System

Abstract

An object of the system according to the embodiment is to provide an environment in which even a person who is not confident in English conversation can continuously practice.SOLUTION: A system includes an English conversation practice part, a voice setting part, a scheduling part, and a conversation analysis part. An English conversation practice part practices English conversation by using the generated AI. The vocalization setting unit sets the vocalization of the AI generated by the English conversation practice unit. A scheduling part sets a practice time by using the voices of the generation AI set by the voice setting part. The conversation analysis unit analyzes the conversation history on the basis of the practice time set by the scheduling unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technology has the problem that it is difficult for people who are not confident in their English conversation skills to continue practicing, making it difficult to learn effectively.

[0005] The system according to the embodiment aims to provide an environment in which even people who are not confident in their English conversation skills can continue to practice. [Means for solving the problem]

[0006] The system according to the embodiment includes an English conversation practice unit, a voice setting unit, a scheduling unit, and a conversation analysis unit. The English conversation practice unit practices English conversation using a generation AI. The voice setting unit sets the voice of the generation AI generated by the English conversation practice unit. The scheduling unit sets a practice time using the voice of the generation AI set by the voice setting unit. The conversation analysis unit analyzes the conversation history based on the practice time set by the scheduling unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide an environment where even people who are not confident in their English conversation skills can continue to practice. [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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[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 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[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 practice system according to an embodiment of the present invention is a service that uses AI to help people who lack confidence in their English skills practice English conversation. Because the other party in this system is AI, the system provides an English conversation service that allows users to make as many mistakes and redo as they like. This allows the English conversation practice system to enable users to practice English conversation with confidence and improve their skills.

[0029] An English conversation practice system according to an embodiment includes an English conversation practice unit, a voice setting unit, a scheduling unit, and a conversation analysis unit. The English conversation practice unit uses a generation AI to practice English conversation. For example, when a user says, "Hello, how are you?", the generation AI responds, "I'm fine, thank you. How about you?" The English conversation practice unit also allows the generation AI to evaluate the user's pronunciation in real time and provide specific feedback on areas for improvement. For example, the generation AI analyzes the user's pronunciation, identifies pronunciation errors based on the speech waveform, and instructs the user to practice by emphasizing those phonemes. The voice setting unit sets the voice of the generation AI. For example, a male voice, a female voice, a young voice, or an older voice can be selected according to the user's preferences. The voice setting unit can also add emotional expressions to the voice of the generation AI to achieve more natural conversation. For example, speech synthesis technology can be used to adjust the intensity and type of emotions, reflecting joy or sadness in the voice. The scheduling unit sets the practice time. For example, when a user sets a desired practice time, the generation AI automatically sends a reminder at that time to encourage practice. The scheduling unit can also use the generation AI to learn the user's daily rhythm and suggest optimal practice times. For example, the practice time can be set based on the user's wake-up time and bedtime. The conversation analysis unit analyzes the conversation history. For example, the generation AI can analyze the conversation history in detail to identify the user's weaknesses and provide an individualized learning plan. The conversation analysis unit can also use the generation AI to visualize the user's progress and give them a sense of accomplishment. For example, the practice results can be displayed in graphs or charts. This allows the English conversation practice system according to the embodiment to enable users to practice English conversation with confidence and improve their skills. For example, the output unit displays the practice results to the user via a web application or a mobile application. If the user desires feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending them directly to the user.

[0030] The English conversation practice unit can evaluate the user's pronunciation in real time and provide specific feedback on areas for improvement. In the English conversation practice unit, for example, the generation AI analyzes the user's pronunciation in real time and identifies pronunciation errors based on the audio waveform. For example, if a specific phoneme is not pronounced correctly, the unit instructs the user to emphasize that phoneme and practice. The English conversation practice unit can also analyze the user's pronunciation, identify pronunciation errors based on the audio waveform, and instruct the user to emphasize that phoneme and practice. For example, the generation AI analyzes the user's pronunciation, identify pronunciation errors based on the audio waveform, and instruct the user to emphasize that phoneme and practice. This allows the user's pronunciation to be improved quickly.

[0031] The English conversation practice unit can analyze the user's conversational fluency and suggest practice methods to improve fluency. In the English conversation practice unit, for example, the generation AI analyzes the user's conversational fluency and evaluates the speaking speed and rhythm. For example, if the user speaks slowly, it suggests practice methods to speed up. In addition, the English conversation practice unit can analyze the user's conversational fluency and evaluate the speaking speed and rhythm and suggest practice methods to speed up. For example, if the user speaks slowly, it suggests practice methods to speed up. This improves the user's conversational fluency.

[0032] The voice setting unit can make the voice of the generation AI sound more familiar by making it sound more similar to the user's voice. For example, the voice setting unit collects user voice samples and adjusts the voice of the generation AI using voice synthesis technology to make the voice of the generation AI sound more similar to the user's voice. For example, it can reflect the tone and pitch of the user's voice. The voice setting unit can also collect user voice samples and adjust the voice of the generation AI using voice synthesis technology to make the voice of the generation AI sound more similar to the user's voice. For example, it can reflect the tone and pitch of the user's voice. This allows the user to practice English conversation in a familiar way.

[0033] The voice setting unit can add emotional expressions to the voice of the generated AI to achieve natural conversation. For example, the voice setting unit can use voice synthesis technology to adjust the intensity and type of emotion to add emotional expressions to the voice of the generated AI. For example, emotions such as joy and sadness can be reflected in the voice. The voice setting unit can also use voice synthesis technology to adjust the intensity and type of emotion to add emotional expressions to the voice of the generated AI. For example, emotions such as joy and sadness can be reflected in the voice. This allows the user to enjoy natural conversation.

[0034] The scheduling unit can use the generation AI to learn the user's daily rhythm and suggest the optimal practice time. For example, the generation AI in the scheduling unit can learn the user's daily rhythm and suggest the optimal practice time. For example, the practice time can be set based on the user's wake-up time and bedtime. The scheduling unit can also use the generation AI to learn the user's daily rhythm and suggest the optimal practice time. For example, the practice time can be set based on the user's wake-up time and bedtime. This allows the user to practice English conversation at the optimal time.

[0035] The scheduling unit can use the generation AI to monitor the user's progress in real time and send reminders at appropriate times. For example, the scheduling unit can use the generation AI to monitor the user's progress in real time and send reminders at appropriate times. For example, a reminder is sent if the user has not practiced for a certain period of time. The scheduling unit can also use the generation AI to monitor the user's progress in real time and send reminders at appropriate times. For example, a reminder is sent if the user has not practiced for a certain period of time. This allows the user to practice at appropriate times.

[0036] The conversation analysis unit can use the generation AI to analyze the conversation history in detail, identify the user's weak points, and provide an individualized learning plan. For example, the generation AI can analyze the conversation history in detail to identify the user's weak points. For example, it can extract specific grammatical errors or pronunciation errors. The conversation analysis unit can also use the generation AI to analyze the conversation history in detail to identify the user's weak points and provide an individualized learning plan. For example, it can extract specific grammatical errors or pronunciation errors. This can identify the user's weak points and provide an individualized learning plan.

[0037] The conversation analysis unit can use the generation AI to visualize the user's progress, allowing the user to feel a sense of accomplishment. For example, the generation AI can visualize the user's progress, allowing the user to feel a sense of accomplishment. For example, the results of practice can be displayed in a graph or chart. The conversation analysis unit can also use the generation AI to visualize the user's progress, allowing the user to feel a sense of accomplishment. For example, the results of practice can be displayed in a graph or chart. This allows the user to progress with their studies while feeling a sense of accomplishment.

[0038] The conversation analysis unit can use the generation AI to provide learning content related to the user's interests and hobbies based on the conversation history. For example, the generation AI can provide learning content related to the user's interests and hobbies based on the conversation history. For example, if the user is interested in sports, learning content related to sports can be provided. The conversation analysis unit can also use the generation AI to provide learning content related to the user's interests and hobbies based on the conversation history. For example, if the user is interested in sports, learning content related to sports can be provided. This allows the user to study with content related to their interests and hobbies.

[0039] The conversation analysis unit can use the generation AI to provide a content format (video, audio, text) that matches the user's learning style. For example, the generation AI can provide a content format that matches the user's learning style. For example, it can provide learning content in video format. The conversation analysis unit can also use the generation AI to provide a content format that matches the user's learning style. For example, it can provide learning content in video format. This allows the user to progress with their studies in the most optimal format.

[0040] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0041] The English conversation practice system further includes a translation unit. The translation unit can translate what the user says in English into other languages ​​in real time. For example, when the user says, "Hello, how are you?", the translation unit translates the content into Spanish as "Hola, ?como estas?". The translation unit can also translate what the user says in other languages ​​into English. For example, when the user says, "Hello, how are you?" in Japanese, the translation unit translates the content into English as "Hello, how are you?". This allows the user to improve their multilingual communication skills.

[0042] The English conversation practice system further includes a cultural information providing unit. The cultural information providing unit can provide cultural information related to topics that the user touches on during the conversation. For example, if the user talks about "Thanksgiving," the cultural information providing unit can provide information about the history and customs of Thanksgiving. Also, if the user talks about "British tea time," the cultural information providing unit can provide information about British tea time traditions and etiquette. This allows the user to deepen their understanding of different cultures through English conversation.

[0043] The English conversation practice system further includes a game section. The game section can provide practice content in the form of a game so that the user can practice English conversation while having fun. For example, the game section can provide a game in which the user earns points by answering English quizzes, or an adventure game in which the user controls a character using English phrases. The game section can also provide an online competitive game in which the user can compete against other users. This allows the user to improve their English conversation skills while having fun.

[0044] The English conversation practice system further includes a reward section. The reward section can provide rewards according to the results of practice to motivate users to continue practicing. For example, when a user achieves a certain amount of practice time, the user can earn badges or points. The reward section can also provide a system that allows users to exchange the points they earn for special offers or prizes. This can motivate users to continue practicing.

[0045] The English conversation practice system further includes a feedback unit. The feedback unit can provide detailed feedback on the content the user has practiced. For example, it can provide specific improvements and advice on the content of the conversation or pronunciation the user has had. The feedback unit can also provide a function that allows the user to record what the user has practiced and play it back later for review. This allows the user to check their progress and study effectively.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The English conversation practice section uses the generation AI to practice English conversation. For example, if a user says, "Hello, how are you?", the generation AI will respond with, "I'm fine, thank you. How about you?" The English conversation practice section can also use the generation AI to evaluate the user's pronunciation in real time and provide specific feedback on areas for improvement. For example, the generation AI can analyze the user's pronunciation, identify pronunciation errors based on the audio waveform, and instruct the user to practice by emphasizing those phonemes. Step 2: The voice setting unit sets the voice of the generated AI. For example, users can select a voice based on their preferences, such as a male or female voice, a young or old voice, etc. The voice setting unit can also add emotional expressions to the generated AI's voice to achieve more natural conversations. For example, speech synthesis technology can be used to adjust the intensity and type of emotion, allowing the voice to reflect emotions such as joy or sadness. Step 3: The scheduling unit sets the practice time. For example, if the user sets the desired practice time, the generation AI automatically sends a reminder at that time to encourage practice. The scheduling unit can also use the generation AI to learn the user's daily rhythm and suggest the optimal practice time. For example, it can set the practice time based on the user's wake-up time and bedtime. Step 4: The conversation analysis unit analyzes the conversation history. For example, the generation AI can analyze the conversation history in detail, identify the user's weaknesses, and provide an individualized learning plan. The conversation analysis unit can also use the generation AI to visualize the user's progress and give them a sense of accomplishment. For example, the results of their practice can be displayed in graphs or charts.

[0048] (Example 2) The English conversation practice system according to an embodiment of the present invention is a service that uses AI to help people who lack confidence in their English skills practice English conversation. Because the other party in this system is AI, the system provides an English conversation service that allows users to make as many mistakes and redo as they like. This allows the English conversation practice system to enable users to practice English conversation with confidence and improve their skills.

[0049] An English conversation practice system according to an embodiment includes an English conversation practice unit, a voice setting unit, a scheduling unit, and a conversation analysis unit. The English conversation practice unit uses a generation AI to practice English conversation. For example, when a user says, "Hello, how are you?", the generation AI responds, "I'm fine, thank you. How about you?" The English conversation practice unit also allows the generation AI to evaluate the user's pronunciation in real time and provide specific feedback on areas for improvement. For example, the generation AI analyzes the user's pronunciation, identifies pronunciation errors based on the speech waveform, and instructs the user to practice by emphasizing those phonemes. The voice setting unit sets the voice of the generation AI. For example, a male voice, a female voice, a young voice, or an older voice can be selected according to the user's preferences. The voice setting unit can also add emotional expressions to the voice of the generation AI to achieve more natural conversation. For example, speech synthesis technology can be used to adjust the intensity and type of emotions, reflecting joy or sadness in the voice. The scheduling unit sets the practice time. For example, when a user sets a desired practice time, the generation AI automatically sends a reminder at that time to encourage practice. The scheduling unit can also use the generation AI to learn the user's daily rhythm and suggest optimal practice times. For example, the practice time can be set based on the user's wake-up time and bedtime. The conversation analysis unit analyzes the conversation history. For example, the generation AI can analyze the conversation history in detail to identify the user's weaknesses and provide an individualized learning plan. The conversation analysis unit can also use the generation AI to visualize the user's progress and give them a sense of accomplishment. For example, the practice results can be displayed in graphs or charts. This allows the English conversation practice system according to the embodiment to enable users to practice English conversation with confidence and improve their skills. For example, the output unit displays the practice results to the user via a web application or a mobile application. If the user desires feedback in paper form, the results can be printed using a printer. Sending the results via email provides quick feedback by sending them directly to the user.

[0050] The English conversation practice unit can evaluate the user's pronunciation in real time and provide specific feedback on areas for improvement. In the English conversation practice unit, for example, the generation AI analyzes the user's pronunciation in real time and identifies pronunciation errors based on the audio waveform. For example, if a specific phoneme is not pronounced correctly, the unit instructs the user to emphasize that phoneme and practice. The English conversation practice unit can also analyze the user's pronunciation, identify pronunciation errors based on the audio waveform, and instruct the user to emphasize that phoneme and practice. For example, the generation AI analyzes the user's pronunciation, identify pronunciation errors based on the audio waveform, and instruct the user to emphasize that phoneme and practice. This allows the user's pronunciation to be improved quickly.

[0051] The English conversation practice unit can analyze the user's conversational fluency and suggest practice methods to improve fluency. In the English conversation practice unit, for example, the generation AI analyzes the user's conversational fluency and evaluates the speaking speed and rhythm. For example, if the user speaks slowly, it suggests practice methods to speed up. In addition, the English conversation practice unit can analyze the user's conversational fluency and evaluate the speaking speed and rhythm and suggest practice methods to speed up. For example, if the user speaks slowly, it suggests practice methods to speed up. This improves the user's conversational fluency.

[0052] The English conversation practice unit uses the emotion estimation function to generate responses according to the user's emotional state, providing an environment in which the user can converse in a relaxed manner. In the English conversation practice unit, for example, the generation AI analyzes the user's voice and facial expression to estimate the user's emotional state. For example, if the user is nervous, it generates a response that will help the user relax. In addition, the English conversation practice unit can also analyze the user's voice and facial expression to estimate the user's emotional state and generate a response that will help the user relax. For example, if the user is nervous, it generates a response that will help the user relax. This allows the user to practice English conversation in a relaxed manner.

[0053] The voice setting unit can make the voice of the generation AI sound more familiar by making it sound more similar to the user's voice. For example, the voice setting unit collects user voice samples and adjusts the voice of the generation AI using voice synthesis technology to make the voice of the generation AI sound more similar to the user's voice. For example, it can reflect the tone and pitch of the user's voice. The voice setting unit can also collect user voice samples and adjust the voice of the generation AI using voice synthesis technology to make the voice of the generation AI sound more similar to the user's voice. For example, it can reflect the tone and pitch of the user's voice. This allows the user to practice English conversation in a familiar way.

[0054] The voice setting unit can add emotional expressions to the voice of the generated AI to achieve natural conversation. For example, the voice setting unit can use voice synthesis technology to adjust the intensity and type of emotion to add emotional expressions to the voice of the generated AI. For example, emotions such as joy and sadness can be reflected in the voice. The voice setting unit can also use voice synthesis technology to adjust the intensity and type of emotion to add emotional expressions to the voice of the generated AI. For example, emotions such as joy and sadness can be reflected in the voice. This allows the user to enjoy natural conversation.

[0055] The scheduling unit can use the generation AI to learn the user's daily rhythm and suggest the optimal practice time. For example, the generation AI in the scheduling unit can learn the user's daily rhythm and suggest the optimal practice time. For example, the practice time can be set based on the user's wake-up time and bedtime. The scheduling unit can also use the generation AI to learn the user's daily rhythm and suggest the optimal practice time. For example, the practice time can be set based on the user's wake-up time and bedtime. This allows the user to practice English conversation at the optimal time.

[0056] The scheduling unit can use the generation AI to monitor the user's progress in real time and send reminders at appropriate times. For example, the scheduling unit can use the generation AI to monitor the user's progress in real time and send reminders at appropriate times. For example, a reminder is sent if the user has not practiced for a certain period of time. The scheduling unit can also use the generation AI to monitor the user's progress in real time and send reminders at appropriate times. For example, a reminder is sent if the user has not practiced for a certain period of time. This allows the user to practice at appropriate times.

[0057] The scheduling unit can use the emotion estimation function to send reminders to encourage the user to practice when the user's motivation is high. The scheduling unit, for example, uses the emotion estimation function to send reminders to encourage the user to practice when the user's motivation is high. For example, the scheduling unit sends a reminder when the user is showing positive emotions. The scheduling unit can also use the emotion estimation function to send reminders to encourage the user to practice when the user's motivation is high. For example, the scheduling unit sends a reminder when the user is showing positive emotions. This allows the user to practice effectively when the user's motivation is high.

[0058] The conversation analysis unit can use the generation AI to analyze the conversation history in detail, identify the user's weak points, and provide an individualized learning plan. For example, the generation AI can analyze the conversation history in detail to identify the user's weak points. For example, it can extract specific grammatical errors or pronunciation errors. The conversation analysis unit can also use the generation AI to analyze the conversation history in detail to identify the user's weak points and provide an individualized learning plan. For example, it can extract specific grammatical errors or pronunciation errors. This can identify the user's weak points and provide an individualized learning plan.

[0059] The conversation analysis unit can use the generation AI to visualize the user's progress, allowing the user to feel a sense of accomplishment. For example, the generation AI can visualize the user's progress, allowing the user to feel a sense of accomplishment. For example, the results of practice can be displayed in a graph or chart. The conversation analysis unit can also use the generation AI to visualize the user's progress, allowing the user to feel a sense of accomplishment. For example, the results of practice can be displayed in a graph or chart. This allows the user to progress with their studies while feeling a sense of accomplishment.

[0060] The conversation analysis unit can use the generation AI to provide learning content related to the user's interests and hobbies based on the conversation history. For example, the generation AI can provide learning content related to the user's interests and hobbies based on the conversation history. For example, if the user is interested in sports, learning content related to sports can be provided. The conversation analysis unit can also use the generation AI to provide learning content related to the user's interests and hobbies based on the conversation history. For example, if the user is interested in sports, learning content related to sports can be provided. This allows the user to study with content related to their interests and hobbies.

[0061] The conversation analysis unit can use the generation AI to provide a content format (video, audio, text) that matches the user's learning style. For example, the generation AI can provide a content format that matches the user's learning style. For example, it can provide learning content in video format. The conversation analysis unit can also use the generation AI to provide a content format that matches the user's learning style. For example, it can provide learning content in video format. This allows the user to progress with their studies in the most optimal format.

[0062] The conversation analysis unit can use the emotion estimation function to suggest a content format that allows the user to most enjoy learning. The conversation analysis unit can, for example, use the emotion estimation function to suggest a content format that allows the user to most enjoy learning. For example, it can analyze the degree of enjoyment from the user's facial expressions and voice. The conversation analysis unit can also use the emotion estimation function to suggest a content format that allows the user to most enjoy learning. For example, it can analyze the degree of enjoyment from the user's facial expressions and voice. This allows the user to enjoy learning.

[0063] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0064] The English conversation practice system further includes a translation unit. The translation unit can translate what the user says in English into other languages ​​in real time. For example, when the user says, "Hello, how are you?", the translation unit translates the content into Spanish as "Hola, ?como estas?". The translation unit can also translate what the user says in other languages ​​into English. For example, when the user says, "Hello, how are you?" in Japanese, the translation unit translates the content into English as "Hello, how are you?". This allows the user to improve their multilingual communication skills.

[0065] The English conversation practice system further includes a cultural information providing unit. The cultural information providing unit can provide cultural information related to topics that the user touches on during the conversation. For example, if the user talks about "Thanksgiving," the cultural information providing unit can provide information about the history and customs of Thanksgiving. Also, if the user talks about "British tea time," the cultural information providing unit can provide information about British tea time traditions and etiquette. This allows the user to deepen their understanding of different cultures through English conversation.

[0066] The English conversation practice system further includes a game section. The game section can provide practice content in the form of a game so that the user can practice English conversation while having fun. For example, the game section can provide a game in which the user earns points by answering English quizzes, or an adventure game in which the user controls a character using English phrases. The game section can also provide an online competitive game in which the user can compete against other users. This allows the user to improve their English conversation skills while having fun.

[0067] The English conversation practice system further includes a reward section. The reward section can provide rewards according to the results of practice to motivate users to continue practicing. For example, when a user achieves a certain amount of practice time, the user can earn badges or points. The reward section can also provide a system that allows users to exchange the points they earn for special offers or prizes. This can motivate users to continue practicing.

[0068] The English conversation practice system further includes a feedback unit. The feedback unit can provide detailed feedback on the content the user has practiced. For example, it can provide specific improvements and advice on the content of the conversation or pronunciation the user has had. The feedback unit can also provide a function that allows the user to record what the user has practiced and play it back later for review. This allows the user to check their progress and study effectively.

[0069] The English conversation practice system can also use emotion estimation to monitor the user's stress level and suggest appropriate relaxation methods. For example, if the user is tense, it can suggest deep breathing or relaxing music. It can also encourage the user to take a short break if they are feeling stressed. This allows the user to practice English conversation in a relaxed state.

[0070] The English conversation practice system can also use the emotion estimation function to provide encouraging messages to motivate the user. For example, if the user is feeling down, an encouraging message can be displayed. Also, if the user is working hard, a message of praise can be displayed. This allows the user to maintain their motivation while practicing English conversation.

[0071] The English conversation practice system can also use its emotion estimation function to provide learning content that matches the user's emotions. For example, if the user is tired, it can provide relaxing conversations. If the user is excited, it can provide challenging conversations. This allows the user to study optimally according to their emotional state.

[0072] The English conversation practice system can also use the emotion estimation function to provide feedback based on the user's emotions. For example, if the user is confident, it can provide feedback encouraging further challenges. If the user is anxious, it can provide reassuring feedback. This allows the user to receive appropriate feedback according to their emotions.

[0073] The English conversation practice system can also use its emotion estimation function to suggest study plans based on the user's emotions. For example, if the user is expressing positive emotions, it can suggest more difficult tasks. On the other hand, if the user is expressing negative emotions, it can suggest easier tasks. This allows the user to proceed with the optimal study plan tailored to their emotional state.

[0074] The processing flow of the second embodiment will be briefly explained below.

[0075] Step 1: The English conversation practice section uses the generation AI to practice English conversation. For example, if a user says, "Hello, how are you?", the generation AI will respond with, "I'm fine, thank you. How about you?" The English conversation practice section can also use the generation AI to evaluate the user's pronunciation in real time and provide specific feedback on areas for improvement. For example, the generation AI can analyze the user's pronunciation, identify pronunciation errors based on the audio waveform, and instruct the user to practice by emphasizing those phonemes. Step 2: The voice setting unit sets the voice of the generated AI. For example, users can select a voice based on their preferences, such as a male or female voice, a young or old voice, etc. The voice setting unit can also add emotional expressions to the generated AI's voice to achieve more natural conversations. For example, speech synthesis technology can be used to adjust the intensity and type of emotion, allowing the voice to reflect emotions such as joy or sadness. Step 3: The scheduling unit sets the practice time. For example, if the user sets the desired practice time, the generation AI automatically sends a reminder at that time to encourage practice. The scheduling unit can also use the generation AI to learn the user's daily rhythm and suggest the optimal practice time. For example, it can set the practice time based on the user's wake-up time and bedtime. Step 4: The conversation analysis unit analyzes the conversation history. For example, the generation AI can analyze the conversation history in detail, identify the user's weaknesses, and provide an individualized learning plan. The conversation analysis unit can also use the generation AI to visualize the user's progress and give them a sense of accomplishment. For example, the results of their practice can be displayed in graphs or charts.

[0076] 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.

[0077] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<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.

[0078] 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.

[0079] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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.

[0084] 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).

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0089] 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. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] 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.

[0094] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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).

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0104] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0110] 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.

[0111] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0112] The 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.

[0113] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0114] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0115] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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 the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0120] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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).

[0129] 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.

[0130] 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."

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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]

[0143] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. An English conversation practice section that uses generative AI to practice English conversation, a voice setting unit that sets the voice of the AI ​​generated by the English conversation practice unit; a scheduling unit that sets a practice time using the voice of the generation AI set by the voice setting unit; a conversation analysis unit that analyzes the conversation history based on the practice time set by the scheduling unit. A system characterized by:

2. The voice setting unit By making the voice of the generated AI sound more similar to the user's voice, it creates a sense of familiarity.

2. The system of claim 1.

3. The scheduling unit The generative AI learns the user's daily rhythm and suggests optimal practice times.

2. The system of claim 1.

4. The conversation analysis unit The generative AI is used to analyze the conversation history in detail, identify the user's weaknesses, and provide a personalized learning plan.

2. The system of claim 1.

5. The English conversation practice section: Generates responses according to the user's emotional state, providing an environment in which the user can have a relaxed conversation 2. The system of claim 1.

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