system

Abstract

The system according to this embodiment aims to provide individualized feedback and support tailored to each child's level of understanding and progress. [Solution] The system according to the embodiment comprises a reception unit, an analysis unit, a provision unit, a generation unit, and a connection unit. The reception unit receives input of photos, text, and audio. The analysis unit analyzes the information received by the reception unit to grasp the child's level of understanding and progress. The provision unit provides answers and feedback based on the level of understanding and progress grasped by the analysis unit. The generation unit generates visual text and graphics. The connection unit connects to an online tutor as needed.

Description

Technical Field

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

Background Art

[0002] Patent Document 1 discloses a persona chatbot control method performed by at least one processor, including steps of receiving a user utterance, adding the user utterance to a prompt including an instruction sentence related to an explanation of a 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

Summary of the Invention

Problems to be Solved by the Invention

[0004] In the prior art, there was a problem that it was difficult to provide individual feedback and support according to the understanding level and progress of children.

[0005] The system according to the embodiment aims to provide individual feedback and support according to the understanding level and progress of children.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a generation unit, and a connection unit. The reception unit receives input of photos, text, and audio. The analysis unit analyzes the information received by the reception unit to understand the child's level of understanding and progress. The provision unit provides answers and feedback based on the level of understanding and progress understood by the analysis unit. The generation unit generates visual text and graphics. The connection unit connects to an online tutor as needed. [Effects of the Invention]

[0007] The system according to this embodiment can provide individualized feedback and support tailored to each child's level of understanding and progress. [Brief explanation of the drawing]

[0008] [Figure 1] This is a conceptual diagram showing an example of the configuration of a data processing system according to the first embodiment. [Figure 2] This is a conceptual diagram showing an example of the essential functions of a data processing device and a smart device according to the first embodiment. [Figure 3] This is a conceptual diagram showing an example of the configuration of a data processing system according to the second embodiment. [Figure 4] This is a conceptual diagram showing an example of the main functions of a data processing device and smart glasses according to the second embodiment. [Figure 5] This is a conceptual diagram showing an example of the configuration of a data processing system according to the third embodiment. [Figure 6] This is a conceptual diagram showing an example of the main functions of a data processing device and a headset-type terminal according to the third embodiment. [Figure 7] This is a conceptual diagram showing an example of the configuration of a data processing system according to the fourth embodiment. [Figure 8] This is a conceptual diagram showing an example of the main functions of a data processing device and a robot according to the fourth embodiment. [Figure 9] This shows an emotion map where multiple emotions are mapped. [Figure 10]This shows an emotion map where multiple emotions are mapped. [Modes for carrying out the invention]

[0009] Hereinafter, an example of an embodiment of the system relating to the technology of this disclosure will be described with reference to the attached drawings.

[0010] First, let's explain the terminology used in the following explanation.

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

[0012] In the following embodiments, signed RAM (Random Access Memory) is a memory that temporarily stores information and is used as work memory by the processor.

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

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

[0015] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B". That is, "A and / or B" means that it may be only A, only B, or a combination of A and B. Also, in this specification, when expressing three or more matters connected by "and / or", the same concept as "A and / or B" is applied.

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

[0017] As shown in FIG. 1, the 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, the RAM 30, and the storage 32 are connected to a bus 34. Also, the database 24 and the communication I / F 26 are 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 comprises a computer 36, a receiving device 38, an output device 40, a camera 42, and a communication interface 44. The computer 36 comprises a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The receiving device 38, output device 40, and camera 42 are also connected to the bus 52.

[0020] The reception device 38 is equipped with a touch panel 38A and a microphone 38B, and accepts user input. The touch panel 38A accepts user input via touch by detecting contact with an object (e.g., a pen or finger). The microphone 38B accepts user input via voice by detecting the user's voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and microphone 38B to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 (see Figure 2) acquires the data indicating the user input.

[0021] The output device 40 includes a display 40A and a speaker 40B, and presents data to the user by outputting the data in a form perceptible to the user (e.g., audio and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs audio according to instructions from the processor 46. The camera 42 is a small digital camera equipped with an optical system such as a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor.

[0022] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various types of information between processor 46 and processor 28 via network 54.

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

[0024] As shown in Figure 2, in the data processing device 12, a specific processing 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" related to the technology of this 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 processing is realized by the processor 28 operating as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

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

[0026] In the smart device 14, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. The specific processing program 60 is used in conjunction 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 a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart device 14 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0027] Furthermore, other devices besides the data processing device 12 may also 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 processing results (such as prediction results) using the data generation model 58 by communicating with the server device having the data generation model 58. The data processing device 12 may also be a server device or a terminal device owned by a user (e.g., a mobile phone, robot, home appliance, etc.). Next, an example of processing by the data processing system 10 according to the first embodiment will be described.

[0028] (Example of form 1) The AI-assisted service according to an embodiment of the present invention is a system designed to deepen children's joy of learning and cultivate their problem-solving skills. This system allows children to ask the AI ​​questions and homework assignments using photos, text, and audio. The AI ​​analyzes this multimodal information to understand the child's level of comprehension and progress. It provides not only answers but also specific feedback on individual learning gaps. Furthermore, it generates visual text and graphics to make the learning content easier to understand. If necessary, it can connect children to online tutors in real time for deeper support. This enables children to develop the ability to identify and solve problems themselves. First, children ask the AI ​​questions and homework assignments using photos, text, and audio. For example, they can take a picture of a math problem and send it to the AI. They can also input questions in text or record and send questions via audio. Next, the AI ​​analyzes this multimodal information. The AI ​​recognizes characters from photos and understands the content of questions from text and audio. For example, it analyzes a math problem in a photo to understand its content. It also understands the intent of the question from text and audio and derives an appropriate answer. The AI ​​understands children's comprehension and progress. For example, it assesses a child's learning situation based on their past question history and the accuracy of their answers. This allows it to identify areas where children are struggling and provide specific feedback on individual learning gaps. Furthermore, the AI ​​generates visual text and graphics. For instance, it can generate step-by-step graphics for solving math problems, making them easy for children to understand. In a history lesson, it can generate a timeline visually illustrating important events. If needed, the AI ​​can connect children to online tutors in real time. For example, if a child wants a more detailed explanation of a particular problem, they can connect to an online tutor and receive direct support. This allows children to progress in their learning with in-depth support. Through this AI-assisted service, children can develop the ability to identify and solve problems on their own.For example, if a child gets stuck while solving a math problem, the AI ​​can provide appropriate feedback and help them find the solution themselves. Similarly, visual graphics can be helpful in understanding important events in history class. This allows children to deepen their enjoyment of learning and develop their problem-solving skills. In short, AI-assisted services can enhance children's enjoyment of learning and cultivate their problem-solving abilities.

[0029] The AI ​​support service according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a generation unit, and a connection unit. The reception unit accepts input of photos, text, and audio. The reception unit can accept, for example, photos in JPEG format, text in PDF format, and audio in MP3 format. The reception unit can also scan photos and convert them into digital data. For example, the reception unit can scan handwritten notes and convert them into digital data. The reception unit can also record audio and convert it into text. For example, the reception unit can convert audio to text using speech recognition technology. The analysis unit analyzes the information received by the reception unit to grasp the child's level of understanding and progress. The analysis unit can, for example, recognize characters from photos using OCR technology. The analysis unit can also understand the content of questions from text and audio using natural language processing technology. For example, the analysis unit extracts keywords from text and understands the intent of the question. The analysis unit can also estimate emotions from audio. For example, the analysis unit analyzes the tone and speed of the voice to estimate emotions. The providing unit provides answers and feedback based on the level of understanding and progress grasped by the analysis unit. The providing unit evaluates the child's learning situation based, for example, on past question history and the accuracy of answers. The providing unit can also provide answers in text format. For example, the providing unit displays the answer as text. The providing unit can also provide audio feedback. For example, the providing unit explains the answer aloud. The generating unit generates visual text and graphics. For example, the generating unit generates a graphic that shows the solution to a math problem step by step. The generating unit can also generate infographics. For example, the generating unit generates a timeline that visually shows historical events. The connecting unit connects to an online tutor as needed. For example, the connecting unit connects to an online tutor via video call. The connecting unit can also provide chat support. For example, the connecting unit connects to an online tutor via text chat. In this way, the AI-assisted service according to the embodiment can deepen children's joy of learning and cultivate their problem-solving skills.

[0030] The reception unit accepts input in the form of photos, text, and audio. For example, it can accept JPEG photos, PDF text files, and MP3 audio files. Specifically, the reception unit has the function to automatically scan user-uploaded photos and convert them into digital data. For example, it can scan handwritten notes and charts and save them as digital data. The reception unit can also record audio and convert it to text using speech recognition technology. For example, if a user inputs a question by voice, the system records the audio and converts it to text using speech recognition technology, making it easier for the subsequent analysis unit to process. Furthermore, the reception unit can accept multiple data formats simultaneously, so it can process photos, text, and audio uploaded by users at once without any problems. This allows users to input information in a variety of ways, improving convenience. The reception unit also has a function to check the quality of the input data and automatically filter out data unsuitable for analysis, such as blurry photos and noisy audio. This allows the analysis unit to handle high-quality data, improving overall accuracy.

[0031] The analysis unit analyzes the information received by the reception unit to understand the child's level of comprehension and progress. For example, the analysis unit uses OCR technology to recognize text from photographs. Specifically, it uses OCR technology to convert handwritten notes and printed text into digital text, making it an analyzable format. The analysis unit can also understand the content of questions from text and audio using natural language processing technology. For example, it extracts keywords from text and performs contextual and semantic analysis to understand the intent of the question. Furthermore, the analysis unit can estimate emotions from audio. For example, it analyzes the tone and speed of the voice to estimate whether the user is excited, calm, or confused. This allows the analysis unit to provide feedback that takes the user's emotional state into account. The analysis unit combines these technologies to comprehensively evaluate the user's level of comprehension and progress. For example, it analyzes past answer history and question trends to identify which areas the user excels in and which areas need reinforcement. The analysis unit can also process data in real time and provide immediate feedback. This allows the user to constantly understand their learning progress and take appropriate measures.

[0032] The service provider provides answers and feedback based on the level of understanding and progress grasped by the analysis unit. Specifically, it evaluates the child's learning situation based on past question history and the accuracy of answers, and provides appropriate feedback. For example, the service provider may re-present questions that the user previously answered incorrectly to check their understanding. The service provider can also provide answers in text format. For example, it may display answers in text and add detailed explanations to make them easier for the user to understand. Furthermore, the service provider can also provide audio feedback. For example, it may explain answers in audio, allowing the user to utilize not only visual information but also auditory information. This allows the service provider to provide diverse feedback tailored to the user's learning style, maximizing learning effectiveness. In addition, the service provider can collect user feedback and continuously improve the accuracy of answers and feedback. For example, it may record how the user reacted to the answers and adjust the content of the feedback based on that data. This allows the service provider to provide more effective learning support to the user.

[0033] The generation unit generates visual text and graphics. Specifically, it can generate graphics that show the solution to a math problem step by step. For example, it can visually demonstrate the solution to a complex equation, making it easier for users to understand each step. The generation unit can also generate infographics. For example, it can generate a timeline that visually shows historical events, making it easier for users to grasp information chronologically. Furthermore, the generation unit can generate graphics customized according to the user's learning progress. For example, it can generate graphics that highlight areas where the user is struggling in a particular subject, allowing them to focus their learning on those areas. By automatically generating and providing this visual content to the user, the generation unit can deepen their understanding of what they are learning. In addition, the generation unit can adjust the content of the graphics based on user feedback, providing more effective learning support. For example, if a user finds a particular graphic difficult to understand, the design and content of the graphic can be improved based on that feedback. In this way, the generation unit can always provide the user with the most optimal visual content, maximizing the learning effect.

[0034] The connection unit connects to online tutors as needed. Specifically, it has the functionality to connect to online tutors via video call. For example, if a user requests a detailed explanation on a particular issue, the connection unit connects them to a professional tutor via video call, who can answer their questions in real time. The connection unit can also provide chat support. For example, users can connect to online tutors via text chat and answer questions immediately. Furthermore, the connection unit provides tutors with the user's learning history and progress, enabling tutors to understand the user's situation and provide appropriate guidance. This allows the connection unit to provide an environment where users can receive expert support when needed, thereby enhancing learning effectiveness. In addition, the connection unit can collect user feedback and continuously improve the quality of the connection and support. For example, it can evaluate whether users were satisfied with the quality of the video call and whether the tutor's response was appropriate, and use this data to improve the service. This allows the connection unit to consistently provide users with high-quality support and maximize learning efficiency.

[0035] The reception desk can accept input in the form of photos, text, and audio. For example, it can accept photos in JPEG format, text in PDF format, and audio in MP3 format. The reception desk can also scan photos and convert them into digital data. For example, it can scan handwritten notes and convert them into digital data. The reception desk can also record audio and convert it into text. For example, it can use speech recognition technology to convert audio to text. This allows children to ask the AI ​​questions and homework in a variety of ways. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input audio data into a generating AI and have the generating AI perform the conversion from audio data to text data.

[0036] The analysis unit can recognize characters from photographs and understand the content of questions from text and audio. For example, the analysis unit can recognize characters from photographs using OCR technology. For example, the analysis unit can recognize handwritten characters with high accuracy. The analysis unit can also understand the content of questions from text and audio using natural language processing technology. For example, the analysis unit can extract keywords from text and understand the intent of the question. The analysis unit can also estimate emotions from audio. For example, the analysis unit analyzes the tone and speed of the voice to estimate emotions. This allows for an accurate understanding of children's questions and homework. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input photographic data into a generating AI and have the generating AI perform character recognition from the photographic data.

[0037] The service provider can evaluate a child's learning progress based on past question history and the accuracy of their answers, and provide specific feedback on individual learning gaps. For example, the service provider can evaluate a child's learning progress based on past question history and the accuracy of their answers. For example, the service provider can analyze the question history stored in the database to understand the child's learning progress. The service provider can also provide answers in text format. For example, the service provider can display the answers as text. The service provider can also provide audio feedback. For example, the service provider can explain the answers aloud. This allows the service provider to identify children's learning gaps and provide appropriate feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past question history into a generating AI and have the generating AI perform the evaluation of the learning progress.

[0038] The generation unit can generate graphics that show the solution to a math problem step by step. For example, the generation unit can generate a flowchart to visually show the steps to the solution. The generation unit can also generate infographics. For example, the generation unit can generate a timeline that visually shows historical events. This makes it possible to provide learning content in a way that is easy for children to understand visually. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input math problem data into a generation AI and have the generation AI perform step-by-step graphic generation.

[0039] The connection unit can connect a child to an online tutor if the child needs further explanation on a particular issue. The connection unit can connect to the online tutor, for example, via video call. For example, the connection unit can interact with the tutor in real time via video call. The connection unit can also provide chat support. For example, the connection unit can connect to the online tutor via text chat. This allows children to progress in their learning with in-depth support. Some or all of the above processes in the connection unit may be performed using AI, for example, or not. For example, the connection unit can input the child's question data into a generating AI and have the generating AI execute the connection to the online tutor.

[0040] The reception desk can analyze a child's past input history and suggest the optimal input method. For example, the reception desk can prioritize suggesting input methods (photo, text, voice) that the child has frequently used in the past. The reception desk can also predict and suggest input methods to be used at specific times of the day based on the child's past input history. The reception desk can also suggest relevant input methods based on the content of questions the child has previously answered. This allows the child to input information in the most optimal way. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0041] The reception unit can filter input based on the child's current learning situation and areas of interest. For example, the reception unit can prioritize questions related to the subject the child is currently studying. The reception unit can also filter relevant questions based on the child's areas of interest. The reception unit can also accept questions of appropriate difficulty according to the child's learning progress. This ensures that questions that match the child's learning situation and interests are prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the child's learning situation data into a generating AI and have the generating AI perform the filtering.

[0042] The reception desk can prioritize receiving highly relevant information by considering the child's geographical location when receiving input. For example, if the child is at school, the reception desk can prioritize questions related to school lessons. If the child is at home, the reception desk can also prioritize questions related to home learning. If the child is at the library, the reception desk can also prioritize questions related to library materials. This allows for prioritizing questions based on the child's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the child's geographical location data into a generating AI and have the generating AI prioritize receiving highly relevant information.

[0043] The reception desk can analyze a child's social media activity and receive relevant information upon receiving input. For example, the reception desk can prioritize questions related to learning content shared by the child on social media. The reception desk can also receive relevant questions based on information from educational accounts that the child follows on social media. The reception desk can also analyze the activities of learning groups that the child participates in on social media and receive relevant questions. This allows the reception desk to receive questions based on the child's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the child's social media data into a generating AI and have the generating AI perform the task of receiving relevant information.

[0044] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also extract specific patterns from past analysis data and adjust the analysis algorithm. The analysis unit can also analyze past analysis data to improve the accuracy of the analysis algorithm. As a result, the accuracy of the analysis is improved by optimizing the analysis algorithm based on past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0045] The analysis unit can improve the accuracy of its analysis by considering the child's learning history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the child's past learning history. The analysis unit can also improve the accuracy of its analysis by extracting specific learning patterns from the child's learning history. The analysis unit can also analyze the child's learning history and optimize the analysis algorithm. This improves the accuracy of the analysis by considering the child's learning history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's learning history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0046] The analysis unit can perform analysis while taking into account the child's geographical location information. For example, if the child is at school, the analysis unit can perform analysis related to school lessons. If the child is at home, the analysis unit can also perform analysis related to home learning. If the child is at the library, the analysis unit can also perform analysis related to library materials. This allows for analysis tailored to the child's geographical location. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's geographical location data into a generating AI and have the generating AI perform the analysis.

[0047] The analysis unit can improve the accuracy of its analysis by referring to relevant academic literature during the analysis process. For example, the analysis unit improves the accuracy of its analysis based on relevant academic literature. The analysis unit can also improve the accuracy of its analysis by extracting specific knowledge from academic literature. The analysis unit can also optimize its analysis algorithm by referring to academic literature. This improves the accuracy of the analysis by referring to relevant academic literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input academic literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0048] The feedback provider can provide optimal feedback by referring to the child's learning history when providing feedback. For example, the provider can provide optimal feedback based on the child's past learning history. The provider can also extract specific learning patterns from the child's learning history and provide optimal feedback. The provider can also analyze the child's learning history to improve the accuracy of the feedback. This allows the provider to provide optimal feedback based on the child's learning history. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input the child's learning history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0049] The service provider can customize the content of the feedback based on the child's current learning situation when providing feedback. For example, the service provider can customize the content of the feedback based on the child's current learning situation. The service provider can also adjust the content of the feedback according to the child's learning progress. The service provider can also analyze the child's learning situation and provide optimal feedback. This allows the service provider to provide feedback that is appropriate to the child's current learning situation. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's learning situation data into a generating AI and have the generating AI perform the feedback customization.

[0050] The service provider can provide optimal feedback by taking into account the child's geographical location when providing feedback. For example, if the child is at school, the service provider can provide feedback related to school lessons. If the child is at home, the service provider can also provide feedback related to home learning. If the child is at the library, the service provider can also provide feedback related to library materials. This allows for the provision of feedback tailored to the child's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's geographical location data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0051] The service provider can analyze a child's social media activity and adjust the content of the feedback when providing it. For example, the service provider can provide feedback related to learning content shared by the child on social media. The service provider can also provide relevant feedback based on information from educational accounts that the child follows on social media. The service provider can also analyze the activities of learning groups that the child participates in on social media and provide relevant feedback. This allows the service provider to provide feedback based on the child's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the child's social media data into a generating AI and have the generating AI adjust the content of the feedback.

[0052] The generation unit can generate optimal graphics by referring to the child's learning history during graphic generation. For example, the generation unit can generate easy-to-understand graphics based on the child's past learning history. The generation unit can also extract specific learning patterns from the child's learning history and generate optimal graphics. The generation unit can also analyze the child's learning history and generate visually effective graphics. This allows the generation of optimal graphics based on the child's learning history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's learning history data into a generation AI and have the generation AI perform the generation of optimal graphics.

[0053] The generation unit can customize the content of a graphic based on the child's current learning status when generating the graphic. For example, the generation unit customizes the content of the graphic based on the child's current learning status. The generation unit can also adjust the content of the graphic according to the child's learning progress. The generation unit can also analyze the child's learning status and generate the optimal graphic. This makes it possible to generate a graphic that corresponds to the child's current learning status. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's learning status data into a generation AI and have the generation AI perform the graphic customization.

[0054] The generation unit can generate optimal graphics by considering the child's geographical location information during graphic generation. For example, if the child is at school, the generation unit can generate graphics related to school lessons. If the child is at home, the generation unit can also generate graphics related to home learning. If the child is at the library, the generation unit can also generate graphics related to library materials. This allows for the generation of graphics tailored to the child's geographical location. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's geographical location data into a generation AI and have the generation AI perform the generation of optimal graphics.

[0055] The generation unit can adjust the content of graphics by referring to relevant academic literature during graphic generation. For example, the generation unit adjusts the content of graphics based on relevant academic literature. The generation unit can also extract specific knowledge from academic literature and adjust the content of graphics. The generation unit can also generate visually effective graphics by referring to academic literature. This allows for the optimization of graphic content by referring to relevant academic literature. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input academic literature data into a generation AI and have the generation AI perform the graphic content adjustment.

[0056] The connection unit can select the most suitable tutor by referring to the child's learning history when connecting to an online tutor. For example, the connection unit can select the most suitable tutor based on the child's past learning history. The connection unit can also extract specific learning patterns from the child's learning history and select the most suitable tutor. The connection unit can also analyze the child's learning history and select the most suitable tutor. This allows for the selection of the most suitable tutor based on the child's learning history. Some or all of the above processing in the connection unit may be performed using AI, for example, or without AI. For example, the connection unit can input the child's learning history data into a generating AI and have the generating AI perform the selection of the most suitable tutor.

[0057] The connection unit can customize the connection content based on the child's current learning situation when connecting to an online tutor. For example, the connection unit customizes the connection content based on the child's current learning situation. The connection unit can also adjust the connection content according to the child's learning progress. The connection unit can also analyze the child's learning situation and provide the optimal connection content. This allows the connection content to be provided according to the child's current learning situation. Some or all of the above processing in the connection unit may be performed using AI, for example, or without AI. For example, the connection unit can input the child's learning situation data into a generating AI and have the generating AI perform the customization of the connection content.

[0058] The connection unit can select the most suitable tutor when connecting to an online tutor, taking into account the child's geographical location. For example, if the child is at school, the connection unit can select a tutor related to school lessons. If the child is at home, the connection unit can also select a tutor related to home learning. If the child is at the library, the connection unit can also select a tutor related to library materials. This allows for the selection of a tutor that is appropriate to the child's geographical location. Some or all of the above processing in the connection unit may be performed using AI, for example, or without AI. For example, the connection unit can input the child's geographical location data into a generating AI and have the generating AI perform the selection of the most suitable tutor.

[0059] The connection unit can analyze a child's social media activity and adjust the connection content when connecting with an online tutor. For example, the connection unit can select a tutor related to the learning content the child has shared on social media. The connection unit can also select a relevant tutor based on information from educational accounts the child follows on social media. The connection unit can also analyze the activities of learning groups the child participates in on social media and select a relevant tutor. This allows the connection unit to provide connection content based on the child's social media activity. Some or all of the above processing in the connection unit may be performed using AI, for example, or not using AI. For example, the connection unit can input the child's social media data into a generating AI and have the generating AI adjust the connection content.

[0060] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0061] The reception desk can suggest the optimal input method based on the child's learning history. For example, it prioritizes suggesting input methods that the child has frequently used in the past (photo, text, audio). It can also suggest appropriate input methods according to the child's learning progress and level of understanding. Furthermore, it can predict and suggest input methods that the child will use at specific times of day based on their learning history. This allows children to ask questions and complete homework assignments to the AI ​​in the most optimal way for them.

[0062] The analysis unit can improve the accuracy of its analysis based on the child's learning history. For example, it can evaluate the child's learning situation and optimize the analysis algorithm based on past question history and the accuracy of answers. It can also extract specific learning patterns from the child's learning history to improve the accuracy of the analysis. Furthermore, it can select the optimal analysis algorithm by referring to past analysis data. In this way, the accuracy of the analysis is improved by taking the child's learning history into consideration.

[0063] The generation unit can generate optimal graphics based on a child's learning history. For example, it can generate easy-to-understand graphics based on past learning history. It can also extract specific learning patterns from a child's learning history and generate optimal graphics. Furthermore, it can analyze a child's learning history and generate visually effective graphics. In this way, it can generate optimal graphics based on a child's learning history.

[0064] The reception desk can prioritize receiving highly relevant information by taking into account the child's geographical location. For example, if the child is at school, questions related to school lessons will be prioritized. If the child is at home, questions related to homework will be prioritized. Furthermore, if the child is at the library, questions related to library materials will be prioritized. This allows for prioritizing questions based on the child's geographical location.

[0065] The feedback provider can analyze a child's social media activity and adjust the content of the feedback accordingly. For example, it can provide feedback related to learning content shared by the child on social media. It can also provide relevant feedback based on information from educational accounts the child follows on social media. Furthermore, it can analyze the activities of learning groups the child participates in on social media and provide relevant feedback. This allows for the provision of feedback based on the child's social media activity.

[0066] The connection unit can select the most suitable tutor by referring to the child's learning history when connecting to an online tutor. For example, it can select the most suitable tutor based on past learning history. It can also extract specific learning patterns from the child's learning history and select the most suitable tutor. Furthermore, it can analyze the child's learning history and select the most suitable tutor. In this way, the most suitable tutor can be selected based on the child's learning history.

[0067] The following briefly describes the processing flow for example form 1.

[0068] Step 1: The reception desk accepts input in the form of photos, text, and audio. For example, it can accept photos in JPEG format, text in PDF format, and audio in MP3 format. It can also scan handwritten notes and convert them into digital data, and use speech recognition technology to convert audio to text. Step 2: The analysis unit analyzes the information received by the reception unit to understand the child's level of comprehension and progress. For example, it uses OCR technology to recognize characters from photographs and natural language processing technology to understand the content of questions from text and audio. It can also analyze the tone and speed of speech to estimate emotions. Step 3: The provisioning unit provides answers and feedback based on the level of understanding and progress identified by the analysis unit. For example, it evaluates the learning situation based on past question history and the accuracy of answers, and provides answers and feedback in text or audio format. Step 4: The generation unit generates visual text and graphics. For example, it can generate graphics that show the solution to a math problem step by step, or timelines that visually represent historical events. Step 5: The connection unit connects to an online tutor as needed. For example, it connects to an online tutor via video call or text chat to provide support.

[0069] (Example of form 2) The AI-assisted service according to an embodiment of the present invention is a system designed to deepen children's joy of learning and cultivate their problem-solving skills. This system allows children to ask the AI ​​questions and homework assignments using photos, text, and audio. The AI ​​analyzes this multimodal information to understand the child's level of comprehension and progress. It provides not only answers but also specific feedback on individual learning gaps. Furthermore, it generates visual text and graphics to make the learning content easier to understand. If necessary, it can connect children to online tutors in real time for deeper support. This enables children to develop the ability to identify and solve problems themselves. First, children ask the AI ​​questions and homework assignments using photos, text, and audio. For example, they can take a picture of a math problem and send it to the AI. They can also input questions in text or record and send questions via audio. Next, the AI ​​analyzes this multimodal information. The AI ​​recognizes characters from photos and understands the content of questions from text and audio. For example, it analyzes a math problem in a photo to understand its content. It also understands the intent of the question from text and audio and derives an appropriate answer. The AI ​​understands children's comprehension and progress. For example, it assesses a child's learning situation based on their past question history and the accuracy of their answers. This allows it to identify areas where children are struggling and provide specific feedback on individual learning gaps. Furthermore, the AI ​​generates visual text and graphics. For instance, it can generate step-by-step graphics for solving math problems, making them easy for children to understand. In a history lesson, it can generate a timeline visually illustrating important events. If needed, the AI ​​can connect children to online tutors in real time. For example, if a child wants a more detailed explanation of a particular problem, they can connect to an online tutor and receive direct support. This allows children to progress in their learning with in-depth support. Through this AI-assisted service, children can develop the ability to identify and solve problems on their own.For example, if a child gets stuck while solving a math problem, the AI ​​can provide appropriate feedback and help them find the solution themselves. Similarly, visual graphics can be helpful in understanding important events in history class. This allows children to deepen their enjoyment of learning and develop their problem-solving skills. In short, AI-assisted services can enhance children's enjoyment of learning and cultivate their problem-solving abilities.

[0070] The AI ​​support service according to this embodiment comprises a reception unit, an analysis unit, a provision unit, a generation unit, and a connection unit. The reception unit accepts input of photos, text, and audio. The reception unit can accept, for example, photos in JPEG format, text in PDF format, and audio in MP3 format. The reception unit can also scan photos and convert them into digital data. For example, the reception unit can scan handwritten notes and convert them into digital data. The reception unit can also record audio and convert it into text. For example, the reception unit can convert audio to text using speech recognition technology. The analysis unit analyzes the information received by the reception unit to grasp the child's level of understanding and progress. The analysis unit can, for example, recognize characters from photos using OCR technology. The analysis unit can also understand the content of questions from text and audio using natural language processing technology. For example, the analysis unit extracts keywords from text and understands the intent of the question. The analysis unit can also estimate emotions from audio. For example, the analysis unit analyzes the tone and speed of the voice to estimate emotions. The providing unit provides answers and feedback based on the level of understanding and progress grasped by the analysis unit. The providing unit evaluates the child's learning situation based, for example, on past question history and the accuracy of answers. The providing unit can also provide answers in text format. For example, the providing unit displays the answer as text. The providing unit can also provide audio feedback. For example, the providing unit explains the answer aloud. The generating unit generates visual text and graphics. For example, the generating unit generates a graphic that shows the solution to a math problem step by step. The generating unit can also generate infographics. For example, the generating unit generates a timeline that visually shows historical events. The connecting unit connects to an online tutor as needed. For example, the connecting unit connects to an online tutor via video call. The connecting unit can also provide chat support. For example, the connecting unit connects to an online tutor via text chat. In this way, the AI-assisted service according to the embodiment can deepen children's joy of learning and cultivate their problem-solving skills.

[0071] The reception unit accepts input in the form of photos, text, and audio. For example, it can accept JPEG photos, PDF text files, and MP3 audio files. Specifically, the reception unit has the function to automatically scan user-uploaded photos and convert them into digital data. For example, it can scan handwritten notes and charts and save them as digital data. The reception unit can also record audio and convert it to text using speech recognition technology. For example, if a user inputs a question by voice, the system records the audio and converts it to text using speech recognition technology, making it easier for the subsequent analysis unit to process. Furthermore, the reception unit can accept multiple data formats simultaneously, so it can process photos, text, and audio uploaded by users at once without any problems. This allows users to input information in a variety of ways, improving convenience. The reception unit also has a function to check the quality of the input data and automatically filter out data unsuitable for analysis, such as blurry photos and noisy audio. This allows the analysis unit to handle high-quality data, improving overall accuracy.

[0072] The analysis unit analyzes the information received by the reception unit to understand the child's level of comprehension and progress. For example, the analysis unit uses OCR technology to recognize text from photographs. Specifically, it uses OCR technology to convert handwritten notes and printed text into digital text, making it an analyzable format. The analysis unit can also understand the content of questions from text and audio using natural language processing technology. For example, it extracts keywords from text and performs contextual and semantic analysis to understand the intent of the question. Furthermore, the analysis unit can estimate emotions from audio. For example, it analyzes the tone and speed of the voice to estimate whether the user is excited, calm, or confused. This allows the analysis unit to provide feedback that takes the user's emotional state into account. The analysis unit combines these technologies to comprehensively evaluate the user's level of comprehension and progress. For example, it analyzes past answer history and question trends to identify which areas the user excels in and which areas need reinforcement. The analysis unit can also process data in real time and provide immediate feedback. This allows the user to constantly understand their learning progress and take appropriate measures.

[0073] The service provider provides answers and feedback based on the level of understanding and progress grasped by the analysis unit. Specifically, it evaluates the child's learning situation based on past question history and the accuracy of answers, and provides appropriate feedback. For example, the service provider may re-present questions that the user previously answered incorrectly to check their understanding. The service provider can also provide answers in text format. For example, it may display answers in text and add detailed explanations to make them easier for the user to understand. Furthermore, the service provider can also provide audio feedback. For example, it may explain answers in audio, allowing the user to utilize not only visual information but also auditory information. This allows the service provider to provide diverse feedback tailored to the user's learning style, maximizing learning effectiveness. In addition, the service provider can collect user feedback and continuously improve the accuracy of answers and feedback. For example, it may record how the user reacted to the answers and adjust the content of the feedback based on that data. This allows the service provider to provide more effective learning support to the user.

[0074] The generation unit generates visual text and graphics. Specifically, it can generate graphics that show the solution to a math problem step by step. For example, it can visually demonstrate the solution to a complex equation, making it easier for users to understand each step. The generation unit can also generate infographics. For example, it can generate a timeline that visually shows historical events, making it easier for users to grasp information chronologically. Furthermore, the generation unit can generate graphics customized according to the user's learning progress. For example, it can generate graphics that highlight areas where the user is struggling in a particular subject, allowing them to focus their learning on those areas. By automatically generating and providing this visual content to the user, the generation unit can deepen their understanding of what they are learning. In addition, the generation unit can adjust the content of the graphics based on user feedback, providing more effective learning support. For example, if a user finds a particular graphic difficult to understand, the design and content of the graphic can be improved based on that feedback. In this way, the generation unit can always provide the user with the most optimal visual content, maximizing the learning effect.

[0075] The connection unit connects to online tutors as needed. Specifically, it has the functionality to connect to online tutors via video call. For example, if a user requests a detailed explanation on a particular issue, the connection unit connects them to a professional tutor via video call, who can answer their questions in real time. The connection unit can also provide chat support. For example, users can connect to online tutors via text chat and answer questions immediately. Furthermore, the connection unit provides tutors with the user's learning history and progress, enabling tutors to understand the user's situation and provide appropriate guidance. This allows the connection unit to provide an environment where users can receive expert support when needed, thereby enhancing learning effectiveness. In addition, the connection unit can collect user feedback and continuously improve the quality of the connection and support. For example, it can evaluate whether users were satisfied with the quality of the video call and whether the tutor's response was appropriate, and use this data to improve the service. This allows the connection unit to consistently provide users with high-quality support and maximize learning efficiency.

[0076] The reception desk can accept input in the form of photos, text, and audio. For example, it can accept photos in JPEG format, text in PDF format, and audio in MP3 format. The reception desk can also scan photos and convert them into digital data. For example, it can scan handwritten notes and convert them into digital data. The reception desk can also record audio and convert it into text. For example, it can use speech recognition technology to convert audio to text. This allows children to ask the AI ​​questions and homework in a variety of ways. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input audio data into a generating AI and have the generating AI perform the conversion from audio data to text data.

[0077] The analysis unit can recognize characters from photographs and understand the content of questions from text and audio. For example, the analysis unit can recognize characters from photographs using OCR technology. For example, the analysis unit can recognize handwritten characters with high accuracy. The analysis unit can also understand the content of questions from text and audio using natural language processing technology. For example, the analysis unit can extract keywords from text and understand the intent of the question. The analysis unit can also estimate emotions from audio. For example, the analysis unit analyzes the tone and speed of the voice to estimate emotions. This allows for an accurate understanding of children's questions and homework. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input photographic data into a generating AI and have the generating AI perform character recognition from the photographic data.

[0078] The service provider can evaluate a child's learning progress based on past question history and the accuracy of their answers, and provide specific feedback on individual learning gaps. For example, the service provider can evaluate a child's learning progress based on past question history and the accuracy of their answers. For example, the service provider can analyze the question history stored in the database to understand the child's learning progress. The service provider can also provide answers in text format. For example, the service provider can display the answers as text. The service provider can also provide audio feedback. For example, the service provider can explain the answers aloud. This allows the service provider to identify children's learning gaps and provide appropriate feedback. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input past question history into a generating AI and have the generating AI perform the evaluation of the learning progress.

[0079] The generation unit can generate graphics that show the solution to a math problem step by step. For example, the generation unit can generate a flowchart to visually show the steps to the solution. The generation unit can also generate infographics. For example, the generation unit can generate a timeline that visually shows historical events. This makes it possible to provide learning content in a way that is easy for children to understand visually. Some or all of the above processes in the generation unit may be performed using AI, for example, or not using AI. For example, the generation unit can input math problem data into a generation AI and have the generation AI perform step-by-step graphic generation.

[0080] The connection unit can connect a child to an online tutor if the child needs further explanation on a particular issue. The connection unit can connect to the online tutor, for example, via video call. For example, the connection unit can interact with the tutor in real time via video call. The connection unit can also provide chat support. For example, the connection unit can connect to the online tutor via text chat. This allows children to progress in their learning with in-depth support. Some or all of the above processes in the connection unit may be performed using AI, for example, or not. For example, the connection unit can input the child's question data into a generating AI and have the generating AI execute the connection to the online tutor.

[0081] The reception unit can estimate the child's emotions and adjust the timing of input acceptance based on the estimated emotions. For example, if the child is excited, the reception unit can speed up the timing of input acceptance and immediately accept questions. If the child is tired, the reception unit can delay the timing of input acceptance to encourage a break. If the child is concentrating, the reception unit can adjust the timing of input acceptance to avoid interrupting their concentration. This allows for input to be accepted at the appropriate time according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0082] The reception desk can analyze a child's past input history and suggest the optimal input method. For example, the reception desk can prioritize suggesting input methods (photo, text, voice) that the child has frequently used in the past. The reception desk can also predict and suggest input methods to be used at specific times of the day based on the child's past input history. The reception desk can also suggest relevant input methods based on the content of questions the child has previously answered. This allows the child to input information in the most optimal way. Some or all of the above processes in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input past input history data into a generating AI and have the generating AI suggest the optimal input method.

[0083] The reception unit can filter input based on the child's current learning situation and areas of interest. For example, the reception unit can prioritize questions related to the subject the child is currently studying. The reception unit can also filter relevant questions based on the child's areas of interest. The reception unit can also accept questions of appropriate difficulty according to the child's learning progress. This ensures that questions that match the child's learning situation and interests are prioritized. Some or all of the above processing in the reception unit may be performed using AI, for example, or without AI. For example, the reception unit can input the child's learning situation data into a generating AI and have the generating AI perform the filtering.

[0084] The reception unit can estimate a child's emotions and determine the priority of input reception based on the estimated emotions. For example, if a child is excited, the reception unit can set a high priority for input reception and immediately accept questions. If a child is tired, the reception unit can also set a low priority for input reception and encourage them to take a break. If a child is concentrating, the reception unit can adjust the priority of input reception to avoid interrupting their concentration. This allows for input to be received with appropriate priority according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the reception unit may be performed using AI, for example, or not using AI. For example, the reception unit can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0085] The reception desk can prioritize receiving highly relevant information by considering the child's geographical location when receiving input. For example, if the child is at school, the reception desk can prioritize questions related to school lessons. If the child is at home, the reception desk can also prioritize questions related to home learning. If the child is at the library, the reception desk can also prioritize questions related to library materials. This allows for prioritizing questions based on the child's geographical location. Some or all of the above processing in the reception desk may be performed using AI, for example, or not. For example, the reception desk can input the child's geographical location data into a generating AI and have the generating AI prioritize receiving highly relevant information.

[0086] The reception desk can analyze a child's social media activity and receive relevant information upon receiving input. For example, the reception desk can prioritize questions related to learning content shared by the child on social media. The reception desk can also receive relevant questions based on information from educational accounts that the child follows on social media. The reception desk can also analyze the activities of learning groups that the child participates in on social media and receive relevant questions. This allows the reception desk to receive questions based on the child's social media activity. Some or all of the above processing in the reception desk may be performed using AI, for example, or not using AI. For example, the reception desk can input the child's social media data into a generating AI and have the generating AI perform the task of receiving relevant information.

[0087] The analysis unit can estimate the child's emotions and adjust the analysis method based on the estimated emotions. For example, if the child is relaxed, the analysis unit can perform a detailed analysis to facilitate a deeper understanding. If the child is in a hurry, the analysis unit can also perform a concise analysis to provide a quick answer. If the child is excited, the analysis unit can also provide a visually stimulating analysis result. This allows the analysis to be performed in an appropriate manner according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or not using AI. For example, the analysis unit can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0088] The analysis unit can optimize the analysis algorithm by referring to past analysis data during the analysis. For example, the analysis unit can select the optimal analysis algorithm based on past analysis data. The analysis unit can also extract specific patterns from past analysis data and adjust the analysis algorithm. The analysis unit can also analyze past analysis data to improve the accuracy of the analysis algorithm. As a result, the accuracy of the analysis is improved by optimizing the analysis algorithm based on past data. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without using AI. For example, the analysis unit can input past analysis data into a generating AI and have the generating AI perform the optimization of the analysis algorithm.

[0089] The analysis unit can improve the accuracy of its analysis by considering the child's learning history during the analysis process. For example, the analysis unit can improve the accuracy of its analysis based on the child's past learning history. The analysis unit can also improve the accuracy of its analysis by extracting specific learning patterns from the child's learning history. The analysis unit can also analyze the child's learning history and optimize the analysis algorithm. This improves the accuracy of the analysis by considering the child's learning history. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's learning history data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0090] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the child is nervous, the analysis unit can provide a simple and highly visible display method. If the child is relaxed, the analysis unit can also provide a display method that includes detailed information. If the child is in a hurry, the analysis unit can also provide a concise display method. This allows the analysis results to be provided in an appropriate display method according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generative AI. The generative AI is, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above processing in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0091] The analysis unit can perform analysis while taking into account the child's geographical location information. For example, if the child is at school, the analysis unit can perform analysis related to school lessons. If the child is at home, the analysis unit can also perform analysis related to home learning. If the child is at the library, the analysis unit can also perform analysis related to library materials. This allows for analysis tailored to the child's geographical location. Some or all of the above-described processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input the child's geographical location data into a generating AI and have the generating AI perform the analysis.

[0092] The analysis unit can improve the accuracy of its analysis by referring to relevant academic literature during the analysis process. For example, the analysis unit improves the accuracy of its analysis based on relevant academic literature. The analysis unit can also improve the accuracy of its analysis by extracting specific knowledge from academic literature. The analysis unit can also optimize its analysis algorithm by referring to academic literature. This improves the accuracy of the analysis by referring to relevant academic literature. Some or all of the above processes in the analysis unit may be performed using AI, for example, or without AI. For example, the analysis unit can input academic literature data into a generating AI and have the generating AI perform the analysis accuracy improvement.

[0093] The service provider can estimate a child's emotions and adjust the way it expresses feedback based on the estimated emotions. For example, if the child is relaxed, the service provider can provide detailed feedback. If the child is in a hurry, the service provider can also provide concise feedback. If the child is excited, the service provider can also provide visually stimulating feedback. This allows the service provider to provide feedback in an appropriate way according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, with an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the processing described above in the service provider may be performed using AI or not using AI. For example, the service provider can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0094] The feedback provider can provide optimal feedback by referring to the child's learning history when providing feedback. For example, the provider can provide optimal feedback based on the child's past learning history. The provider can also extract specific learning patterns from the child's learning history and provide optimal feedback. The provider can also analyze the child's learning history to improve the accuracy of the feedback. This allows the provider to provide optimal feedback based on the child's learning history. Some or all of the above processing in the provider may be performed using AI, for example, or without AI. For example, the provider can input the child's learning history data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0095] The service provider can customize the content of the feedback based on the child's current learning situation when providing feedback. For example, the service provider can customize the content of the feedback based on the child's current learning situation. The service provider can also adjust the content of the feedback according to the child's learning progress. The service provider can also analyze the child's learning situation and provide optimal feedback. This allows the service provider to provide feedback that is appropriate to the child's current learning situation. Some or all of the above processes in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's learning situation data into a generating AI and have the generating AI perform the feedback customization.

[0096] The service provider can estimate a child's emotions and determine the priority of feedback based on the estimated emotions. For example, if a child is excited, the service provider can set a high priority for feedback and provide it immediately. If a child is tired, the service provider can also set a low priority for feedback and encourage a break. If a child is concentrating, the service provider can adjust the priority of feedback so as not to interrupt their concentration. This allows for the provision of feedback with appropriate priority according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input a child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0097] The service provider can provide optimal feedback by taking into account the child's geographical location when providing feedback. For example, if the child is at school, the service provider can provide feedback related to school lessons. If the child is at home, the service provider can also provide feedback related to home learning. If the child is at the library, the service provider can also provide feedback related to library materials. This allows for the provision of feedback tailored to the child's geographical location. Some or all of the above processing in the service provider may be performed using AI, for example, or without AI. For example, the service provider can input the child's geographical location data into a generating AI and have the generating AI perform the task of providing optimal feedback.

[0098] The service provider can analyze a child's social media activity and adjust the content of the feedback when providing it. For example, the service provider can provide feedback related to learning content shared by the child on social media. The service provider can also provide relevant feedback based on information from educational accounts that the child follows on social media. The service provider can also analyze the activities of learning groups that the child participates in on social media and provide relevant feedback. This allows the service provider to provide feedback based on the child's social media activity. Some or all of the above processing in the service provider may be performed using AI, for example, or not using AI. For example, the service provider can input the child's social media data into a generating AI and have the generating AI adjust the content of the feedback.

[0099] The generation unit can estimate a child's emotions and adjust the way the generated graphics are represented based on the estimated emotions. For example, if the child is relaxed, the generation unit can generate graphics with calm colors. If the child is excited, the generation unit can also generate graphics with bright colors. If the child is tired, the generation unit can also generate simple, highly visible graphics. This allows for the generation of graphics with an appropriate representation depending on the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input child facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0100] The generation unit can generate optimal graphics by referring to the child's learning history during graphic generation. For example, the generation unit can generate easy-to-understand graphics based on the child's past learning history. The generation unit can also extract specific learning patterns from the child's learning history and generate optimal graphics. The generation unit can also analyze the child's learning history and generate visually effective graphics. This allows the generation of optimal graphics based on the child's learning history. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's learning history data into a generation AI and have the generation AI perform the generation of optimal graphics.

[0101] The generation unit can customize the content of a graphic based on the child's current learning status when generating the graphic. For example, the generation unit customizes the content of the graphic based on the child's current learning status. The generation unit can also adjust the content of the graphic according to the child's learning progress. The generation unit can also analyze the child's learning status and generate the optimal graphic. This makes it possible to generate a graphic that corresponds to the child's current learning status. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's learning status data into a generation AI and have the generation AI perform the graphic customization.

[0102] The generation unit can estimate a child's emotions and determine the priority of graphics to generate based on the estimated emotions. For example, if the child is excited, the generation unit may prioritize generating visually stimulating graphics. If the child is tired, the generation unit may also prioritize generating simple, easily recognizable graphics. If the child is focused, the generation unit may also prioritize generating detailed graphics. This allows graphics to be generated with appropriate priority according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or a generation AI. The generation AI may 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 processes in the generation unit may be performed using AI, or not using AI. For example, the generation unit can input the child's facial expression data into the generation AI and have the generation AI perform emotion estimation.

[0103] The generation unit can generate optimal graphics by considering the child's geographical location information during graphic generation. For example, if the child is at school, the generation unit can generate graphics related to school lessons. If the child is at home, the generation unit can also generate graphics related to home learning. If the child is at the library, the generation unit can also generate graphics related to library materials. This allows for the generation of graphics tailored to the child's geographical location. Some or all of the above-described processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input the child's geographical location data into a generation AI and have the generation AI perform the generation of optimal graphics.

[0104] The generation unit can adjust the content of graphics by referring to relevant academic literature during graphic generation. For example, the generation unit adjusts the content of graphics based on relevant academic literature. The generation unit can also extract specific knowledge from academic literature and adjust the content of graphics. The generation unit can also generate visually effective graphics by referring to academic literature. This allows for the optimization of graphic content by referring to relevant academic literature. Some or all of the above processes in the generation unit may be performed using AI, for example, or without AI. For example, the generation unit can input academic literature data into a generation AI and have the generation AI perform the graphic content adjustment.

[0105] The connection unit can estimate the child's emotions and adjust the timing of the connection to the online tutor based on the estimated emotions. For example, if the child is excited, the connection unit can connect to the online tutor immediately. If the child is tired, the connection unit can also encourage a break and connect to the online tutor later. If the child is concentrating, the connection unit can adjust the connection timing so as not to interrupt their concentration. This allows for connecting to the online tutor at the appropriate time according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the connection unit may be performed using AI or not using AI. For example, the connection unit can input the child's facial expression data into the generative AI and have the generative AI perform emotion estimation.

[0106] The connection unit can select the most suitable tutor by referring to the child's learning history when connecting to an online tutor. For example, the connection unit can select the most suitable tutor based on the child's past learning history. The connection unit can also extract specific learning patterns from the child's learning history and select the most suitable tutor. The connection unit can also analyze the child's learning history and select the most suitable tutor. This allows for the selection of the most suitable tutor based on the child's learning history. Some or all of the above processing in the connection unit may be performed using AI, for example, or without AI. For example, the connection unit can input the child's learning history data into a generating AI and have the generating AI perform the selection of the most suitable tutor.

[0107] The connection unit can customize the connection content based on the child's current learning situation when connecting to an online tutor. For example, the connection unit customizes the connection content based on the child's current learning situation. The connection unit can also adjust the connection content according to the child's learning progress. The connection unit can also analyze the child's learning situation and provide the optimal connection content. This allows the connection content to be provided according to the child's current learning situation. Some or all of the above processing in the connection unit may be performed using AI, for example, or without AI. For example, the connection unit can input the child's learning situation data into a generating AI and have the generating AI perform the customization of the connection content.

[0108] The connection unit can estimate the child's emotions and determine the priority of connecting to an online tutor based on the estimated emotions. For example, if the child is excited, the connection unit can set a high priority for the connection and connect immediately. If the child is tired, the connection unit can also set a low priority for the connection and encourage a break. If the child is concentrating, the connection unit can adjust the connection priority to avoid interrupting their concentration. This allows for connecting to an online tutor with the appropriate priority according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. Generative AI is, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the connection unit may be performed using AI or not using AI. For example, the connection unit can input the child's facial expression data into a generative AI and have the generative AI perform emotion estimation.

[0109] The connection unit can select the most suitable tutor when connecting to an online tutor, taking into account the child's geographical location. For example, if the child is at school, the connection unit can select a tutor related to school lessons. If the child is at home, the connection unit can also select a tutor related to home learning. If the child is at the library, the connection unit can also select a tutor related to library materials. This allows for the selection of a tutor that is appropriate to the child's geographical location. Some or all of the above processing in the connection unit may be performed using AI, for example, or without AI. For example, the connection unit can input the child's geographical location data into a generating AI and have the generating AI perform the selection of the most suitable tutor.

[0110] The connection unit can analyze a child's social media activity and adjust the connection content when connecting with an online tutor. For example, the connection unit can select a tutor related to the learning content the child has shared on social media. The connection unit can also select a relevant tutor based on information from educational accounts the child follows on social media. The connection unit can also analyze the activities of learning groups the child participates in on social media and select a relevant tutor. This allows the connection unit to provide connection content based on the child's social media activity. Some or all of the above processing in the connection unit may be performed using AI, for example, or not using AI. For example, the connection unit can input the child's social media data into a generating AI and have the generating AI adjust the connection content.

[0111] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0112] The reception desk can suggest the optimal input method based on the child's learning history. For example, it prioritizes suggesting input methods that the child has frequently used in the past (photo, text, audio). It can also suggest appropriate input methods according to the child's learning progress and level of understanding. Furthermore, it can predict and suggest input methods that the child will use at specific times of day based on their learning history. This allows children to ask questions and complete homework assignments to the AI ​​in the most optimal way for them.

[0113] The analysis unit can improve the accuracy of its analysis based on the child's learning history. For example, it can evaluate the child's learning situation and optimize the analysis algorithm based on past question history and the accuracy of answers. It can also extract specific learning patterns from the child's learning history to improve the accuracy of the analysis. Furthermore, it can select the optimal analysis algorithm by referring to past analysis data. In this way, the accuracy of the analysis is improved by taking the child's learning history into consideration.

[0114] The system can estimate a child's emotions and adjust the way it presents feedback based on those estimates. For example, if a child is relaxed, it can provide detailed feedback. If a child is in a hurry, it can provide concise feedback. Furthermore, if a child is excited, it can provide visually stimulating feedback. This allows for providing feedback in an appropriate way depending on the child's emotions.

[0115] The generation unit can generate optimal graphics based on a child's learning history. For example, it can generate easy-to-understand graphics based on past learning history. It can also extract specific learning patterns from a child's learning history and generate optimal graphics. Furthermore, it can analyze a child's learning history and generate visually effective graphics. In this way, it can generate optimal graphics based on a child's learning history.

[0116] The connection unit can estimate the child's emotions and adjust the timing of the connection to the online tutor based on those emotions. For example, if the child is excited, it can connect to the online tutor immediately. If the child is tired, it can encourage a break and connect to the online tutor later. Furthermore, if the child is concentrating, it can adjust the connection timing to avoid interrupting their concentration. This allows for connecting to the online tutor at the appropriate time according to the child's emotions.

[0117] The reception desk can prioritize receiving highly relevant information by taking into account the child's geographical location. For example, if the child is at school, questions related to school lessons will be prioritized. If the child is at home, questions related to homework will be prioritized. Furthermore, if the child is at the library, questions related to library materials will be prioritized. This allows for prioritizing questions based on the child's geographical location.

[0118] The analysis unit can estimate the child's emotions and adjust the display method of the analysis results based on the estimated emotions. For example, if the child is nervous, it can provide a simple and easy-to-read display method. If the child is relaxed, it can also provide a display method that includes detailed information. Furthermore, if the child is in a hurry, it can provide a display method that gets straight to the point. This allows the analysis results to be provided in an appropriate display method according to the child's emotions.

[0119] The feedback provider can analyze a child's social media activity and adjust the content of the feedback accordingly. For example, it can provide feedback related to learning content shared by the child on social media. It can also provide relevant feedback based on information from educational accounts the child follows on social media. Furthermore, it can analyze the activities of learning groups the child participates in on social media and provide relevant feedback. This allows for the provision of feedback based on the child's social media activity.

[0120] The generation unit can estimate the child's emotions and adjust the graphic representation based on the estimated emotions. For example, if the child is relaxed, it can generate graphics with calm colors. If the child is excited, it can also generate graphics with bright colors. Furthermore, if the child is tired, it can generate simple, highly visible graphics. This allows for the generation of graphics with an appropriate representation depending on the child's emotions.

[0121] The connection unit can select the most suitable tutor by referring to the child's learning history when connecting to an online tutor. For example, it can select the most suitable tutor based on past learning history. It can also extract specific learning patterns from the child's learning history and select the most suitable tutor. Furthermore, it can analyze the child's learning history and select the most suitable tutor. In this way, the most suitable tutor can be selected based on the child's learning history.

[0122] The following briefly describes the processing flow for example form 2.

[0123] Step 1: The reception desk accepts input in the form of photos, text, and audio. For example, it can accept photos in JPEG format, text in PDF format, and audio in MP3 format. It can also scan handwritten notes and convert them into digital data, and use speech recognition technology to convert audio to text. Step 2: The analysis unit analyzes the information received by the reception unit to understand the child's level of comprehension and progress. For example, it uses OCR technology to recognize characters from photographs and natural language processing technology to understand the content of questions from text and audio. It can also analyze the tone and speed of speech to estimate emotions. Step 3: The provisioning unit provides answers and feedback based on the level of understanding and progress identified by the analysis unit. For example, it evaluates the learning situation based on past question history and the accuracy of answers, and provides answers and feedback in text or audio format. Step 4: The generation unit generates visual text and graphics. For example, it can generate graphics that show the solution to a math problem step by step, or timelines that visually represent historical events. Step 5: The connection unit connects to an online tutor as needed. For example, it connects to an online tutor via video call or text chat to provide support.

[0124] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating 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.

[0125] Data generation model 58 is a form of so-called generative AI (Artificial Intelligence). An example of data generation model 58 is ChatGPT (registered trademark) (Internet search).<URL: https: / / openai.com / blog / chatgpt> Examples of generative AI include text generation AI, image generation AI, and multimodal generation AI. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 is input with prompts containing instructions, and with inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats from audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, 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), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each of the above parts is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example.Furthermore, processing performed by AI, including generative AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by AI, including generative AI.

[0126] Furthermore, the processing performed by the data processing system 10 described above is carried out by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but it may also be carried out by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. In addition, 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.

[0127] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, generation unit, and connection unit, is implemented, for example, in at least one of the smart device 14 and the data processing unit 12. For example, the reception unit is implemented by the reception device 38 of the smart device 14 and accepts input of photos, text, and voice. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information to grasp the child's level of understanding and progress. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides answers and feedback. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates visual text and graphics. The connection unit connects to the online tutor via the communication I / F 44 of the smart device 14. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0128] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0129] As shown in Figure 3, the data processing system 210 includes a data processing device 12 and smart glasses 214. An example of the data processing device 12 is a server.

[0130] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0131] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication interface 44. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, and camera 42 are also connected to the bus 52.

[0132] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0133] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0134] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0135] Figure 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Figure 4, the data processing device 12 performs specific processing by the processor 28. The storage 32 stores the specific processing program 56.

[0136] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0137] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0138] In the smart glasses 214, specific processing is performed by the processor 46. The storage 50 stores a specific processing program 60. 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 acting as a control unit 46A according to the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0139] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0140] 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 user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0141] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0142] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart glasses 214 or an external device, and the smart glasses 214 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0143] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, generation unit, and connection unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the smart glasses 214 and receives input of photos, text, and voice. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information to grasp the child's level of understanding and progress. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides answers and feedback. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates visual text and graphics. The connection unit connects to an online tutor via the communication I / F 44 of the smart glasses 214. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0144] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0145] As shown in Figure 5, the data processing system 310 includes a data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0146] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0147] The headset terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a display 343. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and display 343 are also connected to the bus 52.

[0148] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0149] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0150] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0151] Figure 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Figure 6, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0152] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0153] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0154] In the headset terminal 314, specific processing is performed by the processor 46. The storage 50 stores a specific program 60. The processor 46 reads the specific program 60 from the storage 50 and executes the read specific program 60 on the RAM 48. The specific processing is realized by the processor 46 acting as a control unit 46A according to the specific program 60 executed on the RAM 48. The headset terminal 314 also has a data generation model 58 and an emotion identification model 59, similar to the data generation model and emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0155] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0156] The specific processing unit 290 transmits the result of the specific processing to the headset terminal 314. In the headset terminal 314, the control unit 46A causes the speaker 240 and display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0157] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0158] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset terminal 314, but may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset terminal 314. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the headset terminal 314 or an external device, and the headset terminal 314 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0159] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, generation unit, and connection unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the headset terminal 314 and accepts input of photos, text, and voice. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information to grasp the child's level of understanding and progress. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides answers and feedback. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates visual text and graphics. The connection unit connects to the online tutor via the communication I / F 44 of the headset terminal 314. The correspondence between each unit and the device or control unit is not limited to the example described above and can be modified in various ways.

[0160] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0161] As shown in Figure 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0162] The data processing device 12 comprises a computer 22, a database 24, and a communication interface 26. The computer 22 comprises a processor 28, RAM 30, and storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication interface 26 are also connected to the bus 34. The communication interface 26 is connected to a network 54. An example of the network 54 is a WAN and / or LAN.

[0163] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication interface 44, and a controlled object 443. The computer 36 includes a processor 46, RAM 48, and storage 50. The processor 46, RAM 48, and storage 50 are connected to a bus 52. The microphone 238, speaker 240, camera 42, and controlled object 443 are also connected to the bus 52.

[0164] The microphone 238 receives voice commands and other instructions from the user by receiving voice signals. The microphone 238 captures the voice signals from the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to the instructions from the processor 46.

[0165] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an image sensor such as a CMOS image sensor or CCD image sensor, which captures images of the area around the user (for example, an imaging range defined by a field of view equivalent to the field of vision of a typical healthy person).

[0166] Communication interface 44 is connected to network 54. Communication interfaces 44 and 26 are responsible for the exchange of various information between processor 46 and processor 28 via network 54. The exchange of various information between processor 46 and processor 28 using communication interfaces 44 and 26 is performed in a secure manner.

[0167] The controlled 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 robot 414's emotions can be expressed by controlling these motors. The robot 414's facial expressions can also be expressed by controlling the illumination state of the LEDs in its eyes.

[0168] Figure 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Figure 8, the data processing device 12 performs specific processing using the processor 28. The storage 32 stores the specific processing program 56.

[0169] The processor 28 reads a 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 acting as a specific processing unit 290 according to the specific processing program 56 executed on the RAM 30.

[0170] Storage 32 stores the data generation model 58 and the emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotions using the emotion identification model 59 and perform identification processing using the user's emotions. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotions, including but not limited to these examples. Furthermore, emotion estimation and prediction also include, for example, emotion analysis.

[0171] In robot 414, specific processing is performed by processor 46. A specific program 60 is stored in storage 50. Processor 46 reads the specific program 60 from storage 50 and executes it on RAM 48. The specific processing is achieved by processor 46 acting as a control unit 46A according to the specific program 60 executed on RAM 48. Robot 414 also has data generation model 58 and emotion identification model 59, similar to those of the robot, and can perform processing similar to that of the specific processing unit 290 using these models.

[0172] Furthermore, other devices besides the data processing device 12 may also 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 obtains processing results (such as prediction results) using the data generation model 58 by communicating with the server device that has the data generation model 58. Also, the data processing device 12 may be a server device or a terminal device owned by the user (for example, a mobile phone, robot, home appliance, etc.).

[0173] 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 controlled object 443 to output the result of the specific processing. The microphone 238 acquires audio indicating user input for the result of the specific processing. The control unit 46A transmits the audio data indicating user input acquired by the microphone 238 to the data processing unit 12. In the data processing unit 12, the specific processing unit 290 acquires the audio data.

[0174] The data generation model 58 is a so-called generative AI. An example of a 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 is input with prompts containing instructions, and inference data such as audio data representing speech, text data representing text, and image data representing images (e.g., still image data or video data). The data generation model 58 infers from the input inference data according to the instructions indicated by the prompts, and outputs the inference result in one or more data formats such as audio data, text data, and image data. The data generation model 58 includes, for example, text generation AI, image generation AI, and multimodal generation AI. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specific processing unit 290 performs the specific processing described above using the data generation model 58. The data generation model 58 may be a fine-tuned model that outputs inference results from prompts that do not contain instructions, in which case the data generation model 58 can output inference results from prompts that do not contain instructions. In the data processing device 12, etc., there are multiple types of data generation models 58, and the data generation model 58 includes AI other than generative AI. AI other than generative AI includes, for example, linear regression, logistic regression, decision trees, random forests, support vector machines (SVM), k-means clustering, convolutional neural networks (CNN), recurrent neural networks (RNN), generative adversarial networks (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. Also, the AI ​​may be an AI agent. Furthermore, when the processing of each part described above is performed by the AI, the processing may be performed by the AI ​​in part or in whole, but is not limited to this example. Also, processing performed by an AI including a generative AI may be replaced by rule-based processing, and rule-based processing may be replaced by processing performed by an AI including a generative AI.

[0175] 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 performed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but it may also be performed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. In addition, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the robot 414 or an external device, and the robot 414 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0176] Each of the multiple elements described above, including the reception unit, analysis unit, provision unit, generation unit, and connection unit, is implemented, for example, by at least one of the robot 414 and the data processing unit 12. For example, the reception unit is implemented by the microphone 238 of the robot 414 and receives input of photos, text, and voice. The analysis unit is implemented by the specific processing unit 290 of the data processing unit 12 and analyzes the received information to grasp the child's level of understanding and progress. The provision unit is implemented by the specific processing unit 290 of the data processing unit 12 and provides answers and feedback. The generation unit is implemented by the specific processing unit 290 of the data processing unit 12 and generates visual text and graphics. The connection unit connects to an online tutor via the communication I / F 44 of the robot 414. The correspondence between each unit and the device or control unit is not limited to the example described above and can be changed in various ways.

[0177] Furthermore, the emotion identification model 59, acting 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 a specific mapping, which is an emotion map (see Figure 9). Similarly, the emotion identification model 59 may also determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0178] Figure 9 shows the emotion map 400, in which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive the emotions are located. Further out of the concentric circles, emotions representing states and actions arising from mental states are located. Emotion is a concept that includes feelings and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions occurring in the brain are located. On the right side of the concentric circles, emotions that are generally induced by situational judgment are located. Above and below the concentric circles, emotions that are generally generated from reactions occurring in the brain and induced by situational judgment are located. In addition, the emotion of "pleasure" is located on the upper side of the concentric circles, and the emotion of "displeasure" is located on the lower side. Thus, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions arise, and emotions that are likely to occur simultaneously are mapped close together.

[0179] These emotions are distributed at the 3 o'clock position on the Emotion Map 400, and usually fluctuate between feelings of security and anxiety. In the right half of the Emotion Map 400, situational awareness takes precedence over internal feelings, resulting in a calm impression.

[0180] The inside of the Emotion Map 400 represents inner thoughts, while the outside represents actions. Therefore, the further you go from the outside of the Emotion Map 400, the more visible (expressed in actions) your emotions become.

[0181] Here, human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. Similarly, in robots, cars, and motorcycles, emotions can be created based on various balances, such as posture and battery level. When these balances deviate from the ideal, it results in discomfort, and when they approach the ideal, it results in pleasure. The emotion map can be generated based, for example, on Dr. Mitsuyoshi's emotion map (Research on a system for analyzing brain physiological signals of speech emotion recognition and emotion, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map contains emotions belonging to a region called "response," where sensation is dominant. The right half of the emotion map contains emotions belonging to a region called "situation," where situational awareness is dominant.

[0182] The emotion map defines two emotions that promote learning. One is the emotion around the middle of the negative "repentance" and "reflection" on the situation side. In other words, it is 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 the emotion around the positive "desire" on the reaction side. In other words, it is when the robot has positive feelings such as "I want more" or "I want to know more."

[0183] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​representing each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple training data sets, which are combinations of user input and emotion values ​​representing each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions located close together have similar values, as shown in the emotion map 900 in Figure 10. Figure 10 shows an example where multiple emotions such as "reassured," "calm," and "confident" have similar emotion values.

[0184] In the above embodiment, an example was given in which a specific process is performed by a single computer 22. However, the technology of this disclosure is not limited thereto, and a distributed processing method for the specific process may be used, which includes computer 22 and multiple other computers.

[0185] In the above embodiment, an example was given in which the specific processing program 56 is stored in the storage 32, but the technology of this disclosure is not limited thereto. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-temporary storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-temporary storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes specific processing according to the specific processing program 56.

[0186] 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.

[0187] Furthermore, it is not necessary to store the entirety 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 the entirety of the specific processing program 56 in the storage 32; it is acceptable to store only a portion of the specific processing program 56.

[0188] The following types of processors can be used as hardware resources to perform specific processing. Examples of processors include a CPU, a general-purpose processor that functions as a hardware resource to perform specific processing by executing software, i.e., a program. Other examples of processors include dedicated electrical circuits, such as FPGAs (Field-Programmable Gate Arrays), PLDs (Programmable Logic Devices), or ASICs (Application Specific Integrated Circuits), which have circuit configurations specifically designed to perform specific processing. All of these processors have built-in or connected memory, and all of them perform specific processing by using memory.

[0189] The hardware resource that performs a specific process may consist of one of these various processors, or it may consist of 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). Alternatively, the hardware resource that performs a specific process may consist of a single processor.

[0190] Examples of configurations using a single processor include, firstly, a configuration in which one or more CPUs and software are combined to form a single processor, and this processor functions as a hardware resource that performs a specific process. Secondly, there is a configuration using a processor that realizes the functions of the entire system, including multiple hardware resources that perform a specific process, on a single IC chip, as exemplified by SoCs (System-on-a-chip). In this way, a specific process is realized using one or more of the above types of processors as hardware resources.

[0191] Furthermore, the hardware structure of these various processors can more specifically utilize electrical circuits that combine circuit elements such as semiconductor devices. Also, the specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps can be deleted, new steps added, or the processing order rearranged, as long as it does not deviate from the main purpose.

[0192] Furthermore, although the above-described examples were divided into four embodiments, some or all of these embodiments may be combined. Also, the smart device 14, smart glasses 214, headset terminal 314, and robot 414 are just examples, and they may be combined, or other devices may be used. Also, although the above-described examples were divided into two embodiments, Embodiment 1 and Embodiment 2, these may be combined.

[0193] The descriptions and illustrations presented above are detailed explanations of the technical aspects of this disclosure and are merely examples of the technical aspects. For example, the above descriptions of the structure, function, operation, and effect are examples of the structure, function, operation, and effect of the technical aspects of this disclosure. Therefore, it goes without saying that you may delete unnecessary parts, add new elements, or replace elements in the descriptions and illustrations presented above, as long as you do not deviate from the essence of the technical aspects of this disclosure. Furthermore, in order to avoid confusion and facilitate understanding of the technical aspects of this disclosure, explanations of common technical knowledge and other things that do not require special explanation to enable the implementation of the technical aspects of this disclosure have been omitted from the descriptions and illustrations presented above.

[0194] All documents, patent applications, and technical standards described herein are incorporated by reference to the same extent as if each individual document, patent application, and technical standard were specifically and individually noted to be incorporated by reference.

[0195] (Note 1) A reception area that accepts photos, text, and audio input, The analysis unit analyzes the information received by the reception unit to understand the child's level of comprehension and progress, A providing unit that provides answers and feedback based on the level of understanding and progress grasped by the analysis unit, A generation unit that generates visual text and graphics, It includes a connection unit that connects to an online tutor as needed. A system characterized by the following features. (Note 2) The aforementioned reception unit is It accepts input of photos, text, and audio. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, It recognizes text from photos and understands the content of questions from text and audio. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned supply unit is, Based on past question history and the accuracy of answers, we evaluate the child's learning progress and provide specific feedback on individual learning gaps. The system described in Appendix 1, characterized by the features described herein. (Note 5) The generating unit is Generate graphics that show the solution to a math problem step by step. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned connection part is If your child wants more detailed explanations about a particular issue, connect them with an online tutor. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reception unit is The system estimates the child's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reception unit is We analyze a child's past input history and suggest the optimal input method. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reception unit is When receiving input, the system filters based on the child's current learning situation and areas of interest. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reception unit is The system estimates the child's emotions and determines the priority of input requests based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reception unit is When receiving input, the system prioritizes accepting highly relevant information, taking into account the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reception unit is When receiving input, the system analyzes the child's social media activity and collects relevant information. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, We estimate the child's emotions and adjust the analysis method based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During analysis, the analysis algorithm is optimized by referring to past analysis data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During analysis, the accuracy of the analysis is improved by considering the child's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 16) The aforementioned analysis unit, The system estimates the child's emotions and adjusts how the analysis results are displayed based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 17) The aforementioned analysis unit, During the analysis, the child's geographical location information will be taken into consideration. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, we refer to relevant academic literature to improve the accuracy of the analysis. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned supply unit is, The system estimates the child's emotions and adjusts the way feedback is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned supply unit is, When providing feedback, we refer to the child's learning history to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned supply unit is, When providing feedback, customize the content of the feedback based on the child's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned supply unit is, The system estimates the child's emotions and prioritizes feedback based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned supply unit is, When providing feedback, we take the child's geographical location into consideration to provide the most appropriate feedback. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned supply unit is, When providing feedback, we analyze the child's social media activity and adjust the content of the feedback accordingly. The system described in Appendix 1, characterized by the features described herein. (Note 25) The generating unit is It estimates a child's emotions and adjusts the way graphics are represented based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The generating unit is When generating graphics, the system references the child's learning history to generate the most suitable graphics. The system described in Appendix 1, characterized by the features described herein. (Note 27) The generating unit is When generating graphics, customize the content of the graphics based on the child's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 28) The generating unit is It estimates the child's emotions and determines the priority of graphics to generate based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The generating unit is When generating graphics, the system takes into account the child's geographical location to generate the optimal graphics. The system described in Appendix 1, characterized by the features described herein. (Note 30) The generating unit is When generating graphics, the content of the graphics is adjusted by referring to relevant academic literature. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned connection part is The system estimates the child's emotions and adjusts the timing of the connection to the online tutor based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned connection part is When connecting to an online tutor, the system selects the most suitable tutor by referring to the child's learning history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned connection part is When connecting to an online tutor, the connection content is customized based on the child's current learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned connection part is The system estimates the child's emotions and prioritizes connections to online tutors based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned connection part is When connecting to an online tutor, the system selects the most suitable tutor by considering the child's geographical location. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned connection part is When connecting with an online tutor, the system analyzes the child's social media activity and adjusts the connection accordingly. The system described in Appendix 1, characterized by the features described herein. [Explanation of symbols]

[0196] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Devices 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robots

Claims

1. A reception area that accepts photos, text, and audio input, The analysis unit analyzes the information received by the reception unit to understand the child's level of comprehension and progress, A providing unit that provides answers and feedback based on the level of understanding and progress grasped by the analysis unit, A generation unit that generates visual text and graphics, It includes a connection unit that connects to an online tutor as needed. A system characterized by the following features.

2. The aforementioned reception unit is It accepts input of photos, text, and audio. The system according to feature 1.

3. The aforementioned analysis unit, It recognizes text from photos and understands the content of questions from text and audio. The system according to feature 1.

4. The aforementioned supply unit is, Based on past question history and the accuracy of answers, we evaluate the child's learning progress and provide specific feedback on individual learning gaps. The system according to feature 1.

5. The generating unit is Generate graphics that show the solution to a math problem step by step. The system according to feature 1.

6. The aforementioned connection part is If your child wants more detailed explanations about a particular issue, connect them with an online tutor. The system according to feature 1.

7. The aforementioned reception unit is The system estimates the child's emotions and adjusts the timing of input acceptance based on the estimated emotions. The system according to feature 1.

8. The aforementioned reception unit is We analyze a child's past input history and suggest the optimal input method. The system according to feature 1.

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