system

Abstract

The system according to the embodiment aims to effectively support home learning for preschool children. [Solution] A system according to an embodiment includes a reading unit, a reading unit, a support unit, a recording unit, and a reporting unit. The reading unit reads teaching materials. The reading unit reads aloud the content read by the reading unit. The support unit provides learning support based on the content read by the reading unit. The recording unit records the learning progress made by the support unit. The reporting unit reports the progress recorded by the recording unit to a parent.

Description

[Technical Field]

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

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

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

[0004] Conventional technology has made it difficult for parents to constantly support their preschool children's home learning.

[0005] The system according to the embodiment aims to effectively support home learning for preschool children. [Means for solving the problem]

[0006] The system according to the embodiment includes a reading unit, a reading unit, a support unit, a recording unit, and a reporting unit. The reading unit reads the teaching material. The reading unit reads out loud the content read by the reading unit. The support unit provides learning support based on the content read out by the reading unit. The recording unit records the learning progress made by the support unit. The reporting unit reports the progress recorded by the recording unit to the parent. [Effects of the Invention]

[0007] The system according to the embodiment can effectively support home learning for preschool children. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A smart glasses system according to an embodiment of the present invention is designed to support home learning for preschool children. This smart glasses system has a function for reading educational materials. When a child holds a learning material up to the smart glasses' camera, the system reads its contents. It then reads the read contents aloud, allowing the smart glasses to read them for the child even if the child is unable to read the material themselves. It also has a learning support function, providing hints for solving problems and determining whether an answer is correct or incorrect. It also has a function for recording learning progress and reporting it to parents. This allows parents to keep track of their children's learning status even when they are short on time. This smart glasses are particularly useful in households with multiple children or busy parents, as they can provide individualized learning support for each child, reducing the burden on parents. For example, the system uses OCR technology to read educational materials, AI to analyze the content of the questions, and the data is sent to the cloud, where it is sent to the parent's smartphone. The smart glasses are also designed to be comfortable to wear and easy for children to use, ensuring comfortable use even for long periods of time. Furthermore, data encryption and access restrictions are implemented for privacy and security reasons. This allows the smart glasses system to support home learning for preschool children and reduce the burden on parents.

[0029] A smart glasses system according to an embodiment includes a reading unit, a reading unit, a support unit, a recording unit, and a reporting unit. The reading unit reads learning materials. Examples of learning materials include, but are not limited to, paper learning materials, digital learning materials, and audio learning materials. The reading unit reads the learning materials using, for example, OCR technology. The reading unit can also read the learning materials manually. The reading unit reads aloud the content read by the reading unit. For example, synthesized voice can be used for the voice reading. The reading unit can also record and read a recorded human voice. The support unit provides learning support based on the content read by the reading unit. Learning support includes, for example, explanations of problems, provision of hints, and progress management. The recording unit records the learning progress performed by the support unit. The progress record can be recorded, for example, as digital data. The recording unit can also record on paper. The reporting unit reports the progress recorded by the recording unit to a parent. The report can be sent, for example, by email. The reporting unit can also report through an app. This allows the smart glasses system according to the embodiment to support home learning for preschool children and reduce the burden on parents.

[0030] The device includes an OCR unit that reads the teaching materials using OCR technology. The OCR unit uses OCR technology to read the teaching materials. Examples of OCR technology include, but are not limited to, Tesseract (registered trademark) and Google (registered trademark) Cloud Vision. The OCR unit reads the teaching materials using, for example, Tesseract. The OCR unit can also read the teaching materials using Google Cloud Vision. This allows the OCR unit to improve the accuracy of reading the teaching materials. Some or all of the above-mentioned processing in the OCR unit may be performed using, for example, AI, or may be performed without using AI. For example, the OCR unit can input image data of the teaching materials to a generation AI and cause the generation AI to generate text data from the image data.

[0031] The system includes an analysis unit that analyzes the content of the problem using AI. The analysis unit uses AI technology to analyze the content of the problem. AI technology includes, but is not limited to, machine learning and deep learning. The analysis unit analyzes the content of the problem using, for example, machine learning. The analysis unit can also analyze the content of the problem using deep learning. This allows the analysis unit to accurately analyze the content of the problem. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data of the problem to a generation AI and cause the generation AI to analyze the content of the problem.

[0032] The system includes a transmitting unit that transmits data to the cloud. The transmitting unit transmits the data to the cloud. Cloud services include, but are not limited to, AWS (registered trademark), Google Cloud, and Microsoft Azure (registered trademark). The transmitting unit transmits the data using AWS, for example. The transmitting unit can also transmit the data using Google Cloud. Furthermore, the transmitting unit can also transmit the data using Microsoft Azure. This allows the transmitting unit to easily manage the data. Some or all of the above-described processing in the transmitting unit may be performed using AI, for example, or may be performed without using AI. For example, the transmitting unit may input data to a generating AI and cause the generating AI to transmit the data to the cloud.

[0033] The encryption unit includes an encryption unit that encrypts data. The encryption unit encrypts data. Encryption techniques include, but are not limited to, AES and RSA, for example. The encryption unit encrypts data using AES, for example. The encryption unit can also encrypt data using RSA, for example. This allows the encryption unit to improve data security. Some or all of the above-mentioned processing in the encryption unit may be performed using AI, or may be performed without using AI, for example. For example, the encryption unit may input data to a generation AI and have the generation AI perform encryption.

[0034] The system includes an access restriction unit that performs access restriction. The access restriction unit performs access restriction on data. Examples of access restriction include, but are not limited to, password authentication and two-factor authentication. The access restriction unit performs access restriction using, for example, password authentication. The access restriction unit can also perform access restriction using two-factor authentication. This allows the access restriction unit to enable access control of data. Some or all of the above-mentioned processing in the access restriction unit may be performed using, for example, AI, or may be performed without using AI. For example, the access restriction unit can input access restriction settings to a generation AI and have the generation AI execute the access restriction.

[0035] The reading unit can automatically select the optimal reading mode depending on the type of teaching material. For example, in the case of a picture book, the reading unit reads the entire page using image recognition. In addition, in the case of a textbook, the reading unit can also prioritize reading character information using text recognition. In addition, in the case of a worksheet, the reading unit can focus on reading the answer column using handwriting recognition. This enables the reading unit to perform optimal reading depending on the type of teaching material. Reading modes include, but are not limited to, text mode and image mode. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the type of teaching material to the generation AI and cause the generation AI to select the optimal reading mode.

[0036] The reading unit can detect the condition of the teaching material and make corrections to improve reading accuracy. For example, the reading unit can detect torn pages and fill in the missing parts. The reading unit can also detect dirty parts and remove the dirt to improve reading accuracy. The reading unit can also detect creased pages and make corrections to flatten the creases. This enables the reading unit to perform optimal reading according to the condition of the teaching material. Examples of the condition of the teaching material include, but are not limited to, tears, dirt, and creases. Some or all of the above-mentioned processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input data on the condition of the teaching material to a generation AI and have the generation AI perform corrections to the reading accuracy.

[0037] When reading teaching materials, the reading unit can set appropriate reading conditions taking into account the ambient light. For example, in a bright environment, the reading unit can adjust the exposure of the camera to improve reading accuracy. In a dark environment, the reading unit can also increase the sensitivity of the camera to improve reading accuracy. Furthermore, when there is reflection from lighting, the reading unit can read at an angle that avoids the reflection. This enables the reading unit to perform optimal reading according to the ambient light. Ambient light includes, for example, light intensity and light color, but is not limited to such examples. Some or all of the above-mentioned processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input ambient light data into the generation AI and have the generation AI set optimal reading conditions.

[0038] The reading unit can track the child's gaze and automatically adjust the reading range when reading the teaching material. For example, the reading unit prioritizes reading the page the child is looking at. Furthermore, if the child moves their gaze, the reading unit can adjust the reading range in that direction. Furthermore, if the child is gazing at a particular part, the reading unit can enlarge and read that part. This enables the reading unit to optimally read according to the child's gaze. Examples of gaze tracking include, but are not limited to, cameras and sensors. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the child's gaze data into the generation AI and cause the generation AI to adjust the reading range based on the gaze.

[0039] The reading unit can select a character with a different voice depending on the content to be read. For example, when reading a story, the reading unit can use a different voice for each character. Furthermore, the reading unit can read a textbook in a calm voice. Furthermore, the reading unit can read a worksheet in a lively voice. This allows the reading unit to provide a character with the optimal voice depending on the content. Voice characters include, but are not limited to, male voices, female voices, and children's voices. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input content data to be read into a generation AI and have the generation AI select a character with the optimal voice.

[0040] The reading unit can add background sounds and sound effects to the content being read to enhance the sense of realism. For example, when reading a story, the reading unit can add background sounds appropriate to the scene. The reading unit can also add sound effects to important parts when reading a textbook. The reading unit can also add cheering sound effects when the answer is correct when reading a worksheet. This enables the reading unit to read with a sense of realism. Examples of background sounds and sound effects include, but are not limited to, natural sounds, music, and sound effects. Some or all of the above-described processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input content data to be read into a generation AI and have the generation AI add background sounds and sound effects.

[0041] The reading unit monitors the child's reaction in real time while reading aloud and can change the reading content as appropriate. For example, if the child shows no interest, the reading unit can switch to a different content. The reading unit can also add supplementary explanations if the child does not understand. The reading unit can also change the reading tempo if the child becomes bored. This enables the reading unit to read optimally in accordance with the child's reaction. The child's reaction includes, for example, facial expressions, tone of voice, and movements, but is not limited to these examples. Some or all of the above-described processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input the child's reaction data into a generation AI and have the generation AI change the reading content based on the reaction.

[0042] The reading unit can evaluate the child's level of comprehension while reading aloud and provide supplementary explanations as necessary. For example, if the child does not understand, the reading unit can provide supplementary explanations in simple terms. If the child understands, the reading unit can move on to the next content. If the child only partially understands, the reading unit can also focus on the parts that the child does not understand. This allows the reading unit to provide optimal supplementary explanations according to the child's level of comprehension. Examples of evaluations of comprehension include, but are not limited to, quiz results and answer questions. Some or all of the above-mentioned processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input the child's comprehension data into a generation AI and have the generation AI provide supplementary explanations based on the child's level of comprehension.

[0043] The support unit can select different support methods depending on the learning content. For example, the support unit can provide visual hints for math problems. The support unit can also provide audio guidance for reading comprehension problems. The support unit can also provide video explanations for science experiments. This allows the support unit to provide the optimal support method depending on the learning content. Support methods include, but are not limited to, visual hints and audio guidance. Some or all of the above-mentioned processing in the support unit may be performed using, or without, AI. For example, the support unit can input learning content data into a generation AI and have the generation AI select the optimal support method.

[0044] The support unit can analyze the child's learning history and automatically generate an optimal learning support plan. For example, the support unit can generate a plan that focuses support on weak areas based on the child's past learning history. The support unit can also generate a support plan according to the child's progress based on the learning history. The support unit can also analyze the learning history and generate a plan that suggests effective learning methods. This allows the support unit to provide an optimal support plan based on the child's learning history. The learning support plan may include, for example, learning goals and progress management, but is not limited to these examples. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input learning history data into a generation AI and cause the generation AI to generate an optimal learning support plan.

[0045] The support unit can provide the optimal support method by taking into consideration the child's learning environment when providing support. For example, the support unit can provide quiet audio guidance at night. The support unit can also prioritize visual hints outdoors. The support unit can also provide a short, effective support method at school. This allows the support unit to provide the optimal support method according to the learning environment. The learning environment includes, for example, time of day, location, ambient sounds, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input learning environment data into a generation AI and cause the generation AI to provide the optimal support method.

[0046] The support unit can customize the support content according to the child's learning style when providing support. For example, the support unit can provide support that makes extensive use of diagrams and illustrations for visually-oriented children. The support unit can also provide support that makes extensive use of audio guides for auditory-oriented children. The support unit can also provide activities that involve actual hands-on learning for tactile-oriented children. This allows the support unit to provide optimal support content according to the learning style. Learning styles include, but are not limited to, visual, auditory, and experiential styles. Some or all of the above-described processing in the support unit may be performed using, or without, AI, for example. For example, the support unit can input learning style data into a generation AI and cause the generation AI to customize the optimal support content.

[0047] The recording unit can record detailed learning progress and visually display it using graphs and charts. For example, the recording unit can display study time using a graph. The recording unit can also display progress for each subject using a chart. The recording unit can also display the accuracy rate using a pie chart. This allows the recording unit to visually grasp learning progress. Examples of visual displays include, but are not limited to, graphs, charts, icons, etc. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI. For example, the recording unit can input learning progress data into a generation AI and have the generation AI execute a visual display.

[0048] The recording unit can add the child's self-assessment to the learning progress record to improve the accuracy of the record. The recording unit, for example, provides an interface that allows the child to input the self-assessment. The recording unit can also supplement the learning progress record based on the self-assessment. The recording unit can also record a comparison between the self-assessment and actual progress. This enables the recording unit to record with high accuracy, including the self-assessment. Self-assessment includes, for example, a self-assessment sheet or a questionnaire, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input self-assessment data to the generation AI and cause the generation AI to improve the accuracy of the record based on the self-assessment.

[0049] The recording unit can adjust the recorded content during recording, taking into account the child's learning environment. For example, the recording unit prioritizes recording learning in a quiet environment at night. The recording unit can also prioritize visual learning records outdoors. The recording unit can also prioritize short, effective learning records at school. This allows the recording unit to provide optimal recorded content according to the learning environment. The recorded content includes, for example, learning progress, level of understanding, emotions, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI. For example, the recording unit can input learning environment data to a generation AI and have the generation AI adjust the optimal recorded content.

[0050] The recording unit can customize the recording method according to the child's learning style when recording. For example, the recording unit can provide a recording method that makes extensive use of diagrams and illustrations for a visual child. The recording unit can also provide a recording method that makes extensive use of voice memos for an auditory child. The recording unit can also provide a recording method that involves actual hands-on activities for a tactile child. This allows the recording unit to provide an optimal recording method according to the learning style. Recording methods include, but are not limited to, digital recording and paper recording. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input learning style data into the generation AI and cause the generation AI to customize the optimal recording method.

[0051] The reporting unit can periodically send detailed reports of the child's learning progress to the parent. For example, the reporting unit can send a report summarizing the child's learning progress every week. The reporting unit can also send a report detailing the progress for each subject. The reporting unit can also send a report showing the study time and the correct answer rate in graphs. This allows the reporting unit to provide the parent with a detailed understanding of the child's learning progress. The detailed report may include, for example, the child's learning progress, level of understanding, and emotions, but is not limited to these examples. Some or all of the above-described processing in the reporting unit may be performed using, or without, AI. For example, the reporting unit can input learning progress data into a generation AI and cause the generation AI to generate and send a detailed report.

[0052] The reporting unit can receive parent feedback and continuously improve the report content. For example, the reporting unit can adjust the report content based on parent feedback. The reporting unit can also change the format of the report according to parent requests. The reporting unit can also reflect parent feedback and use it in the next report. This allows the reporting unit to provide optimal report content that reflects parent feedback. Feedback includes, for example, questionnaires and interviews, but is not limited to such examples. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input parent feedback data into a generation AI and cause the generation AI to improve the report content based on the feedback.

[0053] The reporting unit can set the optimal timing for reporting, taking the parent's schedule into consideration. For example, the reporting unit can report by avoiding times when the parent is busy. The reporting unit can also report during times when the parent is relaxed. The reporting unit can also adjust the timing of reporting to match the parent's schedule. This allows the reporting unit to provide the optimal timing for reporting according to the parent's schedule. Examples of the timing for reporting include, but are not limited to, the parent's schedule and learning progress. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, AI. For example, the reporting unit can input the parent's schedule data into the generation AI and cause the generation AI to set the optimal timing for reporting.

[0054] The reporting unit can customize the reporting format according to the parent's preferences when reporting. For example, if the parent prefers text format, the reporting unit can report in text. Also, if the parent prefers video format, the reporting unit can report in video. The reporting unit can also allow the parent to select the reporting format according to their preferences. This allows the reporting unit to provide the optimal reporting format according to the parent's preferences. Reporting formats include, but are not limited to, text, video, and audio. Some or all of the above-described processing in the reporting unit may be performed using, or without, AI. For example, the reporting unit can input parental preference data into the generation AI and cause the generation AI to customize the optimal reporting format.

[0055] The OCR unit can display the OCR reading results in real time and immediately correct any misrecognitions. For example, the OCR unit can display the reading results on a screen and immediately correct any misrecognitions. The OCR unit can also read the reading results aloud and immediately correct any misrecognitions. The OCR unit can also notify the parent of the reading results and immediately correct any misrecognitions. This allows the OCR unit to immediately correct any misrecognitions, enabling accurate reading. Correction of misrecognitions includes, but is not limited to, manual correction and automatic correction. Some or all of the above-described processing in the OCR unit may be performed using, for example, AI, or may be performed without using AI. For example, the OCR unit can input the reading result data to a generation AI and have the generation AI correct any misrecognitions.

[0056] The OCR unit can be added with a function to display the OCR reading results in multiple languages. For example, the OCR unit can display the reading results in English and Japanese. The OCR unit can also display the reading results in Spanish and French. The OCR unit can also display the reading results in Chinese and Korean. This enables the OCR unit to display in multiple languages. Examples of multiple languages ​​include, but are not limited to, English, Japanese, and Chinese. Some or all of the above-described processing in the OCR unit may be performed using, or without, AI. For example, the OCR unit can input the reading result data to a generation AI and have the generation AI display the results in multiple languages.

[0057] The OCR unit can automatically select optimal settings depending on the format of the learning material when reading the OCR. For example, in the case of handwritten learning materials, the OCR unit prioritizes handwritten character recognition. In addition, in the case of printed learning materials, the OCR unit can also prioritize printed character recognition. In addition, in the case of mixed learning materials, the OCR unit can apply both types of recognition. This allows the OCR unit to provide optimal OCR settings depending on the format of the learning material. Learning material formats include, but are not limited to, handwritten, printed, and digital. Some or all of the above-described processing in the OCR unit may be performed using, or without, AI. For example, the OCR unit can input learning material format data into a generation AI and have the generation AI automatically select optimal OCR settings.

[0058] The analysis unit can improve the accuracy of analysis by referring to past learning data during analysis. The analysis unit, for example, analyzes the current learning content based on past learning data. The analysis unit can also reduce errors by referring to past learning data. The analysis unit can also analyze past learning data and select the optimal analysis method. In this way, the analysis unit improves the accuracy of analysis by referring to past learning data. Past learning data includes, for example, learning history and test results, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past learning data into a generation AI and cause the generation AI to improve the accuracy of analysis.

[0059] The analysis unit can visually display the analysis results to make them easier for children to understand. The analysis unit, for example, displays the analysis results in a graph. The analysis unit can also display the analysis results in a chart. The analysis unit can also display the analysis results in a diagram. This makes it easier for children to understand by visually displaying the results. Examples of visual displays include, but are not limited to, graphs, charts, icons, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the analysis result data to a generation AI and have the generation AI execute the visual display.

[0060] During analysis, the analysis unit can adjust the analysis content taking into account the child's learning environment. For example, the analysis unit may prioritize analysis in a quiet environment at night. The analysis unit may also prioritize visual analysis outdoors. The analysis unit may also prioritize short, effective analysis at school. This allows the analysis unit to provide optimal analysis content according to the learning environment. The learning environment includes, for example, time of day, location, and ambient sounds, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input learning environment data into the generation AI and cause the generation AI to adjust the optimal analysis content.

[0061] When transmitting data, the transmitting unit can select a transmission method depending on the importance of the transmission content. For example, the transmitting unit immediately transmits important data to the cloud. The transmitting unit can also periodically transmit general data to the cloud. The transmitting unit can also transmit low-importance data to the cloud using batch processing. This allows the transmitting unit to provide an optimal transmission method depending on the importance of the transmission content. Transmission methods include, but are not limited to, email, the cloud, and an app. Some or all of the above-mentioned processing in the transmitting unit may be performed using, or without, AI. For example, the transmitting unit can input transmission content data to a generating AI and have the generating AI select a transmission method based on the importance.

[0062] The transmitting unit can record the history of data transmission so that it can be referenced later. The transmitting unit, for example, records the transmission date and time and the transmission content. The transmitting unit can also record the transmission destination and the transmission method. The transmitting unit can also record the transmission result so that it can be referenced later. In this way, the transmitting unit can record the transmission history so that it can be referenced later. The transmission history includes, for example, digital records and paper records, but is not limited to such examples. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the transmission history data to a generating AI and have the generating AI record and reference the history.

[0063] When transmitting data, the transmitting unit can select the optimal transmission method taking into account the device information of the destination. For example, when transmitting to the parent's smartphone, the transmitting unit can use push notification. When transmitting to the parent's tablet, the transmitting unit can also use email. When transmitting to the parent's PC, the transmitting unit can also use cloud storage. This allows the transmitting unit to provide the optimal transmission method according to the device information of the destination. The device information of the destination includes, for example, a smartphone, a tablet, a PC, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the device information of the destination into the generating AI and have the generating AI select the optimal transmission method.

[0064] The encryption unit can apply different encryption algorithms depending on the type of data during encryption. For example, the encryption unit applies AES encryption to personal information. The encryption unit can also apply RSA encryption to training data. The encryption unit can also apply SHA-256 encryption to general data. This allows the encryption unit to provide the optimal encryption algorithm depending on the type of data. Encryption algorithms include, but are not limited to, AES, RSA, DES, etc. Some or all of the above-mentioned processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit can input the type of data to the generation AI and cause the generation AI to apply the optimal encryption algorithm.

[0065] The encryption unit can record the encryption history so that it can be referenced later. The encryption unit records, for example, the encryption date and time and the encryption algorithm. The encryption unit can also record the data to be encrypted and the encryption key. The encryption unit can also record the encryption result so that it can be referenced later. In this way, the encryption unit can record the encryption history so that it can be referenced later. The encryption history includes, for example, a digital record or a paper record, but is not limited to such examples. Some or all of the above-mentioned processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit can input encryption history data to a generation AI and have the generation AI record and reference the history.

[0066] The encryption unit can select the optimal encryption method depending on the destination of the data when encrypting it. For example, the encryption unit applies AES encryption to data to be sent to the cloud. The encryption unit can also apply RSA encryption to data to be sent to a parent's smartphone. The encryption unit can also apply SHA-256 encryption to data to be sent to a school server. This allows the encryption unit to provide the optimal encryption method depending on the destination. Encryption methods include, but are not limited to, symmetric key encryption and public key encryption. Some or all of the above-mentioned processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit can input destination data into a generation AI and have the generation AI select the optimal encryption method.

[0067] The access restriction unit can apply different restrictions depending on the user's attribute information when restricting access. For example, the access restriction unit may grant a parent the authority to access all data. The access restriction unit may also grant a teacher the authority to access only learning data. The access restriction unit may also grant a child the authority to access only self-learning data. This allows the access restriction unit to provide optimal access restrictions according to the user's attribute information. User attribute information includes, but is not limited to, parent, teacher, child, etc. Some or all of the above-described processing in the access restriction unit may be performed using, or without, AI. For example, the access restriction unit may input the user's attribute information into a generation AI and cause the generation AI to apply optimal access restrictions.

[0068] The access restriction unit can record the access restriction history for later reference. The access restriction unit, for example, records the access date and time and the accessing user. The access restriction unit can also record the accessed data and access authority. The access restriction unit can also record the access results for later reference. In this way, the access restriction unit can record the access restriction history for later reference. The access restriction history includes, for example, a digital record or a paper record, but is not limited to such examples. Some or all of the above-mentioned processing in the access restriction unit may be performed using, for example, AI, or may be performed without using AI. For example, the access restriction unit can input the access restriction history data to a generation AI and have the generation AI record and reference the history.

[0069] When restricting access, the access restriction unit can select the optimal restriction method depending on the type of device. For example, the access restriction unit may grant the parent's smartphone permission to access all data. The access restriction unit may also grant the teacher's tablet permission to access only learning data. The access restriction unit may also grant the child's computer permission to access only self-learning data. This allows the access restriction unit to provide the optimal access restriction method depending on the type of device. Device types include, but are not limited to, smartphones, tablets, and computers. Some or all of the above-described processing in the access restriction unit may be performed using, or without, AI. For example, the access restriction unit may input device information into a generation AI and cause the generation AI to select the optimal access restriction method.

[0070] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0071] The smart glasses system can further include a style analysis unit that analyzes children's learning styles. The style analysis unit analyzes children's learning history and behavioral patterns to identify their learning style, such as visual, auditory, or experiential. For example, visually-oriented children can be provided with learning materials that make extensive use of diagrams and illustrations. Auditory-oriented children can also be provided with learning materials that make extensive use of audio guides. Furthermore, experiential-oriented children can be provided with learning materials that include hands-on activities. In this way, the style analysis unit can provide the optimal learning materials according to each child's learning style.

[0072] The OCR unit can further include a summarization unit that summarizes the content of the learning material. The summarization unit analyzes the content of the learning material read by the OCR unit, extracts and summarizes the important points. For example, in the case of long learning materials, the summarization unit can display the main points in a short summary. The summarization unit can also integrate the content of multiple learning materials to create a single summary. Furthermore, the summarization unit can read the summary content aloud. In this way, the summarization unit can help children efficiently understand the content of the learning material.

[0073] The analysis unit may further include a prediction unit that predicts a child's learning progress. The prediction unit predicts a child's learning progress based on the data collected by the analysis unit. For example, it can suggest what content to study next based on past learning history. The prediction unit can also predict a child's level of understanding and suggest that they focus on studying areas where they lack understanding. Furthermore, the prediction unit can automatically adjust the learning plan according to their learning progress. In this way, the prediction unit can efficiently advance a child's learning.

[0074] The transmission unit may further include a selection unit that automatically selects a data destination. The selection unit selects the optimal destination depending on the content and importance of the data to be transmitted. For example, important data can be transmitted directly to the parent's smartphone. Ordinary data can be stored in the cloud so that the parent can access it as needed. Furthermore, the selection unit can select multiple data destinations and transmit the data simultaneously. This allows the selection unit to optimize the data destination and achieve efficient data management.

[0075] The encryption unit may further include a strength adjustment unit that adjusts the encryption strength according to the importance of the data. The strength adjustment unit analyzes the importance of the data to be transmitted and selects an appropriate encryption strength. For example, high-strength encryption may be applied to personal information or confidential data. Normal encryption may also be applied to general data. Furthermore, the strength adjustment unit may adjust the encryption strength according to the destination of the data. In this way, the strength adjustment unit can optimize data security and achieve efficient data protection.

[0076] The processing flow of the first embodiment will be briefly explained below.

[0077] Step 1: The reading unit reads the teaching materials. The teaching materials include paper teaching materials, digital teaching materials, audio teaching materials, etc. The reading unit can read the teaching materials using OCR technology, or it can read the teaching materials manually. Step 2: The reading unit reads out the content read by the reading unit. For the reading out, a synthesized voice can be used, or a human voice can be recorded and read out. Step 3: The support section provides learning support based on the content read out by the reading section. This support includes explaining the problem, providing hints, and managing progress. Step 4: The recording unit records the progress of the learning carried out by the support unit. The progress record can be recorded as digital data or on paper. Step 5: The reporting department reports the progress recorded by the recording department to the parents. Reports can be sent by email or via the app.

[0078] (Example 2) A smart glasses system according to an embodiment of the present invention is designed to support home learning for preschool children. This smart glasses system has a function for reading educational materials. When a child holds a learning material up to the smart glasses' camera, the system reads its contents. It then reads the read contents aloud, allowing the smart glasses to read them for the child even if the child is unable to read the material themselves. It also has a learning support function, providing hints for solving problems and determining whether an answer is correct or incorrect. It also has a function for recording learning progress and reporting it to parents. This allows parents to keep track of their children's learning status even when they are short on time. This smart glasses are particularly useful in households with multiple children or busy parents, as they can provide individualized learning support for each child, reducing the burden on parents. For example, the system uses OCR technology to read educational materials, AI to analyze the content of the questions, and the data is sent to the cloud, where it is sent to the parent's smartphone. The smart glasses are also designed to be comfortable to wear and easy for children to use, ensuring comfortable use even for long periods of time. Furthermore, data encryption and access restrictions are implemented for privacy and security reasons. This allows the smart glasses system to support home learning for preschool children and reduce the burden on parents.

[0079] A smart glasses system according to an embodiment includes a reading unit, a reading unit, a support unit, a recording unit, and a reporting unit. The reading unit reads learning materials. Examples of learning materials include, but are not limited to, paper learning materials, digital learning materials, and audio learning materials. The reading unit reads the learning materials using, for example, OCR technology. The reading unit can also read the learning materials manually. The reading unit reads aloud the content read by the reading unit. For example, synthesized voice can be used for the voice reading. The reading unit can also record and read a recorded human voice. The support unit provides learning support based on the content read by the reading unit. Learning support includes, for example, explanations of problems, provision of hints, and progress management. The recording unit records the learning progress performed by the support unit. The progress record can be recorded, for example, as digital data. The recording unit can also record on paper. The reporting unit reports the progress recorded by the recording unit to a parent. The report can be sent, for example, by email. The reporting unit can also report through an app. This allows the smart glasses system according to the embodiment to support home learning for preschool children and reduce the burden on parents.

[0080] The device includes an OCR unit that reads the teaching materials using OCR technology. The OCR unit uses OCR technology to read the teaching materials. Examples of OCR technology include, but are not limited to, Tesseract and Google Cloud Vision. The OCR unit reads the teaching materials using, for example, Tesseract. The OCR unit can also read the teaching materials using Google Cloud Vision. This allows the OCR unit to improve the accuracy of reading the teaching materials. Some or all of the above-mentioned processing in the OCR unit may be performed using, for example, AI, or may be performed without using AI. For example, the OCR unit can input image data of the teaching materials to a generation AI and cause the generation AI to generate text data from the image data.

[0081] The system includes an analysis unit that analyzes the content of the problem using AI. The analysis unit uses AI technology to analyze the content of the problem. AI technology includes, but is not limited to, machine learning and deep learning. The analysis unit analyzes the content of the problem using, for example, machine learning. The analysis unit can also analyze the content of the problem using deep learning. This allows the analysis unit to accurately analyze the content of the problem. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input data of the problem to a generation AI and cause the generation AI to analyze the content of the problem.

[0082] The system includes a transmitting unit that transmits data to the cloud. The transmitting unit transmits the data to the cloud. Cloud services include, but are not limited to, AWS, Google Cloud, Microsoft Azure, and the like. The transmitting unit transmits the data using, for example, AWS. The transmitting unit can also transmit the data using Google Cloud. Furthermore, the transmitting unit can also transmit the data using Microsoft Azure. This allows the transmitting unit to easily manage the data. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit may input data to a generating AI and cause the generating AI to transmit the data to the cloud.

[0083] The encryption unit includes an encryption unit that encrypts data. The encryption unit encrypts data. Encryption techniques include, but are not limited to, AES and RSA, for example. The encryption unit encrypts data using AES, for example. The encryption unit can also encrypt data using RSA, for example. This allows the encryption unit to improve data security. Some or all of the above-mentioned processing in the encryption unit may be performed using AI, or may be performed without using AI, for example. For example, the encryption unit may input data to a generation AI and have the generation AI perform encryption.

[0084] The system includes an access restriction unit that performs access restriction. The access restriction unit performs access restriction on data. Examples of access restriction include, but are not limited to, password authentication and two-factor authentication. The access restriction unit performs access restriction using, for example, password authentication. The access restriction unit can also perform access restriction using two-factor authentication. This allows the access restriction unit to enable access control of data. Some or all of the above-mentioned processing in the access restriction unit may be performed using, for example, AI, or may be performed without using AI. For example, the access restriction unit can input access restriction settings to a generation AI and have the generation AI execute the access restriction.

[0085] The reading unit can estimate the child's emotions and adjust the reading speed of the learning material based on the estimated child's emotions. For example, if the child is excited, the reading unit can increase the reading speed to progress at a good pace. Furthermore, if the child is relaxed, the reading unit can slow down the reading speed to allow the child to carefully understand the content. Furthermore, if the child is tired, the reading unit can further slow down the reading speed and proceed with breaks. This allows the reading unit to provide an optimal reading speed according to the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, with an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reading unit can be performed using, for example, an AI, or without an AI. For example, the reading unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the reading speed based on the emotion.

[0086] The reading unit can automatically select the optimal reading mode depending on the type of teaching material. For example, in the case of a picture book, the reading unit reads the entire page using image recognition. In addition, in the case of a textbook, the reading unit can also prioritize reading character information using text recognition. In addition, in the case of a worksheet, the reading unit can focus on reading the answer column using handwriting recognition. This enables the reading unit to perform optimal reading depending on the type of teaching material. Reading modes include, but are not limited to, text mode and image mode. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the type of teaching material to the generation AI and cause the generation AI to select the optimal reading mode.

[0087] The reading unit can detect the condition of the teaching material and make corrections to improve reading accuracy. For example, the reading unit can detect torn pages and fill in the missing parts. The reading unit can also detect dirty parts and remove the dirt to improve reading accuracy. The reading unit can also detect creased pages and make corrections to flatten the creases. This enables the reading unit to perform optimal reading according to the condition of the teaching material. Examples of the condition of the teaching material include, but are not limited to, tears, dirt, and creases. Some or all of the above-mentioned processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input data on the condition of the teaching material to a generation AI and have the generation AI perform corrections to the reading accuracy.

[0088] The reading unit can estimate the child's emotions and prioritize the learning materials to be read based on the estimated child's emotions. For example, if the child is excited, the reading unit can prioritize learning materials that interest the child. Furthermore, if the child is relaxed, the reading unit can prioritize learning materials that are effective for learning. Furthermore, if the child is tired, the reading unit can prioritize easy learning materials. This enables the reading unit to read optimal learning materials according to the child's emotions. Prioritization of learning materials includes, but is not limited to, learning progress and difficulty. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or without AI. For example, the reading unit can input the child's emotion data into the generation AI and cause the generation AI to determine the priority of learning materials based on emotions.

[0089] When reading teaching materials, the reading unit can set appropriate reading conditions taking into account the ambient light. For example, in a bright environment, the reading unit can adjust the exposure of the camera to improve reading accuracy. In a dark environment, the reading unit can also increase the sensitivity of the camera to improve reading accuracy. Furthermore, when there is reflection from lighting, the reading unit can read at an angle that avoids the reflection. This enables the reading unit to perform optimal reading according to the ambient light. Ambient light includes, for example, light intensity and light color, but is not limited to such examples. Some or all of the above-mentioned processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input ambient light data into the generation AI and have the generation AI set optimal reading conditions.

[0090] The reading unit can track the child's gaze and automatically adjust the reading range when reading the teaching material. For example, the reading unit prioritizes reading the page the child is looking at. Furthermore, if the child moves their gaze, the reading unit can adjust the reading range in that direction. Furthermore, if the child is gazing at a particular part, the reading unit can enlarge and read that part. This enables the reading unit to optimally read according to the child's gaze. Examples of gaze tracking include, but are not limited to, cameras and sensors. Some or all of the above-mentioned processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input the child's gaze data into the generation AI and cause the generation AI to adjust the reading range based on the gaze.

[0091] The reading unit can estimate the child's emotions and adjust the reading tone and speed based on the estimated child's emotions. For example, if the child is excited, the reading unit can read faster in a bright and energetic tone. If the child is relaxed, the reading unit can read more slowly in a calm tone. If the child is tired, the reading unit can read even more slowly in a gentle tone. This enables the reading unit to optimally read according to the child's emotions. Examples of the reading tone and speed include, but are not limited to, changing the tone and adjusting the speed according to the emotion. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or without AI. For example, the reading unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the reading tone and speed based on the emotion.

[0092] The reading unit can select a character with a different voice depending on the content to be read. For example, when reading a story, the reading unit can use a different voice for each character. Furthermore, the reading unit can read a textbook in a calm voice. Furthermore, the reading unit can read a worksheet in a lively voice. This allows the reading unit to provide a character with the optimal voice depending on the content. Voice characters include, but are not limited to, male voices, female voices, and children's voices. Some or all of the above-described processing in the reading unit may be performed using, for example, AI, or may be performed without using AI. For example, the reading unit can input content data to be read into a generation AI and have the generation AI select a character with the optimal voice.

[0093] The reading unit can add background sounds and sound effects to the content being read to enhance the sense of realism. For example, when reading a story, the reading unit can add background sounds appropriate to the scene. The reading unit can also add sound effects to important parts when reading a textbook. The reading unit can also add cheering sound effects when the answer is correct when reading a worksheet. This enables the reading unit to read with a sense of realism. Examples of background sounds and sound effects include, but are not limited to, natural sounds, music, and sound effects. Some or all of the above-described processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input content data to be read into a generation AI and have the generation AI add background sounds and sound effects.

[0094] The reading unit can estimate the child's emotions and adjust the order of the content to be read based on the estimated child's emotions. For example, if the child is excited, the reading unit can prioritize content that attracts the child's interest. Furthermore, if the child is relaxed, the reading unit can prioritize content that is effective for learning. Furthermore, if the child is tired, the reading unit can prioritize content that is easy to read. This enables the reading unit to read optimal content according to the child's emotions. The order of content can include, but is not limited to, learning progress and the child's interests. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reading unit can be performed using, for example, AI, or without AI. For example, the reading unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the order of content based on the emotion.

[0095] The reading unit monitors the child's reaction in real time while reading aloud and can change the reading content as appropriate. For example, if the child shows no interest, the reading unit can switch to a different content. The reading unit can also add supplementary explanations if the child does not understand. The reading unit can also change the reading tempo if the child becomes bored. This enables the reading unit to read optimally in accordance with the child's reaction. The child's reaction includes, for example, facial expressions, tone of voice, and movements, but is not limited to these examples. Some or all of the above-described processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input the child's reaction data into a generation AI and have the generation AI change the reading content based on the reaction.

[0096] The reading unit can evaluate the child's level of comprehension while reading aloud and provide supplementary explanations as necessary. For example, if the child does not understand, the reading unit can provide supplementary explanations in simple terms. If the child understands, the reading unit can move on to the next content. If the child only partially understands, the reading unit can also focus on the parts that the child does not understand. This allows the reading unit to provide optimal supplementary explanations according to the child's level of comprehension. Examples of evaluations of comprehension include, but are not limited to, quiz results and answer questions. Some or all of the above-mentioned processing in the reading unit may be performed using, or without, AI. For example, the reading unit can input the child's comprehension data into a generation AI and have the generation AI provide supplementary explanations based on the child's level of comprehension.

[0097] The support unit can estimate the child's emotions and adjust the difficulty of the hints provided based on the estimated child's emotions. For example, if the child is excited, the support unit can provide a more difficult hint. If the child is relaxed, the support unit can provide a more moderately difficult hint. If the child is tired, the support unit can provide an easier hint. This enables the support unit to provide optimal hints according to the child's emotions. The difficulty of the hints can include, but is not limited to, the difficulty of the question and the child's level of understanding. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the support unit can be performed using, for example, AI, or without AI. For example, the support unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the difficulty of the hints based on the emotion.

[0098] The support unit can select different support methods depending on the learning content. For example, the support unit can provide visual hints for math problems. The support unit can also provide audio guidance for reading comprehension problems. The support unit can also provide video explanations for science experiments. This allows the support unit to provide the optimal support method depending on the learning content. Support methods include, but are not limited to, visual hints and audio guidance. Some or all of the above-mentioned processing in the support unit may be performed using, or without, AI. For example, the support unit can input learning content data into a generation AI and have the generation AI select the optimal support method.

[0099] The support unit can analyze the child's learning history and automatically generate an optimal learning support plan. For example, the support unit can generate a plan that focuses support on weak areas based on the child's past learning history. The support unit can also generate a support plan according to the child's progress based on the learning history. The support unit can also analyze the learning history and generate a plan that suggests effective learning methods. This allows the support unit to provide an optimal support plan based on the child's learning history. The learning support plan may include, for example, learning goals and progress management, but is not limited to these examples. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input learning history data into a generation AI and cause the generation AI to generate an optimal learning support plan.

[0100] The support unit can estimate the child's emotions and prioritize support content based on the estimated child's emotions. For example, if the child is excited, the support unit can prioritize support content that attracts the child's interest. Furthermore, if the child is relaxed, the support unit can prioritize support content that is highly effective for learning. Furthermore, if the child is tired, the support unit can prioritize support content that is easy to understand. This enables the support unit to provide optimal support content according to the child's emotions. Support content priorities include, but are not limited to, learning progress and the child's interests. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the support unit may be performed using, for example, AI, or without AI. For example, the support unit can input the child's emotion data into the generation AI and cause the generation AI to determine the priority of support content based on emotions.

[0101] The support unit can provide the optimal support method by taking into consideration the child's learning environment when providing support. For example, the support unit can provide quiet audio guidance at night. The support unit can also prioritize visual hints outdoors. The support unit can also provide a short, effective support method at school. This allows the support unit to provide the optimal support method according to the learning environment. The learning environment includes, for example, time of day, location, ambient sounds, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the support unit may be performed using, for example, AI, or may be performed without using AI. For example, the support unit can input learning environment data into a generation AI and cause the generation AI to provide the optimal support method.

[0102] The support unit can customize the support content according to the child's learning style when providing support. For example, the support unit can provide support that makes extensive use of diagrams and illustrations for visually-oriented children. The support unit can also provide support that makes extensive use of audio guides for auditory-oriented children. The support unit can also provide activities that involve actual hands-on learning for tactile-oriented children. This allows the support unit to provide optimal support content according to the learning style. Learning styles include, but are not limited to, visual, auditory, and experiential styles. Some or all of the above-described processing in the support unit may be performed using, or without, AI, for example. For example, the support unit can input learning style data into a generation AI and cause the generation AI to customize the optimal support content.

[0103] The recording unit can estimate the child's emotions and adjust the recording method of the learning progress based on the estimated emotions. For example, if the child is excited, the recording unit can record in detail. If the child is relaxed, the recording unit can record with appropriate detail. If the child is tired, the recording unit can record in a simplified manner. This allows the recording unit to provide an optimal recording method according to the child's emotions. Recording methods include, but are not limited to, digital recording and paper recording. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the recording method based on the emotion.

[0104] The recording unit can record detailed learning progress and visually display it using graphs and charts. For example, the recording unit can display study time using a graph. The recording unit can also display progress for each subject using a chart. The recording unit can also display the accuracy rate using a pie chart. This allows the recording unit to visually grasp learning progress. Examples of visual displays include, but are not limited to, graphs, charts, icons, etc. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI. For example, the recording unit can input learning progress data into a generation AI and have the generation AI execute a visual display.

[0105] The recording unit can add the child's self-assessment to the learning progress record to improve the accuracy of the record. The recording unit, for example, provides an interface that allows the child to input the self-assessment. The recording unit can also supplement the learning progress record based on the self-assessment. The recording unit can also record a comparison between the self-assessment and actual progress. This enables the recording unit to record with high accuracy, including the self-assessment. Self-assessment includes, for example, a self-assessment sheet or a questionnaire, but is not limited to such examples. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input self-assessment data to the generation AI and cause the generation AI to improve the accuracy of the record based on the self-assessment.

[0106] The recording unit can estimate the child's emotions and determine the priority of items to be recorded based on the estimated child's emotions. For example, if the child is excited, the recording unit can prioritize items that interest the child. Furthermore, if the child is relaxed, the recording unit can prioritize items that are effective for learning. Furthermore, if the child is tired, the recording unit can prioritize items that are easy to learn. This allows the recording unit to provide optimal prioritization of record items according to the child's emotions. Examples of record item prioritization include, but are not limited to, learning progress and the child's interests. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the recording unit may be performed using, for example, AI, or without AI. For example, the recording unit can input the child's emotion data into the generation AI and cause the generation AI to determine the priority of record items based on emotions.

[0107] The recording unit can adjust the recorded content during recording, taking into account the child's learning environment. For example, the recording unit prioritizes recording learning in a quiet environment at night. The recording unit can also prioritize visual learning records outdoors. The recording unit can also prioritize short, effective learning records at school. This allows the recording unit to provide optimal recorded content according to the learning environment. The recorded content includes, for example, learning progress, level of understanding, emotions, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the recording unit may be performed using, or without, AI. For example, the recording unit can input learning environment data to a generation AI and have the generation AI adjust the optimal recorded content.

[0108] The recording unit can customize the recording method according to the child's learning style when recording. For example, the recording unit can provide a recording method that makes extensive use of diagrams and illustrations for a visual child. The recording unit can also provide a recording method that makes extensive use of voice memos for an auditory child. The recording unit can also provide a recording method that involves actual hands-on activities for a tactile child. This allows the recording unit to provide an optimal recording method according to the learning style. Recording methods include, but are not limited to, digital recording and paper recording. Some or all of the above-mentioned processing in the recording unit may be performed using, for example, AI, or may be performed without using AI. For example, the recording unit can input learning style data into the generation AI and cause the generation AI to customize the optimal recording method.

[0109] The reporting unit can estimate the child's emotions and adjust the way the report content is expressed based on the estimated child's emotions. For example, if the child is excited, the reporting unit can report using bright and cheerful expressions. If the child is relaxed, the reporting unit can report using calm expressions. If the child is tired, the reporting unit can report using concise and easy-to-understand expressions. This allows the reporting unit to provide optimal report content expression according to the child's emotions. Examples of how the report content is expressed include, but are not limited to, text, graphs, charts, etc. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or without AI. For example, the reporting unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the way the report content is expressed based on the emotion.

[0110] The reporting unit can periodically send detailed reports of the child's learning progress to the parent. For example, the reporting unit can send a report summarizing the child's learning progress every week. The reporting unit can also send a report detailing the progress for each subject. The reporting unit can also send a report showing the study time and the correct answer rate in graphs. This allows the reporting unit to provide the parent with a detailed understanding of the child's learning progress. The detailed report may include, for example, the child's learning progress, level of understanding, and emotions, but is not limited to these examples. Some or all of the above-described processing in the reporting unit may be performed using, or without, AI. For example, the reporting unit can input learning progress data into a generation AI and cause the generation AI to generate and send a detailed report.

[0111] The reporting unit can receive parent feedback and continuously improve the report content. For example, the reporting unit can adjust the report content based on parent feedback. The reporting unit can also change the format of the report according to parent requests. The reporting unit can also reflect parent feedback and use it in the next report. This allows the reporting unit to provide optimal report content that reflects parent feedback. Feedback includes, for example, questionnaires and interviews, but is not limited to such examples. Some or all of the above-mentioned processing in the reporting unit may be performed using, for example, AI, or may be performed without using AI. For example, the reporting unit can input parent feedback data into a generation AI and cause the generation AI to improve the report content based on the feedback.

[0112] The reporting unit can estimate the child's emotions and prioritize report content based on the estimated child's emotions. For example, if the child is excited, the reporting unit can prioritize reporting content that is interesting. Furthermore, if the child is relaxed, the reporting unit can prioritize reporting content that is highly effective for learning. Furthermore, if the child is tired, the reporting unit can prioritize reporting content that is easy to understand. This allows the reporting unit to provide optimal prioritization of report content according to the child's emotions. Prioritization of report content includes, but is not limited to, learning progress and the child's interests. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reporting unit may be performed using, for example, AI, or without AI. For example, the reporting unit can input the child's emotion data into the generation AI and cause the generation AI to determine the priority of report content based on emotions.

[0113] The reporting unit can set the optimal timing for reporting, taking the parent's schedule into consideration. For example, the reporting unit can report by avoiding times when the parent is busy. The reporting unit can also report during times when the parent is relaxed. The reporting unit can also adjust the timing of reporting to match the parent's schedule. This allows the reporting unit to provide the optimal timing for reporting according to the parent's schedule. Examples of the timing for reporting include, but are not limited to, the parent's schedule and learning progress. Some or all of the above-mentioned processing in the reporting unit may be performed using, or without, AI. For example, the reporting unit can input the parent's schedule data into the generation AI and cause the generation AI to set the optimal timing for reporting.

[0114] The reporting unit can customize the reporting format according to the parent's preferences when reporting. For example, if the parent prefers text format, the reporting unit can report in text. Also, if the parent prefers video format, the reporting unit can report in video. The reporting unit can also allow the parent to select the reporting format according to their preferences. This allows the reporting unit to provide the optimal reporting format according to the parent's preferences. Reporting formats include, but are not limited to, text, video, and audio. Some or all of the above-described processing in the reporting unit may be performed using, or without, AI. For example, the reporting unit can input parental preference data into the generation AI and cause the generation AI to customize the optimal reporting format.

[0115] The OCR unit can estimate a child's emotions and adjust the OCR reading accuracy based on the estimated child's emotions. For example, if the child is excited, the OCR unit can increase the reading accuracy and process the data quickly. If the child is relaxed, the OCR unit can also read with normal accuracy. If the child is tired, the OCR unit can further increase the reading accuracy to prevent misrecognition. This allows the OCR unit to provide optimal OCR reading accuracy according to the child's emotions. Examples of OCR reading accuracy include, but are not limited to, character recognition rates and methods for correcting misrecognition. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the OCR unit may be performed using, for example, AI, or without AI. For example, the OCR unit can input child's emotion data into the generation AI and cause the generation AI to adjust the OCR reading accuracy based on the emotion.

[0116] The OCR unit can display the OCR reading results in real time and immediately correct any misrecognitions. For example, the OCR unit can display the reading results on a screen and immediately correct any misrecognitions. The OCR unit can also read the reading results aloud and immediately correct any misrecognitions. The OCR unit can also notify the parent of the reading results and immediately correct any misrecognitions. This allows the OCR unit to immediately correct any misrecognitions, enabling accurate reading. Correction of misrecognitions includes, but is not limited to, manual correction and automatic correction. Some or all of the above-described processing in the OCR unit may be performed using, for example, AI, or may be performed without using AI. For example, the OCR unit can input the reading result data to a generation AI and have the generation AI correct any misrecognitions.

[0117] The OCR unit can be added with a function to display the OCR reading results in multiple languages. For example, the OCR unit can display the reading results in English and Japanese. The OCR unit can also display the reading results in Spanish and French. The OCR unit can also display the reading results in Chinese and Korean. This enables the OCR unit to display in multiple languages. Examples of multiple languages ​​include, but are not limited to, English, Japanese, and Chinese. Some or all of the above-described processing in the OCR unit may be performed using, or without, AI. For example, the OCR unit can input the reading result data to a generation AI and have the generation AI display the results in multiple languages.

[0118] The OCR unit can estimate a child's emotions and adjust the OCR reading order based on the estimated child's emotions. For example, if a child is excited, the OCR unit can prioritize reading interesting parts. Also, if a child is relaxed, the OCR unit can prioritize reading parts that are highly effective for learning. Also, if a child is tired, the OCR unit can prioritize reading easy parts. This allows the OCR unit to provide an optimal OCR reading order based on the child's emotions. The OCR reading order can be based on, but is not limited to, importance and content relevance. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the OCR unit can be performed using, for example, AI, or without AI. For example, the OCR unit can input child's emotion data into the generation AI and have the generation AI adjust the OCR reading order based on the emotion.

[0119] The OCR unit can automatically select optimal settings depending on the format of the learning material when reading the OCR. For example, in the case of handwritten learning materials, the OCR unit prioritizes handwritten character recognition. In addition, in the case of printed learning materials, the OCR unit can also prioritize printed character recognition. In addition, in the case of mixed learning materials, the OCR unit can apply both types of recognition. This allows the OCR unit to provide optimal OCR settings depending on the format of the learning material. Learning material formats include, but are not limited to, handwritten, printed, and digital. Some or all of the above-described processing in the OCR unit may be performed using, or without, AI. For example, the OCR unit can input learning material format data into a generation AI and have the generation AI automatically select optimal OCR settings.

[0120] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated child's emotions. For example, if the child is excited, the analysis unit can provide a visually stimulating display method. If the child is relaxed, the analysis unit can provide a calming display method. If the child is tired, the analysis unit can provide a concise and easy-to-understand display method. This allows the analysis unit to display the analysis results optimally according to the child's emotions. Examples of display methods for the analysis results include, but are not limited to, graphs, charts, text, etc. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the display method of the analysis results based on the emotion.

[0121] The analysis unit can improve the accuracy of analysis by referring to past learning data during analysis. The analysis unit, for example, analyzes the current learning content based on past learning data. The analysis unit can also reduce errors by referring to past learning data. The analysis unit can also analyze past learning data and select the optimal analysis method. In this way, the analysis unit improves the accuracy of analysis by referring to past learning data. Past learning data includes, for example, learning history and test results, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input past learning data into a generation AI and cause the generation AI to improve the accuracy of analysis.

[0122] The analysis unit can visually display the analysis results to make them easier for children to understand. The analysis unit, for example, displays the analysis results in a graph. The analysis unit can also display the analysis results in a chart. The analysis unit can also display the analysis results in a diagram. This makes it easier for children to understand by visually displaying the results. Examples of visual displays include, but are not limited to, graphs, charts, icons, etc. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit can input the analysis result data to a generation AI and have the generation AI execute the visual display.

[0123] The analysis unit can estimate the child's emotions and prioritize the analysis content based on the estimated child's emotions. For example, if the child is excited, the analysis unit can prioritize analyzing content that is interesting. Furthermore, if the child is relaxed, the analysis unit can prioritize analyzing content that is highly effective for learning. Furthermore, if the child is tired, the analysis unit can prioritize analyzing easy content. This allows the analysis unit to provide optimal priorities for analysis content according to the child's emotions. Priorities for analysis content include, but are not limited to, learning progress and the child's interests. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or without AI. For example, the analysis unit can input the child's emotion data into the generation AI and cause the generation AI to determine the priority of analysis content based on emotions.

[0124] During analysis, the analysis unit can adjust the analysis content taking into account the child's learning environment. For example, the analysis unit may prioritize analysis in a quiet environment at night. The analysis unit may also prioritize visual analysis outdoors. The analysis unit may also prioritize short, effective analysis at school. This allows the analysis unit to provide optimal analysis content according to the learning environment. The learning environment includes, for example, time of day, location, and ambient sounds, but is not limited to these examples. Some or all of the above-mentioned processing in the analysis unit may be performed using, or without, AI. For example, the analysis unit may input learning environment data into the generation AI and cause the generation AI to adjust the optimal analysis content.

[0125] The transmitting unit can estimate the child's emotions and adjust the timing of data transmission based on the estimated child's emotions. For example, if the child is excited, the transmitting unit can transmit data immediately. Furthermore, if the child is relaxed, the transmitting unit can transmit data at an appropriate timing. Furthermore, if the child is tired, the transmitting unit can transmit data after a break. This allows the transmitting unit to provide optimal data transmission timing according to the child's emotions. Examples of data transmission timing include, but are not limited to, learning progress and the child's emotions. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or without AI. For example, the transmitting unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the timing of data transmission based on the emotion.

[0126] When transmitting data, the transmitting unit can select a transmission method depending on the importance of the transmission content. For example, the transmitting unit immediately transmits important data to the cloud. The transmitting unit can also periodically transmit general data to the cloud. The transmitting unit can also transmit low-importance data to the cloud using batch processing. This allows the transmitting unit to provide an optimal transmission method depending on the importance of the transmission content. Transmission methods include, but are not limited to, email, the cloud, and an app. Some or all of the above-mentioned processing in the transmitting unit may be performed using, or without, AI. For example, the transmitting unit can input transmission content data to a generating AI and have the generating AI select a transmission method based on the importance.

[0127] The transmitting unit can record the history of data transmission so that it can be referenced later. The transmitting unit, for example, records the transmission date and time and the transmission content. The transmitting unit can also record the transmission destination and the transmission method. The transmitting unit can also record the transmission result so that it can be referenced later. In this way, the transmitting unit can record the transmission history so that it can be referenced later. The transmission history includes, for example, digital records and paper records, but is not limited to such examples. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the transmission history data to a generating AI and have the generating AI record and reference the history.

[0128] The transmitting unit can estimate the child's emotions and prioritize the content to be transmitted based on the estimated emotions. For example, if the child is excited, the transmitting unit can prioritize important content. Furthermore, if the child is relaxed, the transmitting unit can prioritize content that is highly effective for learning. Furthermore, if the child is tired, the transmitting unit can prioritize simple content. This allows the transmitting unit to provide optimal prioritization of content to be transmitted according to the child's emotions. Prioritization of content to be transmitted includes, but is not limited to, learning progress and the child's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the transmitting unit may be performed using, for example, AI, or without AI. For example, the transmitting unit can input the child's emotion data into the generation AI and cause the generation AI to determine the priority of content to be transmitted based on emotions.

[0129] When transmitting data, the transmitting unit can select the optimal transmission method taking into account the device information of the destination. For example, when transmitting to the parent's smartphone, the transmitting unit can use push notification. When transmitting to the parent's tablet, the transmitting unit can also use email. When transmitting to the parent's PC, the transmitting unit can also use cloud storage. This allows the transmitting unit to provide the optimal transmission method according to the device information of the destination. The device information of the destination includes, for example, a smartphone, a tablet, a PC, etc., but is not limited to these examples. Some or all of the above-mentioned processing in the transmitting unit may be performed using, for example, AI, or may be performed without using AI. For example, the transmitting unit can input the device information of the destination into the generating AI and have the generating AI select the optimal transmission method.

[0130] The encryption unit can estimate the child's emotions and adjust the encryption strength based on the estimated child's emotions. For example, the encryption unit applies high-strength encryption when the child is excited. The encryption unit can also apply normal encryption when the child is relaxed. The encryption unit can also apply high-strength encryption when the child is tired. This allows the encryption unit to provide optimal encryption strength according to the child's emotions. Examples of encryption strength include, but are not limited to, the length of the encryption key and the type of algorithm. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the encryption unit can be performed using, for example, AI, or without AI. For example, the encryption unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the encryption strength based on the emotion.

[0131] The encryption unit can apply different encryption algorithms depending on the type of data during encryption. For example, the encryption unit applies AES encryption to personal information. The encryption unit can also apply RSA encryption to training data. The encryption unit can also apply SHA-256 encryption to general data. This allows the encryption unit to provide the optimal encryption algorithm depending on the type of data. Encryption algorithms include, but are not limited to, AES, RSA, DES, etc. Some or all of the above-mentioned processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit can input the type of data to the generation AI and cause the generation AI to apply the optimal encryption algorithm.

[0132] The encryption unit can record the encryption history so that it can be referenced later. The encryption unit records, for example, the encryption date and time and the encryption algorithm. The encryption unit can also record the data to be encrypted and the encryption key. The encryption unit can also record the encryption result so that it can be referenced later. In this way, the encryption unit can record the encryption history so that it can be referenced later. The encryption history includes, for example, a digital record or a paper record, but is not limited to such examples. Some or all of the above-mentioned processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit can input encryption history data to a generation AI and have the generation AI record and reference the history.

[0133] The encryption unit can estimate the child's emotions and determine encryption priorities based on the estimated child's emotions. For example, if the child is excited, the encryption unit can prioritize encrypting important data. Furthermore, if the child is relaxed, the encryption unit can prioritize encrypting learning data. Furthermore, if the child is tired, the encryption unit can prioritize encrypting personal information. This allows the encryption unit to provide optimal encryption priorities according to the child's emotions. Encryption priorities include, but are not limited to, the importance of data and the security level of the destination. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the encryption unit may be performed using, for example, AI, or without AI. For example, the encryption unit can input the child's emotion data into the generation AI and cause the generation AI to determine encryption priorities based on emotions.

[0134] The encryption unit can select the optimal encryption method depending on the destination of the data when encrypting it. For example, the encryption unit applies AES encryption to data to be sent to the cloud. The encryption unit can also apply RSA encryption to data to be sent to a parent's smartphone. The encryption unit can also apply SHA-256 encryption to data to be sent to a school server. This allows the encryption unit to provide the optimal encryption method depending on the destination. Encryption methods include, but are not limited to, symmetric key encryption and public key encryption. Some or all of the above-mentioned processing in the encryption unit may be performed using, or without, AI. For example, the encryption unit can input destination data into a generation AI and have the generation AI select the optimal encryption method.

[0135] The access restriction unit can estimate the child's emotions and adjust the level of access restriction based on the estimated child's emotions. For example, the access restriction unit applies strict access restriction when the child is excited. The access restriction unit can also apply normal access restriction when the child is relaxed. The access restriction unit can also apply strict access restriction when the child is tired. This allows the access restriction unit to provide an optimal access restriction level according to the child's emotions. The access restriction level includes, but is not limited to, read-only, editable, and administrator privileges. The emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the access restriction unit may be performed using, for example, AI, or without AI. For example, the access restriction unit can input the child's emotion data into the generation AI and cause the generation AI to adjust the access restriction level based on the emotion.

[0136] The access restriction unit can apply different restrictions depending on the user's attribute information when restricting access. For example, the access restriction unit may grant a parent the authority to access all data. The access restriction unit may also grant a teacher the authority to access only learning data. The access restriction unit may also grant a child the authority to access only self-learning data. This allows the access restriction unit to provide optimal access restrictions according to the user's attribute information. User attribute information includes, but is not limited to, parent, teacher, child, etc. Some or all of the above-described processing in the access restriction unit may be performed using, or without, AI. For example, the access restriction unit may input the user's attribute information into a generation AI and cause the generation AI to apply optimal access restrictions.

[0137] The access restriction unit can record the access restriction history for later reference. The access restriction unit, for example, records the access date and time and the accessing user. The access restriction unit can also record the accessed data and access authority. The access restriction unit can also record the access results for later reference. In this way, the access restriction unit can record the access restriction history for later reference. The access restriction history includes, for example, a digital record or a paper record, but is not limited to such examples. Some or all of the above-mentioned processing in the access restriction unit may be performed using, for example, AI, or may be performed without using AI. For example, the access restriction unit can input the access restriction history data to a generation AI and have the generation AI record and reference the history.

[0138] The access restriction unit can estimate the child's emotions and determine the priority of access restrictions based on the estimated child's emotions. For example, if the child is excited, the access restriction unit can prioritize restricting access to important data. Furthermore, if the child is relaxed, the access restriction unit can prioritize restricting access to learning data. Furthermore, if the child is tired, the access restriction unit can prioritize restricting access to personal information. This allows the access restriction unit to provide optimal access restriction priorities according to the child's emotions. Examples of access restriction priorities include, but are not limited to, the importance of data and the user's authority level. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the access restriction unit may be performed using, for example, AI, or without AI. For example, the access restriction unit can input the child's emotion data into the generation AI and cause the generation AI to determine the priority of access restrictions based on emotions.

[0139] When restricting access, the access restriction unit can select the optimal restriction method depending on the type of device. For example, the access restriction unit may grant the parent's smartphone permission to access all data. The access restriction unit may also grant the teacher's tablet permission to access only learning data. The access restriction unit may also grant the child's computer permission to access only self-learning data. This allows the access restriction unit to provide the optimal access restriction method depending on the type of device. Device types include, but are not limited to, smartphones, tablets, and computers. Some or all of the above-described processing in the access restriction unit may be performed using, or without, AI. For example, the access restriction unit may input device information into a generation AI and cause the generation AI to select the optimal access restriction method. === Hard Collateral 1-1 === Each of the multiple elements, including the reading unit, reading-out unit, support unit, recording unit, reporting unit, OCR unit, analysis unit, transmission unit, encryption unit, and access restriction unit, is realized, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reading unit reads the learning material using the camera 42 of the smart device 14. The reading unit reads the read content aloud using the speaker 40B of the smart device 14. The support unit provides learning support via the specific processing unit 290 of the data processing device 12. The recording unit records learning progress in the database 24 of the data processing device 12. The reporting unit reports progress from the data processing device 12 to the parent's smartphone. The OCR unit reads the learning material using OCR technology via the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the content of the questions using AI via the specific processing unit 290 of the data processing device 12. The transmission unit transmits data from the data processing device 12 to the cloud. The encryption unit encrypts the data via the specific processing unit 290 of the data processing device 12. The access restriction unit restricts access by using the specific processing unit 290 of the data processing device 12 . === Hard Collateral 1-2 === Each of the multiple elements, including the reading unit, reading-out unit, support unit, recording unit, reporting unit, OCR unit, analysis unit, transmission unit, encryption unit, and access restriction unit, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reading unit reads the learning material using the camera 42 of the smart glasses 214. The reading unit reads the read content aloud using the speaker 240 of the smart glasses 214. The support unit provides learning support via the specific processing unit 290 of the data processing device 12. The recording unit records the learning progress in the database 24 of the data processing device 12. The reporting unit reports the progress from the data processing device 12 to the parent's smartphone. The OCR unit reads the learning material using OCR technology via the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the content of the questions using AI via the specific processing unit 290 of the data processing device 12. The transmission unit transmits data from the data processing device 12 to the cloud. The encryption unit encrypts data using the specific processing unit 290 of the data processing device 12. The access restriction unit restricts access using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-3 === Each of the multiple elements including the reading unit, reading-out unit, support unit, recording unit, reporting unit, OCR unit, analysis unit, transmission unit, encryption unit, and access restriction unit described above is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reading unit reads the learning material using the camera 42 of the headset-type terminal 314. The reading unit reads the read content aloud using the speaker 240 of the headset-type terminal 314. The support unit provides learning support via the specific processing unit 290 of the data processing device 12. The recording unit records the learning progress in the database 24 of the data processing device 12. The reporting unit reports the progress from the data processing device 12 to the parent's smartphone. The OCR unit reads the learning material using OCR technology via the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the content of the questions using AI via the specific processing unit 290 of the data processing device 12. The transmission unit transmits data from the data processing device 12 to the cloud. The encryption unit encrypts data using the specific processing unit 290 of the data processing device 12. The access restriction unit restricts access using the specific processing unit 290 of the data processing device 12. === Hard Collateral 1-4 === Each of the multiple elements, including the reading unit, reading-out unit, support unit, recording unit, reporting unit, OCR unit, analysis unit, transmission unit, encryption unit, and access restriction unit, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reading unit reads the learning material using the camera 42 of the robot 414. The reading unit reads the read content aloud using the speaker 240 of the robot 414. The support unit provides learning support via the specific processing unit 290 of the data processing device 12. The recording unit records learning progress in the database 24 of the data processing device 12. The reporting unit reports progress from the data processing device 12 to the parent's smartphone. The OCR unit reads the learning material using OCR technology via the specific processing unit 290 of the data processing device 12. The analysis unit analyzes the content of the questions using AI via the specific processing unit 290 of the data processing device 12. The transmission unit transmits data from the data processing device 12 to the cloud. The encryption unit encrypts the data via the specific processing unit 290 of the data processing device 12. The access restriction unit restricts access by using the specific processing unit 290 of the data processing device 12 .

[0140] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0141] The smart glasses system can further include a style analysis unit that analyzes children's learning styles. The style analysis unit analyzes children's learning history and behavioral patterns to identify their learning style, such as visual, auditory, or experiential. For example, visually-oriented children can be provided with learning materials that make extensive use of diagrams and illustrations. Auditory-oriented children can also be provided with learning materials that make extensive use of audio guides. Furthermore, experiential-oriented children can be provided with learning materials that include hands-on activities. In this way, the style analysis unit can provide the optimal learning materials according to each child's learning style.

[0142] The OCR unit can further include a summarization unit that summarizes the content of the learning material. The summarization unit analyzes the content of the learning material read by the OCR unit, extracts and summarizes the important points. For example, in the case of long learning materials, the summarization unit can display the main points in a short summary. The summarization unit can also integrate the content of multiple learning materials to create a single summary. Furthermore, the summarization unit can read the summary content aloud. In this way, the summarization unit can help children efficiently understand the content of the learning material.

[0143] The analysis unit may further include a prediction unit that predicts a child's learning progress. The prediction unit predicts a child's learning progress based on the data collected by the analysis unit. For example, it can suggest what content to study next based on past learning history. The prediction unit can also predict a child's level of understanding and suggest that they focus on studying areas where they lack understanding. Furthermore, the prediction unit can automatically adjust the learning plan according to their learning progress. In this way, the prediction unit can efficiently advance a child's learning.

[0144] The transmission unit may further include a selection unit that automatically selects a data destination. The selection unit selects the optimal destination depending on the content and importance of the data to be transmitted. For example, important data can be transmitted directly to the parent's smartphone. Ordinary data can be stored in the cloud so that the parent can access it as needed. Furthermore, the selection unit can select multiple data destinations and transmit the data simultaneously. This allows the selection unit to optimize the data destination and achieve efficient data management.

[0145] The encryption unit may further include a strength adjustment unit that adjusts the encryption strength according to the importance of the data. The strength adjustment unit analyzes the importance of the data to be transmitted and selects an appropriate encryption strength. For example, high-strength encryption may be applied to personal information or confidential data. Normal encryption may also be applied to general data. Furthermore, the strength adjustment unit may adjust the encryption strength according to the destination of the data. In this way, the strength adjustment unit can optimize data security and achieve efficient data protection.

[0146] The reading unit can estimate the child's emotions and adjust the reading order of the learning materials based on the estimated child's emotions. For example, if the child is excited, it can prioritize reading interesting learning materials. Also, if the child is relaxed, it can prioritize reading learning materials that are highly effective for learning. Furthermore, if the child is tired, it can prioritize reading easy learning materials. In this way, the reading unit can provide the optimal reading order of the learning materials according to the child's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI.

[0147] The reading unit can estimate the child's emotions and adjust the order of the content to be read based on the estimated child's emotions. For example, if the child is excited, it can prioritize reading content that will interest the child. Also, if the child is relaxed, it can prioritize reading content that will be highly educational. Furthermore, if the child is tired, it can prioritize reading simple content. This allows the reading unit to provide the optimal reading order for the content according to the child's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI.

[0148] The support unit can estimate the child's emotions and adjust the difficulty of the hints it provides based on the estimated child's emotions. For example, if the child is excited, it can provide a more difficult hint. If the child is relaxed, it can provide a more moderately difficult hint. Furthermore, if the child is tired, it can provide an easier hint. This allows the support unit to provide optimal hints according to the child's emotions. Emotion estimation is achieved using, for example, an emotion engine or a generative AI.

[0149] The recording unit can estimate the child's emotions and adjust the recording method of the learning progress based on the estimated child's emotions. For example, if the child is excited, detailed recording can be performed. If the child is relaxed, recording can be performed with moderate detail. Furthermore, if the child is tired, simplified recording can be performed. In this way, the recording unit can provide the optimal recording method according to the child's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI.

[0150] The reporting unit can estimate the child's emotions and adjust the way the report is expressed based on the estimated child's emotions. For example, if the child is excited, the report can be expressed in a bright and cheerful manner. If the child is relaxed, the report can be expressed in a calm manner. Furthermore, if the child is tired, the report can be expressed in a concise and easy-to-understand manner. This allows the reporting unit to provide the optimal report expression according to the child's emotions. Emotion estimation is achieved using, for example, an emotion engine or generative AI.

[0151] The processing flow of the second embodiment will be briefly explained below.

[0152] Step 1: The reading unit reads the teaching materials. The teaching materials include paper teaching materials, digital teaching materials, audio teaching materials, etc. The reading unit can read the teaching materials using OCR technology, or it can read the teaching materials manually. Step 2: The reading unit reads out the content read by the reading unit. For the reading out, a synthesized voice can be used, or a human voice can be recorded and read out. Step 3: The support section provides learning support based on the content read out by the reading section. This support includes explaining the problem, providing hints, and managing progress. Step 4: The recording unit records the progress of the learning carried out by the support unit. The progress record can be recorded as digital data or on paper. Step 5: The reporting department reports the progress recorded by the recording department to the parents. Reports can be sent by email or via the app.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). 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 (e.g., still image data or video data). 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 one or more data formats of voice data, text data, image data, etc. 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 may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0155] 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.

[0156] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0157] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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).

[0163] 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.

[0164] 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.

[0165] 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.

[0166] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0167] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0168] 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.

[0169] 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.

[0170] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0171] 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.

[0172] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0173] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0174] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0175] 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.

[0176] 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.

[0177] 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.

[0178] 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).

[0179] 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.

[0180] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset 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.

[0181] 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.

[0182] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0183] 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 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0184] 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.

[0185] 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.

[0186] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0187] 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.

[0188] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0189] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0190] 7, a 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] 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).

[0195] 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.

[0196] 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.

[0197] 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.

[0198] 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.

[0199] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0200] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. 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 the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0201] 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.

[0202] 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.

[0203] 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 including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). 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 one or more data formats, such as audio data, text data, and image 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 models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0204] 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.

[0205] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0206] 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.

[0207] 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.

[0208] 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.

[0209] 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).

[0210] 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.

[0211] 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."

[0212] 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.

[0213] 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.

[0214] 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.

[0215] 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.

[0216] 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.

[0217] 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.

[0218] 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.

[0219] 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.

[0220] 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.

[0221] 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.

[0222] 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.

[0223] 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.

[0224] [Explanation of symbols]

[0225] 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 section that reads the teaching materials; a reading unit that reads out the content read by the reading unit; a support unit that provides learning support based on the content read out by the reading unit; a recording unit that records the progress of the learning performed by the support unit; a reporting unit that reports the progress recorded by the recording unit to a parent; A system comprising:

2. Equipped with an OCR section that uses OCR technology to read teaching materials 2. The system of claim 1.

3. Equipped with an analysis unit that uses AI to analyze the content of the problem 2. The system of claim 1.

4. Equipped with a transmitter that transmits data to the cloud 2. The system of claim 1.

5. Equipped with an encryption unit that encrypts data 2. The system of claim 1.

6. An access restriction unit is provided to restrict access 2. The system of claim 1.

7. The reading unit Estimate the child's emotions and adjust the reading speed of the material based on the estimated emotions of the child 2. The system of claim 1.

8. The reading unit Automatically selects the appropriate reading mode depending on the type of teaching material 2. The system of claim 1.

9. The reading unit Detects the condition of the teaching material and makes corrections to improve reading accuracy 2. The system of claim 1.

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