System

Abstract

An object of the system according to the embodiment is to quickly provide an appropriate answer or solution when a student works on homework or a learning task.SOLUTION: A system includes a question photographing part, an analysis part, an answer providing part, and a learning content providing part. The question photographing unit photographs a question of homework of a student. The analysis unit analyzes the question photographed by the question photographing unit. The answer providing unit provides an appropriate answer or solution to the problem analyzed by the analysis unit. The learning content providing unit provides learning content related to the answer or solution provided by the answer providing unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the problem that it is difficult for students to quickly obtain appropriate answers or solutions when working on homework or study tasks.

[0005] The system according to the embodiment aims to quickly provide students with appropriate answers and solutions when they are working on homework or study tasks. [Means for solving the problem]

[0006] The system according to the embodiment includes a question capturing unit, an analysis unit, an answer providing unit, and a learning content providing unit. The question capturing unit captures a question for a student's homework. The analysis unit analyzes the question captured by the question capturing unit. The answer providing unit provides an appropriate answer or solution to the question analyzed by the analysis unit. The learning content providing unit provides learning content related to the answer or solution provided by the answer providing unit. [Effects of the Invention]

[0007] The system according to the embodiment can quickly provide students with appropriate answers and solutions when they are working on homework or study tasks. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0025] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention uses a generative AI to support students in their homework and learning tasks. When a student takes a photo of a homework problem, the generative AI analyzes the content of the problem and provides an appropriate answer or solution. This allows the learning support system to assist students in their learning by having them take a photo of their homework, analyze the problem, provide an appropriate answer or solution, and provide related learning content.

[0029] A learning support system according to an embodiment includes a question capturing unit, an analysis unit, an answer providing unit, and a learning content providing unit. The question capturing unit captures a student's homework problem. For example, the problem can be captured using a smartphone or tablet. The question capturing unit then transmits the captured image to a generation AI. The analysis unit analyzes the problem captured by the question capturing unit. For example, the generation AI uses image analysis technology to understand the content of the problem. The generation AI recognizes mathematical formulas and figures and determines the type and difficulty of the problem. The answer providing unit provides an appropriate answer or solution method for the problem analyzed by the analysis unit. For example, the generation AI generates an answer based on the content of the problem and provides detailed explanations of the solution steps and solution method. The generation AI indicates the calculations and operations required for each step to help students understand. The learning content providing unit provides learning content related to the answer or solution method provided by the answer providing unit. For example, the generation AI generates content including the solution steps, explanations of related concepts, example problems, and practice problems. This allows students to study at their own pace and improve their understanding. The learning support system according to the embodiment can support students' learning by photographing and analyzing students' homework problems, providing appropriate answers and solutions, and providing related learning content. For example, when a student photographs a homework problem, the generation AI analyzes the problem and provides appropriate answers and solutions. The generation AI also provides explanations of the solution steps and related concepts, as well as example problems and practice problems, allowing students to study at their own pace. This improves students' understanding and enhances their learning effectiveness.

[0030] When analyzing a problem, the analysis unit refers to background information and related learning history, allowing for more accurate analysis. For example, when the generation AI analyzes a problem, the analysis unit refers to the student's past learning history to identify similar problems and related concepts. For example, it refers to a database of previously solved problems to understand what types of problems the student is strong at and what types of problems they are weak at. The analysis unit also refers to the background information of the problem to understand the source of the problem and related topics. For example, it can analyze the content of the problem more accurately based on the source of the problem and related topics. In this way, by referring to background information and learning history, the accuracy of problem analysis improves.

[0031] The question capture unit can evaluate the quality of the image in real time and instruct the student on the optimal shooting angle and lighting conditions. For example, the question capture unit uses a generation AI to evaluate the quality of the image in real time and instruct the student on the optimal shooting angle. For example, if the image is blurry, it instructs the student to adjust the camera position. The question capture unit also evaluates the lighting conditions and instructs the student on the optimal lighting conditions. For example, if the image is dark, it instructs the student to adjust the position of the light source. Furthermore, the question capture unit evaluates the image resolution and instructs the student to shoot at the optimal resolution. For example, if the image is low resolution, it instructs the student to change the camera settings. This improves the accuracy of question capture by evaluating image quality in real time and instructing the student on the optimal shooting conditions.

[0032] The question capture unit also supports voice input and handwriting input, allowing students to input questions in the way that is most convenient for them. In the question capture unit, for example, the generation AI supports voice input, allowing students to input questions by verbally explaining the question. For example, when a student reads a question aloud, the generation AI analyzes the content. In addition, the question capture unit supports handwriting input, allowing students to input questions by hand. For example, when a question is input by hand using a tablet, the generation AI analyzes the content. In addition, the question capture unit supports both voice input and handwriting input, allowing students to input questions in the way that is most convenient for them. For example, students can choose between voice input and handwriting input. This allows students to input questions in the way that is most convenient for them by supporting voice input and handwriting input.

[0033] The analysis unit can automatically adjust the difficulty and content of questions to accommodate different grades and subjects. For example, the generation AI automatically adjusts the difficulty of questions to accommodate different grades and subjects. For example, it distinguishes between questions for elementary school students and questions for high school students and analyzes them separately. The analysis unit also automatically adjusts the content of questions to accommodate different grades and subjects. For example, it distinguishes between math questions and science questions and analyzes them separately. Furthermore, the analysis unit comprehensively adjusts the difficulty and content of questions to accommodate different grades and subjects. For example, it adjusts the difficulty and content of questions based on the complexity of the question and the difficulty of the answer. This makes it possible to automatically adjust the difficulty and content of questions to accommodate different grades and subjects.

[0034] When providing an answer, the answer providing unit can present multiple solution methods so that the student can select the method that is easiest to understand. For example, the answer providing unit may present multiple solution methods using a generative AI so that the student can select the method that is easiest to understand. For example, it may present an algebraic solution method and a geometric solution method. The answer providing unit may also compare the multiple solution methods so that the student can select the method that is easiest to understand. For example, it may explain the advantages and disadvantages of each solution method. Furthermore, the answer providing unit may provide a detailed answer based on the solution method selected by the student. For example, it may provide a step-by-step explanation based on the selected solution method. This allows the student to select the method that is easiest to understand by presenting multiple solution methods.

[0035] The answer providing unit can display the steps of the solution using animations or interactive visuals to make it easier for students to understand visually. For example, the answer providing unit displays the steps of the solution using animations to make it easier for students to understand visually. For example, the process of transforming a mathematical formula is shown using animation. The answer providing unit also uses interactive visuals to allow students to experience the steps of the solution. For example, clickable elements allow students to progress through the steps of the solution. Furthermore, the answer providing unit combines animations and interactive visuals to allow students to gain a deeper understanding of the steps of the solution. For example, by interactively manipulating the steps shown in the animation, students can experience the steps of the solution. In this way, the use of animations and interactive visuals makes it easier for students to understand visually.

[0036] The answer providing unit adds an interactive function that allows students to input questions about the answers provided by the generation AI, thereby supporting real-time question resolution. The answer providing unit adds an interactive function that allows students to input questions about the answers provided by the generation AI. For example, the answer providing unit inputs questions about the steps to the answer. The answer providing unit also introduces an instant response system to support real-time question resolution. For example, it uses a chatbot to provide instant responses to students' questions. Furthermore, the answer providing unit provides detailed explanations and additional materials for questions input by students. For example, it displays video or audio guides related to the questions. In this way, adding interactive functions makes it possible to resolve questions in real time.

[0037] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0038] The learning assistance system can further include an audio feedback unit. The audio feedback unit explains the steps and methods of solving a problem by voice when the student is solving it. For example, the generative AI can explain the steps of solving a problem by voice, and the student can solve the problem while listening. The audio feedback unit can also provide audio hints when the student is unsure of the answer. For example, if the student stumbles on a particular step, an audio hint regarding that step can be provided. Furthermore, the audio feedback unit can also provide audio feedback on the accuracy of the answer after the student has completed the answer. For example, if the answer is correct, an audio message praising the student is provided, and if the answer is incorrect, an audio message encouraging the student to try again is provided. In this way, the use of audio feedback can improve the student's learning experience.

[0039] The analysis unit can further include a personalized study plan provider. The personalized study plan provider generates an individual study plan based on the student's study history and the analysis results. For example, the generation AI analyzes the student's past grades and study history to identify areas that need strengthening and new topics that need to be learned. The personalized study plan provider can also provide a study plan that suits the student's learning style and pace. For example, it can suggest a plan for intensive study in a short period of time or a plan for studying gradually over a long period of time. Furthermore, the personalized study plan provider can monitor the progress of the study plan in real time and adjust the plan as necessary. This makes it possible to maximize the student's learning effectiveness by providing an individual study plan.

[0040] The question capture unit can further be equipped with an environmental recognition unit. The environmental recognition unit recognizes the surrounding environment when students are photographing the questions and suggests optimal shooting conditions. For example, the generative AI detects the ambient brightness and noise level and suggests the optimal shooting location and time. The environmental recognition unit can also evaluate the student's posture and how they hold the camera when taking the photo and instruct them on the optimal shooting method. For example, if the camera is at an angle, it will instruct them to hold it horizontally. Furthermore, the environmental recognition unit can detect obstacles and reflections that may occur during shooting and provide advice on how to avoid them. This makes it possible to improve the accuracy of question capture by using environmental recognition.

[0041] The question capture unit can further include a multilingual support unit. The multilingual support unit allows students to input questions in different languages. For example, the generation AI can support multiple languages, such as English and Spanish, allowing students to input questions in their native language. The multilingual support unit can also automatically translate the input questions and send them to the analysis unit. For example, a question input in English can be translated into Japanese and analyzed. Furthermore, the multilingual support unit can provide answers and solutions in the student's native language. For example, answers can be provided in Japanese. In this way, multilingual support allows students who speak different languages ​​to use the learning support system.

[0042] The analysis unit can further include a learning progress monitoring unit. The learning progress monitoring unit monitors the student's learning progress in real time and provides feedback according to the progress. For example, the generative AI analyzes the student's learning progress and evaluates the degree of goal achievement. The learning progress monitoring unit can also provide additional learning content if the student has not reached the goal. For example, it can provide additional practice questions on a specific topic. Furthermore, the learning progress monitoring unit can display a message of praise if the student achieves the goal. In this way, monitoring the learning progress can improve the student's learning effectiveness.

[0043] The answer providing unit can further include a gamification unit. The gamification unit makes learning fun by incorporating game elements as students progress through the answers. For example, the generating AI can award points according to the progress of the answers, and students can earn rewards by collecting points. The gamification unit can also enable students to level up or earn badges each time they complete an answer. For example, solving a specific problem can earn a special badge. Furthermore, the gamification unit can also incorporate elements that encourage competition between students. For example, a ranking system can be introduced, allowing students to compete with other students and increase their motivation to learn. In this way, gamification can be used to increase students' motivation to learn.

[0044] The processing flow of the first embodiment will be briefly explained below.

[0045] Step 1: The problem capture unit takes a photo of the student's homework problem. For example, the problem can be captured using a smartphone or tablet. The problem capture unit then sends the captured image to the generation AI. Step 2: The analysis unit analyzes the problem captured by the problem capture unit. For example, the generation AI uses image analysis technology to understand the content of the problem. The generation AI recognizes mathematical formulas and figures and determines the type and difficulty of the problem. Step 3: The answer provider provides appropriate answers and solutions to the problems analyzed by the analysis unit. For example, the generation AI generates an answer based on the content of the problem and provides detailed explanations of the steps and methods for solving the problem. The generation AI shows the calculations and operations required at each step, helping students to understand the problem more easily. Step 4: The learning content provider provides learning content related to the answers and solutions provided by the answer provider. For example, the generative AI generates content including solution procedures, explanations of related concepts, example problems, and practice questions. This allows students to study at their own pace and improve their understanding.

[0046] (Example 2) A learning support system according to an embodiment of the present invention uses a generative AI to support students in their homework and learning tasks. When a student takes a photo of a homework problem, the generative AI analyzes the content of the problem and provides an appropriate answer or solution. This allows the learning support system to assist students in their learning by having them take a photo of their homework, analyze the problem, provide an appropriate answer or solution, and provide related learning content.

[0047] A learning support system according to an embodiment includes a question capturing unit, an analysis unit, an answer providing unit, and a learning content providing unit. The question capturing unit captures a student's homework problem. For example, the problem can be captured using a smartphone or tablet. The question capturing unit then transmits the captured image to a generation AI. The analysis unit analyzes the problem captured by the question capturing unit. For example, the generation AI uses image analysis technology to understand the content of the problem. The generation AI recognizes mathematical formulas and figures and determines the type and difficulty of the problem. The answer providing unit provides an appropriate answer or solution method for the problem analyzed by the analysis unit. For example, the generation AI generates an answer based on the content of the problem and provides detailed explanations of the solution steps and solution method. The generation AI indicates the calculations and operations required for each step to help students understand. The learning content providing unit provides learning content related to the answer or solution method provided by the answer providing unit. For example, the generation AI generates content including the solution steps, explanations of related concepts, example problems, and practice problems. This allows students to study at their own pace and improve their understanding. The learning support system according to the embodiment can support students' learning by photographing and analyzing students' homework problems, providing appropriate answers and solutions, and providing related learning content. For example, when a student photographs a homework problem, the generation AI analyzes the problem and provides appropriate answers and solutions. The generation AI also provides explanations of the solution steps and related concepts, as well as example problems and practice problems, allowing students to study at their own pace. This improves students' understanding and enhances their learning effectiveness.

[0048] When analyzing a problem, the analysis unit refers to background information and related learning history, allowing for more accurate analysis. For example, when the generation AI analyzes a problem, the analysis unit refers to the student's past learning history to identify similar problems and related concepts. For example, it refers to a database of previously solved problems to understand what types of problems the student is strong at and what types of problems they are weak at. The analysis unit also refers to the background information of the problem to understand the source of the problem and related topics. For example, it can analyze the content of the problem more accurately based on the source of the problem and related topics. In this way, by referring to background information and learning history, the accuracy of problem analysis improves.

[0049] The question capture unit can evaluate the quality of the image in real time and instruct the student on the optimal shooting angle and lighting conditions. For example, the question capture unit uses a generation AI to evaluate the quality of the image in real time and instruct the student on the optimal shooting angle. For example, if the image is blurry, it instructs the student to adjust the camera position. The question capture unit also evaluates the lighting conditions and instructs the student on the optimal lighting conditions. For example, if the image is dark, it instructs the student to adjust the position of the light source. Furthermore, the question capture unit evaluates the image resolution and instructs the student to shoot at the optimal resolution. For example, if the image is low resolution, it instructs the student to change the camera settings. This improves the accuracy of question capture by evaluating image quality in real time and instructing the student on the optimal shooting conditions.

[0050] The question capturing unit can use the emotion estimation function to analyze the emotion a student feels when capturing a question and provide advice to reduce stress and anxiety. The question capturing unit, for example, uses the emotion estimation function to analyze the emotion a student feels when capturing a question. For example, if the student is nervous, the question capturing unit provides advice to relax. Furthermore, if the student is feeling stressed, the question capturing unit provides advice to reduce stress. For example, the question capturing unit instructs the student to take a deep breath. Furthermore, if the student is feeling anxious, the question capturing unit provides advice to reduce anxiety. For example, the question capturing unit instructs the student to have a positive mindset. In this way, the emotion estimation function can reduce the student's stress and anxiety and improve learning effectiveness.

[0051] The question capture unit also supports voice input and handwriting input, allowing students to input questions in the way that is most convenient for them. In the question capture unit, for example, the generation AI supports voice input, allowing students to input questions by verbally explaining the question. For example, when a student reads a question aloud, the generation AI analyzes the content. In addition, the question capture unit supports handwriting input, allowing students to input questions by hand. For example, when a question is input by hand using a tablet, the generation AI analyzes the content. In addition, the question capture unit supports both voice input and handwriting input, allowing students to input questions in the way that is most convenient for them. For example, students can choose between voice input and handwriting input. This allows students to input questions in the way that is most convenient for them by supporting voice input and handwriting input.

[0052] The analysis unit can automatically adjust the difficulty and content of questions to accommodate different grades and subjects. For example, the generation AI automatically adjusts the difficulty of questions to accommodate different grades and subjects. For example, it distinguishes between questions for elementary school students and questions for high school students and analyzes them separately. The analysis unit also automatically adjusts the content of questions to accommodate different grades and subjects. For example, it distinguishes between math questions and science questions and analyzes them separately. Furthermore, the analysis unit comprehensively adjusts the difficulty and content of questions to accommodate different grades and subjects. For example, it adjusts the difficulty and content of questions based on the complexity of the question and the difficulty of the answer. This makes it possible to automatically adjust the difficulty and content of questions to accommodate different grades and subjects.

[0053] The question capture unit can use the emotion estimation function to monitor in real time the emotions that students feel when they capture questions and provide feedback to elicit positive emotions. For example, the question capture unit uses the emotion estimation function to monitor in real time the emotions that students feel when they capture questions. For example, if a student is feeling anxious, the question capture unit provides advice to help them relax. The question capture unit also provides feedback to help students feel positive emotions. For example, it displays a message encouraging the student to gain confidence. Furthermore, the question capture unit suggests specific actions to help students feel positive emotions. For example, it instructs the student to recall a successful experience. In this way, the emotion estimation function can be used to elicit positive emotions in students and improve learning effectiveness.

[0054] When providing an answer, the answer providing unit can present multiple solution methods so that the student can select the method that is easiest to understand. For example, the answer providing unit may present multiple solution methods using a generative AI so that the student can select the method that is easiest to understand. For example, it may present an algebraic solution method and a geometric solution method. The answer providing unit may also compare the multiple solution methods so that the student can select the method that is easiest to understand. For example, it may explain the advantages and disadvantages of each solution method. Furthermore, the answer providing unit may provide a detailed answer based on the solution method selected by the student. For example, it may provide a step-by-step explanation based on the selected solution method. This allows the student to select the method that is easiest to understand by presenting multiple solution methods.

[0055] The answer providing unit can display the steps of the solution using animations or interactive visuals to make it easier for students to understand visually. For example, the answer providing unit displays the steps of the solution using animations to make it easier for students to understand visually. For example, the process of transforming a mathematical formula is shown using animation. The answer providing unit also uses interactive visuals to allow students to experience the steps of the solution. For example, clickable elements allow students to progress through the steps of the solution. Furthermore, the answer providing unit combines animations and interactive visuals to allow students to gain a deeper understanding of the steps of the solution. For example, by interactively manipulating the steps shown in the animation, students can experience the steps of the solution. In this way, the use of animations and interactive visuals makes it easier for students to understand visually.

[0056] The answer providing unit uses the emotion estimation function to analyze the emotions of students when they understand the answer and can provide focused explanations on the parts that are difficult to understand. The answer providing unit, for example, uses the emotion estimation function to analyze the emotions of students when they understand the answer. For example, if the student is confused, the answer providing unit will focus on explaining that part. The answer providing unit also identifies the parts that are difficult for the student to understand and provides detailed explanations. For example, it will analyze incorrect answers and explain which parts the student made mistakes in. Furthermore, the answer providing unit will provide additional example problems or practice questions for the parts that are difficult to understand. For example, if the student does not understand a particular concept, it will provide additional questions related to that concept. In this way, by using the emotion estimation function, the answer providing unit can provide focused explanations on the parts that are difficult for the student to understand.

[0057] The answer providing unit adds an interactive function that allows students to input questions about the answers provided by the generation AI, thereby supporting real-time question resolution. The answer providing unit adds an interactive function that allows students to input questions about the answers provided by the generation AI. For example, the answer providing unit inputs questions about the steps to the answer. The answer providing unit also introduces an instant response system to support real-time question resolution. For example, it uses a chatbot to provide instant responses to students' questions. Furthermore, the answer providing unit provides detailed explanations and additional materials for questions input by students. For example, it displays video or audio guides related to the questions. In this way, adding interactive functions makes it possible to resolve questions in real time.

[0058] The answer providing unit can use the emotion estimation function to monitor in real time the emotions that students feel when they understand an answer and provide feedback to elicit positive emotions. The answer providing unit, for example, uses the emotion estimation function to monitor in real time the emotions that students feel when they understand an answer. For example, if a student feels anxious, it provides advice to help them relax. The answer providing unit also provides feedback to help students feel positive emotions. For example, it displays a message encouraging the student to gain confidence. Furthermore, the answer providing unit suggests specific actions to help students feel positive emotions. For example, it instructs the student to recall a successful experience. In this way, the emotion estimation function can be used to elicit positive emotions in students and improve learning effectiveness.

[0059] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0060] The learning assistance system can further include an audio feedback unit. The audio feedback unit explains the steps and methods of solving a problem by voice when the student is solving it. For example, the generative AI can explain the steps of solving a problem by voice, and the student can solve the problem while listening. The audio feedback unit can also provide audio hints when the student is unsure of the answer. For example, if the student stumbles on a particular step, an audio hint regarding that step can be provided. Furthermore, the audio feedback unit can also provide audio feedback on the accuracy of the answer after the student has completed the answer. For example, if the answer is correct, an audio message praising the student is provided, and if the answer is incorrect, an audio message encouraging the student to try again is provided. In this way, the use of audio feedback can improve the student's learning experience.

[0061] The analysis unit can further include a personalized study plan provider. The personalized study plan provider generates an individual study plan based on the student's study history and the analysis results. For example, the generation AI analyzes the student's past grades and study history to identify areas that need strengthening and new topics that need to be learned. The personalized study plan provider can also provide a study plan that suits the student's learning style and pace. For example, it can suggest a plan for intensive study in a short period of time or a plan for studying gradually over a long period of time. Furthermore, the personalized study plan provider can monitor the progress of the study plan in real time and adjust the plan as necessary. This makes it possible to maximize the student's learning effectiveness by providing an individual study plan.

[0062] The question capture unit can further be equipped with an environmental recognition unit. The environmental recognition unit recognizes the surrounding environment when students are photographing the questions and suggests optimal shooting conditions. For example, the generative AI detects the ambient brightness and noise level and suggests the optimal shooting location and time. The environmental recognition unit can also evaluate the student's posture and how they hold the camera when taking the photo and instruct them on the optimal shooting method. For example, if the camera is at an angle, it will instruct them to hold it horizontally. Furthermore, the environmental recognition unit can detect obstacles and reflections that may occur during shooting and provide advice on how to avoid them. This makes it possible to improve the accuracy of question capture by using environmental recognition.

[0063] The question capture unit can use the emotion estimation function to analyze the emotions that students feel when capturing questions and provide advice to improve their motivation to learn. For example, if a student is tired, the unit instructs them to take a break. Furthermore, if a student is lacking concentration, the question capture unit provides advice to improve their concentration, for example, by instructing them to take a short break. Furthermore, if a student is losing motivation, the question capture unit provides advice to increase their motivation, for example, by instructing them to reaffirm their goals. In this way, the emotion estimation function can be used to improve students' motivation to learn.

[0064] The question capture unit can further include a multilingual support unit. The multilingual support unit allows students to input questions in different languages. For example, the generation AI can support multiple languages, such as English and Spanish, allowing students to input questions in their native language. The multilingual support unit can also automatically translate the input questions and send them to the analysis unit. For example, a question input in English can be translated into Japanese and analyzed. Furthermore, the multilingual support unit can provide answers and solutions in the student's native language. For example, answers can be provided in Japanese. In this way, multilingual support allows students who speak different languages ​​to use the learning support system.

[0065] The analysis unit can further include a learning progress monitoring unit. The learning progress monitoring unit monitors the student's learning progress in real time and provides feedback according to the progress. For example, the generative AI analyzes the student's learning progress and evaluates the degree of goal achievement. The learning progress monitoring unit can also provide additional learning content if the student has not reached the goal. For example, it can provide additional practice questions on a specific topic. Furthermore, the learning progress monitoring unit can display a message of praise if the student achieves the goal. In this way, monitoring the learning progress can improve the student's learning effectiveness.

[0066] The question capture unit can use the emotion estimation function to analyze the emotions students feel when capturing questions and provide advice to make learning more enjoyable. For example, if a student is bored, it can suggest activities to make learning more fun. If a student is losing interest, the question capture unit can provide advice to rekindle their interest. For example, it can introduce related, interesting topics. The question capture unit can also provide encouraging messages to encourage students to have positive emotions toward learning. In this way, the emotion estimation function can bring out the enjoyment of learning in students and improve their learning effectiveness.

[0067] The answer providing unit can further include a gamification unit. The gamification unit makes learning fun by incorporating game elements as students progress through the answers. For example, the generating AI can award points according to the progress of the answers, and students can earn rewards by collecting points. The gamification unit can also enable students to level up or earn badges each time they complete an answer. For example, solving a specific problem can earn a special badge. Furthermore, the gamification unit can also incorporate elements that encourage competition between students. For example, a ranking system can be introduced, allowing students to compete with other students and increase their motivation to learn. In this way, gamification can be used to increase students' motivation to learn.

[0068] The answer providing unit can further use the emotion estimation function to analyze the emotions felt by students when they understand an answer and provide feedback to enhance their sense of accomplishment in learning. For example, when a student understands an answer, it can display a message praising the student so that the student feels a sense of accomplishment. The answer providing unit also provides feedback to the student so that the student has positive emotions when understanding the answer. For example, it can instruct the student to recall a successful experience. Furthermore, the answer providing unit can suggest specific actions to enhance the student's sense of accomplishment when the student understands the answer. For example, it can instruct the student to reflect on their progress before moving on to the next step. In this way, the emotion estimation function can enhance the student's sense of accomplishment in learning and improve learning effectiveness.

[0069] The answer providing unit can further use the emotion estimation function to analyze the student's emotions when understanding the answer and provide feedback to reduce learning frustration. For example, if the student feels frustrated with the answer, the unit can focus on explaining that part. Furthermore, if the student feels frustrated, the answer providing unit can provide advice to help the student relax. For example, the unit can instruct the student to take a deep breath. Furthermore, if the student feels frustrated, the answer providing unit can suggest specific actions to help the student feel positive. For example, the unit can instruct the student to recall a successful experience. In this way, the emotion estimation function can reduce the student's frustration and improve learning effectiveness.

[0070] The processing flow of the second embodiment will be briefly explained below.

[0071] Step 1: The problem capture unit takes a photo of the student's homework problem. For example, the problem can be captured using a smartphone or tablet. The problem capture unit then sends the captured image to the generation AI. Step 2: The analysis unit analyzes the problem captured by the problem capture unit. For example, the generation AI uses image analysis technology to understand the content of the problem. The generation AI recognizes mathematical formulas and figures and determines the type and difficulty of the problem. Step 3: The answer provider provides appropriate answers and solutions to the problems analyzed by the analysis unit. For example, the generation AI generates an answer based on the content of the problem and provides detailed explanations of the steps and methods for solving the problem. The generation AI shows the calculations and operations required at each step, helping students to understand the problem more easily. Step 4: The learning content provider provides learning content related to the answers and solutions provided by the answer provider. For example, the generative AI generates content including solution procedures, explanations of related concepts, example problems, and practice questions. This allows students to study at their own pace and improve their understanding.

[0072] The specific processing unit 290 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0073] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0074] Furthermore, the processing by the data processing system 10 described above is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart device 14, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart device 14. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information necessary for processing from the smart device 14 or an external device, and the smart device 14 acquires or collects information necessary for processing from the data processing device 12 or an external device.

[0075] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0076] 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.

[0077] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0078] The smart glasses 214 include a computer 36, a microphone 238, a speaker 240, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, and the camera 42 are also connected to the bus 52.

[0079] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0080] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0081] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0082] Fig. 4 shows an example of the main functions of the data processing device 12 and the smart glasses 214. As shown in Fig. 4, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0083] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0084] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0085] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0086] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0087] The specific processing unit 290 transmits the result of the specific processing to the smart glasses 214. In the smart glasses 214, the control unit 46A causes the speaker 240 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0088] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0089] The data processing system 210 according to the second embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 210 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the smart glasses 214, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the smart glasses 214. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the smart glasses 214 or an external device, etc., and the smart glasses 214 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0090] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0091] 5, the data processing system 310 includes the data processing device 12 and a headset type terminal 314. An example of the data processing device 12 is a server.

[0092] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0093] The headset type terminal 314 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a display 343. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the display 343 are also connected to the bus 52.

[0094] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0095] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS (Complementary Metal-Oxide-Semiconductor) image sensor or a CCD (Charge Coupled Device) image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0096] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0097] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset type terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0098] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0099] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0100] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0101] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0102] The specific processing unit 290 transmits the result of the specific processing to the headset type terminal 314. In the headset type terminal 314, the control unit 46A causes the speaker 240 and the display 343 to output the result of the specific processing. The microphone 238 acquires audio indicating a user input regarding the result of the specific processing. The control unit 46A transmits audio data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the audio data.

[0103] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0104] The data processing system 310 according to the third embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 310 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the headset type terminal 314, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the headset type terminal 314. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the headset type terminal 314 or an external device, etc., and the headset type terminal 314 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0105] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0106] 7, a data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0107] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0108] The robot 414 includes a computer 36, a microphone 238, a speaker 240, a camera 42, a communication I / F 44, and a control target 443. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The microphone 238, the speaker 240, the camera 42, and the control target 443 are also connected to the bus 52.

[0109] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0110] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS image sensor or a CCD image sensor, and captures images of the user's surroundings (for example, an imaging range defined by an angle of view equivalent to the field of vision of a typical healthy person).

[0111] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0112] The control object 443 includes a display device, LEDs in the eyes, and motors that drive the arms, hands, and feet. The posture and gestures of the robot 414 are controlled by controlling the motors of the arms, hands, and feet. Some of the emotions of the robot 414 can be expressed by controlling these motors. In addition, the facial expressions of the robot 414 can also be expressed by controlling the light emission state of the LEDs in the eyes of the robot 414.

[0113] Fig. 8 shows an example of the main functions of the data processing device 12 and the robot 414. As shown in Fig. 8, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0114] The processor 28 reads the specific processing program 56 from the storage 32 and executes the read specific processing program 56 on the RAM 30. The specific processing is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific processing program 56 executed on the RAM 30.

[0115] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0116] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0117] Note that a device other than the data processing device 12 may have the data generation model 58. For example, a server device may have the data generation model 58. In this case, the data processing device 12 communicates with the server device having the data generation model 58 to obtain a processing result (such as a prediction result) using the data generation model 58. Furthermore, the data processing device 12 may be a server device, or may be a terminal device (for example, a mobile phone, a robot, a home appliance, etc.) owned by a user.

[0118] The specific processing unit 290 transmits the result of the specific processing to the robot 414. In the robot 414, the control unit 46A causes the speaker 240 and the control target 443 to output the result of the specific processing. The microphone 238 acquires voice indicating a user input regarding the result of the specific processing. The control unit 46A transmits voice data indicating the user input acquired by the microphone 238 to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0119] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0120] The data processing system 410 according to the fourth embodiment performs the same processing as the data processing system 10 according to the first embodiment. The processing by the data processing system 410 is executed by the specific processing unit 290 of the data processing device 12 or the control unit 46A of the robot 414, but may also be executed by the specific processing unit 290 of the data processing device 12 and the control unit 46A of the robot 414. Furthermore, the specific processing unit 290 of the data processing device 12 acquires or collects information required for processing from the robot 414 or an external device, etc., and the robot 414 acquires or collects information required for processing from the data processing device 12 or an external device, etc.

[0121] The emotion identification model 59 as an emotion engine may determine the user's emotion according to a specific mapping. Specifically, the emotion identification model 59 may determine the user's emotion according to an emotion map (see FIG. 9), which is a specific mapping. Similarly, the emotion identification model 59 may determine the robot's emotion, and the identification processing unit 290 may perform identification processing using the robot's emotion.

[0122] FIG. 9 illustrates an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. Emotions closer to the center of the concentric circles are more primitive. Emotions representing states and behaviors arising from a state of mind are arranged on the outer edges of the concentric circles. The concept of emotion encompasses both emotions and mental states. Emotions generally generated from reactions occurring in the brain are arranged on the left side of the concentric circles. Emotions generally induced by situational judgment are arranged on the right side of the concentric circles. Emotions generally generated from reactions occurring in the brain and induced by situational judgment are arranged on the upper and lower sides of the concentric circles. Furthermore, the emotion of "pleasure" is arranged on the upper side of the concentric circles, and the emotion of "discomfort" is arranged on the lower side. In this way, in the emotion map 400, multiple emotions are mapped based on the structure by which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0123] These emotions are distributed in the 3 o'clock direction on emotion map 400, and typically fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0124] The inside of emotion map 400 represents what is going on in the mind, and the outside of emotion map 400 represents behavior, so the further you go outside emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0125] Human emotions are based on various balances, such as posture and blood sugar levels. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. Emotions can also be created for robots, cars, and motorcycles, based on various balances, such as posture and remaining battery life. When these balances deviate from the ideal, a state of discomfort is expressed, and when they approach the ideal, a state of pleasure is expressed. An emotion map can be generated, for example, based on Dr. Mitsuyoshi's emotion map (Research on speech emotion recognition and brain physiological signal analysis systems for emotions, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). The left half of the emotion map lists emotions belonging to the area called "reaction," where sensation is dominant. The right half of the emotion map lists emotions belonging to the area called "situation," where situational awareness is dominant.

[0126] The emotion map defines two emotions that promote learning. One is a negative emotion on the situation side, around the middle of "repentance" or "reflection." In other words, this occurs when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is a positive emotion on the response side, around "desire." In other words, this occurs when the robot experiences positive feelings such as "I want more" or "I want to know more."

[0127] The emotion identification model 59 inputs user input into a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the user's emotion. This neural network is pre-trained based on multiple pieces of training data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. Furthermore, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 10. FIG. 10 shows an example in which multiple emotions, "relieved," "calm," and "reassuring," have similar emotion values.

[0128] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers including computer 22.

[0129] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable, computer-readable, non-transitory storage medium such as a USB (Universal Serial Bus) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0130] 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.

[0131] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0132] The hardware resource for executing a specific process can be any of the following types of processors: A processor, for example, is a CPU, which is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A processor also includes a dedicated electrical circuit, such as an FPGA (Field-Programmable Gate Array), a PLD (Programmable Logic Device), or an ASIC (Application Specific Integrated Circuit), which is a processor with a circuit configuration designed specifically for executing a specific process. Each processor has built-in or connected memory, and each processor uses the memory to execute the specific process.

[0133] The hardware resource that executes the specific process may be configured with one of these various processors, or may be configured with a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0134] As an example of a system configured with a single processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a system that uses a processor that realizes the functions of an entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0135] Furthermore, the hardware structure of these various processors can be, more specifically, an electric circuit that combines circuit elements such as semiconductor devices. The specific processing described above is merely an example. Therefore, it goes without saying that unnecessary steps may be deleted, new steps may be added, or the processing order may be rearranged, without departing from the spirit of the invention.

[0136] In the above example, the first to fourth embodiments have been described separately, but some or all of these embodiments may be combined. The smart device 14, smart glasses 214, headset terminal 314, and robot 414 are merely examples, and they may be combined, or other devices may be used. In the above example, the first and second embodiments have been described separately, but they may be combined.

[0137] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0138] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0139] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. The problem photography department takes photos of students' homework problems, an analysis unit that analyzes the questions photographed by the question photographing unit; an answer providing unit that provides an appropriate answer or solution to the problem analyzed by the analysis unit; a learning content providing unit that provides learning content related to the answers and solutions provided by the answer providing unit; A system characterized by:

2. The question photographing unit Evaluate image quality in real time and guide students to the best shooting angles and lighting conditions 2. The system of claim 1.

3. The analysis unit Automatically adjust the difficulty and content of the questions to accommodate different grade levels and subjects 2. The system of claim 1.

4. The answer providing unit: When providing the answer, present multiple solutions and allow students to choose the method that is easiest for them to understand.

2. The system of claim 1.

5. The question photographing unit Emotion estimation function analyzes the emotions students express when taking photos of the questions and provides advice to reduce stress and anxiety.

2. The system of claim 1.

6. The answer providing unit: Using emotion estimation, the system analyzes the emotions students have when trying to understand the answer and provides focused explanations on the parts that are difficult to understand.

2. The system of claim 1.

7. The question photographing unit Using emotion estimation, we monitor students' emotions in real time as they shoot questions and provide feedback to elicit positive emotions.

2. The system of claim 1.

8. The answer providing unit: Using emotion estimation, we monitor students' emotions in real time as they understand the answer and provide feedback to elicit positive emotions.

2. The system of claim 1.

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