System

Abstract

An object of the system according to the embodiment is to enable a user to learn a foreign language based on an arbitrary conversation topic and receive feedback in real time.SOLUTION: A system includes a conversation topic investigation unit, a lesson providing unit, and a feedback unit. When a user casts an arbitrary conversation topic, the conversation topic investigation unit automatically investigates information on the topic. The lesson providing unit provides a lesson based on the information investigated by the conversation topic investigation unit. The feedback unit analyzes the user's pronunciation and grammar in real time during the lesson and provides appropriate feedback.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the drawback of making it difficult for users to learn a foreign language based on an arbitrary conversation topic, and they do not provide real-time feedback.

[0005] The system according to the embodiment aims to enable users to learn a foreign language based on any conversation topic and receive feedback in real time. [Means for solving the problem]

[0006] The system according to the embodiment includes a conversation topic research unit, a lesson provision unit, and a feedback unit. When a user submits a conversation topic, the conversation topic research unit automatically researches information related to that topic. The lesson provision unit provides lessons based on the information researched by the conversation topic research unit. The feedback unit analyzes the user's pronunciation and grammar in real time during the lesson and provides appropriate feedback. [Effects of the Invention]

[0007] The system according to the embodiment allows users to learn a foreign language based on any conversation topic and receive feedback in real time. [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 AI ​​tutor system according to an embodiment of the present invention is a system for helping users learn a foreign language through conversation topics of their choice. This system allows users to submit their favorite conversation topics to an AI tutor, who then automatically conducts research based on the topic, providing new knowledge to users and providing lessons tailored to their individual levels. This allows the AI ​​tutor system to help users learn a foreign language efficiently and effectively.

[0029] The AI ​​tutor system according to the embodiment includes a conversation topic research unit, a lesson provision unit, and a feedback unit. When a user submits a conversation topic, the conversation topic research unit automatically researches information related to that topic. For example, if the user wants to talk about "space exploration," the generation AI collects the latest information and basic knowledge about space exploration and advances the conversation based on that information. The generation AI receives input from a prompt containing instructions on what the user wants the generation AI to do. The generation AI then collects information based on the prompt and provides a lesson. The lesson provision unit provides lessons based on the information researched by the conversation topic research unit. For example, the generation AI advances lessons with content tailored to the user's level. The generation AI can adjust the difficulty of the lesson according to the user's level of understanding. The feedback unit analyzes the user's pronunciation and grammar in real time during the lesson and provides appropriate feedback. For example, if the user says, "I went to the store," the generation AI immediately provides feedback such as, "'goed' is incorrect; the correct word is 'went'." This allows the user to correct their mistake and learn the correct expression on the spot. As a result, the AI ​​tutor system according to the embodiment allows users to learn a foreign language through any conversation topic and receive feedback in real time.

[0030] The conversation topic research unit can analyze the user's past conversation history and suggest related topics based on the user's interests and concerns. For example, the generation AI analyzes the user's past conversation history to identify topics that the user frequently talks about. For example, if the user has talked a lot about "space exploration" in the past, the generation AI will suggest new related topics such as "Mars exploration" and "life on a space station." This can increase the user's motivation to learn by suggesting related topics based on the user's interests and concerns.

[0031] The conversation topic research unit can track the user's learning progress in real time and automatically insert review topics at appropriate times. For example, the generation AI in the conversation topic research unit tracks the user's learning progress in real time and suggests reviewing previously learned topics at appropriate times. For example, after a user learns "how to use the past tense," it suggests that they review that topic again after a certain period of time has passed. In this way, by tracking the user's learning progress and providing review topics at appropriate times, it is possible to improve learning effectiveness.

[0032] The conversation topic research unit can collect information on the same topic in different languages, allowing users to study in multiple languages ​​simultaneously. For example, the generation AI can collect information on the same topic in multiple languages, allowing users to study in multiple languages. For example, the conversation topic research unit can provide information on "space exploration" in English, French, and Spanish. This allows users to study in multiple languages ​​simultaneously, broadening the scope of their language learning.

[0033] The conversation topic research unit can provide region-specific topics based on the user's geographic location information, deepening cultural understanding. For example, the generation AI can provide region-specific topics based on the user's geographic location information. For example, if the user is in Japan, it can suggest topics about "Japanese festivals" and "Japanese food culture." This allows for a deeper cultural understanding by providing region-specific topics based on the user's geographic location information.

[0034] The feedback unit can evaluate the user's pronunciation in detail using speech waveform analysis and suggest specific ways to improve pronunciation. For example, the generation AI can evaluate the user's pronunciation in detail using speech waveform analysis and suggest specific ways to improve pronunciation. For example, if the user is having trouble pronouncing the sound "th," the generation AI can provide specific advice such as "place your tongue between your front teeth when pronouncing it." This allows the user's pronunciation to be evaluated in detail and specific ways to improve pronunciation to improve pronunciation accuracy.

[0035] The feedback unit can analyze the user's grammatical mistakes using pattern recognition technology and provide special practice questions for frequently occurring mistakes. For example, in the feedback unit, the generation AI can analyze the user's grammatical mistakes using pattern recognition technology and provide special practice questions for frequently occurring mistakes. For example, if the user frequently makes mistakes in using the "past tense," the generation AI can provide "past tense practice questions." This allows the user's grammatical mistakes to be analyzed and special practice questions to be provided, thereby improving grammatical accuracy.

[0036] The feedback unit can automatically generate an individual learning plan based on the user's feedback history to support continuous learning. For example, the generation AI can automatically generate an individual learning plan based on the user's feedback history to support continuous learning. For example, the generation AI can analyze feedback that the user has received in the past and create a learning plan that focuses on a specific topic. This makes it possible to support continuous learning by automatically generating an individual learning plan based on the user's feedback history.

[0037] The feedback unit can compare the user's feedback with other learners and suggest group lessons for common issues. For example, the generation AI can compare the user's feedback with other learners and suggest group lessons for common issues. For example, if multiple users make mistakes in "using the past tense," the generation AI can suggest a "group lesson on the past tense." This makes it possible to improve learning effectiveness by comparing the user's feedback with other learners and suggesting group lessons for common issues.

[0038] The system can learn the user's lifestyle and suggest the optimal study timing. For example, the generation AI learns the user's lifestyle and suggests the optimal study timing. For example, if the user wakes up at 7am every morning and goes to bed at 10pm, the generation AI will suggest studying in the morning hours. This allows the system to learn the user's lifestyle and suggest the optimal study timing, thereby improving learning effectiveness.

[0039] The system can automatically generate mini-lessons that allow for effective learning in a short amount of time based on the user's learning history. For example, the system uses a generation AI to automatically generate mini-lessons that allow for effective learning in a short amount of time based on the user's learning history. For example, the system can provide a five-minute mini-lesson for the user to review topics that they have previously learned. This can improve learning efficiency by automatically generating mini-lessons that allow for effective learning in a short amount of time based on the user's learning history.

[0040] The system can automatically adjust the user's schedule and send reminders to ensure they set aside time to study. For example, the system uses a generative AI to automatically adjust the user's schedule and send reminders to ensure they set aside time to study. For example, the system analyzes the user's calendar and sends study reminders when the user has free time. This automatically adjusts the user's schedule and sends reminders to ensure they set aside time to study, thereby increasing the continuity of their studies.

[0041] The system can share the user's learning progress with family and friends and provide support to increase motivation to learn. For example, the system uses a generative AI to share the user's learning progress with family and friends and provide support to increase motivation to learn. For example, the system notifies family and friends of the learning goals the user has achieved. This allows the user to share the user's learning progress with family and friends and provide support to increase motivation to learn, thereby maintaining motivation to learn.

[0042] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0043] The conversation topic research unit can suggest related topics based on the user's hobbies and skills. For example, if the user is interested in music, the generative AI can suggest topics such as "music theory" and "famous composers." If the user is interested in sports, it can also suggest topics such as "Olympic history" and "soccer tactics." This can increase learning motivation by suggesting related topics based on the user's hobbies and skills.

[0044] The conversation topic research unit can suggest topics that match the user's learning style. For example, visual topics such as "historical paintings" or "how to read maps" can be suggested to visual learners. Auditory topics such as "podcast recommendations" or "music history" can also be suggested to auditory learners. Furthermore, practical topics such as "cooking recipes" or "DIY projects" can be suggested to hands-on learners. This allows the system to suggest topics that match the user's learning style, thereby enhancing learning effectiveness.

[0045] The conversation topic research unit can suggest topics based on the user's learning goals. For example, if a user wants to learn business English, the generation AI can suggest topics such as "How to conduct a business meeting" or "Presentation tips." If a user wants to learn travel English, the AI ​​can suggest topics such as "Airport conversations" or "How to book a hotel." Furthermore, if a user wants to prepare for an exam, the AI ​​can suggest topics such as "How to prepare for the TOEFL" or "Tips for the IELTS speaking test." This improves learning effectiveness by suggesting topics based on the user's learning goals.

[0046] The conversation topic research unit can suggest topics related to the user's occupation. For example, if the user is a medical professional, the generative AI can suggest topics such as "latest medical technology" and "medical ethics." If the user is an engineer, it can also suggest topics such as "new programming languages" and "technological trends." Furthermore, if the user is an educator, it can suggest topics such as "educational methodology" and "ways to improve student motivation." This can increase the user's motivation to learn by suggesting topics related to their occupation.

[0047] The conversation topic research unit can provide related information about topics previously learned based on the user's learning history. For example, if the user previously learned about "space exploration," the generation AI can provide related information such as "the latest space exploration missions" and "the history of space exploration." If the user learned about "environmental issues," the AI ​​can provide related information such as "the latest environmental protection technology" and "trends in environmental policy." Furthermore, if the user learned about a "historical event," the AI ​​can provide related information such as "people associated with the event" and "the impact of the event." This allows the AI ​​to provide related information based on the user's learning history, thereby enhancing the depth of learning.

[0048] The conversation topic research unit can compare the user's learning progress with other learners and provide rankings to stimulate competitive spirit. For example, the generation AI analyzes the user's learning progress and displays a ranking in comparison with other learners. Also, if the user receives high marks on a specific topic, the ranking for that topic can be displayed. Furthermore, if the user achieves their learning goal, a badge or title can be provided according to their level of achievement. This allows the user's learning progress to be compared with other learners, stimulating competitive spirit and increasing motivation to learn.

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

[0050] Step 1: When a user proposes a topic, the conversation topic research unit automatically researches information about that topic. For example, if a user wants to talk about "space exploration," the AI ​​generator will gather the latest information and basic knowledge about space exploration and use that information to advance the conversation. Step 2: The lesson provider provides lessons based on the information collected by the conversation topic researcher. For example, the AI ​​generator can provide lessons tailored to the user's level and adjust the difficulty of the lessons according to the user's level of understanding. Step 3: The feedback section analyzes the user's pronunciation and grammar in real time during the lesson and provides appropriate feedback. For example, if the user says, "I went to the store," the generation AI immediately provides feedback such as, "'goed' is incorrect; the correct word is 'went'."

[0051] (Example 2) The AI ​​tutor system according to an embodiment of the present invention is a system for helping users learn a foreign language through conversation topics of their choice. This system allows users to submit their favorite conversation topics to an AI tutor, who then automatically conducts research based on the topic, providing new knowledge to users and providing lessons tailored to their individual levels. This allows the AI ​​tutor system to help users learn a foreign language efficiently and effectively.

[0052] The AI ​​tutor system according to the embodiment includes a conversation topic research unit, a lesson provision unit, and a feedback unit. When a user submits a conversation topic, the conversation topic research unit automatically researches information related to that topic. For example, if the user wants to talk about "space exploration," the generation AI collects the latest information and basic knowledge about space exploration and advances the conversation based on that information. The generation AI receives input from a prompt containing instructions on what the user wants the generation AI to do. The generation AI then collects information based on the prompt and provides a lesson. The lesson provision unit provides lessons based on the information researched by the conversation topic research unit. For example, the generation AI advances lessons with content tailored to the user's level. The generation AI can adjust the difficulty of the lesson according to the user's level of understanding. The feedback unit analyzes the user's pronunciation and grammar in real time during the lesson and provides appropriate feedback. For example, if the user says, "I went to the store," the generation AI immediately provides feedback such as, "'goed' is incorrect; the correct word is 'went'." This allows the user to correct their mistake and learn the correct expression on the spot. As a result, the AI ​​tutor system according to the embodiment allows users to learn a foreign language through any conversation topic and receive feedback in real time.

[0053] The conversation topic research unit can analyze the user's past conversation history and suggest related topics based on the user's interests and concerns. For example, the generation AI analyzes the user's past conversation history to identify topics that the user frequently talks about. For example, if the user has talked a lot about "space exploration" in the past, the generation AI will suggest new related topics such as "Mars exploration" and "life on a space station." This can increase the user's motivation to learn by suggesting related topics based on the user's interests and concerns.

[0054] The conversation topic research unit can track the user's learning progress in real time and automatically insert review topics at appropriate times. For example, the generation AI in the conversation topic research unit tracks the user's learning progress in real time and suggests reviewing previously learned topics at appropriate times. For example, after a user learns "how to use the past tense," it suggests that they review that topic again after a certain period of time has passed. In this way, by tracking the user's learning progress and providing review topics at appropriate times, it is possible to improve learning effectiveness.

[0055] The conversation topic research unit uses the emotion estimation function to select topics that correspond to the user's emotional state, thereby increasing motivation to learn. For example, the conversation topic research unit uses the emotion estimation function to analyze the user's emotional state in real time and select topics that elicit positive emotions. For example, if the user is tired, the unit suggests topics about "ways to relax" or "hobbies." This allows the user's motivation to learn to be increased by selecting topics that correspond to the user's emotional state.

[0056] The conversation topic research unit can collect information on the same topic in different languages, allowing users to study in multiple languages ​​simultaneously. For example, the generation AI can collect information on the same topic in multiple languages, allowing users to study in multiple languages. For example, the conversation topic research unit can provide information on "space exploration" in English, French, and Spanish. This allows users to study in multiple languages ​​simultaneously, broadening the scope of their language learning.

[0057] The conversation topic research unit can provide region-specific topics based on the user's geographic location information, deepening cultural understanding. For example, the generation AI can provide region-specific topics based on the user's geographic location information. For example, if the user is in Japan, it can suggest topics about "Japanese festivals" and "Japanese food culture." This allows for a deeper cultural understanding by providing region-specific topics based on the user's geographic location information.

[0058] The conversation topic research unit can use the emotion estimation function to identify the topic in which the user is most interested and suggest subtopics related to that topic. For example, if the user is interested in "space exploration," the conversation topic research unit can suggest subtopics such as "Mars exploration" and "life on a space station." This can increase motivation to learn by identifying the topic in which the user is most interested and suggesting subtopics related to that topic.

[0059] The feedback unit can evaluate the user's pronunciation in detail using speech waveform analysis and suggest specific ways to improve pronunciation. For example, the generation AI can evaluate the user's pronunciation in detail using speech waveform analysis and suggest specific ways to improve pronunciation. For example, if the user is having trouble pronouncing the sound "th," the generation AI can provide specific advice such as "place your tongue between your front teeth when pronouncing it." This allows the user's pronunciation to be evaluated in detail and specific ways to improve pronunciation to improve pronunciation accuracy.

[0060] The feedback unit can analyze the user's grammatical mistakes using pattern recognition technology and provide special practice questions for frequently occurring mistakes. For example, in the feedback unit, the generation AI can analyze the user's grammatical mistakes using pattern recognition technology and provide special practice questions for frequently occurring mistakes. For example, if the user frequently makes mistakes in using the "past tense," the generation AI can provide "past tense practice questions." This allows the user's grammatical mistakes to be analyzed and special practice questions to be provided, thereby improving grammatical accuracy.

[0061] The feedback unit can use the emotion estimation function to analyze the emotional reaction of the user when receiving feedback and reinforce positive feedback. For example, the feedback unit can use the emotion estimation function to analyze the emotional reaction of the user when receiving feedback and reinforce positive feedback. For example, if the user has a positive reaction to feedback, the generation AI adds encouraging words such as "Well done!". In this way, by analyzing the user's emotional reaction and reinforcing positive feedback, it is possible to increase motivation to learn.

[0062] The feedback unit can automatically generate an individual learning plan based on the user's feedback history to support continuous learning. For example, the generation AI can automatically generate an individual learning plan based on the user's feedback history to support continuous learning. For example, the generation AI can analyze feedback that the user has received in the past and create a learning plan that focuses on a specific topic. This makes it possible to support continuous learning by automatically generating an individual learning plan based on the user's feedback history.

[0063] The feedback unit can compare the user's feedback with other learners and suggest group lessons for common issues. For example, the generation AI can compare the user's feedback with other learners and suggest group lessons for common issues. For example, if multiple users make mistakes in "using the past tense," the generation AI can suggest a "group lesson on the past tense." This makes it possible to improve learning effectiveness by comparing the user's feedback with other learners and suggesting group lessons for common issues.

[0064] The feedback unit can use the emotion estimation function to identify a feedback format in which the user can learn most effectively and provide feedback in that format. For example, the feedback unit can use the emotion estimation function to identify a feedback format in which the user can learn most effectively and provide feedback in that format. For example, the feedback unit can identify a feedback format in which the user shows positive emotions and provide feedback in that format. This can improve learning effectiveness by identifying a feedback format in which the user can learn most effectively and providing feedback in that format.

[0065] The system can learn the user's lifestyle and suggest the optimal study timing. For example, the generation AI learns the user's lifestyle and suggests the optimal study timing. For example, if the user wakes up at 7am every morning and goes to bed at 10pm, the generation AI will suggest studying in the morning hours. This allows the system to learn the user's lifestyle and suggest the optimal study timing, thereby improving learning effectiveness.

[0066] The system can automatically generate mini-lessons that allow for effective learning in a short amount of time based on the user's learning history. For example, the system uses a generation AI to automatically generate mini-lessons that allow for effective learning in a short amount of time based on the user's learning history. For example, the system can provide a five-minute mini-lesson for the user to review topics that they have previously learned. This can improve learning efficiency by automatically generating mini-lessons that allow for effective learning in a short amount of time based on the user's learning history.

[0067] The system can use the emotion estimation function to analyze the user's stress level and provide a relaxing learning environment. For example, if the user shows a high stress level, the generation AI can play relaxing music while the user studies. This can improve learning effectiveness by analyzing the user's stress level and providing a relaxing learning environment.

[0068] The system can automatically adjust the user's schedule and send reminders to ensure they set aside time to study. For example, the system uses a generative AI to automatically adjust the user's schedule and send reminders to ensure they set aside time to study. For example, the system analyzes the user's calendar and sends study reminders when the user has free time. This automatically adjusts the user's schedule and sends reminders to ensure they set aside time to study, thereby increasing the continuity of their studies.

[0069] The system can share the user's learning progress with family and friends and provide support to increase motivation to learn. For example, the system uses a generative AI to share the user's learning progress with family and friends and provide support to increase motivation to learn. For example, the system notifies family and friends of the learning goals the user has achieved. This allows the user to share the user's learning progress with family and friends and provide support to increase motivation to learn, thereby maintaining motivation to learn.

[0070] The system can use the emotion estimation function to identify the time period when the user can concentrate best and send a notification encouraging the user to study during that time period. For example, the system can use the emotion estimation function to identify the time period when the user can concentrate best and send a notification encouraging the user to study during that time period. For example, if the user can concentrate best in the morning, the system sends a study notification during that time period. This allows the system to improve the effectiveness of learning by identifying the time period when the user can concentrate best and sending a notification encouraging the user to study during that time period.

[0071] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0072] The conversation topic research unit can suggest related topics based on the user's hobbies and skills. For example, if the user is interested in music, the generative AI can suggest topics such as "music theory" and "famous composers." If the user is interested in sports, it can also suggest topics such as "Olympic history" and "soccer tactics." This can increase learning motivation by suggesting related topics based on the user's hobbies and skills.

[0073] The conversation topic research unit can suggest topics that match the user's learning style. For example, visual topics such as "historical paintings" or "how to read maps" can be suggested to visual learners. Auditory topics such as "podcast recommendations" or "music history" can also be suggested to auditory learners. Furthermore, practical topics such as "cooking recipes" or "DIY projects" can be suggested to hands-on learners. This allows the system to suggest topics that match the user's learning style, thereby enhancing learning effectiveness.

[0074] The conversation topic research unit can use the emotion estimation function to adjust the learning pace according to the user's emotional state. For example, if the user is feeling stressed, the generation AI can slow down the learning pace and suggest relaxing topics. If the user is excited, the generation AI can also speed up the learning pace and suggest challenging topics. Furthermore, if the user is feeling down, the generation AI can suggest positive topics with encouraging words. This allows the learning pace to be adjusted according to the user's emotional state, helping to maintain motivation to learn.

[0075] The conversation topic research unit can suggest topics based on the user's learning goals. For example, if a user wants to learn business English, the generation AI can suggest topics such as "How to conduct a business meeting" or "Presentation tips." If a user wants to learn travel English, the AI ​​can suggest topics such as "Airport conversations" or "How to book a hotel." Furthermore, if a user wants to prepare for an exam, the AI ​​can suggest topics such as "How to prepare for the TOEFL" or "Tips for the IELTS speaking test." This improves learning effectiveness by suggesting topics based on the user's learning goals.

[0076] The conversation topic research unit can use the emotion estimation function to adjust the form of feedback according to the user's emotional state. For example, if the user is nervous, the generation AI can provide feedback in a gentle tone. If the user is confident, the generation AI can also provide feedback that points out specific areas for improvement. Furthermore, if the user is feeling down, the generation AI can provide feedback with words of encouragement. This allows the user to maintain their motivation to learn by adjusting the form of feedback according to their emotional state.

[0077] The conversation topic research unit can suggest topics related to the user's occupation. For example, if the user is a medical professional, the generative AI can suggest topics such as "latest medical technology" and "medical ethics." If the user is an engineer, it can also suggest topics such as "new programming languages" and "technological trends." Furthermore, if the user is an educator, it can suggest topics such as "educational methodology" and "ways to improve student motivation." This can increase the user's motivation to learn by suggesting topics related to their occupation.

[0078] The conversation topic research unit can use the emotion estimation function to make suggestions to help users study in the most relaxing environment. For example, if a user is feeling stressed, the generation AI can suggest playing relaxing music. Also, if a user is lacking concentration, the generation AI can suggest taking a short break. Furthermore, if a user is tired, the generation AI can suggest doing some light stretching. This makes it possible to improve learning effectiveness by making suggestions to help users study in the most relaxing environment.

[0079] The conversation topic research unit can provide related information about topics previously learned based on the user's learning history. For example, if the user previously learned about "space exploration," the generation AI can provide related information such as "the latest space exploration missions" and "the history of space exploration." If the user learned about "environmental issues," the AI ​​can provide related information such as "the latest environmental protection technology" and "trends in environmental policy." Furthermore, if the user learned about a "historical event," the AI ​​can provide related information such as "people associated with the event" and "the impact of the event." This allows the AI ​​to provide related information based on the user's learning history, thereby enhancing the depth of learning.

[0080] The conversation topic research unit can use the emotion estimation function to identify the topics that interest the user most and provide practical tasks related to those topics. For example, if a user is interested in "space exploration," the generation AI can provide practical tasks such as "create a presentation about space exploration" or "research the history of space exploration." If a user is interested in "environmental issues," the generation AI can provide practical tasks such as "think of ideas for environmental protection" or "hold a discussion about environmental issues." Furthermore, if a user is interested in "historical events," the generation AI can provide practical tasks such as "write an essay about that event" or "hold a debate about that event." This allows the system to identify the topics that interest the user most and provide practical tasks related to those topics, thereby increasing learning motivation.

[0081] The conversation topic research unit can compare the user's learning progress with other learners and provide rankings to stimulate competitive spirit. For example, the generation AI analyzes the user's learning progress and displays a ranking in comparison with other learners. Also, if the user receives high marks on a specific topic, the ranking for that topic can be displayed. Furthermore, if the user achieves their learning goal, a badge or title can be provided according to their level of achievement. This allows the user's learning progress to be compared with other learners, stimulating competitive spirit and increasing motivation to learn.

[0082] The processing flow of the second embodiment will be briefly explained below.

[0083] Step 1: When a user proposes a topic, the conversation topic research unit automatically researches information about that topic. For example, if a user wants to talk about "space exploration," the AI ​​generator will gather the latest information and basic knowledge about space exploration and use that information to advance the conversation. Step 2: The lesson provider provides lessons based on the information collected by the conversation topic researcher. For example, the AI ​​generator can provide lessons tailored to the user's level and adjust the difficulty of the lessons according to the user's level of understanding. Step 3: The feedback section analyzes the user's pronunciation and grammar in real time during the lesson and provides appropriate feedback. For example, if the user says, "I went to the store," the generation AI immediately provides feedback such as, "'goed' is incorrect; the correct word is 'went'."

[0084] 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.

[0085] 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.

[0086] 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.

[0087] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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).

[0093] 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.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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).

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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."

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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, in order to avoid confusion and to 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.

[0150] 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]

[0151] 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. When a user posts a conversation topic, The conversation topic research section automatically researches information about the topic, a lesson providing unit providing lessons based on the information investigated by the conversation topic investigating unit; The feedback section analyzes the user's pronunciation and grammar in real time during the lesson and provides appropriate feedback. A system characterized by:

2. The conversation topic research unit Analyzing the user's past conversation history and suggesting relevant topics based on the user's interests 2. The system of claim 1.

3. The conversation topic research unit Collecting information on the same topic in different languages, allowing the user to study in multiple languages ​​simultaneously 2. The system of claim 1.

4. The feedback unit The user's pronunciation is evaluated in detail using speech waveform analysis, and specific pronunciation improvement methods are presented.

2. The system of claim 1.

5. The system comprises: Learn the user's daily rhythm and suggest the best time to study 2. The system of claim 1.

6. The conversation topic research unit Selecting a topic according to the user's emotional state to enhance their motivation to learn 2. The system of claim 1.

7. The feedback unit Analyzing the emotional response of the user when receiving feedback and reinforcing positive feedback 2. The system of claim 1.

8. The system comprises: Analyze the user's stress level and provide a relaxing learning environment 2. The system of claim 1.

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