System

Abstract

An object of a system according to an embodiment is to enable a dual-income parent to grasp the progress of a child's reading aloud and provide appropriate advice.SOLUTION: A system according to an embodiment includes a read aloud capturing unit, a read aloud analyzing unit, an advice providing unit, and a homework recording unit. The reading-aloud capturing unit captures the child's reading-aloud. The reading-aloud analyzing unit analyzes the reading-aloud captured by the reading-aloud capturing unit. The advice providing section provides advice based on a result of the analysis by the reading-aloud analyzing section. The homework recording unit records the progress of the read-aloud analyzed by the read-aloud analysis 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, it was difficult for working parents to supervise their children's reading aloud, making it difficult to grasp their children's progress in reading aloud.

[0005] The system according to the embodiment aims to enable dual-income parents to grasp their children's progress in reading aloud and provide them with appropriate advice. [Means for solving the problem]

[0006] The system according to the embodiment includes a reading aloud capture unit, a reading aloud analysis unit, an advice providing unit, and a homework recording unit. The reading aloud capture unit captures a child's reading aloud. The reading aloud analysis unit analyzes the reading aloud captured by the reading aloud capture unit. The advice providing unit provides advice based on the results of the analysis by the reading aloud analysis unit. The homework recording unit records the progress of the reading aloud analyzed by the reading aloud analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment allows dual-income parents to grasp their children's progress in reading aloud and provide them with appropriate advice. [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 reading aloud support system according to an embodiment of the present invention automatically captures children's reading aloud, analyzes it using a generation AI, provides advice, and records homework. This allows the reading aloud support system to solve the problem of dual-income parents being unable to supervise their children's reading aloud, and to support children's learning.

[0029] A reading aloud support system according to an embodiment includes a reading aloud capture unit, a reading aloud analysis unit, an advice providing unit, and a homework recording unit. The reading aloud capture unit captures a child's reading aloud. For example, when a child reads aloud from a textbook or picture book, the voice is recorded using a microphone on a smartphone or tablet. The reading aloud capture unit can also transmit the voice data to a generation AI. For example, the voice data can be uploaded to the cloud via a smartphone app. The reading aloud analysis unit allows the generation AI to analyze the reading aloud captured by the reading aloud capture unit. For example, the generation AI can evaluate the accuracy and fluency of the pronunciation of the reading aloud using a text generation AI (e.g., LLM). The generation AI can also analyze the intonation of the reading aloud using a multimodal generation AI. The generation AI can also analyze the speed and rhythm of the reading aloud. The advice providing unit provides advice based on the results of the analysis by the reading aloud analysis unit. For example, the generation AI can generate specific advice such as, "Your pronunciation of this part is a little different. Try it again." The generation AI can also provide advice such as, "Maybe you should read a little more slowly." The generation AI can also provide positive feedback such as, "You read very well! Let's try harder next time!" The homework recording unit records the progress of the reading aloud analyzed by the reading aloud analysis unit. For example, it records which pages the child read aloud and how long it took. The homework recording unit can also manage the progress of the reading aloud. For example, it can evaluate the progress based on the number of pages and time the child read aloud. In this way, the reading aloud support system according to the embodiment can solve the problem of dual-income parents being unable to supervise their children's reading aloud and support their children's learning. For example, even if parents are busy, they can keep track of their children's reading aloud progress and receive appropriate advice. Furthermore, the children themselves can receive feedback from the AI ​​to increase their motivation to learn.

[0030] The reading aloud capture unit can automatically filter background and environmental sounds when capturing a reading aloud, improving the quality of the audio data. For example, when capturing a reading aloud, the reading aloud capture unit uses AI to detect background and environmental sounds in real time and automatically remove them. For example, it filters out ambient noise and television sounds while a child is reading aloud. The reading aloud capture unit can also use noise-canceling technology to improve the quality of the audio data. For example, a noise-canceling headset can be used to reduce ambient noise. This improves the quality of the audio data, enabling more accurate analysis and advice.

[0031] The reading aloud capture unit can adjust the tone and tempo of a child's voice in real time when capturing the reading aloud, thereby providing an optimal reading aloud environment. For example, when capturing the reading aloud, the reading aloud capture unit uses AI to analyze the tone and tempo of a child's voice in real time, providing an optimal reading aloud environment. For example, if the child's voice is too high-pitched, the tone is adjusted. The reading aloud capture unit can also adjust the tempo if the reading speed is too fast. For example, the reading speed is adjusted to an appropriate range. In this way, by adjusting the tone and tempo of a child's voice, an optimal reading aloud environment can be provided.

[0032] The reading aloud capture unit can capture reading aloud through a smart speaker or a wearable device, enabling use with a wider variety of devices. For example, the reading aloud capture unit can capture reading aloud through a smart speaker, allowing children to read aloud naturally without being conscious of the device. For example, Amazon Echo or Google Home can be used. The reading aloud capture unit can also capture reading aloud through a wearable device. For example, an Apple Watch or Fitbit can be used to record reading aloud. This allows reading aloud to be captured on a wider variety of devices.

[0033] The reading aloud capture unit provides visual feedback when capturing the reading aloud, thereby enhancing a child's ability to concentrate. For example, when capturing the reading aloud, the reading aloud capture unit displays a bar indicating the progress of the reading aloud as visual feedback, thereby enhancing a child's ability to concentrate. For example, the completed reading portion is displayed in color. The reading aloud capture unit can also visually display the progress of the reading aloud using graphs or charts. For example, it displays a line graph indicating the progress of the reading aloud. In this way, by providing visual feedback, a child's ability to concentrate can be enhanced.

[0034] The reading aloud analysis unit can individually learn the characteristics of a child's voice when analyzing reading aloud and provide advice that is optimized for each child. For example, when analyzing reading aloud, the reading aloud analysis unit uses AI to individually learn the characteristics of a child's voice and provide advice that is optimized for that child. For example, advice to correct specific pronunciation habits. The reading aloud analysis unit can also analyze the pitch, tone, and rhythm of the voice to learn individual characteristics. For example, it can analyze the pitch and speed of a child's voice and provide advice based on those characteristics. The reading aloud analysis unit can also create individual learning patterns based on past reading aloud data and provide advice based on those patterns. For example, it can analyze past reading aloud data and identify specific pronunciation areas for improvement. This can improve learning effectiveness by providing advice that is optimized for each child.

[0035] The reading aloud analysis unit can display the results of the analysis of the reading aloud in a visual graph or chart, allowing the child to intuitively understand his or her progress. The reading aloud analysis unit, for example, displays the results of the analysis of the reading aloud in a visual graph, allowing the child to intuitively understand his or her progress. For example, a graph showing the accuracy and fluency of pronunciation. The reading aloud analysis unit can also visually display the progress of the reading aloud using a chart. For example, it displays a bar graph showing the progress of the reading aloud. The reading aloud analysis unit can also display a line graph showing the progress. For example, it displays a graph showing the progress of the reading aloud on a time axis. This allows the child to intuitively understand his or her progress by providing visual feedback.

[0036] The reading aloud analysis unit can provide a dashboard that allows parents and teachers to monitor the analysis results of reading aloud in real time. The reading aloud analysis unit provides, for example, a dashboard that allows parents and teachers to monitor the analysis results of reading aloud in real time. For example, the progress and evaluation of reading aloud can be confirmed at a glance. The reading aloud analysis unit can also display graphs and charts on the dashboard to allow progress to be visually grasped. For example, it displays a bar graph or line graph showing the progress of reading aloud. The reading aloud analysis unit can also send alerts to parents and teachers through the dashboard. For example, it sends an alert if progress in reading aloud is lagging behind. This allows parents and teachers to understand the child's learning situation in real time.

[0037] The homework recording unit can record homework taking into account not only the content of the reading aloud but also the child's level of concentration and fatigue. For example, when recording homework, the homework recording unit uses AI to evaluate the child's level of concentration and reflects that data in the record. For example, it identifies time periods when concentration is high and records the reading aloud during those time periods. The homework recording unit can also use voice analysis technology to evaluate the child's level of concentration based on the tone and speed of their voice. For example, it can determine that a child's level of concentration is high if the tone of their voice is steady. The homework recording unit can also analyze changes in heart rate to evaluate the level of fatigue. For example, it can determine that a child's level of fatigue is high if the heart rate is rising. This allows for more accurate learning records by taking into account the child's level of concentration and fatigue.

[0038] The homework recorder can store homework records on a cloud-based platform that is easily accessible by parents and teachers. The homework recorder, for example, stores homework records on a cloud-based platform to make them easily accessible by parents and teachers. For example, they can be accessed through a dedicated app or website. The homework recorder can also update homework progress in real time through the cloud-based platform. For example, each time reading aloud progress is recorded, it is uploaded to the cloud. This makes it easier to manage learning records by storing them on a cloud-based platform that is easily accessible by parents and teachers.

[0039] The homework record unit can link homework records with other learning activities to create a comprehensive learning record. The homework record unit, for example, links homework records with other learning activities to create a comprehensive learning record. For example, it integrates records of reading aloud with records of solving math problems. The homework record unit can also link with records of science experiment results and social studies projects. For example, it compares progress in reading aloud with the results of science experiments to create a comprehensive learning record. The homework record unit can also integrate past learning data with current data to create a comprehensive learning record. For example, it compares past reading aloud data with current data to evaluate progress. In this way, by linking with other learning activities, a comprehensive learning record can be created.

[0040] The homework recording unit allows parents and teachers to customize homework records, allowing recording to be tailored to individual learning goals. The homework recording unit, for example, allows parents and teachers to customize homework records, allowing recording to be tailored to individual learning goals. For example, recording can be focused on practicing specific pronunciation or vocabulary. The homework recording unit can also record based on learning goals set by parents or teachers. For example, a specific number of pages or time can be set as a goal, and the progress toward achieving that goal can be recorded. The homework recording unit can also provide a customized recording method. For example, recording can be based on items set by parents or teachers. This allows parents and teachers to customize homework records, allowing recording to be tailored to individual learning goals.

[0041] The advice providing unit can provide reports to parents as audio or video messages, thereby providing them in a more user-friendly format. The advice providing unit can, for example, provide reports to parents as audio messages, thereby providing them in a more user-friendly format. For example, AI reports the child's learning progress by audio. The advice providing unit can also provide reports as video messages. For example, the child's learning progress can be filmed on video and sent to the parent. The advice providing unit can also provide visual feedback to the parent when they receive the report. For example, graphs and charts can be displayed in the video message. This allows the parent to understand the child's learning progress in a more user-friendly format.

[0042] The advice providing unit can add a function that allows reports to parents to be shared with other parents and teachers, thereby forming a learning community. The advice providing unit can, for example, add a function that allows reports to parents to be shared with other parents and teachers, thereby forming a learning community. For example, a dedicated app can be provided for sharing learning progress. The advice providing unit can also enable parents and teachers to provide comments and feedback based on the shared information. For example, it can send messages of advice or encouragement regarding learning progress. The advice providing unit can also provide a function for information exchange and discussion within the learning community. For example, parents and teachers can exchange opinions through a forum or chat function. In this way, by forming a learning community, parents and teachers can share information and support children's learning.

[0043] The advice providing unit can provide customized learning content based on a child's interests and concerns to improve motivation to learn. For example, the advice providing unit provides customized learning content based on a child's interests and concerns to improve motivation to learn. For example, it provides learning materials using a child's favorite character. The advice providing unit can also create an individual learning plan based on the child's learning history and interests. For example, it provides learning content based on a specific theme. The advice providing unit can also update the learning content according to the child's interests. For example, if a new interest is discovered, it provides learning materials based on that interest. In this way, by providing learning content based on a child's interests and concerns, it is possible to improve motivation to learn.

[0044] The advice providing unit can develop a learning program that incorporates game elements to improve motivation to learn. The advice providing unit, for example, develops a learning program that incorporates game elements to improve motivation to learn. For example, a system that allows points to be earned according to progress in reading aloud is introduced. The advice providing unit can also introduce a level-up function to enable children to feel a sense of accomplishment. For example, a system is provided in which the level increases each time a certain level of progress is achieved. The advice providing unit can also provide rewards such as badges or trophies. For example, a badge is earned when a specific goal is achieved. In this way, by incorporating game elements, children's motivation to learn can be improved.

[0045] The advice providing unit can introduce a ranking system that allows children to compete with other children to improve their motivation to study. The advice providing unit, for example, introduces a ranking system that allows children to compete with other children to improve their motivation to study. For example, a system is provided in which the ranking changes according to the progress of reading aloud. The advice providing unit can also provide rewards based on the ranking. For example, a special badge is awarded to children who rank highly. The advice providing unit can also update the ranking in real time so that children can check their ranking. For example, the ranking is displayed through a dedicated app. In this way, introducing a ranking system can increase children's motivation to study.

[0046] The advice providing unit can automatically generate encouraging messages from parents or teachers and send them to children to improve their motivation to study. The advice providing unit can, for example, automatically generate encouraging messages from parents or teachers and send them to children to improve their motivation to study. For example, it can provide encouraging messages according to the progress of reading aloud. The advice providing unit can also use a generation AI to generate messages that imitate the voices of parents or teachers. For example, it can send a message in a parent's voice saying, "Do your best!" The advice providing unit can also automatically generate video messages and send them to children. For example, it can send a video message showing the teacher. In this way, automatically generating encouraging messages from parents and teachers can increase a child's motivation to study.

[0047] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0048] The reading support system can also use the reading capture unit to detect a child's posture when reading aloud and provide advice on maintaining proper posture. For example, it can use a camera to monitor a child's posture in real time, and if the child's back is hunched, provide advice such as "Try reading with a straighter back." The reading capture unit can also detect changes in posture and encourage the child to take a break to prevent fatigue from long periods of reading aloud. For example, it can display a message such as "Try taking a short break" at regular intervals. This allows children to read aloud in proper posture and maintain a healthy learning environment.

[0049] The reading aloud support system can also use the reading aloud analysis unit to provide related knowledge and information based on the content of the child's reading aloud. For example, it can provide historical background or scientific facts related to the content of the textbook the child is reading aloud. The reading aloud analysis unit can also generate quizzes and questions related to the content of the reading aloud to check the child's level of understanding. For example, it can ask a quiz such as "Who is the main character in this story?" based on the content read aloud. This can deepen the child's learning.

[0050] The reading aloud support system can also incorporate a reward system in the advice providing unit according to the child's progress in reading aloud. For example, digital badges or points can be awarded when a certain level of progress is achieved. The advice providing unit can also allow the child to purchase virtual items using the points they have collected. For example, they can use the points to customize a character's costume. This can increase the child's motivation to learn.

[0051] The reading aloud support system can also provide a weekly summary report in the homework recording section, allowing parents and teachers to easily check a child's reading aloud progress. For example, a report summarizing weekly reading aloud progress and achievement level can be automatically generated and sent to parents and teachers by email. The homework recording section can also include graphs and charts in the report, allowing progress to be visually grasped. For example, a bar graph or line graph showing reading aloud progress can be displayed. This allows parents and teachers to easily understand a child's learning situation.

[0052] The reading aloud support system can also provide a function in the reading aloud capture unit to optimize the environment in which a child reads aloud. For example, if the lighting in the room is dim, the system can provide advice such as "Try reading in a brighter place." The reading aloud capture unit can also detect the ambient noise level and encourage the child to read aloud in a quieter environment. For example, if the noise level is too high, the system can display a message such as "Move to a quieter place and try reading." This allows the child to read aloud in the optimal environment.

[0053] The processing flow of the first embodiment will be briefly explained below.

[0054] Step 1: The reading aloud capture unit captures the child's reading aloud. For example, when a child reads aloud from a textbook or picture book, the audio is recorded using the microphone on a smartphone or tablet. The reading aloud capture unit can also send the audio data to the generation AI. For example, the audio data can be uploaded to the cloud via a smartphone app. Step 2: In the reading analysis unit, the generation AI analyzes the reading captured by the reading capture unit. For example, the generation AI may use a text generation AI (e.g., LLM) to evaluate the accuracy and fluency of the pronunciation of the reading. The generation AI may also use a multimodal generation AI to analyze the intonation of the reading. The generation AI may also analyze the speed and rhythm of the reading. Step 3: The advice provider provides advice based on the results of the analysis by the reading aloud analyzer. For example, the generation AI generates specific advice such as, "Your pronunciation in this part is a little different. Try it again." The generation AI can also provide advice such as, "Perhaps it would be better to read a little more slowly." The generation AI can also provide positive feedback such as, "You read very well! Let's try harder next time!" Step 4: The homework recorder records the progress of the reading aloud analyzed by the reading aloud analyzer. For example, it records which pages the child read aloud and how long it took. The homework recorder can also manage the progress of the reading aloud. For example, it can evaluate the progress based on the number of pages and time the child read aloud.

[0055] (Example 2) The reading aloud support system according to an embodiment of the present invention automatically captures children's reading aloud, analyzes it using a generation AI, provides advice, and records homework. This allows the reading aloud support system to solve the problem of dual-income parents being unable to supervise their children's reading aloud, and to support children's learning.

[0056] A reading aloud support system according to an embodiment includes a reading aloud capture unit, a reading aloud analysis unit, an advice providing unit, and a homework recording unit. The reading aloud capture unit captures a child's reading aloud. For example, when a child reads aloud from a textbook or picture book, the voice is recorded using a microphone on a smartphone or tablet. The reading aloud capture unit can also transmit the voice data to a generation AI. For example, the voice data can be uploaded to the cloud via a smartphone app. The reading aloud analysis unit allows the generation AI to analyze the reading aloud captured by the reading aloud capture unit. For example, the generation AI can evaluate the accuracy and fluency of the pronunciation of the reading aloud using a text generation AI (e.g., LLM). The generation AI can also analyze the intonation of the reading aloud using a multimodal generation AI. The generation AI can also analyze the speed and rhythm of the reading aloud. The advice providing unit provides advice based on the results of the analysis by the reading aloud analysis unit. For example, the generation AI can generate specific advice such as, "Your pronunciation of this part is a little different. Try it again." The generation AI can also provide advice such as, "Maybe you should read a little more slowly." The generation AI can also provide positive feedback such as, "You read very well! Let's try harder next time!" The homework recording unit records the progress of the reading aloud analyzed by the reading aloud analysis unit. For example, it records which pages the child read aloud and how long it took. The homework recording unit can also manage the progress of the reading aloud. For example, it can evaluate the progress based on the number of pages and time the child read aloud. In this way, the reading aloud support system according to the embodiment can solve the problem of dual-income parents being unable to supervise their children's reading aloud and support their children's learning. For example, even if parents are busy, they can keep track of their children's reading aloud progress and receive appropriate advice. Furthermore, the children themselves can receive feedback from the AI ​​to increase their motivation to learn.

[0057] The reading aloud capture unit can automatically filter background and environmental sounds when capturing a reading aloud, improving the quality of the audio data. For example, when capturing a reading aloud, the reading aloud capture unit uses AI to detect background and environmental sounds in real time and automatically remove them. For example, it filters out ambient noise and television sounds while a child is reading aloud. The reading aloud capture unit can also use noise-canceling technology to improve the quality of the audio data. For example, a noise-canceling headset can be used to reduce ambient noise. This improves the quality of the audio data, enabling more accurate analysis and advice.

[0058] The reading aloud capture unit can adjust the tone and tempo of a child's voice in real time when capturing the reading aloud, thereby providing an optimal reading aloud environment. For example, when capturing the reading aloud, the reading aloud capture unit uses AI to analyze the tone and tempo of a child's voice in real time, providing an optimal reading aloud environment. For example, if the child's voice is too high-pitched, the tone is adjusted. The reading aloud capture unit can also adjust the tempo if the reading speed is too fast. For example, the reading speed is adjusted to an appropriate range. In this way, by adjusting the tone and tempo of a child's voice, an optimal reading aloud environment can be provided.

[0059] The reading aloud capture unit can use its emotion estimation function to detect stress or tension felt by a child while reading aloud and provide advice to help them relax. For example, when recording a reading aloud, the reading aloud capture unit uses AI to analyze the tone and tempo of a child's voice to detect stress or tension. For example, if the child's voice is trembling, the unit provides advice to help them relax. The reading aloud capture unit can also use facial expression analysis technology to detect stress or tension from a child's facial expressions. For example, it can analyze facial muscle movements to detect signs of stress. The reading aloud capture unit can also analyze changes in heart rate to detect stress or tension. For example, if the heart rate is elevated, the unit provides advice to help the child relax while reading aloud.

[0060] The reading aloud capture unit can capture reading aloud through a smart speaker or a wearable device, enabling use with a wider variety of devices. For example, the reading aloud capture unit can capture reading aloud through a smart speaker, allowing children to read aloud naturally without being conscious of the device. For example, Amazon Echo or Google Home can be used. The reading aloud capture unit can also capture reading aloud through a wearable device. For example, an Apple Watch or Fitbit can be used to record reading aloud. This allows reading aloud to be captured on a wider variety of devices.

[0061] The reading aloud capture unit provides visual feedback when capturing the reading aloud, thereby enhancing a child's ability to concentrate. For example, when capturing the reading aloud, the reading aloud capture unit displays a bar indicating the progress of the reading aloud as visual feedback, thereby enhancing a child's ability to concentrate. For example, the completed reading portion is displayed in color. The reading aloud capture unit can also visually display the progress of the reading aloud using graphs or charts. For example, it displays a line graph indicating the progress of the reading aloud. In this way, by providing visual feedback, a child's ability to concentrate can be enhanced.

[0062] The reading aloud capture unit can use an emotion estimation function to evaluate a child's emotional state before starting to read aloud and guide the child to start reading aloud at the optimal timing. For example, before reading aloud, the reading aloud capture unit uses AI to evaluate the child's emotional state and guide the child to start reading aloud in a relaxed state. For example, if the child is nervous, the reading aloud capture unit can provide advice on how to relax. The reading aloud capture unit can also use facial expression analysis technology to evaluate a child's emotional state from their facial expression. For example, it can analyze facial muscle movements to determine whether the child is relaxed. The reading aloud capture unit can also analyze changes in heart rate to evaluate the child's emotional state. For example, it can guide the child to start reading aloud if the heart rate is stable. This helps the child start reading aloud at the optimal timing.

[0063] The reading aloud analysis unit can individually learn the characteristics of a child's voice when analyzing reading aloud and provide advice that is optimized for each child. For example, when analyzing reading aloud, the reading aloud analysis unit uses AI to individually learn the characteristics of a child's voice and provide advice that is optimized for that child. For example, advice to correct specific pronunciation habits. The reading aloud analysis unit can also analyze the pitch, tone, and rhythm of the voice to learn individual characteristics. For example, it can analyze the pitch and speed of a child's voice and provide advice based on those characteristics. The reading aloud analysis unit can also create individual learning patterns based on past reading aloud data and provide advice based on those patterns. For example, it can analyze past reading aloud data and identify specific pronunciation areas for improvement. This can improve learning effectiveness by providing advice that is optimized for each child.

[0064] The reading aloud analysis unit can display the results of the analysis of the reading aloud in a visual graph or chart, allowing the child to intuitively understand his or her progress. The reading aloud analysis unit, for example, displays the results of the analysis of the reading aloud in a visual graph, allowing the child to intuitively understand his or her progress. For example, a graph showing the accuracy and fluency of pronunciation. The reading aloud analysis unit can also visually display the progress of the reading aloud using a chart. For example, it displays a bar graph showing the progress of the reading aloud. The reading aloud analysis unit can also display a line graph showing the progress. For example, it displays a graph showing the progress of the reading aloud on a time axis. This allows the child to intuitively understand his or her progress by providing visual feedback.

[0065] The reading aloud analysis unit can use an emotion estimation function to analyze the emotions felt by a child while reading aloud and provide advice based on those emotions. For example, when analyzing reading aloud, the reading aloud analysis unit uses AI to analyze the child's emotional state and provide advice based on those emotions. For example, if the child is feeling anxious, it can provide an encouraging message. The reading aloud analysis unit can also use facial expression analysis technology to analyze emotions from a child's facial expressions. For example, it can analyze facial muscle movements to identify emotions. The reading aloud analysis unit can also use voice analysis technology to analyze emotions from the tone and speed of a child's voice. For example, if the voice tone is low, it can provide an encouraging message. This allows for advice based on the child's emotions, thereby improving learning effectiveness.

[0066] The reading aloud analysis unit can provide a dashboard that allows parents and teachers to monitor the analysis results of reading aloud in real time. The reading aloud analysis unit provides, for example, a dashboard that allows parents and teachers to monitor the analysis results of reading aloud in real time. For example, the progress and evaluation of reading aloud can be confirmed at a glance. The reading aloud analysis unit can also display graphs and charts on the dashboard to allow progress to be visually grasped. For example, it displays a bar graph or line graph showing the progress of reading aloud. The reading aloud analysis unit can also send alerts to parents and teachers through the dashboard. For example, it sends an alert if progress in reading aloud is lagging behind. This allows parents and teachers to understand the child's learning situation in real time.

[0067] The reading aloud analysis unit can use its emotion estimation function to evaluate the sense of accomplishment and satisfaction a child feels after reading aloud and suggest the next learning step based on that emotion. For example, after analyzing the reading aloud, the reading aloud analysis unit uses AI to evaluate the child's sense of accomplishment and satisfaction and suggests the next learning step based on that emotion. For example, if the sense of accomplishment is high, it will suggest a task of the next level of difficulty. The reading aloud analysis unit can also use facial expression analysis technology to evaluate the child's sense of accomplishment and satisfaction from their facial expressions. For example, it can analyze the frequency of smiles to evaluate the sense of accomplishment. The reading aloud analysis unit can also use voice analysis technology to evaluate the child's sense of accomplishment and satisfaction from the tone and speed of their voice. For example, it can determine that a high voice tone indicates a high sense of accomplishment. This can increase the child's motivation to learn by suggesting the next learning step based on their emotions.

[0068] The homework recording unit can record homework taking into account not only the content of the reading aloud but also the child's level of concentration and fatigue. For example, when recording homework, the homework recording unit uses AI to evaluate the child's level of concentration and reflects that data in the record. For example, it identifies time periods when concentration is high and records the reading aloud during those time periods. The homework recording unit can also use voice analysis technology to evaluate the child's level of concentration based on the tone and speed of their voice. For example, it can determine that a child's level of concentration is high if the tone of their voice is steady. The homework recording unit can also analyze changes in heart rate to evaluate the level of fatigue. For example, it can determine that a child's level of fatigue is high if the heart rate is rising. This allows for more accurate learning records by taking into account the child's level of concentration and fatigue.

[0069] The homework recorder can store homework records on a cloud-based platform that is easily accessible by parents and teachers. The homework recorder, for example, stores homework records on a cloud-based platform to make them easily accessible by parents and teachers. For example, they can be accessed through a dedicated app or website. The homework recorder can also update homework progress in real time through the cloud-based platform. For example, each time reading aloud progress is recorded, it is uploaded to the cloud. This makes it easier to manage learning records by storing them on a cloud-based platform that is easily accessible by parents and teachers.

[0070] The homework recording unit can use an emotion estimation function to record a child's emotional state when doing homework and provide feedback to maintain motivation to study. For example, when recording homework, the homework recording unit uses AI to evaluate the child's emotional state and reflects that data in the record. For example, if the child is enjoying the work, positive feedback is provided. The homework recording unit can also use facial expression analysis technology to evaluate a child's emotional state from their facial expressions. For example, it can analyze the frequency of smiles to evaluate their emotional state. The homework recording unit can also use voice analysis technology to evaluate a child's emotional state from the tone and speed of their voice. For example, a high-pitched voice is determined to indicate a positive emotion. This allows the system to record a child's emotional state and provide feedback to maintain their motivation to study, thereby improving learning effectiveness.

[0071] The homework record unit can link homework records with other learning activities to create a comprehensive learning record. The homework record unit, for example, links homework records with other learning activities to create a comprehensive learning record. For example, it integrates records of reading aloud with records of solving math problems. The homework record unit can also link with records of science experiment results and social studies projects. For example, it compares progress in reading aloud with the results of science experiments to create a comprehensive learning record. The homework record unit can also integrate past learning data with current data to create a comprehensive learning record. For example, it compares past reading aloud data with current data to evaluate progress. In this way, by linking with other learning activities, a comprehensive learning record can be created.

[0072] The homework recording unit allows parents and teachers to customize homework records, allowing recording to be tailored to individual learning goals. The homework recording unit, for example, allows parents and teachers to customize homework records, allowing recording to be tailored to individual learning goals. For example, recording can be focused on practicing specific pronunciation or vocabulary. The homework recording unit can also record based on learning goals set by parents or teachers. For example, a specific number of pages or time can be set as a goal, and the progress toward achieving that goal can be recorded. The homework recording unit can also provide a customized recording method. For example, recording can be based on items set by parents or teachers. This allows parents and teachers to customize homework records, allowing recording to be tailored to individual learning goals.

[0073] The homework recording unit can use an emotion estimation function to evaluate a child's emotions when completing homework and implement a reward system based on those emotions. For example, when recording homework, the homework recording unit uses AI to evaluate the child's emotional state and implement a reward system based on those emotions. For example, if a child enjoys completing their homework, points are awarded. The homework recording unit can also use facial expression analysis technology to evaluate emotions from a child's facial expressions. For example, it can analyze the frequency of smiling and determine rewards. The homework recording unit can also use voice analysis technology to evaluate emotions from the tone and speed of a child's voice. For example, it can award a reward if the voice tone is high. This can increase a child's motivation to learn by implementing a reward system based on their emotions.

[0074] The advice providing unit can report to the parent in detail not only the child's learning progress but also the child's emotional state and learning attitude. For example, the advice providing unit reports to the parent in detail not only the child's learning progress but also the child's emotional state and learning attitude. For example, it reports how the child felt while studying. The advice providing unit can also report information regarding the learning attitude. For example, it reports whether the child was concentrating while studying. The advice providing unit can also provide visual feedback so that the parent can understand the child's learning situation in more detail. For example, it can show the learning progress using graphs or charts. This allows the parent to understand the child's learning situation in more detail.

[0075] The advice providing unit can provide reports to parents as audio or video messages, thereby providing them in a more user-friendly format. The advice providing unit can, for example, provide reports to parents as audio messages, thereby providing them in a more user-friendly format. For example, AI reports the child's learning progress by audio. The advice providing unit can also provide reports as video messages. For example, the child's learning progress can be filmed on video and sent to the parent. The advice providing unit can also provide visual feedback to the parent when they receive the report. For example, graphs and charts can be displayed in the video message. This allows the parent to understand the child's learning progress in a more user-friendly format.

[0076] The advice providing unit can use the emotion estimation function to analyze the emotional reaction of the parent when receiving the report and optimize the content of the report. For example, the advice providing unit uses AI to analyze the emotional reaction of the parent when receiving the report and optimizes the content of the report based on that data. For example, if the parent has a positive reaction, it provides a detailed report. The advice providing unit can also use facial expression analysis technology to analyze the emotional reaction from the parent's facial expression. For example, it can analyze the frequency of smiling and adjust the content of the report. The advice providing unit can also use voice analysis technology to analyze the emotional reaction from the tone and speed of the parent's voice. For example, it can provide a detailed report if the voice tone is high. This makes it possible to provide more effective feedback by optimizing the content of the report based on the parent's emotional reaction.

[0077] The advice providing unit can add a function that allows reports to parents to be shared with other parents and teachers, thereby forming a learning community. The advice providing unit can, for example, add a function that allows reports to parents to be shared with other parents and teachers, thereby forming a learning community. For example, a dedicated app can be provided for sharing learning progress. The advice providing unit can also enable parents and teachers to provide comments and feedback based on the shared information. For example, it can send messages of advice or encouragement regarding learning progress. The advice providing unit can also provide a function for information exchange and discussion within the learning community. For example, parents and teachers can exchange opinions through a forum or chat function. In this way, by forming a learning community, parents and teachers can share information and support children's learning.

[0078] The advice providing unit can use the emotion estimation function to evaluate the emotions of the parent after receiving the report and provide additional advice based on those emotions. For example, the advice providing unit uses AI to evaluate the emotions of the parent after receiving the report and provides additional advice based on those emotions. For example, if the parent feels anxious, the advice providing unit can suggest specific measures. The advice providing unit can also use facial expression analysis technology to evaluate the emotions of the parent from their facial expressions. For example, it can analyze the movement of facial muscles to identify emotions. The advice providing unit can also use voice analysis technology to evaluate the emotions of the parent from the tone and speed of their voice. For example, it can provide an encouraging message if the voice tone is low. This enables more effective support by providing additional advice based on the parent's emotions.

[0079] The advice providing unit can provide customized learning content based on a child's interests and concerns to improve motivation to learn. For example, the advice providing unit provides customized learning content based on a child's interests and concerns to improve motivation to learn. For example, it provides learning materials using a child's favorite character. The advice providing unit can also create an individual learning plan based on the child's learning history and interests. For example, it provides learning content based on a specific theme. The advice providing unit can also update the learning content according to the child's interests. For example, if a new interest is discovered, it provides learning materials based on that interest. In this way, by providing learning content based on a child's interests and concerns, it is possible to improve motivation to learn.

[0080] The advice providing unit can develop a learning program that incorporates game elements to improve motivation to learn. The advice providing unit, for example, develops a learning program that incorporates game elements to improve motivation to learn. For example, a system that allows points to be earned according to progress in reading aloud is introduced. The advice providing unit can also introduce a level-up function to enable children to feel a sense of accomplishment. For example, a system is provided in which the level increases each time a certain level of progress is achieved. The advice providing unit can also provide rewards such as badges or trophies. For example, a badge is earned when a specific goal is achieved. In this way, by incorporating game elements, children's motivation to learn can be improved.

[0081] The advice providing unit can use the emotion estimation function to provide feedback to reinforce the positive emotions felt by the child while studying. The advice providing unit, for example, uses the emotion estimation function to provide feedback to reinforce the positive emotions felt by the child while studying. For example, if the child is enjoying themselves, a praising message is provided. The advice providing unit can also use facial expression analysis technology to identify positive emotions from the child's facial expressions. For example, the frequency of smiles can be analyzed to reinforce positive emotions. The advice providing unit can also use voice analysis technology to identify positive emotions from the tone and speed of the child's voice. For example, if the voice tone is high, a praising message is provided. This can reinforce positive emotions and increase the child's motivation to learn.

[0082] The advice providing unit can introduce a ranking system that allows children to compete with other children to improve their motivation to study. The advice providing unit, for example, introduces a ranking system that allows children to compete with other children to improve their motivation to study. For example, a system is provided in which the ranking changes according to the progress of reading aloud. The advice providing unit can also provide rewards based on the ranking. For example, a special badge is awarded to children who rank highly. The advice providing unit can also update the ranking in real time so that children can check their ranking. For example, the ranking is displayed through a dedicated app. In this way, introducing a ranking system can increase children's motivation to study.

[0083] The advice providing unit can automatically generate encouraging messages from parents or teachers and send them to children to improve their motivation to study. The advice providing unit can, for example, automatically generate encouraging messages from parents or teachers and send them to children to improve their motivation to study. For example, it can provide encouraging messages according to the progress of reading aloud. The advice providing unit can also use a generation AI to generate messages that imitate the voices of parents or teachers. For example, it can send a message in a parent's voice saying, "Do your best!" The advice providing unit can also automatically generate video messages and send them to children. For example, it can send a video message showing the teacher. In this way, automatically generating encouraging messages from parents and teachers can increase a child's motivation to study.

[0084] The advice providing unit can use the emotion estimation function to evaluate the emotion of the child after completing a lesson and suggest the next learning step based on that emotion. The advice providing unit can, for example, use the emotion estimation function to evaluate the emotion of the child after completing a lesson and suggest the next learning step based on that emotion. For example, if the child feels a sense of accomplishment, the advice providing unit can suggest a task of the next level of difficulty. The advice providing unit can also use facial expression analysis technology to evaluate the emotion from the child's facial expression. For example, the frequency of smiling can be analyzed to suggest the next learning step. The advice providing unit can also use voice analysis technology to evaluate the emotion from the tone and speed of the child's voice. For example, if the voice tone is high, the advice providing unit can suggest a task of the next level of difficulty. In this way, by suggesting the next learning step based on the child's emotion, it is possible to increase the child's motivation to learn.

[0085] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0086] The reading support system can also use the reading capture unit to detect a child's posture when reading aloud and provide advice on maintaining proper posture. For example, it can use a camera to monitor a child's posture in real time, and if the child's back is hunched, provide advice such as "Try reading with a straighter back." The reading capture unit can also detect changes in posture and encourage the child to take a break to prevent fatigue from long periods of reading aloud. For example, it can display a message such as "Try taking a short break" at regular intervals. This allows children to read aloud in proper posture and maintain a healthy learning environment.

[0087] The reading aloud support system can also use the reading aloud analysis unit to provide related knowledge and information based on the content of the child's reading aloud. For example, it can provide historical background or scientific facts related to the content of the textbook the child is reading aloud. The reading aloud analysis unit can also generate quizzes and questions related to the content of the reading aloud to check the child's level of understanding. For example, it can ask a quiz such as "Who is the main character in this story?" based on the content read aloud. This can deepen the child's learning.

[0088] The reading aloud support system can also incorporate a reward system in the advice providing unit according to the child's progress in reading aloud. For example, digital badges or points can be awarded when a certain level of progress is achieved. The advice providing unit can also allow the child to purchase virtual items using the points they have collected. For example, they can use the points to customize a character's costume. This can increase the child's motivation to learn.

[0089] The reading aloud support system can also provide a weekly summary report in the homework recording section, allowing parents and teachers to easily check a child's reading aloud progress. For example, a report summarizing weekly reading aloud progress and achievement level can be automatically generated and sent to parents and teachers by email. The homework recording section can also include graphs and charts in the report, allowing progress to be visually grasped. For example, a bar graph or line graph showing reading aloud progress can be displayed. This allows parents and teachers to easily understand a child's learning situation.

[0090] The reading aloud support system can also provide a function in the reading aloud capture unit to optimize the environment in which a child reads aloud. For example, if the lighting in the room is dim, the system can provide advice such as "Try reading in a brighter place." The reading aloud capture unit can also detect the ambient noise level and encourage the child to read aloud in a quieter environment. For example, if the noise level is too high, the system can display a message such as "Move to a quieter place and try reading." This allows the child to read aloud in the optimal environment.

[0091] The reading aloud support system further uses an emotion estimation function in the reading aloud analysis unit to analyze the emotions felt by the child while reading aloud and provide advice based on those emotions. For example, if the child is feeling anxious, an encouraging message is provided. The reading aloud analysis unit can also use facial expression analysis technology to analyze emotions from the child's facial expressions. For example, the movement of facial muscles can be analyzed to identify the emotion. The reading aloud analysis unit can also use voice analysis technology to analyze emotions from the tone and speed of the child's voice. For example, if the voice tone is low, an encouraging message is provided. This makes it possible to improve learning effectiveness by providing advice based on the child's emotions.

[0092] The reading aloud support system further uses an emotion estimation function in the advice providing unit to evaluate the child's emotions after finishing reading aloud and suggest the next learning step based on those emotions. For example, if the child feels a sense of accomplishment, the advice providing unit can suggest a task of the next level of difficulty. The advice providing unit can also use facial expression analysis technology to evaluate the child's emotions from their facial expressions. For example, it can analyze the frequency of smiles and suggest the next learning step. The advice providing unit can also use voice analysis technology to evaluate the child's emotions from the tone and speed of their voice. For example, if the voice tone is high, the advice providing unit can suggest a task of the next level of difficulty. In this way, suggesting the next learning step based on the child's emotions can increase their motivation to learn.

[0093] The reading aloud support system further uses an emotion estimation function in the homework recording unit to record the emotional state of a child when doing homework and provide feedback to maintain motivation to study. For example, if a child is enjoying themselves, positive feedback is provided. The homework recording unit can also use facial expression analysis technology to evaluate the emotional state from the child's facial expressions. For example, the frequency of smiling can be analyzed to evaluate the emotional state. The homework recording unit can also use voice analysis technology to evaluate the emotional state from the tone and speed of the child's voice. For example, a high-pitched voice can be determined to indicate a positive emotion. In this way, by recording the child's emotional state and providing feedback to maintain motivation to study, it is possible to improve learning effectiveness.

[0094] The reading aloud support system further uses an emotion estimation function in the advice providing unit to analyze the emotional reaction of the parent when receiving the report and optimize the content of the report. For example, AI analyzes the emotional reaction of the parent when receiving the report and optimizes the content of the report based on that data. For example, if the parent has a positive reaction, a detailed report is provided. The advice providing unit can also use facial expression analysis technology to analyze the emotional reaction from the parent's facial expression. For example, it can analyze the frequency of smiling and adjust the content of the report. The advice providing unit can also use voice analysis technology to analyze the emotional reaction from the tone and speed of the parent's voice. For example, if the voice tone is high, a detailed report is provided. This makes it possible to provide more effective feedback by optimizing the content of the report based on the parent's emotional reaction.

[0095] The reading aloud support system can further use the emotion estimation function in the advice providing unit to provide feedback to reinforce the positive emotions felt by the child while studying. For example, the emotion estimation function can be used to provide feedback to reinforce the positive emotions felt by the child while studying. For example, if the child is enjoying themselves, a praising message can be provided. The advice providing unit can also use facial expression analysis technology to identify positive emotions from the child's facial expressions. For example, the frequency of smiles can be analyzed to reinforce positive emotions. The advice providing unit can also use voice analysis technology to identify positive emotions from the tone and speed of the child's voice. For example, if the voice tone is high, a praising message can be provided. This can reinforce positive emotions and increase the child's motivation to study.

[0096] The processing flow of the second embodiment will be briefly explained below.

[0097] Step 1: The reading aloud capture unit captures the child's reading aloud. For example, when a child reads aloud from a textbook or picture book, the audio is recorded using the microphone on a smartphone or tablet. The reading aloud capture unit can also send the audio data to the generation AI. For example, the audio data can be uploaded to the cloud via a smartphone app. Step 2: In the reading analysis unit, the generation AI analyzes the reading captured by the reading capture unit. For example, the generation AI may use a text generation AI (e.g., LLM) to evaluate the accuracy and fluency of the pronunciation of the reading. The generation AI may also use a multimodal generation AI to analyze the intonation of the reading. The generation AI may also analyze the speed and rhythm of the reading. Step 3: The advice provider provides advice based on the results of the analysis by the reading aloud analyzer. For example, the generation AI generates specific advice such as, "Your pronunciation in this part is a little different. Try it again." The generation AI can also provide advice such as, "Perhaps it would be better to read a little more slowly." The generation AI can also provide positive feedback such as, "You read very well! Let's try harder next time!" Step 4: The homework recorder records the progress of the reading aloud analyzed by the reading aloud analyzer. For example, it records which pages the child read aloud and how long it took. The homework recorder can also manage the progress of the reading aloud. For example, it can evaluate the progress based on the number of pages and time the child read aloud.

[0098] 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.

[0099] 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.

[0100] 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.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] 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.

[0115] 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.

[0116] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0117] 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.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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).

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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).

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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.

[0148] 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.

[0149] 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.

[0150] 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).

[0151] 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.

[0152] 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."

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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]

[0165] 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 reading aloud capture section that captures children's reading aloud, a reading aloud analysis unit that analyzes the reading aloud captured by the reading aloud capture unit; an advice providing unit that provides advice based on the analysis result by the reading aloud analysis unit; a homework recording unit that records the progress of the reading aloud analyzed by the reading aloud analysis unit. A system characterized by:

2. The reading aloud capturing unit Automatically filters out background and environmental noise when capturing speech to improve the quality of the audio data 2. The system of claim 1.

3. The reading aloud capturing unit Reading aloud can be captured through smart speakers and wearable devices, enabling use on a wider variety of devices.

2. The system of claim 1.

4. The reading analysis unit When analyzing reading aloud, the system learns the characteristics of each child's voice and provides advice optimized for each child.

2. The system of claim 1.

5. The homework recording unit Recording homework takes into account not only the content of the reading aloud but also the child's level of concentration and fatigue.

2. The system of claim 1.

6. The advice providing unit Report to parents in detail not only about the child's learning progress but also about their emotional state and learning attitude.

2. The system of claim 1.

7. The advice providing unit Providing feedback to reinforce the positive feelings the child had while learning 2. The system of claim 1.

8. The reading aloud capturing unit Detect stress or tension the child is feeling while reading aloud and provide advice to help them relax 2. The system of claim 1.

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