system

Abstract

The system according to this embodiment aims to identify a child's strengths and weaknesses and provide individually tailored advice. [Solution] The system according to the embodiment comprises a reading unit, an analysis unit, a visualization unit, a presentation unit, and a will-considering unit. The reading unit reads test and homework answers as photographs. The analysis unit analyzes the data read by the reading unit. The visualization unit visualizes the child's strengths and weaknesses from the data analyzed by the analysis unit. The presentation unit provides advice based on the strengths and weaknesses visualized by the visualization unit. The will-considering unit considers the child's will based on the advice provided by the presentation unit.

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 is a problem that it is difficult to efficiently grasp a child's strong and weak fields and provide individually suitable advice.

[0005] The system according to the embodiment aims to grasp a child's strong and weak fields and provide individually suitable advice.

Means for Solving the Problems

[0006] The system according to this embodiment comprises a reading unit, an analysis unit, a visualization unit, a presentation unit, and a will-considering unit. The reading unit reads test and homework answers as photographs. The analysis unit analyzes the data read by the reading unit. The visualization unit visualizes the child's strengths and weaknesses from the data analyzed by the analysis unit. The presentation unit provides advice based on the strengths and weaknesses visualized by the visualization unit. The will-considering unit considers the child's wishes based on the advice provided by the presentation unit. [Effects of the Invention]

[0007] The system according to this embodiment can identify a child's strengths and weaknesses and provide individually tailored advice. [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 learning support system according to an embodiment of the present invention is a system that supports efficient, personalized studying by combining the child's own will with the results of data analysis. This learning support system reads the child's completed tests and homework as photos, and the AI ​​analyzes them, visualizing the child's strengths and weaknesses from the correct and incorrect answer data. Furthermore, it provides advice to further develop strengths and advice to overcome weaknesses. At this time, the child's own will is also taken into consideration, and an efficient study method tailored to the individual is proposed. For example, the learning support system reads the child's completed tests and homework as photos. In this case, a smartphone or tablet is used to take a picture of the test or homework. For example, a child takes a picture of a math problem they have solved with a smartphone and inputs the image data into the system. Next, the learning support system's AI analyzes the read data. The AI ​​uses image recognition technology to analyze the answers to the tests and homework and extracts correct and incorrect answer data. For example, it determines whether the answer to a math problem is correct and generates correct and incorrect answer data. Furthermore, the learning support system uses AI to visualize strengths and weaknesses based on correct and incorrect answer data. For example, if a child gets many math problems right, that area will be displayed as a strength. Conversely, if they get many problems wrong, that area will be displayed as a weakness. Next, the learning support system provides advice to further develop strengths and overcome weaknesses. For example, it suggests solving more difficult problems in strengths and starting with basic problems in weaknesses. Finally, the learning support system takes the child's wishes into consideration and suggests efficient study methods tailored to each individual. For example, if a child wants to focus on a particular subject, it will prioritize providing advice related to that subject. In this way, it realizes learning support that is tailored to each child. As a result, the learning support system can support efficient studying tailored to each individual by combining the child's wishes with the results of data analysis.

[0029] The learning support system according to this embodiment comprises a reading unit, an analysis unit, a visualization unit, a presentation unit, and a decision-making unit. The reading unit reads test and homework answers as photographs. The reading unit takes pictures of tests and homework using a device such as a smartphone or tablet. For example, the reading unit takes a picture of a math problem solved by a child with a smartphone and inputs the image data into the system. The analysis unit analyzes the data read by the reading unit. The analysis unit analyzes the test and homework answers using image recognition technology, for example, and extracts correct and incorrect answer data. For example, the analysis unit determines whether the answer to a math problem is correct and generates correct and incorrect answer data. The visualization unit visualizes the strengths and weaknesses of the student from the data analyzed by the analysis unit. For example, the visualization unit visualizes the strengths and weaknesses of the student from the correct and incorrect answer data. For example, if a child has many correct answers in math problems, the visualization unit displays that area as a strength. On the other hand, if a child has many incorrect answers, that area is displayed as a weakness. The presentation unit provides advice based on the strengths and weaknesses visualized by the visualization unit. For example, the presentation unit suggests solving more difficult problems in areas of strength and starting with basic problems in areas of weakness. The intention consideration unit takes into account the child's wishes based on the advice provided by the presentation unit. For example, if the child wishes to focus on a particular area, the intention consideration unit prioritizes providing advice related to that area. As a result, the learning support system according to this embodiment can support efficient, personalized studying by combining the child's wishes with the results of data analysis.

[0030] The reading unit reads test and homework answers from photographs. The reading unit uses devices such as smartphones and tablets to take pictures of tests and homework. Specifically, it uses the smartphone's camera function to photograph the child's completed math problems or homework answer sheets, and inputs the image data into the system. The captured images are automatically adjusted for resolution, brightness, and contrast, and imported into the system in an optimal state. Furthermore, the reading unit can read multiple images at once and has a function to automatically combine consecutively taken images into a single data file. This allows for efficient reading of test and homework answers spanning multiple pages. The reading unit also performs pre-processing to accurately recognize handwritten characters and shapes, and performs image noise reduction and distortion correction. This allows the reading unit to accurately and quickly import test and homework answers into the system, enabling smooth data processing by the subsequent analysis unit.

[0031] The analysis unit analyzes the data read by the reading unit. For example, the analysis unit uses image recognition technology to analyze test and homework answers and extracts correct and incorrect data. Specifically, it uses OCR (optical character recognition) technology to convert handwritten characters into digital data and extracts the content of the answers to the questions as text data. Furthermore, it uses AI to analyze the extracted text data and determine whether the answers to the questions are correct or incorrect. For example, for mathematics problems, it analyzes the mathematical formulas and verifies the calculation results to determine whether the answer is correct or incorrect. The analysis unit also uses natural language processing technology to analyze word problems and written answers and provides appropriate evaluations. Based on these analysis results, the analysis unit records the correct answer rate and the trend of incorrect answers for each question in a database, making it available for use in the subsequent visualization and presentation units. In addition, the analysis unit has a function to evaluate learning progress and the degree of improvement in performance by comparing it with past data. This allows the analysis unit to perform accurate and detailed data analysis and enhance the overall effectiveness of the learning support system.

[0032] The visualization unit visualizes strengths and weaknesses based on data analyzed by the analysis unit. Specifically, it displays the correct and incorrect answer rates for each subject area in graphs and charts, based on correct and incorrect answer data. For example, it uses bar graphs and pie charts to allow users to quickly grasp areas where a child excels and areas where they struggle. The visualization unit also displays changes in performance over time using line graphs, allowing users to visually check their learning progress. Furthermore, the visualization unit displays detailed analysis results for each individual problem, specifically showing which problems were answered incorrectly and which parts were not understood. This allows children, parents, and teachers to intuitively understand learning outcomes and challenges, and to create effective learning plans. The visualization unit also prioritizes user-friendliness, employing a simple and easy-to-understand design. As a result, the visualization unit effectively visualizes analysis results, making it easy for users of the learning support system to understand and utilize the information.

[0033] The presentation unit provides advice based on the strengths and weaknesses visualized by the visualization unit. Specifically, it suggests solving more difficult problems in areas of strength and starting with basic problems in areas of weakness. For example, in mathematics, if a student frequently gets certain answers right, it will present applied problems and advanced tasks and provide advice to deepen their understanding. On the other hand, if a student frequently makes mistakes, it will encourage reviewing basic concepts and formulas and guide them to progress step by step from the basics. The presentation unit presents this advice as a concrete learning plan to support daily learning. Furthermore, to increase motivation, the presentation unit introduces achievement goals and reward systems to make learning enjoyable for children. For example, it provides a system where students can earn badges or points when they achieve certain goals, visualizing their learning progress. This allows the presentation unit to provide specific and effective advice tailored to each individual learner, improving the quality of their learning.

[0034] The Intention-Consideration Unit takes the child's wishes into account based on the advice provided by the Presentation Unit. Specifically, if a child wishes to focus on a particular subject, it prioritizes providing advice related to that subject. For example, if a child is interested in geometry in mathematics, it will focus on presenting geometry-related problems and assignments to maintain their interest. Also, if a child wants to overcome a weak area, it will prioritize providing basic problems and review assignments in that area. The Intention-Consideration Unit proposes flexible learning plans and provides support tailored to individual needs in order to maximize the child's motivation and interest in learning. Furthermore, the Intention-Consideration Unit collects feedback from the child and improves and adjusts the learning plan. For example, if a child feels they have deepened their understanding of a particular problem, they will proceed with learning in that area, while if their understanding is insufficient, they will be instructed to review the basics again. In this way, the Intention-Consideration Unit can combine the child's wishes with the results of data analysis to support personalized and efficient learning.

[0035] The reading unit can take pictures of tests and homework using devices such as smartphones and tablets. For example, the reading unit can easily take pictures of tests and homework using a smartphone or tablet. For instance, the reading unit can take pictures of tests and homework using a smartphone camera and input the image data into the system. The reading unit can also take pictures of tests and homework using a tablet camera and input the image data into the system. Furthermore, the reading unit can take pictures of tests and homework using a digital camera and input the image data into the system. This makes it easy to take pictures of tests and homework using a smartphone or tablet. Smartphones and tablets include, but are not limited to, iOS devices and Android devices. Some or all of the processing described above in the reading unit may be performed using, for example, AI, or not. For example, the reading unit can input image data taken with a smartphone into a generating AI and have the generating AI perform analysis of the image data.

[0036] The analysis unit can analyze test and homework answers using image recognition technology and extract correct and incorrect data. For example, the analysis unit can determine whether an answer to a math problem is correct and generate correct and incorrect data. The analysis unit can also determine whether an answer to an English problem is correct and generate correct and incorrect data. Furthermore, the analysis unit can determine whether an answer to a science problem is correct and generate correct and incorrect data. In this way, test and homework answers can be accurately analyzed using image recognition technology. Image recognition technology includes, but is not limited to, CNN (Convolutional Neural Network) and SVM (Support Vector Machine). Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input image data read by the reading unit into a generating AI and have the generating AI perform the analysis of the image data.

[0037] The visualization unit can visualize strengths and weaknesses based on correct and incorrect answer data. For example, if a child gets many correct answers in math problems, the visualization unit will display that area as a strength. Conversely, if they get many incorrect answers, that area will be displayed as a weakness. The visualization unit can also display English problems as a strength if the child gets many correct answers, and conversely, if they get many incorrect answers, that area will be displayed as a weakness. Furthermore, the visualization unit can also display science problems as a strength if the child gets many correct answers, and conversely, if they get many incorrect answers, that area will be displayed as a weakness. This allows for a visual understanding of strengths and weaknesses. Visualization methods include, but are not limited to, graph displays and heatmap displays. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or not using AI. For example, the visualization unit can input correct and incorrect answer data generated by the analysis unit into the generating AI, allowing the generating AI to visualize the user's strengths and weaknesses.

[0038] The presentation can suggest solving more difficult problems in areas of strength and starting with basic problems in areas of weakness. For example, the presentation can suggest solving more difficult applied problems in areas of strength, while starting with basic problems in areas of weakness. For example, the presentation can suggest solving more difficult applied problems in areas of strength, while starting with basic problems in areas of weakness. The presentation can also suggest solving more difficult advanced problems in areas of strength, while starting with basic problems in areas of weakness. Furthermore, the presentation can suggest solving more difficult problems in areas of strength, while starting with basic problems in areas of weakness. In this way, advice can be provided to further develop strengths and overcome weaknesses. Difficult problems include, but are not limited to, applied problems and advanced problems. Basic problems include, but are not limited to, basic problems and beginner problems. Some or all of the above-described processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input data on strengths and weaknesses visualized by the visualization unit into a generating AI, and have the generating AI generate advice.

[0039] The intention-considering unit can prioritize providing advice related to a specific field of study if the child wishes to focus on that field. For example, if the child wishes to focus on mathematics, the intention-considering unit will prioritize providing advice related to mathematics. Similarly, if the child wishes to focus on English, the intention-considering unit can prioritize providing advice related to English. Furthermore, if the child wishes to focus on science, the intention-considering unit can prioritize providing advice related to science. This enables efficient learning support that takes the child's wishes into account. Specific fields include, but are not limited to, mathematics, science, and history. Some or all of the processing described above in the intention-considering unit may be performed using, for example, AI, or not. For example, the intention-considering unit can input data about the child's desired fields into a generating AI and have the generating AI generate advice.

[0040] The image reading unit can automatically adjust the light intensity and angle of the shooting environment during reading to acquire the optimal image. For example, the image reading unit can use AI to detect the ambient light intensity and take a picture at the optimal brightness. For example, the image reading unit can use AI to automatically adjust the camera angle and take a picture from the optimal viewpoint. The image reading unit can also use AI to adjust the light intensity and angle in real time in response to changes in the environment while taking pictures. This allows for the acquisition of high-quality images in the optimal shooting environment. Automatic adjustment of light intensity and angle includes, but is not limited to, exposure compensation and white balance adjustment. Some or all of the above processing in the image reading unit may be performed using AI, or not using AI. For example, the image reading unit can input image data captured by the camera into a generating AI and have the generating AI perform adjustments to the light intensity and angle.

[0041] The image processing unit can take multiple photos in succession during the processing and select the sharpest image. For example, the image processing unit can use AI to take multiple photos in succession and automatically select the sharpest image. For example, the image processing unit can use AI to take multiple photos from different angles and select the optimal image. The image processing unit can also use AI to analyze the images taken in succession and select the image with the least blur and noise. This allows for the selection of the sharpest image from multiple photos. The selection of the sharpest image includes, but is not limited to, resolution and noise level. Some or all of the processing described above in the image processing unit may be performed using AI, or not. For example, the image processing unit can input multiple images taken in succession into a generating AI and have the generating AI select the sharpest image.

[0042] The reading unit can perform filtering during the reading process to improve the recognition accuracy of children's handwritten characters. For example, the reading unit can use AI to enhance the contours of handwritten characters to improve recognition accuracy. For example, the reading unit can use AI to remove noise from handwritten characters to improve recognition accuracy. The reading unit can also use AI to adjust the contrast of handwritten characters to improve recognition accuracy. This improves the recognition accuracy of handwritten characters. Filtering includes, but is not limited to, noise reduction and edge enhancement. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input handwritten character image data into a generating AI and have the generating AI perform filtering.

[0043] The reading unit can simultaneously acquire audio data of the child's learning environment during the reading process, enabling a comprehensive understanding of the learning situation. For example, the reading unit can use AI to acquire audio data of the learning environment and evaluate the level of concentration. For example, the reading unit can use AI to analyze the audio data of the learning environment and understand the progress of learning. The reading unit can also use AI to evaluate the efficiency of learning based on the audio data of the learning environment. In this way, by acquiring audio data of the learning environment, a comprehensive understanding of the learning situation can be achieved. Audio data includes, but is not limited to, the type of microphone and sampling rate. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input audio data of the learning environment into a generating AI and have the generating AI perform the analysis of the audio data.

[0044] The analysis unit can analyze performance trends by comparing them with past response data during the analysis process. For example, the analysis unit can use AI to analyze improvements or declines in performance based on past response data. For example, the analysis unit can use AI to compare past response data with current data and display performance trends in a graph. The analysis unit can also use AI to analyze past response data and identify factors influencing performance fluctuations. This allows for analysis of performance trends by comparing them with past data. Analysis of performance trends includes, but is not limited to, time series data analysis and trend analysis. 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 past response data into a generating AI and have the generating AI perform the performance trend analysis.

[0045] The analysis unit can analyze the writing style and handwriting characteristics of the answer during analysis and evaluate the level of understanding. For example, the analysis unit can use AI to analyze the writing style of the answer and evaluate the level of understanding. For example, the analysis unit can use AI to analyze the handwriting characteristics and evaluate the accuracy of the answer. The analysis unit can also use AI to comprehensively analyze the writing style and handwriting characteristics of the answer and evaluate the level of understanding. In this way, the level of understanding can be evaluated by analyzing the writing style and handwriting characteristics of the answer. The analysis of writing style and handwriting characteristics includes, but is not limited to, pen pressure and character shape. 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 data on the writing style and handwriting of the answer into a generating AI and have the generating AI perform the evaluation of the level of understanding.

[0046] The analysis unit can evaluate relative strengths and weaknesses by comparing data with that of other children during analysis. For example, the analysis unit can use AI to evaluate relative strengths and weaknesses by comparing data with that of other children. For example, the analysis unit can use AI to evaluate relative performance based on data from other children. The analysis unit can also use AI to evaluate relative comprehension by comparing data with that of other children. This allows for the evaluation of relative strengths and weaknesses by comparing data with that of other children. The evaluation of relative strengths and weaknesses includes, but is not limited to, methods of comparison with data from other children. 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 data from other children into a generating AI and have the generating AI perform the evaluation of relative strengths and weaknesses.

[0047] The analysis unit can correct the analysis results during analysis by taking into account the speed and time of responses. For example, the analysis unit can analyze the speed of responses by the AI ​​and correct the performance. For example, the analysis unit can analyze the time it took the AI ​​to respond and correct the level of understanding. The analysis unit can also comprehensively analyze the speed and time of responses by the AI ​​and correct the performance. This allows the analysis results to be corrected by taking into account the speed and time of responses. Considerations of speed and time include, but are not limited to, the method of measuring response time and the criteria for evaluating speed. Some or all of the above processing in the analysis unit may be performed using, for example, the AI, or without the use of the AI. For example, the analysis unit can input data on the speed and time of responses into the generating AI and have the generating AI perform the correction of the analysis results.

[0048] The visualization unit can display the progress status in a graph by comparing it with past data during visualization. For example, the visualization unit can use AI to display the progress status in a graph based on past data. For example, the visualization unit can use AI to compare past data with current data and visually display the progress status. The visualization unit can also use AI to analyze past data and display the progress status in chronological order. This allows for a visual understanding of the progress status by comparing it with past data. The display of progress status includes, but is not limited to, the type of graph and the range of data to be displayed. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past data into a generating AI and have the generating AI perform the graph display of the progress status.

[0049] The visualization unit can display strengths and weaknesses in a map format during visualization, making them easier to understand visually. For example, the visualization unit can use AI to display strengths and weaknesses in a map format, making them easier to understand visually. For example, the visualization unit can use AI to color-code strengths and weaknesses and display them in a map format. Alternatively, the visualization unit can use AI to indicate strengths and weaknesses with icons and display them in a map format. This makes it easier to visually understand strengths and weaknesses in a map format. The map format display includes, but is not limited to, the type of map and the range of data to be displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on strengths and weaknesses into a generating AI and have the generating AI perform the map format display.

[0050] The visualization unit can generate detailed reports for parents and teachers during visualization. For example, the visualization unit can use AI to generate detailed reports for parents based on the visualization data. For example, the visualization unit can use AI to generate detailed reports for teachers based on the visualization data. The visualization unit can also use AI to analyze the visualization data and generate customized reports for parents and teachers. This enables the generation of detailed reports for parents and teachers. The generation of detailed reports includes, but is not limited to, report items and data aggregation methods. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input visualization data into a generating AI and have the generating AI perform the generation of detailed reports.

[0051] The visualization unit can attract children's interest by displaying their strengths and weaknesses with animations during visualization. For example, the visualization unit can use AI to display strengths and weaknesses with animations to attract children's interest. For example, the visualization unit can use AI to dynamically display strengths and weaknesses, making them easier to understand visually. The visualization unit can also use AI to display strengths and weaknesses with animations using characters. This allows the visualization unit to attract children's interest by displaying strengths and weaknesses with animations. The display of animations includes, but is not limited to, the format of the animation and the range of data to be displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on strengths and weaknesses into a generating AI and have the generating AI perform the animation display.

[0052] The presentation unit can analyze the effectiveness of past advice and select the optimal advice at the time of presentation. For example, the presentation unit can use AI to analyze the effectiveness of past advice and select the optimal advice. For example, the presentation unit can use AI to provide the most effective advice to the child based on the effectiveness of past advice. The presentation unit can also use AI to evaluate the effectiveness of past advice and adjust the content of the advice. This allows for the selection of the optimal advice by analyzing the effectiveness of past advice. The selection of the optimal advice includes, but is not limited to, methods for evaluating the effectiveness of past advice and criteria for selecting advice. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input data on past advice into a generating AI and have the generating AI perform the selection of the optimal advice.

[0053] The presentation unit can customize the content of the advice to match the child's learning style when presenting it. For example, the presentation unit can use AI to analyze the child's learning style and provide optimal advice. For example, the presentation unit can use AI to customize the content of the advice based on the child's learning style. The presentation unit can also use AI to consider the child's learning style and adjust the format of the advice. This allows the content of the advice to be customized to match the child's learning style. Customization of learning style includes, but is not limited to, visual, auditory, and experiential learning. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input data on the child's learning style into a generating AI and have the generating AI perform the advice customization.

[0054] The presentation unit can adjust advice based on feedback from parents and teachers at the time of presentation. For example, the AI ​​can adjust the content of the advice based on feedback from parents and teachers. For example, the AI ​​can change the priority of the advice based on the opinions of parents and teachers. The AI ​​can also analyze feedback from parents and teachers to improve the effectiveness of the advice. This allows the advice to be adjusted based on feedback from parents and teachers. The reflection of feedback includes, but is not limited to, comments from parents and teachers, evaluation criteria, etc. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input feedback data from parents and teachers into a generating AI and have the generating AI perform the adjustment of the advice.

[0055] The presentation unit can enhance children's motivation to learn by providing advice in a game format during presentation. For example, the presentation unit can use AI to provide advice in a game format to enhance children's motivation to learn. For example, the presentation unit can use AI to provide advice in a quiz format to capture children's interest. The presentation unit can also use AI to provide advice as a mini-game to make learning fun. In this way, providing advice in a game format can enhance children's motivation to learn. Examples of game-format presentations include, but are not limited to, quiz formats and simulation formats. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or without AI. For example, the presentation unit can input advice data into a generating AI and have the generating AI perform the game-format presentation.

[0056] The decision-making unit can select the optimal method of reflecting a decision by referring to past decision-making history when considering a decision. For example, the decision-making unit can use AI to select the optimal method of reflecting a decision based on past decision-making history. For example, the decision-making unit can use AI to analyze past decision-making history and adjust the method of reflecting a decision. The decision-making unit can also use AI to refer to past decision-making history and provide the most suitable method of reflecting a decision for the child. This allows for the selection of the optimal method of reflecting a decision by referring to past decision-making history. The reference to decision-making history includes, but is not limited to, past choices and reasons for choices. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input data on past decision-making history into a generating AI and have the generating AI select the optimal method of reflecting a decision.

[0057] The decision-making unit can customize advice by considering the child's learning goals and future aspirations when considering their intentions. For example, the decision-making unit can use AI to provide optimal advice based on the child's learning goals. For example, the decision-making unit can use AI to consider the child's future aspirations and customize the content of the advice. The decision-making unit can also use AI to comprehensively consider the child's learning goals and future aspirations and adjust the advice accordingly. This allows for the customization of advice by considering the child's learning goals and future aspirations. Consideration of learning goals and future aspirations includes, but is not limited to, short-term goals, long-term goals, and career plans. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input data on the child's learning goals and future aspirations into a generating AI and have the generating AI perform the advice customization.

[0058] The decision-making unit can adjust advice by reflecting the opinions of parents and teachers when considering their opinions. For example, the decision-making unit can use AI to adjust the content of advice based on the opinions of parents and teachers. For example, the decision-making unit can use AI to reflect the opinions of parents and teachers and change the priority of advice. The decision-making unit can also use AI to analyze the opinions of parents and teachers and improve the effectiveness of advice. This allows the advice to be adjusted by reflecting the opinions of parents and teachers. Reflecting the opinions of parents and teachers includes, but is not limited to, comments and evaluation criteria. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input parent and teacher opinion data into a generating AI and have the generating AI perform the adjustment of advice.

[0059] The decision-making unit can provide advice while considering the child's learning environment and lifestyle habits. For example, the decision-making unit can use AI to provide optimal advice based on the child's learning environment. For example, the decision-making unit can use AI to consider the child's lifestyle habits and adjust the content of the advice. The decision-making unit can also use AI to comprehensively consider the child's learning environment and lifestyle habits and provide advice. This allows the unit to provide advice while considering the child's learning environment and lifestyle habits. Consideration of the learning environment and lifestyle habits includes, but is not limited to, learning time, daily routines, and home environment. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input data on the child's learning environment and lifestyle habits into a generating AI and have the generating AI provide advice.

[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 learning support system can also be equipped with a speech recognition unit. This unit can analyze the sounds a child makes while learning and evaluate the accuracy of their pronunciation and intonation. For example, it can determine whether a child's pronunciation of an English word is correct. It can also evaluate whether the intonation is appropriate when a child reads a sentence aloud. Furthermore, it can analyze questions and comments a child makes while learning and provide appropriate feedback. This allows for the evaluation of a child's pronunciation and intonation accuracy using speech recognition technology, thereby improving the quality of their learning.

[0062] The learning support system can also be equipped with a biometrics unit. This unit can acquire the child's biological information and evaluate their learning progress. For example, the biometrics unit can measure the child's heart rate to assess their concentration level. It can also measure the child's skin temperature to assess their stress level. Furthermore, the biometrics unit can measure the child's brainwaves to evaluate their learning efficiency. This allows for a comprehensive evaluation of the child's learning situation using biological information, enabling the provision of appropriate support.

[0063] The learning support system can also be equipped with a gamification section. This section can present learning content in a game format, thereby increasing children's motivation to learn. For example, the gamification section could offer a game where children earn points by solving math problems. It could also offer a game where children level up by memorizing English vocabulary. Furthermore, it could offer a game that simulates science experiments. By presenting learning content in a game format, it can increase children's motivation to learn and make learning more enjoyable.

[0064] The learning support system can also be equipped with a social interaction section. This section facilitates communication among children and allows them to learn collaboratively. For example, the social interaction section can provide a function that allows children to solve problems together online. It can also provide a function that allows them to share their learning outcomes and provide feedback to each other. Furthermore, the social interaction section can provide a function that allows for a deeper understanding of the learning content through group discussions. This enables children to learn collaboratively through communication with each other.

[0065] The learning support system can also include a reminder function. This reminder function can manage a child's learning schedule and provide reminders at appropriate times. For example, it can notify the child of homework deadlines. It can also provide reminders to encourage test preparation. Furthermore, it can provide reminders to help children develop regular study habits. This allows the system to support children's learning by managing their study schedule and providing reminders at the right time.

[0066] The following briefly describes the processing flow for example form 1.

[0067] Step 1: The reading unit reads test and homework answers as photos. For example, a smartphone or tablet is used to take a picture of the test or homework, and the image data is input into the system. Step 2: The analysis unit analyzes the data read by the reading unit. For example, it uses image recognition technology to analyze test or homework answers and extracts data on correct and incorrect answers. Step 3: The visualization unit visualizes the strengths and weaknesses of the student based on the data analyzed by the analysis unit. For example, it visualizes strengths and weaknesses from the data of correct and incorrect answers. If a child gets many correct answers on math problems, that area is displayed as a strength, and if they get many incorrect answers, that area is displayed as a weakness. Step 4: The presentation section provides advice based on the strengths and weaknesses visualized by the visualization section. For example, it suggests solving more difficult problems in strengths and starting with basic problems in weaknesses. Step 5: The Consideration of Wisdom section takes the child's wishes into account based on the advice presented by the Presentation section. For example, if the child wishes to focus on a particular subject, the section will prioritize presenting advice related to that subject.

[0068] (Example of form 2) The learning support system according to an embodiment of the present invention is a system that supports efficient, personalized studying by combining the child's own will with the results of data analysis. This learning support system reads the child's completed tests and homework as photos, and the AI ​​analyzes them, visualizing the child's strengths and weaknesses from the correct and incorrect answer data. Furthermore, it provides advice to further develop strengths and advice to overcome weaknesses. At this time, the child's own will is also taken into consideration, and an efficient study method tailored to the individual is proposed. For example, the learning support system reads the child's completed tests and homework as photos. In this case, a smartphone or tablet is used to take a picture of the test or homework. For example, a child takes a picture of a math problem they have solved with a smartphone and inputs the image data into the system. Next, the learning support system's AI analyzes the read data. The AI ​​uses image recognition technology to analyze the answers to the tests and homework and extracts correct and incorrect answer data. For example, it determines whether the answer to a math problem is correct and generates correct and incorrect answer data. Furthermore, the learning support system uses AI to visualize strengths and weaknesses based on correct and incorrect answer data. For example, if a child gets many math problems right, that area will be displayed as a strength. Conversely, if they get many problems wrong, that area will be displayed as a weakness. Next, the learning support system provides advice to further develop strengths and overcome weaknesses. For example, it suggests solving more difficult problems in strengths and starting with basic problems in weaknesses. Finally, the learning support system takes the child's wishes into consideration and suggests efficient study methods tailored to each individual. For example, if a child wants to focus on a particular subject, it will prioritize providing advice related to that subject. In this way, it realizes learning support that is tailored to each child. As a result, the learning support system can support efficient studying tailored to each individual by combining the child's wishes with the results of data analysis.

[0069] The learning support system according to this embodiment comprises a reading unit, an analysis unit, a visualization unit, a presentation unit, and a decision-making unit. The reading unit reads test and homework answers as photographs. The reading unit takes pictures of tests and homework using a device such as a smartphone or tablet. For example, the reading unit takes a picture of a math problem solved by a child with a smartphone and inputs the image data into the system. The analysis unit analyzes the data read by the reading unit. The analysis unit analyzes the test and homework answers using image recognition technology, for example, and extracts correct and incorrect answer data. For example, the analysis unit determines whether the answer to a math problem is correct and generates correct and incorrect answer data. The visualization unit visualizes the strengths and weaknesses of the student from the data analyzed by the analysis unit. For example, the visualization unit visualizes the strengths and weaknesses of the student from the correct and incorrect answer data. For example, if a child has many correct answers in math problems, the visualization unit displays that area as a strength. On the other hand, if a child has many incorrect answers, that area is displayed as a weakness. The presentation unit provides advice based on the strengths and weaknesses visualized by the visualization unit. For example, the presentation unit suggests solving more difficult problems in areas of strength and starting with basic problems in areas of weakness. The intention consideration unit takes into account the child's wishes based on the advice provided by the presentation unit. For example, if the child wishes to focus on a particular area, the intention consideration unit prioritizes providing advice related to that area. As a result, the learning support system according to this embodiment can support efficient, personalized studying by combining the child's wishes with the results of data analysis.

[0070] The reading unit reads test and homework answers from photographs. The reading unit uses devices such as smartphones and tablets to take pictures of tests and homework. Specifically, it uses the smartphone's camera function to photograph the child's completed math problems or homework answer sheets, and inputs the image data into the system. The captured images are automatically adjusted for resolution, brightness, and contrast, and imported into the system in an optimal state. Furthermore, the reading unit can read multiple images at once and has a function to automatically combine consecutively taken images into a single data file. This allows for efficient reading of test and homework answers spanning multiple pages. The reading unit also performs pre-processing to accurately recognize handwritten characters and shapes, and performs image noise reduction and distortion correction. This allows the reading unit to accurately and quickly import test and homework answers into the system, enabling smooth data processing by the subsequent analysis unit.

[0071] The analysis unit analyzes the data read by the reading unit. For example, the analysis unit uses image recognition technology to analyze test and homework answers and extracts correct and incorrect data. Specifically, it uses OCR (optical character recognition) technology to convert handwritten characters into digital data and extracts the content of the answers to the questions as text data. Furthermore, it uses AI to analyze the extracted text data and determine whether the answers to the questions are correct or incorrect. For example, for mathematics problems, it analyzes the mathematical formulas and verifies the calculation results to determine whether the answer is correct or incorrect. The analysis unit also uses natural language processing technology to analyze word problems and written answers and provides appropriate evaluations. Based on these analysis results, the analysis unit records the correct answer rate and the trend of incorrect answers for each question in a database, making it available for use in the subsequent visualization and presentation units. In addition, the analysis unit has a function to evaluate learning progress and the degree of improvement in performance by comparing it with past data. This allows the analysis unit to perform accurate and detailed data analysis and enhance the overall effectiveness of the learning support system.

[0072] The visualization unit visualizes strengths and weaknesses based on data analyzed by the analysis unit. Specifically, it displays the correct and incorrect answer rates for each subject area in graphs and charts, based on correct and incorrect answer data. For example, it uses bar graphs and pie charts to allow users to quickly grasp areas where a child excels and areas where they struggle. The visualization unit also displays changes in performance over time using line graphs, allowing users to visually check their learning progress. Furthermore, the visualization unit displays detailed analysis results for each individual problem, specifically showing which problems were answered incorrectly and which parts were not understood. This allows children, parents, and teachers to intuitively understand learning outcomes and challenges, and to create effective learning plans. The visualization unit also prioritizes user-friendliness, employing a simple and easy-to-understand design. As a result, the visualization unit effectively visualizes analysis results, making it easy for users of the learning support system to understand and utilize the information.

[0073] The presentation unit provides advice based on the strengths and weaknesses visualized by the visualization unit. Specifically, it suggests solving more difficult problems in areas of strength and starting with basic problems in areas of weakness. For example, in mathematics, if a student frequently gets certain answers right, it will present applied problems and advanced tasks and provide advice to deepen their understanding. On the other hand, if a student frequently makes mistakes, it will encourage reviewing basic concepts and formulas and guide them to progress step by step from the basics. The presentation unit presents this advice as a concrete learning plan to support daily learning. Furthermore, to increase motivation, the presentation unit introduces achievement goals and reward systems to make learning enjoyable for children. For example, it provides a system where students can earn badges or points when they achieve certain goals, visualizing their learning progress. This allows the presentation unit to provide specific and effective advice tailored to each individual learner, improving the quality of their learning.

[0074] The Intention-Consideration Unit takes the child's wishes into account based on the advice provided by the Presentation Unit. Specifically, if a child wishes to focus on a particular subject, it prioritizes providing advice related to that subject. For example, if a child is interested in geometry in mathematics, it will focus on presenting geometry-related problems and assignments to maintain their interest. Also, if a child wants to overcome a weak area, it will prioritize providing basic problems and review assignments in that area. The Intention-Consideration Unit proposes flexible learning plans and provides support tailored to individual needs in order to maximize the child's motivation and interest in learning. Furthermore, the Intention-Consideration Unit collects feedback from the child and improves and adjusts the learning plan. For example, if a child feels they have deepened their understanding of a particular problem, they will proceed with learning in that area, while if their understanding is insufficient, they will be instructed to review the basics again. In this way, the Intention-Consideration Unit can combine the child's wishes with the results of data analysis to support personalized and efficient learning.

[0075] The reading unit can take pictures of tests and homework using devices such as smartphones and tablets. For example, the reading unit can easily take pictures of tests and homework using a smartphone or tablet. For instance, the reading unit can take pictures of tests and homework using a smartphone camera and input the image data into the system. The reading unit can also take pictures of tests and homework using a tablet camera and input the image data into the system. Furthermore, the reading unit can take pictures of tests and homework using a digital camera and input the image data into the system. This makes it easy to take pictures of tests and homework using a smartphone or tablet. Smartphones and tablets include, but are not limited to, iOS devices and Android devices. Some or all of the processing described above in the reading unit may be performed using, for example, AI, or not. For example, the reading unit can input image data taken with a smartphone into a generating AI and have the generating AI perform analysis of the image data.

[0076] The analysis unit can analyze test and homework answers using image recognition technology and extract correct and incorrect data. For example, the analysis unit can determine whether an answer to a math problem is correct and generate correct and incorrect data. The analysis unit can also determine whether an answer to an English problem is correct and generate correct and incorrect data. Furthermore, the analysis unit can determine whether an answer to a science problem is correct and generate correct and incorrect data. In this way, test and homework answers can be accurately analyzed using image recognition technology. Image recognition technology includes, but is not limited to, CNN (Convolutional Neural Network) and SVM (Support Vector Machine). Some or all of the above processing in the analysis unit may be performed using, for example, AI, or not using AI. For example, the analysis unit can input image data read by the reading unit into a generating AI and have the generating AI perform the analysis of the image data.

[0077] The visualization unit can visualize strengths and weaknesses based on correct and incorrect answer data. For example, if a child gets many correct answers in math problems, the visualization unit will display that area as a strength. Conversely, if they get many incorrect answers, that area will be displayed as a weakness. The visualization unit can also display English problems as a strength if the child gets many correct answers, and conversely, if they get many incorrect answers, that area will be displayed as a weakness. Furthermore, the visualization unit can also display science problems as a strength if the child gets many correct answers, and conversely, if they get many incorrect answers, that area will be displayed as a weakness. This allows for a visual understanding of strengths and weaknesses. Visualization methods include, but are not limited to, graph displays and heatmap displays. Some or all of the above-described processing in the visualization unit may be performed using, for example, AI, or not using AI. For example, the visualization unit can input correct and incorrect answer data generated by the analysis unit into the generating AI, allowing the generating AI to visualize the user's strengths and weaknesses.

[0078] The presentation can suggest solving more difficult problems in areas of strength and starting with basic problems in areas of weakness. For example, the presentation can suggest solving more difficult applied problems in areas of strength, while starting with basic problems in areas of weakness. For example, the presentation can suggest solving more difficult applied problems in areas of strength, while starting with basic problems in areas of weakness. The presentation can also suggest solving more difficult advanced problems in areas of strength, while starting with basic problems in areas of weakness. Furthermore, the presentation can suggest solving more difficult problems in areas of strength, while starting with basic problems in areas of weakness. In this way, advice can be provided to further develop strengths and overcome weaknesses. Difficult problems include, but are not limited to, applied problems and advanced problems. Basic problems include, but are not limited to, basic problems and beginner problems. Some or all of the above-described processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input data on strengths and weaknesses visualized by the visualization unit into a generating AI, and have the generating AI generate advice.

[0079] The intention-considering unit can prioritize providing advice related to a specific field of study if the child wishes to focus on that field. For example, if the child wishes to focus on mathematics, the intention-considering unit will prioritize providing advice related to mathematics. Similarly, if the child wishes to focus on English, the intention-considering unit can prioritize providing advice related to English. Furthermore, if the child wishes to focus on science, the intention-considering unit can prioritize providing advice related to science. This enables efficient learning support that takes the child's wishes into account. Specific fields include, but are not limited to, mathematics, science, and history. Some or all of the processing described above in the intention-considering unit may be performed using, for example, AI, or not. For example, the intention-considering unit can input data about the child's desired fields into a generating AI and have the generating AI generate advice.

[0080] The image processing unit can estimate the child's emotions and adjust the timing of photo capture based on the estimated emotions. For example, the AI ​​can automatically select the timing for taking a photo when the child is concentrating. For example, the AI ​​can adjust the timing for taking a photo when the child is relaxed. The AI ​​can also suggest a break when the child is tired and then set the timing for taking a photo. This allows for taking photos at the optimal time according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, such as an emotion engine or generative AI. Generative AI may be, but is not limited to, text generation AI (e.g., LLM) or multimodal generation AI. Some or all of the above processing in the image processing unit may be performed using AI or not. For example, the image processing unit can input image data of the child captured by the camera into a generative AI and have the generative AI perform the estimation of the child's emotions.

[0081] The image reading unit can automatically adjust the light intensity and angle of the shooting environment during reading to acquire the optimal image. For example, the image reading unit can use AI to detect the ambient light intensity and take a picture at the optimal brightness. For example, the image reading unit can use AI to automatically adjust the camera angle and take a picture from the optimal viewpoint. The image reading unit can also use AI to adjust the light intensity and angle in real time in response to changes in the environment while taking pictures. This allows for the acquisition of high-quality images in the optimal shooting environment. Automatic adjustment of light intensity and angle includes, but is not limited to, exposure compensation and white balance adjustment. Some or all of the above processing in the image reading unit may be performed using AI, or not using AI. For example, the image reading unit can input image data captured by the camera into a generating AI and have the generating AI perform adjustments to the light intensity and angle.

[0082] The image processing unit can take multiple photos in succession during the processing and select the sharpest image. For example, the image processing unit can use AI to take multiple photos in succession and automatically select the sharpest image. For example, the image processing unit can use AI to take multiple photos from different angles and select the optimal image. The image processing unit can also use AI to analyze the images taken in succession and select the image with the least blur and noise. This allows for the selection of the sharpest image from multiple photos. The selection of the sharpest image includes, but is not limited to, resolution and noise level. Some or all of the processing described above in the image processing unit may be performed using AI, or not. For example, the image processing unit can input multiple images taken in succession into a generating AI and have the generating AI select the sharpest image.

[0083] The reading unit can estimate the child's emotions and determine the priority of tests and homework to be filmed based on the estimated emotions. For example, if the child is excited, the reading unit will prioritize filming difficult tests and homework. For example, if the child is relaxed, the reading unit will prioritize filming basic tests and homework. Also, if the child is tired, the reading unit can prioritize filming easy homework. This allows tests and homework to be filmed in the optimal order 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 reading unit may be performed using AI or not using AI. For example, the reading unit can input image data of the child captured by the camera into a generative AI and have the generative AI perform the estimation of the child's emotions.

[0084] The reading unit can perform filtering during the reading process to improve the recognition accuracy of children's handwritten characters. For example, the reading unit can use AI to enhance the contours of handwritten characters to improve recognition accuracy. For example, the reading unit can use AI to remove noise from handwritten characters to improve recognition accuracy. The reading unit can also use AI to adjust the contrast of handwritten characters to improve recognition accuracy. This improves the recognition accuracy of handwritten characters. Filtering includes, but is not limited to, noise reduction and edge enhancement. Some or all of the above processing in the reading unit may be performed using, for example, AI, or not using AI. For example, the reading unit can input handwritten character image data into a generating AI and have the generating AI perform filtering.

[0085] The reading unit can simultaneously acquire audio data of the child's learning environment during the reading process, enabling a comprehensive understanding of the learning situation. For example, the reading unit can use AI to acquire audio data of the learning environment and evaluate the level of concentration. For example, the reading unit can use AI to analyze the audio data of the learning environment and understand the progress of learning. The reading unit can also use AI to evaluate the efficiency of learning based on the audio data of the learning environment. In this way, by acquiring audio data of the learning environment, a comprehensive understanding of the learning situation can be achieved. Audio data includes, but is not limited to, the type of microphone and sampling rate. Some or all of the above processing in the reading unit may be performed using AI, for example, or without AI. For example, the reading unit can input audio data of the learning environment into a generating AI and have the generating AI perform the analysis of the audio data.

[0086] The analysis unit can estimate the child's emotions and adjust the analysis algorithm based on the estimated emotions. For example, if the child is relaxed, the analysis unit uses an algorithm that performs a detailed analysis. For example, if the child is in a hurry, the analysis unit uses a simplified analysis algorithm. The analysis unit can also use an algorithm that provides visually stimulating analysis results if the child is excited. This allows the analysis algorithm to be adjusted 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 emotion data into the generative AI and have the generative AI perform the adjustment of the analysis algorithm.

[0087] The analysis unit can analyze performance trends by comparing them with past response data during the analysis process. For example, the analysis unit can use AI to analyze improvements or declines in performance based on past response data. For example, the analysis unit can use AI to compare past response data with current data and display performance trends in a graph. The analysis unit can also use AI to analyze past response data and identify factors influencing performance fluctuations. This allows for analysis of performance trends by comparing them with past data. Analysis of performance trends includes, but is not limited to, time series data analysis and trend analysis. 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 past response data into a generating AI and have the generating AI perform the performance trend analysis.

[0088] The analysis unit can analyze the writing style and handwriting characteristics of the answer during analysis and evaluate the level of understanding. For example, the analysis unit can use AI to analyze the writing style of the answer and evaluate the level of understanding. For example, the analysis unit can use AI to analyze the handwriting characteristics and evaluate the accuracy of the answer. The analysis unit can also use AI to comprehensively analyze the writing style and handwriting characteristics of the answer and evaluate the level of understanding. In this way, the level of understanding can be evaluated by analyzing the writing style and handwriting characteristics of the answer. The analysis of writing style and handwriting characteristics includes, but is not limited to, pen pressure and character shape. 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 data on the writing style and handwriting of the answer into a generating AI and have the generating AI perform the evaluation of the level of understanding.

[0089] 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 provides a simple and highly visible display method. For example, if the child is relaxed, the analysis unit provides a display method that includes detailed information. The analysis unit can also provide a concise display method if the child is in a hurry. This allows the display method of the analysis results to be adjusted 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 emotion data into the generative AI and have the generative AI adjust the display method of the analysis results.

[0090] The analysis unit can evaluate relative strengths and weaknesses by comparing data with that of other children during analysis. For example, the analysis unit can use AI to evaluate relative strengths and weaknesses by comparing data with that of other children. For example, the analysis unit can use AI to evaluate relative performance based on data from other children. The analysis unit can also use AI to evaluate relative comprehension by comparing data with that of other children. This allows for the evaluation of relative strengths and weaknesses by comparing data with that of other children. The evaluation of relative strengths and weaknesses includes, but is not limited to, methods of comparison with data from other children. 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 data from other children into a generating AI and have the generating AI perform the evaluation of relative strengths and weaknesses.

[0091] The analysis unit can correct the analysis results during analysis by taking into account the speed and time of responses. For example, the analysis unit can analyze the speed of responses by the AI ​​and correct the performance. For example, the analysis unit can analyze the time it took the AI ​​to respond and correct the level of understanding. The analysis unit can also comprehensively analyze the speed and time of responses by the AI ​​and correct the performance. This allows the analysis results to be corrected by taking into account the speed and time of responses. Considerations of speed and time include, but are not limited to, the method of measuring response time and the criteria for evaluating speed. Some or all of the above processing in the analysis unit may be performed using, for example, the AI, or without the use of the AI. For example, the analysis unit can input data on the speed and time of responses into the generating AI and have the generating AI perform the correction of the analysis results.

[0092] The visualization unit can estimate a child's emotions and adjust the visualization's color and design based on the estimated emotions. For example, if a child is nervous, the visualization unit provides a visualization with calm colors. For example, if a child is having fun, the visualization unit provides a visualization with bright colors. The visualization unit can also provide a simple and highly visible visualization if a child is tired. This allows the visualization's color and design to be adjusted according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input child emotion data into a generative AI and have the generative AI adjust the visualization's color and design.

[0093] The visualization unit can display the progress status in a graph by comparing it with past data during visualization. For example, the visualization unit can use AI to display the progress status in a graph based on past data. For example, the visualization unit can use AI to compare past data with current data and visually display the progress status. The visualization unit can also use AI to analyze past data and display the progress status in chronological order. This allows for a visual understanding of the progress status by comparing it with past data. The display of progress status includes, but is not limited to, the type of graph and the range of data to be displayed. Some or all of the above-described processes in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input past data into a generating AI and have the generating AI perform the graph display of the progress status.

[0094] The visualization unit can display strengths and weaknesses in a map format during visualization, making them easier to understand visually. For example, the visualization unit can use AI to display strengths and weaknesses in a map format, making them easier to understand visually. For example, the visualization unit can use AI to color-code strengths and weaknesses and display them in a map format. Alternatively, the visualization unit can use AI to indicate strengths and weaknesses with icons and display them in a map format. This makes it easier to visually understand strengths and weaknesses in a map format. The map format display includes, but is not limited to, the type of map and the range of data to be displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on strengths and weaknesses into a generating AI and have the generating AI perform the map format display.

[0095] The visualization unit can estimate the child's emotions and adjust the display order of the visualizations based on the estimated emotions. For example, if the child is nervous, the visualization unit will display important information first. For example, if the child is relaxed, the visualization unit will display detailed information later. Also, if the child is in a hurry, the visualization unit can display the main points first. This allows the display order of the visualizations to be adjusted 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 visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input the child's emotion data into the generative AI and have the generative AI adjust the display order of the visualizations.

[0096] The visualization unit can generate detailed reports for parents and teachers during visualization. For example, the visualization unit can use AI to generate detailed reports for parents based on the visualization data. For example, the visualization unit can use AI to generate detailed reports for teachers based on the visualization data. The visualization unit can also use AI to analyze the visualization data and generate customized reports for parents and teachers. This enables the generation of detailed reports for parents and teachers. The generation of detailed reports includes, but is not limited to, report items and data aggregation methods. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input visualization data into a generating AI and have the generating AI perform the generation of detailed reports.

[0097] The visualization unit can attract children's interest by displaying their strengths and weaknesses with animations during visualization. For example, the visualization unit can use AI to display strengths and weaknesses with animations to attract children's interest. For example, the visualization unit can use AI to dynamically display strengths and weaknesses, making them easier to understand visually. The visualization unit can also use AI to display strengths and weaknesses with animations using characters. This allows the visualization unit to attract children's interest by displaying strengths and weaknesses with animations. The display of animations includes, but is not limited to, the format of the animation and the range of data to be displayed. Some or all of the above processing in the visualization unit may be performed using AI, for example, or without AI. For example, the visualization unit can input data on strengths and weaknesses into a generating AI and have the generating AI perform the animation display.

[0098] The presentation unit can estimate the child's emotions and adjust the way advice is expressed based on the estimated emotions. For example, if the child is nervous, the presentation unit will provide advice in gentle words. For example, if the child is relaxed, the presentation unit will provide detailed advice. The presentation unit can also provide concise and to-the-point advice if the child is in a hurry. This allows the way advice is expressed to be adjusted 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 presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input the child's emotion data into the generative AI and have the generative AI adjust the way advice is expressed.

[0099] The presentation unit can analyze the effectiveness of past advice and select the optimal advice at the time of presentation. For example, the presentation unit can use AI to analyze the effectiveness of past advice and select the optimal advice. For example, the presentation unit can use AI to provide the most effective advice to the child based on the effectiveness of past advice. The presentation unit can also use AI to evaluate the effectiveness of past advice and adjust the content of the advice. This allows for the selection of the optimal advice by analyzing the effectiveness of past advice. The selection of the optimal advice includes, but is not limited to, methods for evaluating the effectiveness of past advice and criteria for selecting advice. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input data on past advice into a generating AI and have the generating AI perform the selection of the optimal advice.

[0100] The presentation unit can customize the content of the advice to match the child's learning style when presenting it. For example, the presentation unit can use AI to analyze the child's learning style and provide optimal advice. For example, the presentation unit can use AI to customize the content of the advice based on the child's learning style. The presentation unit can also use AI to consider the child's learning style and adjust the format of the advice. This allows the content of the advice to be customized to match the child's learning style. Customization of learning style includes, but is not limited to, visual, auditory, and experiential learning. Some or all of the above processing in the presentation unit may be performed using AI, for example, or without AI. For example, the presentation unit can input data on the child's learning style into a generating AI and have the generating AI perform the advice customization.

[0101] The presentation unit can estimate a child's emotions and prioritize advice based on those emotions. For example, if the child is nervous, the presentation unit will prioritize advice on how to relax. For example, if the child is relaxed, the presentation unit will prioritize advice on how to proceed with learning. The presentation unit can also prioritize important advice if the child is in a hurry. This allows the presentation unit to prioritize advice according to the child's emotions. Emotion estimation is achieved using an emotion estimation function, for example, 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 presentation unit may be performed using AI, for example, or not using AI. For example, the presentation unit can input child emotion data into a generative AI and have the generative AI determine the priority of advice.

[0102] The presentation unit can adjust advice based on feedback from parents and teachers at the time of presentation. For example, the AI ​​can adjust the content of the advice based on feedback from parents and teachers. For example, the AI ​​can change the priority of the advice based on the opinions of parents and teachers. The AI ​​can also analyze feedback from parents and teachers to improve the effectiveness of the advice. This allows the advice to be adjusted based on feedback from parents and teachers. The reflection of feedback includes, but is not limited to, comments from parents and teachers, evaluation criteria, etc. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or not using AI. For example, the presentation unit can input feedback data from parents and teachers into a generating AI and have the generating AI perform the adjustment of the advice.

[0103] The presentation unit can enhance children's motivation to learn by providing advice in a game format during presentation. For example, the presentation unit can use AI to provide advice in a game format to enhance children's motivation to learn. For example, the presentation unit can use AI to provide advice in a quiz format to capture children's interest. The presentation unit can also use AI to provide advice as a mini-game to make learning fun. In this way, providing advice in a game format can enhance children's motivation to learn. Examples of game-format presentations include, but are not limited to, quiz formats and simulation formats. Some or all of the above processing in the presentation unit may be performed using, for example, AI, or without AI. For example, the presentation unit can input advice data into a generating AI and have the generating AI perform the game-format presentation.

[0104] The decision-making unit can estimate a child's emotions and adjust how decisions are reflected based on the estimated emotions. For example, if a child is tense, the decision-making unit prioritizes advice on how to relax. For example, if a child is relaxed, the decision-making unit prioritizes advice on how to proceed with learning. The decision-making unit can also prioritize providing important advice if a child is in a hurry. This allows the way decisions are reflected to be adjusted 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 decision-making unit may be performed using AI, for example, or not using AI. For example, the decision-making unit can input child emotion data into a generative AI and have the generative AI adjust how decisions are reflected.

[0105] The decision-making unit can select the optimal method of reflecting a decision by referring to past decision-making history when considering a decision. For example, the decision-making unit can use AI to select the optimal method of reflecting a decision based on past decision-making history. For example, the decision-making unit can use AI to analyze past decision-making history and adjust the method of reflecting a decision. The decision-making unit can also use AI to refer to past decision-making history and provide the most suitable method of reflecting a decision for the child. This allows for the selection of the optimal method of reflecting a decision by referring to past decision-making history. The reference to decision-making history includes, but is not limited to, past choices and reasons for choices. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input data on past decision-making history into a generating AI and have the generating AI select the optimal method of reflecting a decision.

[0106] The decision-making unit can customize advice by considering the child's learning goals and future aspirations when considering their intentions. For example, the decision-making unit can use AI to provide optimal advice based on the child's learning goals. For example, the decision-making unit can use AI to consider the child's future aspirations and customize the content of the advice. The decision-making unit can also use AI to comprehensively consider the child's learning goals and future aspirations and adjust the advice accordingly. This allows for the customization of advice by considering the child's learning goals and future aspirations. Consideration of learning goals and future aspirations includes, but is not limited to, short-term goals, long-term goals, and career plans. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input data on the child's learning goals and future aspirations into a generating AI and have the generating AI perform the advice customization.

[0107] The decision-making unit can estimate a child's emotions and determine the priority of decisions based on the estimated emotions. For example, if the child is nervous, the decision-making unit will prioritize advice on how to relax. For example, if the child is relaxed, the decision-making unit will prioritize advice on how to proceed with learning. The decision-making unit can also prioritize providing important advice if the child is in a hurry. This allows the priority of decisions to be determined 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 decision-making unit may be performed using AI or not using AI. For example, the decision-making unit can input child emotion data into a generative AI and have the generative AI perform the determination of the priority of decisions.

[0108] The decision-making unit can adjust advice by reflecting the opinions of parents and teachers when considering their opinions. For example, the decision-making unit can use AI to adjust the content of advice based on the opinions of parents and teachers. For example, the decision-making unit can use AI to reflect the opinions of parents and teachers and change the priority of advice. The decision-making unit can also use AI to analyze the opinions of parents and teachers and improve the effectiveness of advice. This allows the advice to be adjusted by reflecting the opinions of parents and teachers. Reflecting the opinions of parents and teachers includes, but is not limited to, comments and evaluation criteria. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input parent and teacher opinion data into a generating AI and have the generating AI perform the adjustment of advice.

[0109] The decision-making unit can provide advice while considering the child's learning environment and lifestyle habits. For example, the decision-making unit can use AI to provide optimal advice based on the child's learning environment. For example, the decision-making unit can use AI to consider the child's lifestyle habits and adjust the content of the advice. The decision-making unit can also use AI to comprehensively consider the child's learning environment and lifestyle habits and provide advice. This allows the unit to provide advice while considering the child's learning environment and lifestyle habits. Consideration of the learning environment and lifestyle habits includes, but is not limited to, learning time, daily routines, and home environment. Some or all of the above processing in the decision-making unit may be performed using AI, for example, or without AI. For example, the decision-making unit can input data on the child's learning environment and lifestyle habits into a generating AI and have the generating AI provide advice.

[0110] The system according to the embodiment is not limited to the example described above, and various modifications are possible, for example, as follows.

[0111] The learning support system can also be equipped with a speech recognition unit. This unit can analyze the sounds a child makes while learning and evaluate the accuracy of their pronunciation and intonation. For example, it can determine whether a child's pronunciation of an English word is correct. It can also evaluate whether the intonation is appropriate when a child reads a sentence aloud. Furthermore, it can analyze questions and comments a child makes while learning and provide appropriate feedback. This allows for the evaluation of a child's pronunciation and intonation accuracy using speech recognition technology, thereby improving the quality of their learning.

[0112] The learning support system can also be equipped with a biometrics unit. This unit can acquire the child's biological information and evaluate their learning progress. For example, the biometrics unit can measure the child's heart rate to assess their concentration level. It can also measure the child's skin temperature to assess their stress level. Furthermore, the biometrics unit can measure the child's brainwaves to evaluate their learning efficiency. This allows for a comprehensive evaluation of the child's learning situation using biological information, enabling the provision of appropriate support.

[0113] The learning support system can also be equipped with a gamification section. This section can present learning content in a game format, thereby increasing children's motivation to learn. For example, the gamification section could offer a game where children earn points by solving math problems. It could also offer a game where children level up by memorizing English vocabulary. Furthermore, it could offer a game that simulates science experiments. By presenting learning content in a game format, it can increase children's motivation to learn and make learning more enjoyable.

[0114] The learning support system can also be equipped with a social interaction section. This section facilitates communication among children and allows them to learn collaboratively. For example, the social interaction section can provide a function that allows children to solve problems together online. It can also provide a function that allows them to share their learning outcomes and provide feedback to each other. Furthermore, the social interaction section can provide a function that allows for a deeper understanding of the learning content through group discussions. This enables children to learn collaboratively through communication with each other.

[0115] The learning support system can also include a reminder function. This reminder function can manage a child's learning schedule and provide reminders at appropriate times. For example, it can notify the child of homework deadlines. It can also provide reminders to encourage test preparation. Furthermore, it can provide reminders to help children develop regular study habits. This allows the system to support children's learning by managing their study schedule and providing reminders at the right time.

[0116] The learning support system can also be equipped with an emotion estimation unit. This unit can estimate a child's emotions and adjust learning content based on those emotions. For example, if a child is feeling stressed, the emotion estimation unit can suggest activities to help them relax. It can also suggest activities to improve concentration if the child is excited. Furthermore, if the child is tired, the emotion estimation unit can suggest a break. This allows the learning content to be adjusted according to the child's emotions, supporting effective learning.

[0117] The learning support system can also be equipped with an emotional feedback unit. This unit can estimate a child's emotions and provide feedback based on those estimates. For example, if a child is happy, the unit can provide positive feedback. It can also provide encouraging feedback if a child is feeling down. Furthermore, if a child is feeling anxious, the unit can provide reassuring feedback. This allows for the provision of appropriate feedback according to the child's emotions, thereby increasing their motivation to learn.

[0118] The learning support system can also be equipped with an emotion monitoring unit. This unit can monitor a child's emotions in real time and evaluate their learning progress. For example, it can assess whether the child is concentrating, whether they are tired, and whether they are enjoying themselves. This allows for real-time monitoring of the child's emotions and evaluation of their learning progress.

[0119] The learning support system may also include an emotion regulation unit. This unit can estimate a child's emotions and adjust the learning environment based on those estimates. For example, if a child is feeling stressed, the unit might play relaxing music. It could also suggest a break if the child is tired. Furthermore, if the child is concentrating, the unit could maintain a quiet learning environment. This allows the learning environment to be adjusted according to the child's emotions, supporting effective learning.

[0120] The learning support system can also be equipped with an emotion prediction unit. This unit can predict a child's future emotions based on their past emotional data and adjust the learning plan accordingly. For example, it can predict that a child is more likely to concentrate during certain times and set more challenging tasks for those times. It can also predict that a child is more prone to fatigue on certain days of the week and set lighter tasks for those days. Furthermore, it can predict that a child is more likely to feel stressed after certain events and suggest activities to help them relax afterward. By predicting future emotions and adjusting the learning plan accordingly, the system can support effective learning.

[0121] The following briefly describes the processing flow for example form 2.

[0122] Step 1: The reading unit reads test and homework answers as photos. For example, a smartphone or tablet is used to take a picture of the test or homework, and the image data is input into the system. Step 2: The analysis unit analyzes the data read by the reading unit. For example, it uses image recognition technology to analyze test or homework answers and extracts data on correct and incorrect answers. Step 3: The visualization unit visualizes the strengths and weaknesses of the student based on the data analyzed by the analysis unit. For example, it visualizes strengths and weaknesses from the data of correct and incorrect answers. If a child gets many correct answers on math problems, that area is displayed as a strength, and if they get many incorrect answers, that area is displayed as a weakness. Step 4: The presentation section provides advice based on the strengths and weaknesses visualized by the visualization section. For example, it suggests solving more difficult problems in strengths and starting with basic problems in weaknesses. Step 5: The Consideration of Wisdom section takes the child's wishes into account based on the advice presented by the Presentation section. For example, if the child wishes to focus on a particular subject, the section will prioritize presenting advice related to that subject.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] Each of the multiple elements described above, including the reading unit, analysis unit, visualization unit, presentation unit, and intention consideration unit, is implemented in at least one of the smart device 14 and the data processing unit 12. For example, the reading unit uses the camera 42 of the smart device 14 to take pictures of tests and homework and inputs the image data into the system. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses image recognition technology to analyze the answers to tests and homework and extract correct and incorrect data. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12 and visualizes the child's strengths and weaknesses from the correct and incorrect data. The presentation unit is implemented in the specific processing unit 290 of the data processing unit 12 and presents advice based on the child's strengths and weaknesses. The intention consideration unit is implemented in the specific processing unit 290 of the data processing unit 12 and prioritizes presenting advice while taking into account the child's own intentions. 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.

[0127] [Second Embodiment] Figure 3 shows an example of the configuration of the data processing system 210 according to the second embodiment.

[0128] 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.

[0129] 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.

[0130] 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.

[0131] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0132] 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).

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] Each of the multiple elements described above, including the reading unit, analysis unit, visualization unit, presentation unit, and intention consideration unit, is implemented in at least one of the smart glasses 214 and the data processing unit 12. For example, the reading unit uses the camera 42 of the smart glasses 214 to take pictures of tests and homework and inputs the image data into the system. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses image recognition technology to analyze the answers to tests and homework and extract correct and incorrect data. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12 and visualizes the child's strengths and weaknesses from the correct and incorrect data. The presentation unit is implemented in the specific processing unit 290 of the data processing unit 12 and presents advice based on the child's strengths and weaknesses. The intention consideration unit is implemented in the specific processing unit 290 of the data processing unit 12 and prioritizes presenting advice while taking into account the child's own intentions. 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.

[0143] [Third Embodiment] Figure 5 shows an example of the configuration of the data processing system 310 according to the third embodiment.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0148] 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).

[0149] 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.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.).

[0155] 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.

[0156] 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.

[0157] 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.

[0158] Each of the multiple elements described above, including the reading unit, analysis unit, visualization unit, presentation unit, and intention consideration unit, is implemented in at least one of the headset terminal 314 and the data processing unit 12. For example, the reading unit uses the camera 42 of the headset terminal 314 to take pictures of tests and homework and inputs the image data into the system. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses image recognition technology to analyze the answers to tests and homework and extract correct and incorrect data. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12 and visualizes the child's strengths and weaknesses from the correct and incorrect data. The presentation unit is implemented in the specific processing unit 290 of the data processing unit 12 and presents advice based on the child's strengths and weaknesses. The intention consideration unit is implemented in the specific processing unit 290 of the data processing unit 12 and prioritizes presenting advice while taking into account the child's own intentions. 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.

[0159] [Fourth Embodiment] Figure 7 shows an example of the configuration of the data processing system 410 according to the fourth embodiment.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] The microphone 238 receives voice signals from the user and accepts instructions from the user. 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.

[0164] 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).

[0165] 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.

[0166] 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.

[0167] 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.

[0168] 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.

[0169] 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.

[0170] 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.

[0171] 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.).

[0172] 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.

[0173] 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.

[0174] 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.

[0175] Each of the multiple elements described above, including the reading unit, analysis unit, visualization unit, presentation unit, and intention consideration unit, is implemented in at least one of the robot 414 and the data processing unit 12. For example, the reading unit uses the camera 42 of the robot 414 to take pictures of tests and homework and inputs the image data into the system. The analysis unit is implemented in the specific processing unit 290 of the data processing unit 12 and uses image recognition technology to analyze the answers to tests and homework and extract correct and incorrect data. The visualization unit is implemented in the specific processing unit 290 of the data processing unit 12 and visualizes the child's strengths and weaknesses from the correct and incorrect data. The presentation unit is implemented in the specific processing unit 290 of the data processing unit 12 and presents advice based on the child's strengths and weaknesses. The intention consideration unit is implemented in the specific processing unit 290 of the data processing unit 12 and prioritizes presenting advice while taking into account the child's own intentions. 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.

[0176] 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.

[0177] 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.

[0178] 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.

[0179] 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.

[0180] 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.

[0181] 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."

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] 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.

[0193] 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.

[0194] (Note 1) A reading unit that reads test and homework answers from photos, An analysis unit analyzes the data read by the aforementioned reading unit, A visualization unit visualizes the strengths and weaknesses of the data analyzed by the aforementioned analysis unit, A presentation unit provides advice based on the strengths and weaknesses visualized by the aforementioned visualization unit, The system includes a will consideration unit that considers the child's wishes based on the advice presented by the aforementioned presentation unit. A system characterized by the following features. (Note 2) The aforementioned reading unit, Use a smartphone or tablet to take pictures of your tests or homework. The system described in Appendix 1, characterized by the features described herein. (Note 3) The aforementioned analysis unit, Using image recognition technology, we analyze test and homework answers and extract data on correct and incorrect answers. The system described in Appendix 1, characterized by the features described herein. (Note 4) The aforementioned visualization unit, Visualize your strengths and weaknesses based on correct and incorrect answer data. The system described in Appendix 1, characterized by the features described herein. (Note 5) The aforementioned display unit is, I suggest tackling more difficult problems in areas where you excel, and starting with basic problems in areas where you struggle. The system described in Appendix 1, characterized by the features described herein. (Note 6) The aforementioned intention consideration unit is, If a child wishes to focus on a particular subject, we will prioritize providing advice related to that subject. The system described in Appendix 1, characterized by the features described herein. (Note 7) The aforementioned reading unit, The system estimates the child's emotions and adjusts the timing of photo shoots based on those estimates. The system described in Appendix 1, characterized by the features described herein. (Note 8) The aforementioned reading unit, During loading, the system automatically adjusts the lighting and angle of the shooting environment to obtain the optimal image. The system described in Appendix 1, characterized by the features described herein. (Note 9) The aforementioned reading unit, During loading, multiple photos are taken in succession, and the clearest image is selected. The system described in Appendix 1, characterized by the features described herein. (Note 10) The aforementioned reading unit, The system estimates the child's emotions and uses those estimates to prioritize tests and homework assignments. The system described in Appendix 1, characterized by the features described herein. (Note 11) The aforementioned reading unit, During loading, filtering is performed to improve the accuracy of recognizing children's handwriting. The system described in Appendix 1, characterized by the features described herein. (Note 12) The aforementioned reading unit, During loading, audio data of the child's learning environment is simultaneously acquired to comprehensively understand their learning situation. The system described in Appendix 1, characterized by the features described herein. (Note 13) The aforementioned analysis unit, The system estimates the child's emotions and adjusts the analysis algorithm based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 14) The aforementioned analysis unit, During the analysis, we will analyze the trend of performance by comparing it with past response data. The system described in Appendix 1, characterized by the features described herein. (Note 15) The aforementioned analysis unit, During the analysis, the writing style and handwriting characteristics of the answers are analyzed to evaluate the level of comprehension. 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 analysis, we evaluate relative strengths and weaknesses by comparing them with data from other children. The system described in Appendix 1, characterized by the features described herein. (Note 18) The aforementioned analysis unit, During analysis, the analysis results are corrected to take into account the speed and time of responses. The system described in Appendix 1, characterized by the features described herein. (Note 19) The aforementioned visualization unit, It estimates the child's emotions and adjusts the visualization's colors and design based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 20) The aforementioned visualization unit, When visualizing the data, the progress is displayed in a graph compared to past data. The system described in Appendix 1, characterized by the features described herein. (Note 21) The aforementioned visualization unit, When visualizing the results, strengths and weaknesses are displayed in a map format to make them easier to understand visually. The system described in Appendix 1, characterized by the features described herein. (Note 22) The aforementioned visualization unit, The system estimates the child's emotions and adjusts the display order of the visualizations based on the estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 23) The aforementioned visualization unit, When visualization occurs, a detailed report for parents and teachers is generated. The system described in Appendix 1, characterized by the features described herein. (Note 24) The aforementioned visualization unit, When visualizing, strengths and weaknesses are displayed with animation to capture children's interest. The system described in Appendix 1, characterized by the features described herein. (Note 25) The aforementioned display unit is, The system estimates the child's emotions and adjusts the way advice is expressed based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 26) The aforementioned display unit is, When presenting the advice, we analyze the effectiveness of past advice and select the most suitable advice. The system described in Appendix 1, characterized by the features described herein. (Note 27) The aforementioned display unit is, When presenting the advice, customize the content to suit the child's learning style. The system described in Appendix 1, characterized by the features described herein. (Note 28) The aforementioned display unit is, Estimate the child's emotions and prioritize advice based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 29) The aforementioned display unit is, When presenting the advice, we adjust it to reflect feedback from parents and teachers. The system described in Appendix 1, characterized by the features described herein. (Note 30) The aforementioned display unit is, When presenting the material, advice is offered in a game format to enhance children's motivation to learn. The system described in Appendix 1, characterized by the features described herein. (Note 31) The aforementioned intention consideration unit is, We estimate the child's emotions and adjust the way we express our intentions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 32) The aforementioned intention consideration unit is, When considering a decision, the most appropriate method for reflecting that decision is selected by referring to past decision-making history. The system described in Appendix 1, characterized by the features described herein. (Note 33) The aforementioned intention consideration unit is, When considering a child's wishes, we customize advice by taking into account their learning goals and future aspirations. The system described in Appendix 1, characterized by the features described herein. (Note 34) The aforementioned intention consideration unit is, It estimates the child's emotions and determines the priority of decisions based on those estimated emotions. The system described in Appendix 1, characterized by the features described herein. (Note 35) The aforementioned intention consideration unit is, When considering a student's wishes, the advice given will be adjusted to reflect the opinions of parents and teachers. The system described in Appendix 1, characterized by the features described herein. (Note 36) The aforementioned intention consideration unit is, When considering a child's wishes, provide advice that takes into account their learning environment and lifestyle. The system described in Appendix 1, characterized by the features described herein. [Explanation of Symbols]

[0195] 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 reading unit that reads test and homework answers from photos, An analysis unit analyzes the data read by the aforementioned reading unit, A visualization unit visualizes the strengths and weaknesses of the data analyzed by the aforementioned analysis unit, A presentation unit provides advice based on the strengths and weaknesses visualized by the aforementioned visualization unit, The system includes a will consideration unit that considers the child's wishes based on the advice presented by the aforementioned presentation unit. A system characterized by the following features.

2. The aforementioned reading unit, Use a smartphone or tablet to take pictures of your tests or homework. The system according to feature 1.

3. The aforementioned analysis unit, Using image recognition technology, we analyze test and homework answers and extract data on correct and incorrect answers. The system according to feature 1.

4. The aforementioned visualization unit, Visualize your strengths and weaknesses based on correct and incorrect answer data. The system according to feature 1.

5. The aforementioned display unit is, I suggest tackling more difficult problems in areas where you excel, and starting with basic problems in areas where you struggle. The system according to feature 1.

6. The aforementioned intention consideration unit is, If a child wishes to focus on a particular subject, we will prioritize providing advice related to that subject. The system according to feature 1.

7. The aforementioned reading unit, The system estimates the child's emotions and adjusts the timing of photo shoots based on those estimates. The system according to feature 1.

8. The aforementioned reading unit, During loading, the system automatically adjusts the lighting and angle of the shooting environment to obtain the optimal image. The system according to feature 1.

9. The aforementioned reading unit, During loading, multiple photos are taken in succession, and the clearest image is selected. The system according to feature 1.

10. The aforementioned reading unit, The system estimates the child's emotions and uses those estimates to prioritize tests and homework assignments. The system according to feature 1.

Images