System

Abstract

An object of a system according to an embodiment is to overcome time and cost constraints and to efficiently learn English conversation.SOLUTION: A system according to an embodiment includes a smartphone application and a user. The smartphone application is equipped with a generation AI. The generated AI serves as a question of English conversation from the user or a conversation partner. The generation AI points out errors in pronunciation and grammar of the user in real time and feeds back improvement points.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional techniques have had the problem that time and cost constraints make it difficult to learn English conversation efficiently.

[0005] The system according to the embodiment aims to overcome time and cost constraints and to enable efficient learning of English conversation. [Means for solving the problem]

[0006] The system according to the embodiment includes a smartphone app and a user. The smartphone app is equipped with a generation AI. The generation AI answers English conversation questions from the user and acts as a conversation partner. The generation AI points out the user's pronunciation and grammar errors in real time and provides feedback on areas for improvement. [Effects of the Invention]

[0007] The system according to the embodiment overcomes time and cost constraints and enables efficient learning of English conversation. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) The English conversation learning system according to an embodiment of the present invention is a system that uses a smartphone app in which a generation AI acts as an English conversation instructor, allowing users to learn English conversation at any time and at low cost. This allows users to learn English conversation at their own pace and improve their skills effectively while keeping costs down.

[0029] An English conversation learning system according to an embodiment includes a smartphone app equipped with a generation AI and a user operating the smartphone app. The generation AI responds to the user's English conversation questions and serves as a conversation partner. For example, when a user inputs a prompt such as "I want to introduce myself in English," the generation AI provides appropriate English phrases and grammar for the user to practice. The generation AI also points out the user's pronunciation and grammar errors in real time and provides feedback on areas for improvement. For example, when a user pronounces "apple," the generation AI evaluates the user's pronunciation and provides feedback on necessary corrections. The generation AI also points out the user's grammatical errors and provides correct grammar. For example, if a user says "I went to the store," the generation AI teaches the correct grammar: "I went to the store." This allows users to learn English conversation at their own pace and effectively improve their skills while reducing costs.

[0030] Generative AI can analyze a user's learning history and automatically generate an individualized learning plan. For example, generative AI analyzes a user's past learning history to identify the user's weaknesses and areas of strength. For example, it extracts problems with specific grammar points or pronunciation and creates an individualized learning plan based on that. Generative AI also suggests what to study next based on the user's learning history. For example, it automatically determines when to review previously learned content and the order in which to learn new topics. Generative AI also analyzes the user's learning history and provides a dashboard that visualizes learning progress. For example, it allows users to see at a glance what goals have been achieved and what tasks remain to be completed. This makes it possible to provide an optimal learning plan based on the user's learning history.

[0031] The generative AI can point out the user's pronunciation and grammar errors and provide specific ways to improve them using videos and audio. For example, the generative AI can detect the user's pronunciation and grammar errors and provide specific ways to improve them using videos. For example, it can play a video showing the correct mouth shape and tongue position for pronunciation. The generative AI can also point out the user's grammatical errors and explain how to improve them using audio. For example, it can provide audio example sentences using correct grammatical structure so the user can listen to them and practice. The generative AI can also point out the user's pronunciation and grammar errors and provide an interactive guide that shows step-by-step ways to improve them. For example, it can provide feedback at each step so the user can proceed at their own pace. This allows the user to learn specific ways to improve visually and audibly.

[0032] Generative AI can also support the learning of other languages ​​and function as a multilingual learning platform. Generative AI can support languages ​​other than English, allowing users to learn multiple languages. For example, it provides learning content for Spanish, French, Chinese, and other languages. Furthermore, when a user selects the language they want to learn, Generative AI automatically generates a learning plan for that language. For example, it provides a curriculum that starts with basic greetings and everyday conversations in the selected language. Generative AI also functions as a multilingual learning platform, allowing users to centrally manage their learning progress across different languages. For example, it allows users to check the learning history and progress for each language on a dashboard. This allows users to learn multiple languages.

[0033] The generative AI adds a function that allows users to share their learning progress with family and friends, thereby increasing their motivation to learn. The generative AI provides, for example, a function that allows users to share their learning progress with family and friends. For example, notifications of learning progress and goal achievements are sent via social media or email. The generative AI also adds a function that allows users to receive supportive messages and feedback from family and friends by sharing their learning progress. For example, by sharing their learning results, they can receive encouragement from those around them. The generative AI also provides a function that allows users to set learning goals together with family and friends and promote competition and cooperation by sharing their learning progress. For example, a ranking function that allows users to compete against each other on their learning progress is added. This can increase users' motivation to learn.

[0034] Generative AI can also provide relevant cultural background and historical information in response to user questions. For example, in response to a question such as "Tell me about Thanksgiving in the United States," the generative AI will explain the history and cultural background of Thanksgiving. Furthermore, when a user asks about a specific phrase or expression, the generative AI will explain the cultural situation and historical background in which the phrase is used. For example, it will explain the origin and context in which the expression "break the ice" is used. Furthermore, in response to a user question, the generative AI will provide information on relevant cultural customs and etiquette. For example, in response to a question about "business meeting etiquette in the UK," the generative AI will explain the appropriate etiquette and customs. This allows users to learn not only English conversation but also cultural background and historical information.

[0035] The generative AI can present multiple example answers to a user's questions, allowing the user to choose the most appropriate expression. For example, the generative AI presents multiple example answers to a user's questions. For example, in response to the question "How are you?", the generative AI provides options such as "I'm fine, thank you," "I'm doing well," and "Not too bad." When a user asks about expressions that can be used in a specific situation, the generative AI presents multiple appropriate expressions, allowing the user to choose the most appropriate one. For example, when asked "How to order at a restaurant," the generative AI provides multiple ordering phrases. The generative AI also presents example answers with different nuances and tones in response to a user's questions. For example, when asked "How to apologize," the generative AI provides examples of both formal and casual apologies. This allows the user to choose the most appropriate expression from multiple options.

[0036] The generation AI can add a function that allows users to refer to similar questions and answers asked by other users in response to a user's question. For example, the generation AI can display similar questions and answers asked in the past by other users in response to a user's question. For example, for the question "How to improve my English pronunciation?", the generation AI can reference questions and answers asked by other users. In addition, when a user asks a question on a specific topic, the generation AI can present related past questions and answers for the user's reference. For example, for the question "What are some common English idioms?", the generation AI can display similar past questions and answers. In addition, the generation AI can build a database that allows users to refer to similar questions and answers asked by other users in response to a user's question, making it easier for users to find the best answer to their question. For example, the generation AI can provide a function to search for related questions and answers based on keywords in the question. This allows users to refer to questions and answers asked by other users.

[0037] When pointing out a user's pronunciation or grammar errors, the generative AI can provide specific example sentences and practice questions. For example, the generative AI can point out a user's pronunciation or grammar errors and provide specific example sentences and practice questions. For example, if the user makes the mistake of saying "I went to the store," the generative AI can present the correct example sentence and practice question, "I went to the store." The generative AI can also point out a user's pronunciation errors and provide specific practice questions to improve the pronunciation. For example, if the user's pronunciation of "apple" is incorrect, the generative AI can provide audio practice questions to practice the correct pronunciation. The generative AI can also point out a user's grammatical errors and provide specific example sentences and practice questions to improve the pronunciation. For example, if the user makes the mistake of saying "He don't like apples," the generative AI can present the correct example sentence and practice question, "He doesn't like apples." This allows the user to learn through specific example sentences and practice questions.

[0038] When pointing out a user's pronunciation or grammar errors, the generation AI can provide individualized feedback taking into account the user's learning history. For example, the generation AI can analyze the user's learning history and provide individualized feedback taking into account past mistakes. For example, if the same mistake is repeated, it can provide feedback to specifically call attention to it. The generation AI can also point out pronunciation or grammar errors based on the user's learning history and provide individualized feedback to improve them. For example, it can provide feedback to review content learned in the past. The generation AI can also provide individualized feedback taking into account the user's learning history when pointing out pronunciation or grammar errors. For example, it can provide feedback based on the user's progress to maintain motivation for learning. This allows the user to receive individualized feedback.

[0039] When pointing out a user's pronunciation or grammar errors, the generative AI can share success stories of other users. For example, when pointing out a user's pronunciation or grammar errors, the generative AI can share success stories of other users. For example, it can introduce the experiences and secrets of success of users who overcame the same mistakes. The generative AI can also point out a user's pronunciation or grammar errors and provide success stories of other users to help improve them. For example, it can share methods for practicing pronunciation or learning grammar. The generative AI can also motivate users by sharing success stories of other users when pointing out a user's pronunciation or grammar errors. For example, it can provide specific advice based on success stories. This allows users to refer to the success stories of other users.

[0040] When pointing out a user's pronunciation or grammar errors, the generative AI can provide game-style practice, allowing the user to learn while having fun. For example, the generative AI can point out a user's pronunciation or grammar errors and provide game-style practice to help them improve. For example, pronunciation practice can be gamified so that the user can earn points each time they pronounce something correctly. The generative AI can also point out a user's grammatical errors and provide game-style practice questions to help them improve. For example, it can allow the user to learn correct grammar through grammar quizzes and puzzles. When pointing out a user's pronunciation or grammar errors, the generative AI can provide game-style practice to help them learn while having fun. For example, it can provide pronunciation and grammar practice as mini-games, allowing the user to learn while having fun. This allows the user to progress through their learning while having fun.

[0041] The generation AI can learn the user's lifestyle rhythm and add a reminder function that suggests the optimal time to study. For example, the generation AI can analyze the user's lifestyle rhythm and provide a reminder function that suggests the optimal time to study. For example, if the user has a habit of studying during their morning commute, a reminder can be sent at that time. The generation AI can also customize study reminders based on the user's lifestyle rhythm. For example, if the user often studies at night, a reminder can be sent in the evening. The generation AI can also learn the user's lifestyle rhythm and add a reminder function that suggests the optimal time to study. For example, the generation AI can dynamically adjust study reminders to match the user's schedule. This allows the user to study at the optimal time.

[0042] The generative AI can add a function that automatically suggests what content to study next based on the user's learning history. For example, the generative AI provides a function that analyzes the user's learning history and automatically suggests what content to study next. For example, it suggests the next topic or skill to learn based on what has been learned in the past. The generative AI also customizes what to study next based on the user's learning history. For example, it suggests that the user focus on studying areas in which they are weak. The generative AI also analyzes the user's learning history and adds a function that automatically suggests what to study next. For example, it dynamically adjusts what to study next depending on the user's progress. This allows the user to automatically receive suggestions on what to study next.

[0043] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0044] The English conversation learning system can increase motivation to learn by adding a function that allows users to share their learning progress with family and friends. For example, notifications of learning progress and goal achievement can be sent via social media or email. In addition, a function can be added that allows users to receive supportive messages and feedback from family and friends by sharing their learning progress. For example, sharing their learning results can help them receive encouragement from those around them. In addition, by sharing users' learning progress, a function can be provided that allows them to set learning goals together with family and friends and promote competition and cooperation. For example, a ranking function can be added that allows users to compete based on their learning progress. This can increase users' motivation to learn.

[0045] The English conversation learning system can also support the learning of other languages ​​and function as a multilingual learning platform. For example, it can support languages ​​other than English, allowing users to learn multiple languages. For example, it provides learning content for Spanish, French, Chinese, and other languages. In addition, when a user selects the language they want to learn, it automatically generates a learning plan for that language. For example, it provides a curriculum that starts with basic greetings and everyday conversations in the selected language. It also functions as a multilingual learning platform, allowing users to centrally manage their learning progress across different languages. For example, it allows users to check the learning history and progress for each language on a dashboard. This allows users to learn multiple languages.

[0046] The English conversation learning system can also provide relevant cultural background and historical information in response to user questions. For example, in response to a question such as "Tell me about Thanksgiving in America," the system will explain the history and cultural background of Thanksgiving. Also, when a user asks about a specific phrase or expression, the system will explain the cultural situations and historical background in which the phrase is used. For example, the system will explain the origin of the expression "break the ice" and the situations in which it is used. Also, in response to a user question, the system will provide information on related cultural customs and etiquette. For example, in response to a question about "business meeting etiquette in the UK," the system will explain appropriate etiquette and customs. This allows users to learn not only English conversation but also cultural background and historical information.

[0047] An English conversation learning system can present multiple example answers to a user's questions, allowing the user to choose the most appropriate expression. For example, multiple example answers can be presented to a user's question. For example, in response to the question "How are you?", options such as "I'm fine, thank you," "I'm doing well," and "Not too bad" are provided. Also, when a user asks about expressions that can be used in a specific situation, multiple appropriate expressions can be presented, allowing the user to choose the most appropriate one. For example, when asked "How to order at a restaurant," multiple ordering phrases can be provided. Also, example answers with different nuances and tones can be presented to a user's question. For example, when asked "How to apologize," examples of both formal and casual apologies can be provided. This allows the user to choose the most appropriate expression from multiple options.

[0048] An English conversation learning system can provide specific example sentences and practice questions when pointing out a user's pronunciation or grammar errors. For example, it can point out a user's pronunciation or grammar errors and provide specific example sentences and practice questions. For example, if the user makes a mistake saying "I went to the store," it can present the correct example sentence and practice question, "I went to the store." It can also point out a user's pronunciation errors and provide specific practice questions to improve the pronunciation. For example, if the user's pronunciation of "apple" is incorrect, it can provide audio practice questions to practice the correct pronunciation. It can also point out a user's grammar errors and provide specific example sentences and practice questions to improve the pronunciation. For example, if the user makes a mistake saying "He don't like apples," it can present the correct example sentence and practice question, "He doesn't like apples." This allows the user to learn through specific example sentences and practice questions.

[0049] The processing flow of the first embodiment will be briefly explained below.

[0050] Step 1: The generation AI responds to the user's English conversation questions and acts as a conversation partner. For example, when a user inputs a prompt such as "I want to introduce myself in English," the generation AI provides appropriate English phrases and grammar for the user to practice. Step 2: The AI ​​generator will point out any pronunciation or grammar errors in real time and provide feedback on areas for improvement. For example, when a user pronounces "apple," the AI ​​will evaluate their pronunciation and provide feedback on any necessary corrections. If the user says "I went to the store," the AI ​​will provide the correct grammar: "I went to the store."

[0051] (Example 2) The English conversation learning system according to an embodiment of the present invention is a system that uses a smartphone app in which a generation AI acts as an English conversation instructor, allowing users to learn English conversation at any time and at low cost. This allows users to learn English conversation at their own pace and improve their skills effectively while keeping costs down.

[0052] An English conversation learning system according to an embodiment includes a smartphone app equipped with a generation AI and a user operating the smartphone app. The generation AI responds to the user's English conversation questions and serves as a conversation partner. For example, when a user inputs a prompt such as "I want to introduce myself in English," the generation AI provides appropriate English phrases and grammar for the user to practice. The generation AI also points out the user's pronunciation and grammar errors in real time and provides feedback on areas for improvement. For example, when a user pronounces "apple," the generation AI evaluates the user's pronunciation and provides feedback on necessary corrections. The generation AI also points out the user's grammatical errors and provides correct grammar. For example, if a user says "I went to the store," the generation AI teaches the correct grammar: "I went to the store." This allows users to learn English conversation at their own pace and effectively improve their skills while reducing costs.

[0053] Generative AI can analyze a user's learning history and automatically generate an individualized learning plan. For example, generative AI analyzes a user's past learning history to identify the user's weaknesses and areas of strength. For example, it extracts problems with specific grammar points or pronunciation and creates an individualized learning plan based on that. Generative AI also suggests what to study next based on the user's learning history. For example, it automatically determines when to review previously learned content and the order in which to learn new topics. Generative AI also analyzes the user's learning history and provides a dashboard that visualizes learning progress. For example, it allows users to see at a glance what goals have been achieved and what tasks remain to be completed. This makes it possible to provide an optimal learning plan based on the user's learning history.

[0054] The generative AI can point out the user's pronunciation and grammar errors and provide specific ways to improve them using videos and audio. For example, the generative AI can detect the user's pronunciation and grammar errors and provide specific ways to improve them using videos. For example, it can play a video showing the correct mouth shape and tongue position for pronunciation. The generative AI can also point out the user's grammatical errors and explain how to improve them using audio. For example, it can provide audio example sentences using correct grammatical structure so the user can listen to them and practice. The generative AI can also point out the user's pronunciation and grammar errors and provide an interactive guide that shows step-by-step ways to improve them. For example, it can provide feedback at each step so the user can proceed at their own pace. This allows the user to learn specific ways to improve visually and audibly.

[0055] The generation AI can use its emotion estimation function to analyze the user's emotions while studying and provide encouragement and advice to maintain motivation. For example, the generation AI can use its emotion estimation function to analyze the user's emotions while studying in real time and display encouraging messages if their motivation is declining. For example, it can display a message such as, "Keep it up! You're almost there!". The generation AI can also provide advice based on the user's emotional data according to their progress in studying. For example, if their studies are going well, it can provide positive feedback such as, "Keep it up!". The generation AI can also use its emotion estimation function to monitor the user's stress level while studying and provide advice to help them relax as needed. For example, it can suggest, "Take a short break and refresh yourself." This helps maintain the user's motivation and improve their learning effectiveness.

[0056] Generative AI can also support the learning of other languages ​​and function as a multilingual learning platform. Generative AI can support languages ​​other than English, allowing users to learn multiple languages. For example, it provides learning content for Spanish, French, Chinese, and other languages. Furthermore, when a user selects the language they want to learn, Generative AI automatically generates a learning plan for that language. For example, it provides a curriculum that starts with basic greetings and everyday conversations in the selected language. Generative AI also functions as a multilingual learning platform, allowing users to centrally manage their learning progress across different languages. For example, it allows users to check the learning history and progress for each language on a dashboard. This allows users to learn multiple languages.

[0057] The generative AI adds a function that allows users to share their learning progress with family and friends, thereby increasing their motivation to learn. The generative AI provides, for example, a function that allows users to share their learning progress with family and friends. For example, notifications of learning progress and goal achievements are sent via social media or email. The generative AI also adds a function that allows users to receive supportive messages and feedback from family and friends by sharing their learning progress. For example, by sharing their learning results, they can receive encouragement from those around them. The generative AI also provides a function that allows users to set learning goals together with family and friends and promote competition and cooperation by sharing their learning progress. For example, a ranking function that allows users to compete against each other on their learning progress is added. This can increase users' motivation to learn.

[0058] The generation AI can use its emotion estimation function to automatically select music and background sounds that correspond to the user's emotions while studying, providing a relaxing learning environment. For example, the generation AI uses its emotion estimation function to analyze the user's emotions while studying and automatically select relaxing music and background sounds. For example, if the user is feeling stressed, it will play music that has a relaxing effect. The generation AI also provides music and background sounds to optimize the learning environment based on the user's emotional data. For example, it will play environmental sounds or natural sounds to increase concentration. The generation AI also uses its emotion estimation function to automatically generate a music playlist that corresponds to the user's emotions while studying. For example, it will select upbeat music to maintain positive emotions. This allows the user to study in a relaxed environment.

[0059] Generative AI can also provide relevant cultural background and historical information in response to user questions. For example, in response to a question such as "Tell me about Thanksgiving in the United States," the generative AI will explain the history and cultural background of Thanksgiving. Furthermore, when a user asks about a specific phrase or expression, the generative AI will explain the cultural situation and historical background in which the phrase is used. For example, it will explain the origin and context in which the expression "break the ice" is used. Furthermore, in response to a user question, the generative AI will provide information on relevant cultural customs and etiquette. For example, in response to a question about "business meeting etiquette in the UK," the generative AI will explain the appropriate etiquette and customs. This allows users to learn not only English conversation but also cultural background and historical information.

[0060] The generative AI can present multiple example answers to a user's questions, allowing the user to choose the most appropriate expression. For example, the generative AI presents multiple example answers to a user's questions. For example, in response to the question "How are you?", the generative AI provides options such as "I'm fine, thank you," "I'm doing well," and "Not too bad." When a user asks about expressions that can be used in a specific situation, the generative AI presents multiple appropriate expressions, allowing the user to choose the most appropriate one. For example, when asked "How to order at a restaurant," the generative AI provides multiple ordering phrases. The generative AI also presents example answers with different nuances and tones in response to a user's questions. For example, when asked "How to apologize," the generative AI provides examples of both formal and casual apologies. This allows the user to choose the most appropriate expression from multiple options.

[0061] Using the emotion estimation function, the generation AI can understand the intent and emotions of the user's question and provide a more appropriate answer. For example, using the emotion estimation function, the generation AI can analyze the intent and emotions of the user's question and provide an appropriate answer. For example, if the user is feeling anxious, the generation AI can provide a reassuring answer. The generation AI can also adjust the tone and content of the answer based on the emotional data for the user's question. For example, if the user is excited, the generation AI can provide a calm and collected answer. The generation AI can also use the emotion estimation function to understand the emotions and intentions behind the user's question and provide an appropriate answer. For example, if the user is confused, the generation AI can provide a more detailed explanation or additional information. This allows the generation AI to provide an appropriate answer that matches the user's intent and emotions.

[0062] The generation AI can add a function that allows users to refer to similar questions and answers asked by other users in response to a user's question. For example, the generation AI can display similar questions and answers asked in the past by other users in response to a user's question. For example, for the question "How to improve my English pronunciation?", the generation AI can reference questions and answers asked by other users. In addition, when a user asks a question on a specific topic, the generation AI can present related past questions and answers for the user's reference. For example, for the question "What are some common English idioms?", the generation AI can display similar past questions and answers. In addition, the generation AI can build a database that allows users to refer to similar questions and answers asked by other users in response to a user's question, making it easier for users to find the best answer to their question. For example, the generation AI can provide a function to search for related questions and answers based on keywords in the question. This allows users to refer to questions and answers asked by other users.

[0063] When pointing out a user's pronunciation or grammar errors, the generative AI can provide specific example sentences and practice questions. For example, the generative AI can point out a user's pronunciation or grammar errors and provide specific example sentences and practice questions. For example, if the user makes the mistake of saying "I went to the store," the generative AI can present the correct example sentence and practice question, "I went to the store." The generative AI can also point out a user's pronunciation errors and provide specific practice questions to improve the pronunciation. For example, if the user's pronunciation of "apple" is incorrect, the generative AI can provide audio practice questions to practice the correct pronunciation. The generative AI can also point out a user's grammatical errors and provide specific example sentences and practice questions to improve the pronunciation. For example, if the user makes the mistake of saying "He don't like apples," the generative AI can present the correct example sentence and practice question, "He doesn't like apples." This allows the user to learn through specific example sentences and practice questions.

[0064] When pointing out a user's pronunciation or grammar errors, the generation AI can provide individualized feedback taking into account the user's learning history. For example, the generation AI can analyze the user's learning history and provide individualized feedback taking into account past mistakes. For example, if the same mistake is repeated, it can provide feedback to specifically call attention to it. The generation AI can also point out pronunciation or grammar errors based on the user's learning history and provide individualized feedback to improve them. For example, it can provide feedback to review content learned in the past. The generation AI can also provide individualized feedback taking into account the user's learning history when pointing out pronunciation or grammar errors. For example, it can provide feedback based on the user's progress to maintain motivation for learning. This allows the user to receive individualized feedback.

[0065] The generation AI can use the emotion estimation function to analyze the user's emotional response to a mistake and select an appropriate feedback method. For example, the generation AI can use the emotion estimation function to analyze the user's emotional response to a mistake in real time and select an appropriate feedback method. For example, if the user is feeling down, it can display an encouraging message. The generation AI can also adjust the content and tone of the feedback for the mistake based on the user's emotional data. For example, if the user is feeling stressed, it can provide feedback in a gentle tone. The generation AI can also use the emotion estimation function to build a system that analyzes the user's emotional response to a mistake and select an appropriate feedback method. For example, it can dynamically adjust the content of the feedback based on the user's emotional score. This makes it possible to provide appropriate feedback according to the user's emotions.

[0066] When pointing out a user's pronunciation or grammar errors, the generative AI can share success stories of other users. For example, when pointing out a user's pronunciation or grammar errors, the generative AI can share success stories of other users. For example, it can introduce the experiences and secrets of success of users who overcame the same mistakes. The generative AI can also point out a user's pronunciation or grammar errors and provide success stories of other users to help improve them. For example, it can share methods for practicing pronunciation or learning grammar. The generative AI can also motivate users by sharing success stories of other users when pointing out a user's pronunciation or grammar errors. For example, it can provide specific advice based on success stories. This allows users to refer to the success stories of other users.

[0067] When pointing out a user's pronunciation or grammar errors, the generative AI can provide game-style practice, allowing the user to learn while having fun. For example, the generative AI can point out a user's pronunciation or grammar errors and provide game-style practice to help them improve. For example, pronunciation practice can be gamified so that the user can earn points each time they pronounce something correctly. The generative AI can also point out a user's grammatical errors and provide game-style practice questions to help them improve. For example, it can allow the user to learn correct grammar through grammar quizzes and puzzles. When pointing out a user's pronunciation or grammar errors, the generative AI can provide game-style practice to help them learn while having fun. For example, it can provide pronunciation and grammar practice as mini-games, allowing the user to learn while having fun. This allows the user to progress through their learning while having fun.

[0068] The generative AI can use the emotion estimation function to monitor the user's emotional reactions to mistakes in real time and optimize the timing of feedback. The generative AI can, for example, use the emotion estimation function to monitor the user's emotional reactions to mistakes in real time and optimize the timing of feedback. For example, it can provide feedback when the user is calm. The generative AI can also adjust the timing of feedback based on the user's emotional data. For example, it can provide feedback when the user is concentrating, promoting effective learning. The generative AI can also use the emotion estimation function to build a system that monitors the user's emotional reactions to mistakes in real time and optimizes the timing of feedback. For example, it can dynamically adjust the timing of feedback based on the user's emotion score. This makes it possible to provide feedback at the optimal timing according to the user's emotions.

[0069] The generation AI can learn the user's lifestyle rhythm and add a reminder function that suggests the optimal time to study. For example, the generation AI can analyze the user's lifestyle rhythm and provide a reminder function that suggests the optimal time to study. For example, if the user has a habit of studying during their morning commute, a reminder can be sent at that time. The generation AI can also customize study reminders based on the user's lifestyle rhythm. For example, if the user often studies at night, a reminder can be sent in the evening. The generation AI can also learn the user's lifestyle rhythm and add a reminder function that suggests the optimal time to study. For example, the generation AI can dynamically adjust study reminders to match the user's schedule. This allows the user to study at the optimal time.

[0070] The generative AI can add a function that automatically suggests what content to study next based on the user's learning history. For example, the generative AI provides a function that analyzes the user's learning history and automatically suggests what content to study next. For example, it suggests the next topic or skill to learn based on what has been learned in the past. The generative AI also customizes what to study next based on the user's learning history. For example, it suggests that the user focus on studying areas in which they are weak. The generative AI also analyzes the user's learning history and adds a function that automatically suggests what to study next. For example, it dynamically adjusts what to study next depending on the user's progress. This allows the user to automatically receive suggestions on what to study next.

[0071] The generation AI can use the emotion estimation function to send notifications to increase motivation when a user's motivation to learn decreases. For example, the generation AI uses the emotion estimation function to send notifications to increase motivation when a user's motivation to learn decreases. For example, it sends an encouraging message such as, "Do your best! You're almost there!" The generation AI also customizes notifications to increase motivation to learn based on the user's emotion data. For example, if the user is feeling stressed, it sends advice on how to relax. The generation AI also uses the emotion estimation function to build a system that sends notifications to increase motivation when a user's motivation to learn decreases. For example, it dynamically adjusts the content of the notification based on the user's emotion score. This helps maintain the user's motivation to learn.

[0072] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0073] The English conversation learning system can increase motivation to learn by adding a function that allows users to share their learning progress with family and friends. For example, notifications of learning progress and goal achievement can be sent via social media or email. In addition, a function can be added that allows users to receive supportive messages and feedback from family and friends by sharing their learning progress. For example, sharing their learning results can help them receive encouragement from those around them. In addition, by sharing users' learning progress, a function can be provided that allows them to set learning goals together with family and friends and promote competition and cooperation. For example, a ranking function can be added that allows users to compete based on their learning progress. This can increase users' motivation to learn.

[0074] The English conversation learning system can also support the learning of other languages ​​and function as a multilingual learning platform. For example, it can support languages ​​other than English, allowing users to learn multiple languages. For example, it provides learning content for Spanish, French, Chinese, and other languages. In addition, when a user selects the language they want to learn, it automatically generates a learning plan for that language. For example, it provides a curriculum that starts with basic greetings and everyday conversations in the selected language. It also functions as a multilingual learning platform, allowing users to centrally manage their learning progress across different languages. For example, it allows users to check the learning history and progress for each language on a dashboard. This allows users to learn multiple languages.

[0075] The English conversation learning system can also provide relevant cultural background and historical information in response to user questions. For example, in response to a question such as "Tell me about Thanksgiving in America," the system will explain the history and cultural background of Thanksgiving. Also, when a user asks about a specific phrase or expression, the system will explain the cultural situations and historical background in which the phrase is used. For example, the system will explain the origin of the expression "break the ice" and the situations in which it is used. Also, in response to a user question, the system will provide information on related cultural customs and etiquette. For example, in response to a question about "business meeting etiquette in the UK," the system will explain appropriate etiquette and customs. This allows users to learn not only English conversation but also cultural background and historical information.

[0076] An English conversation learning system can present multiple example answers to a user's questions, allowing the user to choose the most appropriate expression. For example, multiple example answers can be presented to a user's question. For example, in response to the question "How are you?", options such as "I'm fine, thank you," "I'm doing well," and "Not too bad" are provided. Also, when a user asks about expressions that can be used in a specific situation, multiple appropriate expressions can be presented, allowing the user to choose the most appropriate one. For example, when asked "How to order at a restaurant," multiple ordering phrases can be provided. Also, example answers with different nuances and tones can be presented to a user's question. For example, when asked "How to apologize," examples of both formal and casual apologies can be provided. This allows the user to choose the most appropriate expression from multiple options.

[0077] An English conversation learning system can provide specific example sentences and practice questions when pointing out a user's pronunciation or grammar errors. For example, it can point out a user's pronunciation or grammar errors and provide specific example sentences and practice questions. For example, if the user makes a mistake saying "I went to the store," it can present the correct example sentence and practice question, "I went to the store." It can also point out a user's pronunciation errors and provide specific practice questions to improve the pronunciation. For example, if the user's pronunciation of "apple" is incorrect, it can provide audio practice questions to practice the correct pronunciation. It can also point out a user's grammar errors and provide specific example sentences and practice questions to improve the pronunciation. For example, if the user makes a mistake saying "He don't like apples," it can present the correct example sentence and practice question, "He doesn't like apples." This allows the user to learn through specific example sentences and practice questions.

[0078] An English conversation learning system can use emotion estimation to analyze a user's emotions while studying and provide encouragement and advice to maintain motivation. For example, the emotion estimation function can be used to analyze a user's emotions while studying in real time and display encouraging messages if motivation is declining. For example, a message such as "Keep up the good work! You're almost there!" can be displayed. Furthermore, advice based on the user's emotion data can be provided according to the user's progress in studying. For example, if the user's studies are going well, positive feedback such as "Keep it up!" can be given. The emotion estimation function can also be used to monitor the user's stress level while studying and provide advice to help them relax as needed. For example, a suggestion such as "Take a short break and refresh yourself" can be made. This can maintain the user's motivation and improve the effectiveness of their learning.

[0079] An English conversation learning system can use an emotion estimation function to automatically select music and background sounds that correspond to the user's emotions while studying, providing a relaxing learning environment. For example, the emotion estimation function can be used to analyze the user's emotions while studying and automatically select relaxing music and background sounds. For example, if the user is feeling stressed, music with a relaxing effect can be played. Furthermore, music and background sounds can be provided to optimize the learning environment based on the user's emotion data. For example, environmental sounds or natural sounds can be played to improve concentration. Furthermore, the emotion estimation function can be used to automatically generate a music playlist that corresponds to the user's emotions while studying. For example, upbeat music can be selected to maintain positive emotions. This allows the user to study in a relaxed environment.

[0080] The English conversation learning system can use the emotion estimation function to understand the intent and emotions of the user's question and provide a more appropriate answer. For example, the emotion estimation function can be used to analyze the intent and emotions of the user's question and provide an appropriate answer. For example, if the user is feeling anxious, the system can provide a reassuring answer. The tone and content of the answer can also be adjusted based on the emotional data for the user's question. For example, if the user is excited, the system can provide a calm and collected answer. The emotion estimation function can also be used to understand the emotions and intentions behind the user's question and provide an appropriate answer. For example, if the user is confused, the system can provide a more detailed explanation or additional information. This makes it possible to provide an appropriate answer that matches the user's intent and emotions.

[0081] An English conversation learning system can use an emotion estimation function to analyze a user's emotional response to a mistake and select an appropriate feedback method. For example, the emotion estimation function can be used to analyze a user's emotional response to a mistake in real time and select an appropriate feedback method. For example, if the user is feeling down, an encouraging message can be displayed. Furthermore, the content and tone of the feedback for the mistake can be adjusted based on the user's emotion data. For example, if the user is feeling stressed, feedback can be provided in a gentle tone. Furthermore, a system can be constructed that uses the emotion estimation function to analyze a user's emotional response to a mistake and select an appropriate feedback method. For example, the content of the feedback can be dynamically adjusted based on the user's emotion score. This makes it possible to provide appropriate feedback according to the user's emotions.

[0082] An English conversation learning system can use an emotion estimation function to monitor a user's emotional reactions to mistakes in real time and optimize the timing of feedback. For example, the emotion estimation function can be used to monitor a user's emotional reactions to mistakes in real time and optimize the timing of feedback. For example, feedback can be provided when the user is calm. The timing of feedback can also be adjusted based on the user's emotional data. For example, feedback can be provided when the user is concentrating, promoting effective learning. A system can also be built that uses the emotion estimation function to monitor a user's emotional reactions to mistakes in real time and optimize the timing of feedback. For example, the timing of feedback can be dynamically adjusted based on the user's emotional score. This makes it possible to provide feedback at the optimal timing according to the user's emotions.

[0083] The processing flow of the second embodiment will be briefly explained below.

[0084] Step 1: The generation AI responds to the user's English conversation questions and acts as a conversation partner. For example, when a user inputs a prompt such as "I want to introduce myself in English," the generation AI provides appropriate English phrases and grammar for the user to practice. Step 2: The AI ​​generator will point out any pronunciation or grammar errors in real time and provide feedback on areas for improvement. For example, when a user pronounces "apple," the AI ​​will evaluate their pronunciation and provide feedback on any necessary corrections. If the user says "I went to the store," the AI ​​will provide the correct grammar: "I went to the store."

[0085] 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.

[0086] 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.

[0087] 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.

[0088] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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).

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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).

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] In the headset type terminal 314, 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. 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 specific processing unit 290 using these models.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] In the robot 414, 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 robot 414 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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).

[0138] 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.

[0139] 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."

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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. [Explanation of symbols]

[0152] 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 smartphone app equipped with generative AI, a user who operates the smartphone app; The generated AI is Responsible for answering questions and acting as a conversation partner for the user in English, Point out the user's pronunciation and grammar errors in real time and provide feedback on areas for improvement. A system characterized by:

2. The generated AI is Analyze the user's learning history and automatically generate an individual learning plan 2. The system of claim 1.

3. The generated AI is It points out the user's pronunciation and grammar mistakes and provides specific ways to improve them through video and audio.

2. The system of claim 1.

4. The generated AI is Analyze the user's emotions while studying and provide encouragement and advice to maintain motivation 2. The system of claim 1.

5. The generated AI is It also supports learning other languages ​​and functions as a multilingual learning platform.

2. The system of claim 1.

Images