the user. For example, if the user is excited, the progress management unit can also perform an energetic progress evaluation. For example, if the user is tired, the progress management unit can ease the progress evaluation. This allows the learning progress to be evaluated according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-mentioned processing in the progress management unit can be performed using AI, for example, or without AI. For example, the progress management unit can input the user's emotion data into the generation AI to evaluate the learning progress.

[0085] The progress management unit can monitor the learning progress in real time and provide appropriate advice. For example, if the user has achieved progress, the progress management unit can provide advice to proceed to the next step. For example, if the user has not achieved progress, the progress management unit can also provide advice on supplementary learning. For example, if the user has only partially achieved progress, the progress management unit can also provide supplementary explanation. This makes it possible to monitor the learning progress in real time and provide appropriate advice. Some or all of the above-mentioned processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the user's learning data into AI, monitor it in real time, and provide appropriate advice.

[0086] The progress management unit can analyze the user's past learning history and propose an optimal learning plan. The progress management unit can, for example, propose an optimal learning plan based on the user's past learning history. The progress management unit can also, for example, propose an effective learning method based on the user's past learning history. The progress management unit can, for example, analyze the user's past learning history and propose the most efficient learning plan. This makes it possible to propose an optimal learning plan based on the user's past learning history. Some or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's past learning history into AI and propose an optimal learning plan.

[0087] The progress management unit can suggest specific actions to take to advance to the next step according to the progress of learning. For example, if the user has achieved progress, the progress management unit suggests specific actions to take to advance to the next step. For example, if the user has not achieved progress, the progress management unit can also suggest specific actions for supplementary learning. For example, if the user has only partially achieved progress, the progress management unit can also provide supplementary explanations. This makes it possible to suggest specific actions to take to advance to the next step according to the progress of learning. Some or all of the above-described processing in the progress management unit may be performed using AI, for example, or may be performed without using AI. For example, the progress management unit can input the user's learning data into AI and suggest specific actions to take to advance to the next step.

[0088] The progress management unit can estimate the user's emotions and customize the learning progress report based on the estimated user emotions. For example, if the user is tense, the progress management unit can provide a gentler progress report to help the user relax. For example, if the user is excited, the progress management unit can provide an energetic progress report. For example, if the user is tired, the progress management unit can provide a gentler progress report. This allows the learning progress report to be customized according to the user's emotions. 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 progress management unit can be performed using, for example, AI, or without AI. For example, the progress management unit can input the user's emotion data into the generation AI to customize the learning progress report.

[0089] The progress management unit can associate the learning progress with geographical location information and analyze learning trends by region. For example, if the user is in a specific region, the progress management unit can analyze learning trends in that region. For example, if the user is traveling, the progress management unit can also analyze learning trends at the travel destination. For example, if the user is in a specific city, the progress management unit can analyze learning trends in that city. This makes it possible to associate the learning progress with geographical location information and analyze learning trends by region. Some or all of the above-mentioned processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's geographical location information into AI and analyze learning trends by region.

[0090] The progress management unit can improve the user's motivation to learn by associating the user's learning progress with social media activity. The progress management unit can improve motivation by, for example, having the user share their learning content on social media. The progress management unit can also improve the user's motivation to learn by, for example, referring to the activities of the user's friends on social media. The progress management unit can also improve the user's motivation to learn by, for example, analyzing the content of the user's posts on social media. In this way, the user's motivation to learn is improved by associating the user's learning progress with social media activity. Some or all of the above-described processing in the progress management unit may be performed, for example, using AI, or may be performed without using AI. For example, the progress management unit can input the user's social media activity into AI to improve the user's motivation to learn.

[0091] The progress management unit can provide a customized study plan that reflects the user's past feedback based on the user's learning progress. For example, the progress management unit can prioritize providing study plans that the user has previously rated highly. For example, the progress management unit can also avoid providing study plans that the user has previously rated poorly. For example, the progress management unit can also provide a customized study plan based on the user's past feedback. This makes it possible to provide a customized study plan that reflects the user's past feedback based on the user's learning progress. Some or all of the above-described processing in the progress management unit may be performed using, for example, AI, or may be performed without using AI. For example, the progress management unit can input the user's past feedback into AI and provide a customized study plan. === Hard Collateral 1-1 === Each of the multiple elements including the reception unit, conversation unit, and progress management unit described above is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and allows the user to select a teacher using a smartphone app or a web browser. The conversation unit is realized, for example, by the control unit 46A of the smart device 14 and conducts conversations by voice or text. The progress management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the user's learning history and manages progress. === Hard Collateral 1-2 === Each of the multiple elements including the reception unit, conversation unit, and progress management unit described above is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and allows the user to select a teacher using the smart glasses. The conversation unit is realized, for example, by the control unit 46A of the smart glasses 214 and conducts conversations via voice or text. The progress management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the user's learning history and manages progress. === Hard Collateral 1-3 === Each of the multiple elements including the above-mentioned reception unit, conversation unit, and progress management 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 is realized by the control unit 46A of the headset type terminal 314 and allows the user to select a lecturer using the headset type terminal. The conversation unit is realized, for example, by the control unit 46A of the headset type terminal 314 and conducts conversations by voice or text. The progress management unit is realized, for example, by the specific processing unit 290 of the data processing device 12 and records the user's learning history and manages progress. === Hard Collateral 1-4 === Each of the multiple elements including the reception unit, conversation unit, and progress management unit described above is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414, and allows the user to use the robot to select an instructor. The conversation unit is realized, for example, by the control unit 46A of the robot 414, and conducts conversations by voice or text. The progress management unit is realized, for example, by the specific processing unit 290 of the data processing device 12, and records the user's learning history and manages progress.

[0092] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0093] The reception unit can propose an optimal study plan based on the user's past learning history. For example, if the user has demonstrated a high level of understanding of a particular topic in the past, the reception unit can propose a study plan that includes more advanced content related to that topic. In addition, for topics that the user has struggled with in the past, the reception unit can propose a plan that includes supplementary learning resources. Furthermore, it can also suggest activities to accelerate progress according to the user's learning pace. This allows the user to receive an optimal study plan based on their learning history.

[0094] The conversation unit can detect the user's pronunciation and grammar errors in real time and immediately suggest corrections. For example, if the user makes a pronunciation error, the correct pronunciation can be provided via audio. Also, if there is a grammatical error, the correct grammar can be presented in text. Furthermore, if the user uses a particular word incorrectly, the correct usage of that word can be provided along with an example sentence. This allows the user to receive feedback in real time and make corrections immediately.

[0095] The progress management unit can associate a user's learning progress with geographical location information to analyze learning trends by region. For example, it can aggregate the learning progress of users in a specific region to understand the learning trends in that region. It can also provide users who are traveling with learning resources related to the culture and language of their destination. Furthermore, it can propose learning plans that include topics related to that city to users in a specific city. This makes it possible to understand learning trends by region and provide users with the most suitable learning resources.

[0096] The conversation unit can select topics based on the user's interests. For example, it can provide related topics based on topics in which the user has shown interest in the past. It can also analyze the user's social media activity and provide topics related to topics of interest. It can also select related topics based on the user's past conversation history. This allows it to provide topics based on the user's interests and make the conversation more interesting.

[0097] The progress management unit can suggest specific actions to take to advance to the next step based on the user's learning progress. For example, if the user achieves a specific learning goal, it can suggest specific actions to take to advance to the next step. It can also suggest specific actions for supplementary learning if the user has not achieved the desired progress. Furthermore, if the user has only partially achieved the desired progress, it can provide supplementary explanations. This allows the user to receive specific actions according to their learning progress and advance to the next step.

[0098] The reception unit can estimate the user's emotions and recommend the most suitable instructor based on the estimated user's emotions. For example, if the user is nervous, it can recommend an instructor with a gentle tone to help the user relax. Also, if the user is excited, it can recommend an energetic instructor. Furthermore, if the user is tired, it can recommend an instructor with a calm tone. In this way, it is possible to recommend the most suitable instructor according to the user's emotions.

[0099] The conversation unit can estimate the user's emotions and adjust the tone and content of the conversation based on the estimated user's emotions. For example, if the user is nervous, the conversation can be conducted in a gentle tone to relax the user. If the user is excited, the conversation can be conducted in an energetic tone. Furthermore, if the user is tired, the conversation can be conducted in a calm tone. In this way, the tone and content of the conversation can be adjusted according to the user's emotions.

[0100] The progress management unit can estimate the user's emotions and evaluate the learning progress based on the estimated user's emotions. For example, if the user is nervous, the progress evaluation can be made more lenient to help the user relax. Also, if the user is excited, it is possible to make an energetic progress evaluation. Furthermore, if the user is tired, the progress evaluation can be made more lenient. In this way, the learning progress can be evaluated according to the user's emotions.

[0101] The conversation unit can estimate the user's emotions and adjust the conversation progress speed based on the estimated user's emotions. For example, if the user is nervous, the conversation can proceed at a slower speed. Also, if the user is excited, the conversation can proceed at a faster speed. Furthermore, if the user is tired, the conversation can proceed at a slower speed. In this way, the conversation progress speed can be adjusted according to the user's emotions.

[0102] The progress management unit can estimate the user's emotions and customize the learning progress report based on the estimated user's emotions. For example, if the user is nervous, the progress report can be made gentler to help the user relax. Also, if the user is excited, it is possible to make an energetic progress report. Furthermore, if the user is tired, the progress report can be made gentler. In this way, the learning progress report can be customized according to the user's emotions.

[0103] The processing flow of the second embodiment will be briefly explained below.

[0104] Step 1: The reception desk accepts the user's selection of a teacher. Users can access the service using a smartphone app or web browser and choose a teacher from a variety of pre-prepared characters based on their level and preferences. For example, the reception desk may offer characters for beginners or characters specialized in business English. Step 2: The conversation unit conducts a conversation based on the instructor selected by the reception unit. When the conversation unit conducts a voice conversation, the user uses a microphone to converse with the AI ​​instructor. When the conversation unit conducts a text conversation, the user can also use a keyboard to chat with the AI ​​instructor. Furthermore, the conversation unit prepares characters according to the user's level and preferences. For example, the conversation unit can provide characters for beginners or characters specialized in business English. Step 3: The progress management unit records the history of conversations conducted by the conversation unit and manages the learning progress. The progress management unit records information such as what conversations the user had with which characters and the level of understanding. This allows the user to check their learning progress and receive advice on how to proceed to the next step.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0109] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0110] 3, the data processing system 210 includes the data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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).

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0126] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0142] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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).

[0162] 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.

[0163] 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."

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.

[0172] 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.

[0173] 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.

[0174] 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.

[0175] 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.

[0176] [Explanation of symbols]

[0177] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a reception unit that receives a lecturer selection from a user; a conversation unit that conducts a conversation based on the lecturer selected by the reception unit; a progress management unit that records the history of the conversations made by the conversation unit and manages the progress of learning. A system characterized by:

2. The reception unit Accept instructor selection from users via smartphone app or web browser 2. The system of claim 1.

3. The conversation unit is Have a voice or text conversation 2. The system of claim 1.

4. The progress management unit Recording user usage history and managing learning progress 2. The system of claim 1.

5. The conversation unit is Prepare characters according to the user's level and preferences 2. The system of claim 1.

6. The conversation unit is Available 24 hours a day 2. The system of claim 1.

7. The reception unit Estimate user emotions and recommend the most suitable instructor based on the estimated user emotions 2. The system of claim 1.

8. The reception unit Analyze the user's past instructor selection history and automatically suggest the most suitable instructor 2. The system of claim 1.

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