System

Abstract

An object of a system according to an embodiment is to improve the efficiency of homework support for children and reduce the burden on parents.SOLUTION: A system according to an embodiment includes a voice recognition unit, a natural language processing unit, and an answer generation unit. The voice recognizer recognizes a voice of the child. The natural language processing unit analyzes the voice recognized by the voice recognition unit. The answer generation unit generates an appropriate answer on the basis of the content analyzed by the natural language processing 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] With conventional technology, helping children with their homework placed a heavy burden on parents, and it was difficult to provide appropriate answers instantly.

[0005] The system according to the embodiment aims to improve the efficiency of support for children's homework and reduce the burden on parents. [Means for solving the problem]

[0006] The system according to the embodiment includes a speech recognition unit, a natural language processing unit, and an answer generation unit. The speech recognition unit recognizes a child's voice. The natural language processing unit analyzes the speech recognized by the speech recognition unit. The answer generation unit generates an appropriate answer based on the content analyzed by the natural language processing unit. [Effects of the Invention]

[0007] The system according to the embodiment can improve the efficiency of support for children's homework and reduce the burden on parents. [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 homework support system according to an embodiment of the present invention recognizes a child's voice, analyzes the content of the voice using a generation AI, and generates an appropriate answer. This allows the homework support system to support children with their homework and reduce the burden on parents.

[0029] A homework support system according to an embodiment includes a speech recognition unit, a natural language processing unit, and an answer generation unit. The speech recognition unit recognizes a child's voice. For example, the speech recognition unit collects the child's voice using a microphone and saves it as voice data. The speech recognition unit converts the voice data into text data using speech recognition technology. For example, the speech recognition unit analyzes the voice using a deep learning algorithm and converts it into text. The natural language processing unit analyzes the voice recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to analyze the structure of a sentence. The natural language processing unit performs semantic analysis to understand the meaning of the sentence. For example, the natural language processing unit analyzes the meaning of the sentence using a generation AI and extracts information for generating an appropriate answer. The answer generation unit generates an appropriate answer based on the content analyzed by the natural language processing unit. For example, the answer generation unit generates an answer to a child's question using a generation AI. The answer generation unit generates multiple answer candidates using the generation AI and selects the most appropriate answer from among them. For example, the answer generation unit understands the context of the question using the generation AI and generates the most appropriate answer. As a result, the homework support system according to the embodiment can support children's homework and reduce the burden on parents. For example, while a parent is doing housework or work, a child can ask a question to the homework support system, allowing the parent to continue with other tasks.

[0030] The speech recognition unit can improve the accuracy of speech recognition by adding a customization function that learns the characteristics of children's pronunciation. The speech recognition unit can improve the accuracy of speech recognition by adding a customization function that learns the characteristics of children's pronunciation. For example, the speech recognition unit can learn the pronunciation patterns of a specific child and perform speech recognition that is optimal for that child. The speech recognition unit can also improve the overall accuracy of speech recognition by collecting pronunciation data from multiple children and learning common pronunciation patterns. In this way, the accuracy of speech recognition can be improved by learning the characteristics of children's pronunciation.

[0031] The voice recognition unit can use voice recognition technology to analyze environmental sounds other than the voices made by the child and optimize the learning environment. The voice recognition unit, for example, uses voice recognition technology to analyze environmental sounds other than the voices made by the child and optimize the learning environment. For example, it detects ambient noise and recommends a quiet environment. The voice recognition unit also analyzes environmental sounds and adjusts the volume and sound quality to suit learning. For example, it reduces background noise and makes the child's voice clearer. In this way, it is possible to analyze environmental sounds other than the voices made by the child and optimize the learning environment to support the child's learning.

[0032] The voice recognition unit can use voice recognition technology to add a multi-user function that can respond individually to questions from multiple children at the same time. The voice recognition unit can use voice recognition technology to add a multi-user function that can respond individually to questions from multiple children at the same time. For example, it can identify the voice of each child and provide an answer individually. The voice recognition unit can also analyze questions from multiple children at the same time and generate appropriate answers for each. For example, the voice recognition unit can analyze the content of each child's question and provide an answer individually. In this way, children's learning can be supported by adding a multi-user function that can respond individually to questions from multiple children at the same time.

[0033] The natural language processing unit can refer to past question history to gain a deeper understanding of the context of a child's question and improve the accuracy of the answer. The natural language processing unit, for example, refers to past question history to gain a deeper understanding of the context of a child's question and improve the accuracy of the answer. For example, past questions and answers are stored in a database and appropriate answers are provided for similar questions. The natural language processing unit also analyzes the context of the question and generates an answer based on the past question history. For example, the natural language processing unit understands the context of the question and provides the optimal answer by referring to the past question history. In this way, by referring to the past question history, the context of a child's question can be deeply understood and the accuracy of the answer can be improved.

[0034] The natural language processing unit can use natural language processing technology to analyze a child's learning progress in real time and propose an appropriate learning plan. The natural language processing unit, for example, uses natural language processing technology to analyze a child's learning progress in real time and propose an appropriate learning plan. For example, it analyzes learning content and progress and provides an individually customized learning plan. The natural language processing unit also analyzes learning progress and adjusts the learning plan in real time. For example, the natural language processing unit analyzes learning progress and proposes an appropriate learning plan. This makes it possible to support a child's learning by analyzing a child's learning progress in real time and proposing an appropriate learning plan.

[0035] The natural language processing unit can use natural language processing technology to automatically check the grammar and expressions of sentences written by children and point out areas for improvement. The natural language processing unit, for example, uses natural language processing technology to automatically check the grammar and expressions of sentences written by children and point out areas for improvement. For example, it detects grammatical errors and inappropriate expressions and suggests corrections. The natural language processing unit also analyzes the structure of sentences and suggests appropriate expressions. For example, the natural language processing unit analyzes the grammar of sentences and suggests appropriate corrections. In this way, the grammar and expressions of sentences written by children can be automatically checked and points out areas for improvement, thereby supporting children's learning.

[0036] The natural language processing unit can use natural language processing technology to provide additional information related to topics that interest children, thereby increasing their motivation to learn. The natural language processing unit, for example, uses natural language processing technology to provide additional information related to topics that interest children, thereby increasing their motivation to learn. For example, it may suggest articles or videos related to a topic that the child has asked about. The natural language processing unit also analyzes children's interests and provides related information. For example, the natural language processing unit analyzes the content of a child's question and provides related additional information. In this way, providing additional information related to topics that interest children increases their motivation to learn.

[0037] The system adds a function to automatically generate a customized study plan according to the homework content of each family. For example, the system automatically generates a customized study plan according to the homework content of each family. For example, it analyzes the content of the homework and proposes an optimal study plan. The system also adjusts the study plan according to the difficulty of the homework and the child's learning progress. For example, the system analyzes the content of the homework and provides a customized study plan according to the child's learning progress. This makes it possible to support children's learning by automatically generating a customized study plan according to the homework content of each family.

[0038] The system provides support in a variety of ways, such as visually, aurally, and tactilely, to suit a child's learning style. For example, the system provides support in a variety of ways, such as visually, aurally, and tactilely, to suit a child's learning style. For example, the system provides diagrams and illustrations for visual explanations and audio guides for auditory explanations. The system also uses a haptic feedback device to provide tactile feedback. For example, the system uses a haptic feedback device to allow a child to feel the learning content with their sense of touch. This makes it possible to support a child's learning by providing support in a variety of ways to suit a child's learning style.

[0039] The system adds a function to provide a flexible curriculum to accommodate different grades and learning levels. The system adds a function to provide a flexible curriculum to accommodate different grades and learning levels, for example. For example, the system automatically generates and provides a different curriculum for each grade. The system also adjusts the curriculum according to the learning level. For example, the system provides a curriculum according to beginner, intermediate, and advanced learning levels. This makes it possible to support children's learning by providing a flexible curriculum to accommodate different grades and learning levels.

[0040] The system is compatible with portable devices so that it can be used in learning environments other than home. The system is compatible with portable devices so that it can be used in learning environments other than home. For example, an app that can be used on a tablet or smartphone is developed. The system also provides learning support in learning environments other than home using portable devices. For example, the system is compatible with portable devices so that it can be used in learning environments such as libraries and cram schools. This makes it possible to support children's learning by supporting portable devices so that it can be used in learning environments other than home.

[0041] The system provides a dashboard that allows parents to easily check their child's learning progress and automatically generates feedback. For example, the system provides a dashboard that allows parents to easily check their child's learning progress and automatically generates feedback. For example, the system displays learning progress and achievement levels in graphs and automatically generates feedback. The system also allows parents to check their child's learning progress in real time. For example, the system provides a dashboard that allows parents to check their child's learning progress and provide appropriate feedback. As a result, the burden on parents can be reduced by providing a dashboard that allows parents to easily check their child's learning progress and automatically generating feedback.

[0042] The system uses a learning bot to automatically detect times when parents are busy, and provides enhanced support to help children study independently during those times. The system, for example, uses a learning bot to automatically detect times when parents are busy, and provides enhanced support to help children study independently during those times. For example, the system analyzes parents' schedules and provides enhanced learning support during busy times. The system also provides learning guides and a self-assessment system to help children study independently. For example, the system provides learning guides and introduces a self-assessment system to help children study independently. This allows the learning bot to automatically detect times when parents are busy, and provides enhanced support to help children study independently during those times, thereby reducing the burden on parents.

[0043] The system adds a social media integration function that allows parents to easily share their children's learning content. The system adds a social media integration function that allows parents to easily share their children's learning content. For example, it provides a function that allows learning progress and results to be shared on social media. The system also allows parents to share their children's learning content with other parents and teachers. For example, the system makes it possible to share learning content and receive feedback using the social media integration function. By adding a social media integration function that allows parents to easily share their children's learning content, the burden on parents can be reduced.

[0044] The system adds a function that enables parents to ask questions about their children's learning directly to the bot. For example, the system adds a function that enables parents to ask questions about their children's learning directly to the bot. For example, the system provides a function that enables parents to input questions to the bot and receive instant answers. The system also provides a function that enables parents to input questions about their children's learning by voice, and the bot will respond by voice. For example, the system reduces the burden on parents by allowing parents to input questions by voice and the bot to respond by voice. This reduces the burden on parents by adding a function that enables parents to ask questions about their children's learning directly to the bot.

[0045] The system adds a function that allows children to set their own learning goals and automatically evaluate their achievement. The system adds a function that allows children to set their own learning goals and automatically evaluate their achievement. For example, the system inputs learning goals and automatically evaluates progress. The system also provides specific advice to help children achieve their learning goals. For example, the system provides guidelines for children to set learning goals and evaluate their achievement. This improves children's independence by adding a function that allows children to set their own learning goals and automatically evaluate their achievement.

[0046] The system provides guidelines for children to make their own study plans and tracks their progress. For example, the system provides guidelines for children to make their own study plans and tracks their progress. For example, the system explains how to make a study plan and automatically tracks their progress. The system also provides specific advice to children when making their study plans. For example, the system provides step-by-step guidelines for children to make their study plans. In this way, by providing guidelines for children to make their own study plans and tracking their progress, children's independence is improved.

[0047] The system adds a community function that enables children to share their learning content with other children and provide feedback to each other. For example, the system provides an online forum or chat function. The system also provides specific methods for children to share their learning content and provide feedback to each other. For example, the system provides guidelines for children to share their learning content and receive feedback. By adding a community function that enables children to share their learning content with other children and provide feedback to each other, children's independence is improved.

[0048] The system provides interactive graphs and charts that allow children to visually check their learning outcomes. The system, for example, provides interactive graphs and charts that allow children to visually check their learning outcomes. For example, the system displays learning progress and achievement levels in graphs. The system also provides specific methods for children to visually check their learning outcomes. For example, the system allows children to visually check their learning outcomes using interactive graphs and charts. This improves children's independence by providing interactive graphs and charts that allow children to visually check their learning outcomes.

[0049] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0050] Homework support systems can also be equipped with customization functions to suit children's learning styles. For example, for children who prefer visual learning, explanations that make extensive use of diagrams and illustrations can be provided. For children who prefer auditory learning, explanations using audio guides and music can be provided. Furthermore, for children who prefer tactile learning, haptic feedback devices can be used to allow them to experience the learning content. By providing customization functions to suit children's learning styles, learning effectiveness can be improved.

[0051] The homework support system can further be equipped with a function to analyze a child's learning progress in real time and propose an appropriate learning plan. For example, it can analyze the learning content and progress and provide an individually customized learning plan. It can also analyze learning progress and adjust the learning plan in real time. For example, it can analyze learning progress and propose an appropriate learning plan. In this way, it can support a child's learning by analyzing a child's learning progress in real time and proposing an appropriate learning plan.

[0052] Homework support systems can also be equipped with a function that allows children to set their own learning goals and automatically evaluate their achievement. For example, the system can input learning goals and automatically evaluate progress. It can also provide specific advice to help children achieve their learning goals. For example, it can provide guidelines for setting learning goals and evaluating their achievement. This allows children to set their own learning goals and automatically evaluate their achievement, thereby improving their independence.

[0053] The homework support system can also have a community function that enables children to share their learning with other children and provide feedback to each other. For example, it can provide an online forum or chat function. It can also provide specific methods for sharing learning content and providing feedback to each other. For example, it can provide guidelines for sharing learning content and receiving feedback. This allows children to share their learning content with other children and provide feedback to each other, thereby improving their independence.

[0054] The homework support system can further provide a dashboard that allows parents to easily check their child's learning progress and can be equipped with a function to automatically generate feedback. For example, the learning progress and achievement status can be displayed in graphs and feedback can be automatically generated. The system can also enable parents to check their child's learning progress in real time. For example, a dashboard can be provided that allows parents to check their child's learning progress and provide appropriate feedback. This reduces the burden on parents by providing a dashboard that allows parents to easily check their child's learning progress and automatically generating feedback.

[0055] The processing flow of the first embodiment will be briefly explained below.

[0056] Step 1: The voice recognition unit recognizes the child's voice. For example, the voice recognition unit collects the child's voice using a microphone and saves it as voice data. The voice recognition unit then converts the voice data into text data using voice recognition technology. For example, the voice recognition unit uses a deep learning algorithm to analyze the voice and convert it into text. Step 2: The natural language processing unit analyzes the speech recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to analyze the structure of the sentence. The natural language processing unit also performs semantic analysis to understand the meaning of the sentence. For example, the natural language processing unit uses generative AI to analyze the meaning of the sentence and extract information to generate an appropriate response. Step 3: The answer generation unit generates an appropriate answer based on the content analyzed by the natural language processing unit. For example, the answer generation unit uses a generation AI to generate an answer to a child's question. The answer generation unit also uses a generation AI to generate multiple answer candidates and selects the most appropriate answer from among them. For example, the answer generation unit uses a generation AI to understand the context of the question and generate the most appropriate answer.

[0057] (Example 2) The homework support system according to an embodiment of the present invention recognizes a child's voice, analyzes the content of the voice using a generation AI, and generates an appropriate answer. This allows the homework support system to support children with their homework and reduce the burden on parents.

[0058] A homework support system according to an embodiment includes a speech recognition unit, a natural language processing unit, and an answer generation unit. The speech recognition unit recognizes a child's voice. For example, the speech recognition unit collects the child's voice using a microphone and saves it as voice data. The speech recognition unit converts the voice data into text data using speech recognition technology. For example, the speech recognition unit analyzes the voice using a deep learning algorithm and converts it into text. The natural language processing unit analyzes the voice recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to analyze the structure of a sentence. The natural language processing unit performs semantic analysis to understand the meaning of the sentence. For example, the natural language processing unit analyzes the meaning of the sentence using a generation AI and extracts information for generating an appropriate answer. The answer generation unit generates an appropriate answer based on the content analyzed by the natural language processing unit. For example, the answer generation unit generates an answer to a child's question using a generation AI. The answer generation unit generates multiple answer candidates using the generation AI and selects the most appropriate answer from among them. For example, the answer generation unit understands the context of the question using the generation AI and generates the most appropriate answer. As a result, the homework support system according to the embodiment can support children's homework and reduce the burden on parents. For example, while a parent is doing housework or work, a child can ask a question to the homework support system, allowing the parent to continue with other tasks.

[0059] The voice recognition unit can analyze the tone and speed of a child's voice to estimate their emotional state and provide appropriate feedback. The voice recognition unit can, for example, analyze the tone and speed of a child's voice to estimate their emotional state. For example, if a child's voice is high and fast, it can determine that they are excited and provide feedback to calm them down. Alternatively, if a child's voice is low and slow, it can determine that they are tired and provide encouraging feedback. In this way, it is possible to support a child's learning by estimating their emotional state and providing appropriate feedback.

[0060] The speech recognition unit can improve the accuracy of speech recognition by adding a customization function that learns the characteristics of children's pronunciation. The speech recognition unit can improve the accuracy of speech recognition by adding a customization function that learns the characteristics of children's pronunciation. For example, the speech recognition unit can learn the pronunciation patterns of a specific child and perform speech recognition that is optimal for that child. The speech recognition unit can also improve the overall accuracy of speech recognition by collecting pronunciation data from multiple children and learning common pronunciation patterns. In this way, the accuracy of speech recognition can be improved by learning the characteristics of children's pronunciation.

[0061] The voice recognition unit can use the emotion estimation function to provide voice guidance to help a child relax when they are feeling stressed. The voice recognition unit, for example, uses the emotion estimation function to provide voice guidance to help a child relax when they are feeling stressed. For example, it may suggest deep breathing or relaxing music. The voice recognition unit also analyzes the child's emotional state and provides advice on how to relax. For example, if a child is tense, it may suggest specific ways to relax. In this way, by providing voice guidance to help a child relax when they are feeling stressed, it is possible to support the child's learning.

[0062] The voice recognition unit can use voice recognition technology to analyze environmental sounds other than the voices made by the child and optimize the learning environment. The voice recognition unit, for example, uses voice recognition technology to analyze environmental sounds other than the voices made by the child and optimize the learning environment. For example, it detects ambient noise and recommends a quiet environment. The voice recognition unit also analyzes environmental sounds and adjusts the volume and sound quality to suit learning. For example, it reduces background noise and makes the child's voice clearer. In this way, it is possible to analyze environmental sounds other than the voices made by the child and optimize the learning environment to support the child's learning.

[0063] The voice recognition unit can use voice recognition technology to add a multi-user function that can respond individually to questions from multiple children at the same time. The voice recognition unit can use voice recognition technology to add a multi-user function that can respond individually to questions from multiple children at the same time. For example, it can identify the voice of each child and provide an answer individually. The voice recognition unit can also analyze questions from multiple children at the same time and generate appropriate answers for each. For example, the voice recognition unit can analyze the content of each child's question and provide an answer individually. In this way, children's learning can be supported by adding a multi-user function that can respond individually to questions from multiple children at the same time.

[0064] The voice recognition unit can use the emotion estimation function to provide interactive feedback incorporating game elements so that children can enjoy learning. The voice recognition unit, for example, uses the emotion estimation function to provide interactive feedback incorporating game elements so that children can enjoy learning. For example, quiz-style questions or a point system can be introduced. The voice recognition unit also analyzes the child's emotional state and adjusts the feedback so that learning is enjoyable. For example, if the child is excited, the game elements are strengthened to increase motivation to learn. In this way, providing interactive feedback incorporating game elements so that children can enjoy learning increases the child's motivation to learn.

[0065] The natural language processing unit can refer to past question history to gain a deeper understanding of the context of a child's question and improve the accuracy of the answer. The natural language processing unit, for example, refers to past question history to gain a deeper understanding of the context of a child's question and improve the accuracy of the answer. For example, past questions and answers are stored in a database and appropriate answers are provided for similar questions. The natural language processing unit also analyzes the context of the question and generates an answer based on the past question history. For example, the natural language processing unit understands the context of the question and provides the optimal answer by referring to the past question history. In this way, by referring to the past question history, the context of a child's question can be deeply understood and the accuracy of the answer can be improved.

[0066] The natural language processing unit can use natural language processing technology to analyze a child's learning progress in real time and propose an appropriate learning plan. The natural language processing unit, for example, uses natural language processing technology to analyze a child's learning progress in real time and propose an appropriate learning plan. For example, it analyzes learning content and progress and provides an individually customized learning plan. The natural language processing unit also analyzes learning progress and adjusts the learning plan in real time. For example, the natural language processing unit analyzes learning progress and proposes an appropriate learning plan. This makes it possible to support a child's learning by analyzing a child's learning progress in real time and proposing an appropriate learning plan.

[0067] The natural language processing unit can use the emotion estimation function to adjust the tone and wording of the response so that it is easier for the child to understand. The natural language processing unit, for example, uses the emotion estimation function to adjust the tone and wording of the response so that it is easier for the child to understand. For example, if the child is relaxed, it will answer in a gentle tone, and if the child is nervous, it will answer in an encouraging tone. The natural language processing unit also analyzes the child's emotional state and selects appropriate wording. For example, if the child is feeling anxious, it will select wording to reassure the child. In this way, the tone and wording of the response can be adjusted so that it is easier for the child to understand, thereby supporting the child's learning.

[0068] The natural language processing unit can use natural language processing technology to automatically check the grammar and expressions of sentences written by children and point out areas for improvement. The natural language processing unit, for example, uses natural language processing technology to automatically check the grammar and expressions of sentences written by children and point out areas for improvement. For example, it detects grammatical errors and inappropriate expressions and suggests corrections. The natural language processing unit also analyzes the structure of sentences and suggests appropriate expressions. For example, the natural language processing unit analyzes the grammar of sentences and suggests appropriate corrections. In this way, the grammar and expressions of sentences written by children can be automatically checked and points out areas for improvement, thereby supporting children's learning.

[0069] The natural language processing unit can use natural language processing technology to provide additional information related to topics that interest children, thereby increasing their motivation to learn. The natural language processing unit, for example, uses natural language processing technology to provide additional information related to topics that interest children, thereby increasing their motivation to learn. For example, it may suggest articles or videos related to a topic that the child has asked about. The natural language processing unit also analyzes children's interests and provides related information. For example, the natural language processing unit analyzes the content of a child's question and provides related additional information. In this way, providing additional information related to topics that interest children increases their motivation to learn.

[0070] The natural language processing unit can use the emotion estimation function to analyze the emotional state of a child when asking a question and reinforce positive feedback. For example, the natural language processing unit uses the emotion estimation function to analyze the emotional state of a child when asking a question and reinforce positive feedback. For example, if a child is feeling anxious, it provides encouraging words. The natural language processing unit also analyzes the emotional state of a child and provides appropriate feedback. For example, if a child is confident, it provides feedback that encourages further challenges. In this way, by analyzing the emotional state of a child when asking a question and reinforcing positive feedback, the child's motivation to learn is increased.

[0071] The system adds a function to automatically generate a customized study plan according to the homework content of each family. For example, the system automatically generates a customized study plan according to the homework content of each family. For example, it analyzes the content of the homework and proposes an optimal study plan. The system also adjusts the study plan according to the difficulty of the homework and the child's learning progress. For example, the system analyzes the content of the homework and provides a customized study plan according to the child's learning progress. This makes it possible to support children's learning by automatically generating a customized study plan according to the homework content of each family.

[0072] The system provides support in a variety of ways, such as visually, aurally, and tactilely, to suit a child's learning style. For example, the system provides support in a variety of ways, such as visually, aurally, and tactilely, to suit a child's learning style. For example, the system provides diagrams and illustrations for visual explanations and audio guides for auditory explanations. The system also uses a haptic feedback device to provide tactile feedback. For example, the system uses a haptic feedback device to allow a child to feel the learning content with their sense of touch. This makes it possible to support a child's learning by providing support in a variety of ways to suit a child's learning style.

[0073] The system uses the emotion estimation function to strengthen support for a particular subject if the child feels that they are weak in that subject. For example, the system uses the emotion estimation function to strengthen support for a particular subject if the child feels that they are weak in that subject. For example, the system provides more detailed explanations and additional practice questions for subjects that the child is weak in. The system also analyzes the child's emotional state and strengthens support for subjects that the child feels weak in. For example, the system provides positive feedback to help the child gain confidence in subjects that the child is weak in. This makes it possible to support the child's learning if the child feels that they are weak in a particular subject by strengthening support for that subject.

[0074] The system adds a function to provide a flexible curriculum to accommodate different grades and learning levels. The system adds a function to provide a flexible curriculum to accommodate different grades and learning levels, for example. For example, the system automatically generates and provides a different curriculum for each grade. The system also adjusts the curriculum according to the learning level. For example, the system provides a curriculum according to beginner, intermediate, and advanced learning levels. This makes it possible to support children's learning by providing a flexible curriculum to accommodate different grades and learning levels.

[0075] The system is compatible with portable devices so that it can be used in learning environments other than home. The system is compatible with portable devices so that it can be used in learning environments other than home. For example, an app that can be used on a tablet or smartphone is developed. The system also provides learning support in learning environments other than home using portable devices. For example, the system is compatible with portable devices so that it can be used in learning environments such as libraries and cram schools. This makes it possible to support children's learning by supporting portable devices so that it can be used in learning environments other than home.

[0076] The system uses an emotion estimation function to provide content that increases motivation so that children have positive feelings about learning. For example, the system uses the emotion estimation function to provide content that increases motivation so that children have positive feelings about learning. For example, the system provides praise and rewards according to learning progress. The system also analyzes children's emotional states and provides content to increase motivation. For example, the system introduces a reward system so that children have positive feelings about learning. This makes it possible to support children's learning by providing content that increases motivation so that children have positive feelings about learning.

[0077] The system provides a dashboard that allows parents to easily check their child's learning progress and automatically generates feedback. For example, the system provides a dashboard that allows parents to easily check their child's learning progress and automatically generates feedback. For example, the system displays learning progress and achievement levels in graphs and automatically generates feedback. The system also allows parents to check their child's learning progress in real time. For example, the system provides a dashboard that allows parents to check their child's learning progress and provide appropriate feedback. As a result, the burden on parents can be reduced by providing a dashboard that allows parents to easily check their child's learning progress and automatically generating feedback.

[0078] The system uses a learning bot to automatically detect times when parents are busy, and provides enhanced support to help children study independently during those times. The system, for example, uses a learning bot to automatically detect times when parents are busy, and provides enhanced support to help children study independently during those times. For example, the system analyzes parents' schedules and provides enhanced learning support during busy times. The system also provides learning guides and a self-assessment system to help children study independently. For example, the system provides learning guides and introduces a self-assessment system to help children study independently. This allows the learning bot to automatically detect times when parents are busy, and provides enhanced support to help children study independently during those times, thereby reducing the burden on parents.

[0079] The system uses an emotion estimation function to provide parents with advice to help them relax when they are feeling stressed. The system, for example, uses an emotion estimation function to provide parents with advice to help them relax when they are feeling stressed. For example, the system analyzes the parent's emotional state and provides advice to help them relax. The system also suggests specific relaxation methods to help the parent relax. For example, if the parent is feeling stressed, the system suggests relaxation techniques and stress relief methods. In this way, by providing advice to help parents relax when they are feeling stressed, the burden on the parent can be reduced.

[0080] The system adds a social media integration function that allows parents to easily share their children's learning content. The system adds a social media integration function that allows parents to easily share their children's learning content. For example, it provides a function that allows learning progress and results to be shared on social media. The system also allows parents to share their children's learning content with other parents and teachers. For example, the system makes it possible to share learning content and receive feedback using the social media integration function. By adding a social media integration function that allows parents to easily share their children's learning content, the burden on parents can be reduced.

[0081] The system adds a function that enables parents to ask questions about their children's learning directly to the bot. For example, the system adds a function that enables parents to ask questions about their children's learning directly to the bot. For example, the system provides a function that enables parents to input questions to the bot and receive instant answers. The system also provides a function that enables parents to input questions about their children's learning by voice, and the bot will respond by voice. For example, the system reduces the burden on parents by allowing parents to input questions by voice and the bot to respond by voice. This reduces the burden on parents by adding a function that enables parents to ask questions about their children's learning directly to the bot.

[0082] The system uses the emotion estimation function to suggest ways for parents to give positive feedback on their children's learning. For example, the system uses the emotion estimation function to suggest ways for parents to give positive feedback on their children's learning. For example, the system analyzes the parent's emotional state and suggests an appropriate feedback method. The system also suggests specific ways for parents to give positive feedback on their children's learning. For example, the system suggests ways for parents to provide praise or encouraging messages for their children's learning. This reduces the burden on parents by suggesting ways for parents to give positive feedback on their children's learning.

[0083] The system adds a function that allows children to set their own learning goals and automatically evaluate their achievement. The system adds a function that allows children to set their own learning goals and automatically evaluate their achievement. For example, the system inputs learning goals and automatically evaluates progress. The system also provides specific advice to help children achieve their learning goals. For example, the system provides guidelines for children to set learning goals and evaluate their achievement. This improves children's independence by adding a function that allows children to set their own learning goals and automatically evaluate their achievement.

[0084] The system provides guidelines for children to make their own study plans and tracks their progress. For example, the system provides guidelines for children to make their own study plans and tracks their progress. For example, the system explains how to make a study plan and automatically tracks their progress. The system also provides specific advice to children when making their study plans. For example, the system provides step-by-step guidelines for children to make their study plans. In this way, by providing guidelines for children to make their own study plans and tracking their progress, children's independence is improved.

[0085] The system uses an emotion estimation function to provide positive feedback according to the child's learning progress so that the child feels a sense of accomplishment. The system, for example, uses the emotion estimation function to provide positive feedback according to the child's learning progress so that the child feels a sense of accomplishment. For example, the system may provide praise or a reward according to the child's learning progress. The system also analyzes the child's emotional state and provides feedback to help the child feel a sense of accomplishment. For example, the system may provide positive feedback when the child achieves a goal so that the child feels a sense of accomplishment. In this way, providing positive feedback according to the child's learning progress so that the child feels a sense of accomplishment improves the child's independence.

[0086] The system adds a community function that enables children to share their learning content with other children and provide feedback to each other. For example, the system provides an online forum or chat function. The system also provides specific methods for children to share their learning content and provide feedback to each other. For example, the system provides guidelines for children to share their learning content and receive feedback. By adding a community function that enables children to share their learning content with other children and provide feedback to each other, children's independence is improved.

[0087] The system provides interactive graphs and charts that allow children to visually check their learning outcomes. The system, for example, provides interactive graphs and charts that allow children to visually check their learning outcomes. For example, the system displays learning progress and achievement levels in graphs. The system also provides specific methods for children to visually check their learning outcomes. For example, the system allows children to visually check their learning outcomes using interactive graphs and charts. This improves children's independence by providing interactive graphs and charts that allow children to visually check their learning outcomes.

[0088] The system uses an emotion estimation function to provide content that increases motivation so that children have positive feelings about learning. For example, the system uses the emotion estimation function to provide content that increases motivation so that children have positive feelings about learning. For example, the system provides praise and rewards according to learning progress. The system also analyzes children's emotional states and provides content to increase motivation. For example, the system introduces a reward system to increase children's positive feelings about learning. This improves children's independence by providing content that increases motivation so that children have positive feelings about learning.

[0089] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0090] Homework support systems can also be equipped with customization functions to suit children's learning styles. For example, for children who prefer visual learning, explanations that make extensive use of diagrams and illustrations can be provided. For children who prefer auditory learning, explanations using audio guides and music can be provided. Furthermore, for children who prefer tactile learning, haptic feedback devices can be used to allow them to experience the learning content. By providing customization functions to suit children's learning styles, learning effectiveness can be improved.

[0091] The homework support system can further be equipped with a function to analyze a child's learning progress in real time and propose an appropriate learning plan. For example, it can analyze the learning content and progress and provide an individually customized learning plan. It can also analyze learning progress and adjust the learning plan in real time. For example, it can analyze learning progress and propose an appropriate learning plan. In this way, it can support a child's learning by analyzing a child's learning progress in real time and proposing an appropriate learning plan.

[0092] Homework support systems can also be equipped with a function that allows children to set their own learning goals and automatically evaluate their achievement. For example, the system can input learning goals and automatically evaluate progress. It can also provide specific advice to help children achieve their learning goals. For example, it can provide guidelines for setting learning goals and evaluating their achievement. This allows children to set their own learning goals and automatically evaluate their achievement, thereby improving their independence.

[0093] The homework support system can also have a community function that enables children to share their learning with other children and provide feedback to each other. For example, it can provide an online forum or chat function. It can also provide specific methods for sharing learning content and providing feedback to each other. For example, it can provide guidelines for sharing learning content and receiving feedback. This allows children to share their learning content with other children and provide feedback to each other, thereby improving their independence.

[0094] The homework support system can further provide a dashboard that allows parents to easily check their child's learning progress and can be equipped with a function to automatically generate feedback. For example, the learning progress and achievement status can be displayed in graphs and feedback can be automatically generated. The system can also enable parents to check their child's learning progress in real time. For example, a dashboard can be provided that allows parents to check their child's learning progress and provide appropriate feedback. This reduces the burden on parents by providing a dashboard that allows parents to easily check their child's learning progress and automatically generating feedback.

[0095] The homework support system can also use emotion estimation to provide stronger support for weaker subjects to children who feel they have difficulty with them. For example, it can provide more detailed explanations and additional practice problems for weaker subjects. It can also analyze children's emotional state and provide stronger support for weaker subjects. For example, it can provide positive feedback to help children gain confidence in weaker subjects. This allows the system to support children's learning by providing stronger support for weaker subjects to children who feel they have difficulty with them.

[0096] The homework support system can further use its emotion estimation function to provide positive feedback according to the child's learning progress so that the child feels a sense of accomplishment. For example, it can provide praise or rewards according to the child's learning progress. The system can also analyze the child's emotional state and provide feedback that helps the child feel a sense of accomplishment. For example, when a child achieves a goal, it can provide positive feedback that helps the child feel a sense of accomplishment. This can improve the child's independence by providing positive feedback according to the child's learning progress so that the child feels a sense of accomplishment.

[0097] The homework support system can also use emotion estimation to analyze a child's emotional state when asking a question and reinforce positive feedback. For example, if a child is feeling anxious, it can provide encouraging words. It can also analyze a child's emotional state and provide appropriate feedback. For example, if a child is confident, it can provide feedback that encourages further challenges. In this way, by analyzing a child's emotional state when asking a question and reinforcing positive feedback, it is possible to increase a child's motivation to learn.

[0098] The homework support system can also use emotion estimation to provide interactive feedback incorporating game elements to help children enjoy learning. For example, it can introduce quiz-style questions or a point system. It can also analyze a child's emotional state and adjust the feedback to help them enjoy learning. For example, if a child is excited, it can strengthen the game elements and increase their motivation to learn. This allows the system to provide interactive feedback incorporating game elements to help children enjoy learning, thereby increasing their motivation to learn.

[0099] The homework support system can also use its emotion estimation function to adjust the tone and wording of its responses to make them easier for children to understand. For example, if a child is relaxed, it will respond in a gentle tone, and if they are nervous, it will respond in an encouraging tone. It can also analyze a child's emotional state and select appropriate wording. For example, if a child is feeling anxious, it will select reassuring language. This allows it to support children's learning by adjusting the tone and wording of its responses to make them easier for children to understand.

[0100] The processing flow of the second embodiment will be briefly explained below.

[0101] Step 1: The voice recognition unit recognizes the child's voice. For example, the voice recognition unit collects the child's voice using a microphone and saves it as voice data. The voice recognition unit then converts the voice data into text data using voice recognition technology. For example, the voice recognition unit uses a deep learning algorithm to analyze the voice and convert it into text. Step 2: The natural language processing unit analyzes the speech recognized by the speech recognition unit. For example, the natural language processing unit performs morphological analysis to analyze the structure of the sentence. The natural language processing unit also performs semantic analysis to understand the meaning of the sentence. For example, the natural language processing unit uses generative AI to analyze the meaning of the sentence and extract information to generate an appropriate response. Step 3: The answer generation unit generates an appropriate answer based on the content analyzed by the natural language processing unit. For example, the answer generation unit uses a generation AI to generate an answer to a child's question. The answer generation unit also uses a generation AI to generate multiple answer candidates and selects the most appropriate answer from among them. For example, the answer generation unit uses a generation AI to understand the context of the question and generate the most appropriate answer.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0106] 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.

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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).

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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).

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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).

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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).

[0155] 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.

[0156] 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."

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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]

[0169] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. a voice recognition unit that recognizes children's voices; a natural language processing unit that analyzes the speech recognized by the speech recognition unit; a response generation unit that generates an appropriate response based on the content analyzed by the natural language processing unit; A system characterized by:

2. The voice recognition unit Using voice recognition technology, environmental sounds other than the child's voice are analyzed to optimize the learning environment.

2. The system of claim 1.

3. The natural language processing unit Refer to past question history to gain a deeper understanding of the context of a child's question and improve the accuracy of the answer 2. The system of claim 1.

4. The system comprises: Add a customized learning plan based on each family's homework assignments using the auto-generated feature.

2. The system of claim 1.

5. The system comprises: Provides a dashboard that allows parents to easily check their child's learning progress, automatically generating feedback, 2. The system of claim 1.

6. The voice recognition unit Analyzes the tone and rate of a child's voice to estimate their emotional state and provide appropriate feedback 2. The system of claim 1.

7. The natural language processing unit Adjust the tone and language of your answers to make them easier for children to understand 2. The system of claim 1.

8. The system comprises: If your child is not good at a particular subject, you can provide more support for that subject.

2. The system of claim 1.

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