system

Abstract

The system according to the embodiment aims to improve the enjoyment of learning by visually providing answers and explanations to questions. [Solution] A system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit reads a question statement. The analysis unit analyzes the question statement read by the reception unit. The generation unit generates an answer based on the information analyzed by the analysis unit. The display unit visually presents the answer and explanation generated by the generation unit.

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 do not adequately provide answers and explanations to questions visually, and there is room for improvement in terms of making learning more enjoyable.

[0005] The system according to the embodiment aims to improve the enjoyment of learning by visually providing answers and explanations to questions. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. The reception unit reads a question. The analysis unit analyzes the question read by the reception unit. The generation unit generates an answer based on the information analyzed by the analysis unit. The display unit visually provides the answer and explanation generated by the generation unit. [Effects of the Invention]

[0007] The system according to the embodiment visually provides answers and explanations to questions, making learning more enjoyable. [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 pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 (see FIG. 2) acquires the data indicating the user input.

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention combines generative AI and AR technology. When a user inputs a problem, the generative AI analyzes the content and generates an appropriate answer and explanation. Next, using AR technology, the user's favorite character appears and visually provides the answer and explanation. For example, when a user inputs a math problem, the generative AI generates an answer, and a character uses AR technology to explain the answer. This system is expected to make learning more enjoyable and deepen understanding. First, when a user inputs a problem, the reception unit receives the information. Next, the analysis unit analyzes the problem and extracts information for generating an answer. The generation unit generates an answer based on the information obtained from the analysis unit. Finally, the display unit visually provides the generated answer and explanation. At this time, a character appears using AR technology and explains the answer and explanation. This system allows users to intuitively understand the answer and explanation, making learning more enjoyable. Furthermore, the appearance of characters makes learning more engaging and suitable for a wide range of ages, from children to adults. This allows the learning support system to improve the enjoyment of learning by visually providing answers and explanations when the user reads a question.

[0029] A learning support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. When a user reads a question, the reception unit receives the information. For example, the reception unit allows the user to scan the question using a smartphone or tablet or input the question digitally. The analysis unit analyzes the question read by the reception unit and extracts information for generating an answer. For example, the analysis unit analyzes the content of the question using natural language processing technology and extracts important keywords and context. The generation unit generates an answer based on the information obtained from the analysis unit. For example, the generation unit uses a generation AI to generate an appropriate answer for the question. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The display unit visually displays the answer and explanation generated by the generation unit. For example, the display unit may use AR technology to display a user's favorite character to explain the answer and explanation. This allows the user to intuitively understand the answer and explanation, improving the enjoyment of learning. Furthermore, the display unit can provide the generated answers and explanations in the form of text, audio, video, or the like. For example, the display unit can display the answers and explanations in text format, allowing the user to read detailed explanations. Alternatively, the display unit can provide the answers and explanations in audio format, allowing the user to listen and understand. Alternatively, the display unit can provide the answers and explanations in video format, allowing the user to understand visually. In this way, the learning support system can improve the enjoyment of learning by visually providing the answers and explanations when the user reads the question text.

[0030] The learning assistance system includes a character display unit that displays a character. The character display unit displays a user's favorite character. For example, the character display unit can display various types of characters, such as animated characters and still characters. The character display unit can display, for example, a character selected by the user. The user can select their favorite character on a setting screen of the learning assistance system. The character display unit displays the selected character and explains the answer and explanation. For example, the character display unit can have an animated character explain the answer and explanation while moving. The character display unit can also have a still character explain the answer and explanation in text or audio. This makes learning more attractive by having the character appear, and increases the user's motivation to learn. Furthermore, the character display unit can change the character's movements and facial expressions in response to the user's reaction. For example, if the user answers correctly, the character can make a happy movement. Also, if the user makes a mistake, the character can make an encouraging movement. This allows the user to learn while enjoying interaction with the character. Some or all of the above-described processing in the character display unit may be performed, for example, using a generation AI or without using a generation AI. For example, the character display unit can use a generation AI to generate character movements and facial expressions and display them in response to the user's reactions, thereby increasing the user's motivation to learn.

[0031] The learning assistance system includes a reaction acquisition unit that acquires a user's reaction. The reaction acquisition unit acquires the user's reaction. For example, the reaction acquisition unit can acquire reactions such as the user clicking or tapping on the screen. The reaction acquisition unit can also acquire the user's voice input. For example, when the user inputs an answer by voice, the reaction acquisition unit acquires the voice and transmits it to the analysis unit. The reaction acquisition unit can also capture the user's facial expressions and movements with a camera and acquire the data. For example, the reaction acquisition unit can capture the user's facial expressions and movements while the user is thinking about an answer in front of the camera and transmit the data to the analysis unit. This allows the reaction acquisition unit to acquire the user's reaction in various ways and reflect it in the learning assistance system. Some or all of the above-described processing in the reaction acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reaction acquisition unit can analyze the user's voice input using a generation AI and recognize it as an answer. This allows the reaction acquisition unit to efficiently acquire the user's reaction and reflect it in the learning assistance system. Furthermore, the reaction acquisition unit can accumulate the user's reaction data and manage it as a learning history. For example, the reaction acquisition unit records the reactions the user has shown in the past and saves them as a learning history, which allows the reaction acquisition unit to grasp the user's learning progress and provide appropriate feedback.

[0032] The learning assistance system includes a history management unit that manages a learning history. The history management unit manages a user's learning history. For example, the history management unit can record and manage a history of questions that the user has answered in the past. The history management unit accumulates data such as the content of questions answered by the user, whether the answers were correct or incorrect, and the time it took to answer, and saves it as a learning history. This allows the history management unit to grasp the user's learning progress and provide appropriate feedback. Furthermore, the history management unit can analyze the user's learning history and understand learning trends and patterns. For example, the history management unit can analyze which areas of questions the user is strong and weak in and suggest areas for improvement in learning. This allows the history management unit to improve the user's learning effectiveness. Some or all of the above-described processing in the history management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the history management unit can analyze the user's learning history using a generation AI and understand learning trends and patterns. This allows the history management unit to improve the user's learning effectiveness. Furthermore, the history management unit can visually display the user's learning history. For example, the history management unit may display the user's learning history in a visually easy-to-understand format such as a graph or chart, allowing the user to intuitively grasp their learning progress.

[0033] The analysis unit can analyze the question and extract information for generating an answer. For example, the analysis unit can use natural language processing technology to analyze the content of the question and extract important keywords and context. For example, the analysis unit can extract information necessary for the answer from the question and send it to the generation unit. The analysis unit can also analyze the structure of the question using pattern recognition technology to extract the information necessary for the answer. For example, the analysis unit can recognize patterns such as mathematical formulas and graphs in the question and extract the information necessary for the answer based on the patterns. This allows the analysis unit to analyze the question and accurately extract the information necessary for generating an answer. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can analyze the content of the question using a generation AI and extract the information necessary for the answer. This allows the analysis unit to efficiently analyze the question and extract the information necessary for generating an answer. Furthermore, the analysis unit can visually display the analysis results of the question. For example, the analysis unit may display the analysis results of the question as graphs or charts, providing them in a visually easy-to-understand format, allowing the user to intuitively grasp the analysis results of the question.

[0034] The generation unit can generate an answer based on the information obtained from the analysis unit. The generation unit generates an appropriate answer to the question using, for example, a generation AI. The generation AI can use, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit generates an answer to the question using a text generation AI. The text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. This allows the generation unit to generate an appropriate answer to the question. The generation unit can also generate an answer to the question using a multimodal generation AI. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. This allows the generation unit to generate answers to the question in a variety of formats. Furthermore, the generation unit can use the generation AI to build a feedback loop to improve the quality of the answer. For example, the generation unit evaluates the quality of the generated answer and updates the generation AI based on the evaluation results. This allows the generation unit to always provide high-quality answers based on the latest information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may generate an answer using a generation AI and provide the answer to the user. This allows the generation unit to generate an accurate answer based on the information obtained from the analysis unit.

[0035] The reception unit can analyze the user's past learning history and select the optimal method for reading questions. The reception unit, for example, retrieves the user's past learning history from a database and analyzes it using data mining technology. For example, the reception unit can analyze the history of questions the user has answered in the past and identify the user's areas of strength and weakness. The reception unit can also grasp the user's learning progress and select questions of appropriate difficulty. For example, the reception unit can prioritize reading questions in areas in which the user has previously scored high. The reception unit can also avoid question formats that the user has previously struggled with and prioritize reading question formats that the user is good at. This allows the reception unit to analyze the user's past learning history and select the optimal method for reading questions. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's learning history using a generation AI and select the optimal method for reading questions. This allows the reception unit to analyze the user's past learning history and select the optimal method for reading questions.

[0036] When loading questions, the reception unit can filter the questions based on the user's current learning situation and areas of interest. For example, the reception unit obtains the user's current learning situation from a database and selects questions using a filtering algorithm. For example, the reception unit prioritizes loading questions related to the user's current learning area. The reception unit can also identify the user's areas of interest and filter and load related questions. For example, the reception unit filters and loads questions related to topics in which the user is interested. Furthermore, the reception unit can filter and load questions of an appropriate level of difficulty according to the user's learning progress. For example, the reception unit identifies the user's learning progress and selects and loads questions of an appropriate level of difficulty. This allows the reception unit to filter questions based on the user's current learning situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's learning situation and areas of interest using a generation AI, and filter and load the most appropriate questions. This allows the reception unit to filter question sentences based on the user's current learning situation and areas of interest.

[0037] When loading questions, the reception unit can prioritize loading questions that are highly relevant based on the user's geographical location information. The reception unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects highly relevant questions. For example, when the user is in a specific area, the reception unit prioritizes loading questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize loading questions related to the travel destination. For example, when the user is studying at a travel destination, the reception unit can prioritize loading questions related to the history and culture of the area. Furthermore, when the user is at school, the reception unit can prioritize loading questions related to the school curriculum. For example, when the user is studying at school, the reception unit loads questions based on the school curriculum. This allows the reception unit to provide highly relevant questions taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's geographical location information using a generation AI and select the most appropriate question. This allows the reception unit to provide highly relevant question sentences taking into account the user's geographical location information.

[0038] When reading a question, the reception unit can analyze the user's social media activity and read related question statements. The reception unit, for example, retrieves the user's social media activity from a database and analyzes it using data mining technology. For example, the reception unit can read question statements related to topics in which the user has shown interest on social media. The reception unit can also read question statements related to the content of educational accounts the user follows on social media. For example, the reception unit can analyze the content of posts on educational accounts the user follows and select related question statements. The reception unit can also read question statements related to articles the user shared on social media. For example, the reception unit can analyze the content of articles shared by the user and select related question statements. In this way, the reception unit can analyze the user's social media activity and provide related question statements. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's social media activity using a generation AI and select optimal question statements. In this way, the reception unit can analyze the user's social media activity and provide related question statements.

[0039] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. The analysis unit, for example, evaluates the importance of the question and adjusts the level of detail of the analysis based on the evaluation result. For example, the analysis unit performs a detailed analysis on a question with high importance. Specifically, the analysis unit analyzes each element of the question in detail to extract information necessary for the answer. Furthermore, the analysis unit performs a concise analysis on a question with low importance. Specifically, the analysis unit analyzes only the main elements of the question to extract information necessary for the answer. Furthermore, the analysis unit performs an analysis with a moderate level of detail on a question with medium importance. Specifically, the analysis unit analyzes the main elements and some detailed elements of the question to extract information necessary for the answer. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the question. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the analysis unit can use the generation AI to evaluate the importance of the question sentence and adjust the level of analysis detail based on the evaluation results, allowing the analysis unit to perform efficient analysis based on the importance of the question sentence.

[0040] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the problem. For example, the analysis unit classifies the category of the problem and applies an appropriate analysis algorithm depending on the category. For example, the analysis unit applies a mathematical expression analysis algorithm to a mathematics problem. Specifically, the analysis unit analyzes the structure of the mathematical expression and extracts information necessary for the answer. Furthermore, the analysis unit applies a text analysis algorithm to a history problem. Specifically, the analysis unit analyzes information about historical events and people and extracts information necessary for the answer. Furthermore, the analysis unit applies a data analysis algorithm to a science problem. Specifically, the analysis unit analyzes experimental data and observational data and extracts information necessary for the answer. This allows the analysis unit to apply different analysis algorithms depending on the category of the problem. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can classify the category of the problem using a generation AI and apply an appropriate analysis algorithm depending on the category. This allows the analysis unit to perform accurate analysis according to the category of the question sentence.

[0041] During analysis, the analysis unit can determine the analysis priority based on the submission time of the question. The analysis unit, for example, evaluates the submission time of the question and determines the analysis priority based on the evaluation result. For example, the analysis unit prioritizes analysis of question statements with an upcoming submission deadline. Specifically, the analysis unit quickly analyzes question statements with an upcoming submission deadline and generates answers. The analysis unit also postpones question statements with a distant submission deadline. Specifically, the analysis unit analyzes question statements with a distant submission deadline after completing analysis of other question statements. Furthermore, the analysis unit gives moderate priority to question statements with a medium submission deadline. Specifically, the analysis unit analyzes question statements with a medium submission deadline in parallel with analysis of other question statements. This allows the analysis unit to determine the analysis priority based on the submission time of the question. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can use the generation AI to evaluate the timing of question submission and determine the analysis priority based on the evaluation results, allowing the analysis unit to perform efficient analysis based on the timing of question submission.

[0042] The analysis unit can adjust the order of analysis based on the relevance of the questions during analysis. The analysis unit, for example, evaluates the relevance of the questions and adjusts the order of analysis based on the evaluation results. For example, the analysis unit prioritizes analysis of questions related to the user's learning goals. Specifically, the analysis unit quickly analyzes questions related to the user's learning goals and generates answers. The analysis unit can also prioritize analysis of questions related to the user's interests. Specifically, the analysis unit analyzes questions related to the user's interests in parallel with the analysis of other questions. Furthermore, the analysis unit can prioritize analysis of questions related to the user's learning progress. Specifically, the analysis unit analyzes questions related to the user's learning progress after the analysis of other questions has been completed. This allows the analysis unit to adjust the order of analysis based on the relevance of the questions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can evaluate the relevance of questions using a generation AI and adjust the order of analysis based on the evaluation results. This allows the analysis unit to perform efficient analysis based on the relevance of the question sentences.

[0043] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. The generation unit, for example, evaluates the importance of the question and adjusts the level of detail of the answer based on the evaluation result. For example, the generation unit generates a detailed answer for a question with high importance. Specifically, the generation unit analyzes each element of the question in detail and provides information necessary for the answer. Furthermore, the generation unit generates a concise answer for a question with low importance. Specifically, the generation unit analyzes only the main elements of the question and provides information necessary for the answer. Furthermore, the generation unit generates an answer with a moderate level of detail for a question with medium importance. Specifically, the generation unit analyzes the main elements and some of the detailed elements of the question and provides information necessary for the answer. This allows the generation unit to adjust the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generator can use a generation AI to evaluate the importance of the question and adjust the level of detail of the answer based on the evaluation results, thereby enabling the generator to generate an efficient answer based on the importance of the question.

[0044] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit classifies the category of the question and applies an appropriate generation algorithm depending on the category. For example, the generation unit applies a mathematical formula generation algorithm to a mathematics question. Specifically, the generation unit analyzes the structure of the mathematical formula and provides information necessary for the answer. Furthermore, the generation unit applies a text generation algorithm to a history question. Specifically, the generation unit analyzes information about historical events and people and provides information necessary for the answer. Furthermore, the generation unit applies a data generation algorithm to a science question. Specifically, the generation unit analyzes experimental data and observational data and provides information necessary for the answer. This allows the generation unit to apply different generation algorithms depending on the category of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can classify the category of the question using a generation AI and apply an appropriate generation algorithm depending on the category. This allows the generator to generate an accurate answer according to the category of the question sentence.

[0045] When generating answers, the generation unit can determine the priority of answers based on the submission time of the question. The generation unit, for example, evaluates the submission time of the question and determines the priority of answers based on the evaluation result. For example, the generation unit prioritizes generating answers for question statements with an upcoming submission deadline. Specifically, the generation unit quickly generates answers for question statements with an upcoming submission deadline. Furthermore, the generation unit postpones answering of questions with a distant submission deadline. Specifically, the generation unit generates answers for question statements with a distant submission deadline after the answers for other question statements have been completed. Furthermore, the generation unit moderately prioritizes answers for question statements with a medium submission deadline. Specifically, the generation unit generates answers for question statements with a medium submission deadline in parallel with the answers for other question statements. This allows the generation unit to determine the priority of answers based on the submission time of the question. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can use the generation AI to evaluate the timing of question submission and determine the priority of answers based on the evaluation results, thereby enabling the generation unit to generate efficient answers based on the timing of question submission.

[0046] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, the generation unit evaluates the relevance of the questions and adjusts the order of answers based on the evaluation result. For example, the generation unit prioritizes generating answers for questions related to the user's learning goals. Specifically, the generation unit quickly generates answers for questions related to the user's learning goals. The generation unit can also prioritize generating answers for questions related to the user's interests. Specifically, the generation unit generates answers for questions related to the user's interests in parallel with answers for other questions. Furthermore, the generation unit can prioritize generating answers for questions related to the user's learning progress. Specifically, the generation unit generates answers for questions related to the user's learning progress after the answers for the other questions have been completed. This allows the generation unit to adjust the order of answers based on the relevance of the questions. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generator can use a generation AI to evaluate the relevance of the question sentence and adjust the order of the answers based on the evaluation results, thereby enabling the generator to generate efficient answers based on the relevance of the question sentence.

[0047] The display unit can adjust the level of detail of the display based on the importance of the answer when displaying the answer. The display unit, for example, evaluates the importance of the answer and adjusts the level of detail of the display based on the evaluation result. For example, the display unit provides a detailed display for an answer with high importance. Specifically, the display unit displays each element of the answer in detail to provide the user with necessary information. The display unit also provides a concise display for an answer with low importance. Specifically, the display unit displays only the main elements of the answer to provide the user with necessary information. Furthermore, the display unit provides a display with an appropriate level of detail for an answer with medium importance. Specifically, the display unit displays the main elements of the answer and some detailed elements to provide the user with necessary information. This allows the display unit to adjust the level of detail of the display based on the importance of the answer. Some or all of the above-mentioned processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can evaluate the importance of the answer using a generation AI and adjust the level of detail of the display based on the evaluation result. This allows the display unit to perform efficient display based on the importance of the answer.

[0048] The display unit can apply different display algorithms depending on the category of the answer when displaying the answer. For example, the display unit classifies the category of the answer and applies an appropriate display algorithm depending on the category. For example, the display unit applies a mathematical formula display algorithm to a mathematics answer. Specifically, the display unit analyzes the structure of the mathematical formula and provides the user with the necessary information. Furthermore, the display unit applies a text display algorithm to a history answer. Specifically, the display unit analyzes information about historical events and people and provides the user with the necessary information. Furthermore, the display unit applies a data display algorithm to a science answer. Specifically, the display unit analyzes experimental data and observational data and provides the user with the necessary information. This allows the display unit to apply different display algorithms depending on the category of the answer. Some or all of the above-mentioned processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can classify the category of the answer using a generation AI and apply an appropriate display algorithm depending on the category. This allows the display unit to display an accurate answer according to the category of the answer.

[0049] The display unit can determine the display priority based on the submission time of the answers when displaying them. The display unit, for example, evaluates the submission time of the answers and determines the display priority based on the evaluation result. For example, the display unit prioritizes displaying answers with an upcoming submission deadline. Specifically, the display unit quickly displays answers with an upcoming submission deadline. Furthermore, the display unit postpones answers with a distant submission deadline. Specifically, the display unit displays answers with a distant submission deadline after the display of other answers has been completed. Furthermore, the display unit gives moderate priority to answers with a medium submission deadline. Specifically, the display unit displays answers with a medium submission deadline in parallel with the display of other answers. This allows the display unit to determine the display priority based on the submission time of the answers. Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can evaluate the submission time of the answers using a generation AI and determine the display priority based on the evaluation result. This allows the display unit to efficiently display the answers based on the time of submission.

[0050] The display unit can adjust the display order based on the relevance of the answers when displaying them. The display unit, for example, evaluates the relevance of the answers and adjusts the display order based on the evaluation result. For example, the display unit prioritizes displaying answers related to the user's learning goals. Specifically, the display unit quickly displays answers related to the user's learning goals. The display unit can also prioritize displaying answers related to the user's interests. Specifically, the display unit displays answers related to the user's interests in parallel with the display of other answers. Furthermore, the display unit can also prioritize displaying answers related to the user's learning progress. Specifically, the display unit displays answers related to the user's learning progress after the display of other answers has been completed. This allows the display unit to adjust the display order based on the relevance of the answers. Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can evaluate the relevance of the answers using a generation AI and adjust the display order based on the evaluation result. This allows the display unit to efficiently display answers based on their relevance.

[0051] When displaying a character, the character display unit can select an appropriate character by referring to the user's past character selection history. The character display unit, for example, retrieves the user's past character selection history from a database and analyzes it using data mining technology. For example, the character display unit preferentially displays characters previously selected by the user. Specifically, the character display unit suggests new characters that have the characteristics of characters the user previously preferred. The character display unit can also automatically select an optimal character from the user's past selection history. Specifically, the character display unit analyzes the user's past selection history and selects an optimal character. This allows the character display unit to select an appropriate character by referring to the user's past character selection history. Some or all of the above-described processing in the character display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character display unit can analyze the user's past character selection history using a generation AI and select an optimal character. This allows the character display unit to select an appropriate character by referring to the user's past character selection history.

[0052] When displaying a character, the character display unit can select an appropriate character based on the user's geographical location information. The character display unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects a highly relevant character. For example, if the user is in a specific area, the character display unit displays a character related to that area. Specifically, the character display unit selects a character related to the culture or history of that area. Furthermore, if the user is traveling, the character display unit can also display a character related to the travel destination. Specifically, the character display unit selects a character related to tourist attractions and specialties of the travel destination. Furthermore, if the user is at school, the character display unit can also display a character related to the school curriculum. Specifically, the character display unit selects a character related to school lessons and events. In this way, the character display unit can provide a highly relevant character taking the user's geographical location information into consideration. Some or all of the above-described processing in the character display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character display unit can analyze the user's geographical location information using a generation AI and select an optimal character. This allows the character display unit to provide highly relevant characters taking into account the user's geographical location information.

[0053] When acquiring a reaction, the reaction acquisition unit can select an appropriate acquisition method by referring to the user's past reaction history. The reaction acquisition unit, for example, acquires the user's past reaction history from a database and analyzes it using data mining technology. For example, if the user has provided a detailed reaction in the past, the reaction acquisition unit acquires a similar detailed reaction. Specifically, the reaction acquisition unit analyzes the content of the reaction provided by the user in the past and selects an appropriate acquisition method. Furthermore, if the user has provided a brief reaction in the past, the reaction acquisition unit can also acquire a similar brief reaction. Specifically, the reaction acquisition unit analyzes the content of the reaction provided by the user in the past and selects an appropriate acquisition method. Furthermore, the reaction acquisition unit can automatically select an optimal acquisition method from the user's past reaction history. Specifically, the reaction acquisition unit analyzes the user's past reaction history and selects an optimal acquisition method. In this way, the reaction acquisition unit can select an appropriate acquisition method by referring to the user's past reaction history. Some or all of the above-described processing in the reaction acquisition unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reaction acquisition unit can use the generation AI to analyze the user's past reaction history and select the optimal acquisition method. This allows the reaction acquisition unit to select an appropriate acquisition method by referring to the user's past reaction history.

[0054] When acquiring a reaction, the reaction acquisition unit can acquire an appropriate reaction based on the user's geographical location information. The reaction acquisition unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects a highly relevant reaction. For example, if the user is in a specific area, the reaction acquisition unit acquires reactions related to that area. Specifically, the reaction acquisition unit selects reactions related to the culture and history of that area. Furthermore, if the user is traveling, the reaction acquisition unit can also acquire reactions related to the travel destination. Specifically, the reaction acquisition unit selects reactions related to tourist attractions and specialties of the travel destination. Furthermore, if the user is at school, the reaction acquisition unit can also acquire reactions related to the school curriculum. Specifically, the reaction acquisition unit selects reactions related to school class content and events. This allows the reaction acquisition unit to provide a highly relevant reaction taking the user's geographical location information into consideration. Some or all of the above-described processing in the reaction acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reaction acquisition unit can analyze the user's geographical location information using a generation AI and select an optimal reaction. This allows the reaction acquisition unit to provide highly relevant reactions taking into account the user's geographical location information.

[0055] During history management, the history management unit can select an appropriate management method by referring to the user's past learning history. The history management unit, for example, retrieves the user's past learning history from a database and analyzes it using data mining technology. For example, if the user previously managed a detailed history, the history management unit manages a similar detailed history. Specifically, the history management unit analyzes the content of the history previously managed by the user and selects an appropriate management method. Furthermore, if the user previously managed a concise history, the history management unit can also manage a similar concise history. Specifically, the history management unit analyzes the content of the history previously managed by the user and selects an appropriate management method. Furthermore, the history management unit can automatically select an optimal management method from the user's past learning history. Specifically, the history management unit analyzes the user's past learning history and selects an optimal management method. This allows the history management unit to select an appropriate management method by referring to the user's past learning history. Some or all of the above-described processing in the history management unit may be performed, for example, using a generation AI or without using a generation AI. For example, the history management unit can use the generation AI to analyze the user's past learning history and select the optimal management method. This allows the history management unit to select an appropriate management method by referring to the user's past learning history.

[0056] During history management, the history management unit can manage appropriate history based on the user's geographical location information. The history management unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects highly relevant history. For example, if the user is in a specific area, the history management unit manages history related to that area. Specifically, the history management unit selects history related to the culture and history of that area. Furthermore, if the user is traveling, the history management unit can also manage history related to the user's travel destination. Specifically, the history management unit selects history related to tourist attractions and specialties at the travel destination. Furthermore, if the user is at school, the history management unit can also manage history related to the school curriculum. Specifically, the history management unit selects history related to school class content and events. This allows the history management unit to provide highly relevant history taking the user's geographical location information into consideration. Some or all of the above-described processing in the history management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the history management unit can analyze the user's geographical location information using a generation AI and select the most appropriate history. This allows the history management unit to provide highly relevant history taking into account the user's geographical location information.

[0057] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0058] The learning assistance system may further include a learning style analysis unit that analyzes the user's learning style. The learning style analysis unit analyzes the user's preferred learning method based on the user's past learning history and response data. For example, if the user prefers visual information, the learning style analysis unit may instruct the display unit to provide visual content preferentially. If the user prefers auditory information, the learning style analysis unit may intensify audio commentary. Furthermore, if the user prefers interactive learning, the learning assistance system may instruct the character display unit to increase dialogue with the user. This allows the learning assistance system to provide an optimal learning experience tailored to the user's learning style.

[0059] The learning assistance system may further include a goal setting unit that sets learning goals for the user. The goal setting unit sets learning goals that the user wants to achieve and creates a learning plan based on those goals. For example, if the user's goal is to pass a specific exam, the goal setting unit creates a learning plan that covers the knowledge and skills required for that exam. Also, if the user's goal is to acquire a specific skill, questions related to that skill can be provided preferentially. Furthermore, the goal setting unit can monitor the user's progress and provide feedback toward goal achievement. This allows the learning assistance system to provide effective learning assistance tailored to the user's learning goals.

[0060] The learning assistance system can further include an environment optimization unit that optimizes the user's learning environment. The environment optimization unit monitors the user's learning environment (e.g., lighting, volume, temperature, etc.) and provides an optimal learning environment. For example, the environment optimization unit can adjust the brightness of the lighting to make it easier for the user to concentrate. It can also play music that allows the user to study in a relaxed environment. Furthermore, the environment optimization unit can issue an alert if the user's learning environment is not appropriate. In this way, the learning assistance system can support the user in studying in an optimal environment.

[0061] The learning assistance system can further include a motivation improvement unit that improves the user's motivation to learn. The motivation improvement unit provides appropriate motivation improvement measures based on the user's learning progress and achievement status. For example, if the user achieves a goal, the motivation improvement unit can provide the user with praise or a reward. Also, if the user loses motivation to study, the motivation improvement unit can provide an encouraging message or advice. Furthermore, the motivation improvement unit can provide the user with an opportunity to compete with other users to increase their motivation to study. In this way, the learning assistance system can maintain and improve the user's motivation to study.

[0062] The learning assistance system may further include an evaluation unit that evaluates the user's learning outcomes. The evaluation unit evaluates the accuracy and level of understanding of the questions answered by the user and provides feedback on the results. For example, the evaluation unit may calculate the percentage of correct answers for the questions answered by the user and provide feedback to the user. The evaluation unit may also evaluate the user's level of understanding in a specific field and propose a learning plan based on the user's level of understanding. Furthermore, the evaluation unit may visually display the user's learning outcomes, allowing the user to intuitively grasp their progress. This allows the learning assistance system to appropriately evaluate the user's learning outcomes and provide effective feedback.

[0063] The processing flow of the first embodiment will be briefly explained below.

[0064] Step 1: The reception unit receives the information when the user reads the question. For example, the user can scan the question using a smartphone or tablet, or input it digitally. Step 2: The analysis unit analyzes the question text read by the reception unit and extracts information to generate an answer. For example, it uses natural language processing technology to analyze the content of the question text and extract important keywords and context. Step 3: The generator generates an answer based on the information obtained from the analyzer. For example, a generator AI is used to generate an appropriate answer to the question. The generator AI can be a text generator AI (e.g., LLM) or a multimodal generator AI. Step 4: The display unit visually displays the answer and explanation generated by the generator. For example, using AR technology, a character the user likes may appear and explain the answer and explanation. Furthermore, the display unit may display the generated answer and explanation in the form of text, audio, video, or the like.

[0065] (Example 2) A learning support system according to an embodiment of the present invention combines generative AI and AR technology. When a user inputs a problem, the generative AI analyzes the content and generates an appropriate answer and explanation. Next, using AR technology, the user's favorite character appears and visually provides the answer and explanation. For example, when a user inputs a math problem, the generative AI generates an answer, and a character uses AR technology to explain the answer. This system is expected to make learning more enjoyable and deepen understanding. First, when a user inputs a problem, the reception unit receives the information. Next, the analysis unit analyzes the problem and extracts information for generating an answer. The generation unit generates an answer based on the information obtained from the analysis unit. Finally, the display unit visually provides the generated answer and explanation. At this time, a character appears using AR technology and explains the answer and explanation. This system allows users to intuitively understand the answer and explanation, making learning more enjoyable. Furthermore, the appearance of characters makes learning more engaging and suitable for a wide range of ages, from children to adults. This allows the learning support system to improve the enjoyment of learning by visually providing answers and explanations when the user reads a question.

[0066] A learning support system according to an embodiment includes a reception unit, an analysis unit, a generation unit, and a display unit. When a user reads a question, the reception unit receives the information. For example, the reception unit allows the user to scan the question using a smartphone or tablet or input the question digitally. The analysis unit analyzes the question read by the reception unit and extracts information for generating an answer. For example, the analysis unit analyzes the content of the question using natural language processing technology and extracts important keywords and context. The generation unit generates an answer based on the information obtained from the analysis unit. For example, the generation unit uses a generation AI to generate an appropriate answer for the question. The generation AI may be, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. The display unit visually displays the answer and explanation generated by the generation unit. For example, the display unit may use AR technology to display a user's favorite character to explain the answer and explanation. This allows the user to intuitively understand the answer and explanation, improving the enjoyment of learning. Furthermore, the display unit can provide the generated answers and explanations in the form of text, audio, video, or the like. For example, the display unit can display the answers and explanations in text format, allowing the user to read detailed explanations. Alternatively, the display unit can provide the answers and explanations in audio format, allowing the user to listen and understand. Alternatively, the display unit can provide the answers and explanations in video format, allowing the user to understand visually. In this way, the learning support system can improve the enjoyment of learning by visually providing the answers and explanations when the user reads the question text.

[0067] The learning assistance system includes a character display unit that displays a character. The character display unit displays a user's favorite character. For example, the character display unit can display various types of characters, such as animated characters and still characters. The character display unit can display, for example, a character selected by the user. The user can select their favorite character on a setting screen of the learning assistance system. The character display unit displays the selected character and explains the answer and explanation. For example, the character display unit can have an animated character explain the answer and explanation while moving. The character display unit can also have a still character explain the answer and explanation in text or audio. This makes learning more attractive by having the character appear, and increases the user's motivation to learn. Furthermore, the character display unit can change the character's movements and facial expressions in response to the user's reaction. For example, if the user answers correctly, the character can make a happy movement. Also, if the user makes a mistake, the character can make an encouraging movement. This allows the user to learn while enjoying interaction with the character. Some or all of the above-described processing in the character display unit may be performed, for example, using a generation AI or without using a generation AI. For example, the character display unit can use a generation AI to generate character movements and facial expressions and display them in response to the user's reactions, thereby increasing the user's motivation to learn.

[0068] The learning assistance system includes a reaction acquisition unit that acquires a user's reaction. The reaction acquisition unit acquires the user's reaction. For example, the reaction acquisition unit can acquire reactions such as the user clicking or tapping on the screen. The reaction acquisition unit can also acquire the user's voice input. For example, when the user inputs an answer by voice, the reaction acquisition unit acquires the voice and transmits it to the analysis unit. The reaction acquisition unit can also capture the user's facial expressions and movements with a camera and acquire the data. For example, the reaction acquisition unit can capture the user's facial expressions and movements while the user is thinking about an answer in front of the camera and transmit the data to the analysis unit. This allows the reaction acquisition unit to acquire the user's reaction in various ways and reflect it in the learning assistance system. Some or all of the above-described processing in the reaction acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reaction acquisition unit can analyze the user's voice input using a generation AI and recognize it as an answer. This allows the reaction acquisition unit to efficiently acquire the user's reaction and reflect it in the learning assistance system. Furthermore, the reaction acquisition unit can accumulate the user's reaction data and manage it as a learning history. For example, the reaction acquisition unit records the reactions the user has shown in the past and saves them as a learning history, which allows the reaction acquisition unit to grasp the user's learning progress and provide appropriate feedback.

[0069] The learning assistance system includes a history management unit that manages a learning history. The history management unit manages a user's learning history. For example, the history management unit can record and manage a history of questions that the user has answered in the past. The history management unit accumulates data such as the content of questions answered by the user, whether the answers were correct or incorrect, and the time it took to answer, and saves it as a learning history. This allows the history management unit to grasp the user's learning progress and provide appropriate feedback. Furthermore, the history management unit can analyze the user's learning history and understand learning trends and patterns. For example, the history management unit can analyze which areas of questions the user is strong and weak in and suggest areas for improvement in learning. This allows the history management unit to improve the user's learning effectiveness. Some or all of the above-described processing in the history management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the history management unit can analyze the user's learning history using a generation AI and understand learning trends and patterns. This allows the history management unit to improve the user's learning effectiveness. Furthermore, the history management unit can visually display the user's learning history. For example, the history management unit may display the user's learning history in a visually easy-to-understand format such as a graph or chart, allowing the user to intuitively grasp their learning progress.

[0070] The analysis unit can analyze the question and extract information for generating an answer. For example, the analysis unit can use natural language processing technology to analyze the content of the question and extract important keywords and context. For example, the analysis unit can extract information necessary for the answer from the question and send it to the generation unit. The analysis unit can also analyze the structure of the question using pattern recognition technology to extract the information necessary for the answer. For example, the analysis unit can recognize patterns such as mathematical formulas and graphs in the question and extract the information necessary for the answer based on the patterns. This allows the analysis unit to analyze the question and accurately extract the information necessary for generating an answer. Some or all of the above-mentioned processing in the analysis unit can be performed using, for example, a generation AI, or can be performed without using a generation AI. For example, the analysis unit can analyze the content of the question using a generation AI and extract the information necessary for the answer. This allows the analysis unit to efficiently analyze the question and extract the information necessary for generating an answer. Furthermore, the analysis unit can visually display the analysis results of the question. For example, the analysis unit may display the analysis results of the question as graphs or charts, providing them in a visually easy-to-understand format, allowing the user to intuitively grasp the analysis results of the question.

[0071] The generation unit can generate an answer based on the information obtained from the analysis unit. The generation unit generates an appropriate answer to the question using, for example, a generation AI. The generation AI can use, for example, a text generation AI (e.g., LLM) or a multimodal generation AI. For example, the generation unit generates an answer to the question using a text generation AI. The text generation AI has learned a large amount of text data and has advanced natural language processing capabilities. This allows the generation unit to generate an appropriate answer to the question. The generation unit can also generate an answer to the question using a multimodal generation AI. The multimodal generation AI can handle multiple modalities, including not only text but also images and audio. This allows the generation unit to generate answers to the question in a variety of formats. Furthermore, the generation unit can use the generation AI to build a feedback loop to improve the quality of the answer. For example, the generation unit evaluates the quality of the generated answer and updates the generation AI based on the evaluation results. This allows the generation unit to always provide high-quality answers based on the latest information. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may generate an answer using a generation AI and provide the answer to the user. This allows the generation unit to generate an accurate answer based on the information obtained from the analysis unit.

[0072] The reception unit can estimate the user's emotions and adjust the timing of reading the questions based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and adjusts the timing of reading the questions. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the timing of reading the questions. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on fluctuations in heart rate and adjusts the timing of reading the questions. This allows the reception unit to adjust the timing of reading the questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reception unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the reception unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the reception unit to adjust the timing of reading the question sentence according to the user's emotions.

[0073] The reception unit can analyze the user's past learning history and select the optimal method for reading questions. The reception unit, for example, retrieves the user's past learning history from a database and analyzes it using data mining technology. For example, the reception unit can analyze the history of questions the user has answered in the past and identify the user's areas of strength and weakness. The reception unit can also grasp the user's learning progress and select questions of appropriate difficulty. For example, the reception unit can prioritize reading questions in areas in which the user has previously scored high. The reception unit can also avoid question formats that the user has previously struggled with and prioritize reading question formats that the user is good at. This allows the reception unit to analyze the user's past learning history and select the optimal method for reading questions. Some or all of the above-mentioned processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's learning history using a generation AI and select the optimal method for reading questions. This allows the reception unit to analyze the user's past learning history and select the optimal method for reading questions.

[0074] When loading questions, the reception unit can filter the questions based on the user's current learning situation and areas of interest. For example, the reception unit acquires the user's current learning situation from a database and selects questions using a filtering algorithm. For example, the reception unit prioritizes loading questions related to the user's current learning area. The reception unit can also identify the user's areas of interest and filter and load related questions. For example, the reception unit filters and loads questions related to topics in which the user is interested. Furthermore, the reception unit can filter and load questions of an appropriate level of difficulty according to the user's learning progress. For example, the reception unit identifies the user's learning progress and selects and loads questions of an appropriate level of difficulty. This allows the reception unit to filter questions based on the user's current learning situation and areas of interest. Some or all of the above-described processing in the reception unit may be performed using, or without, a generation AI. For example, the reception unit can analyze the user's learning situation and areas of interest using a generation AI and filter and load the most appropriate questions. This allows the reception unit to filter question sentences based on the user's current learning situation and areas of interest.

[0075] The reception unit can estimate the user's emotions and determine the priority of the questions to be read based on the estimated user emotions. For example, the reception unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on changes in facial expression and determines the priority of the questions. The reception unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reception unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the questions. The reception unit can also collect the user's biometric data (heart rate and electrodermal activity) using a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reception unit calculates an emotion score based on heart rate fluctuations and determines the priority of the questions. This allows the reception unit to determine the priority of the questions according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the reception unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the reception unit to determine the priority of question sentences according to the user's emotions.

[0076] When loading questions, the reception unit can prioritize loading questions that are highly relevant based on the user's geographical location information. The reception unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects highly relevant questions. For example, when the user is in a specific area, the reception unit prioritizes loading questions related to that area. Furthermore, when the user is traveling, the reception unit can prioritize loading questions related to the travel destination. For example, when the user is studying at a travel destination, the reception unit can prioritize loading questions related to the history and culture of the area. Furthermore, when the user is at school, the reception unit can prioritize loading questions related to the school curriculum. For example, when the user is studying at school, the reception unit loads questions based on the school curriculum. This allows the reception unit to provide highly relevant questions taking the user's geographical location information into consideration. Some or all of the above-described processing by the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's geographical location information using a generation AI and select the most appropriate question. This allows the reception unit to provide highly relevant question sentences taking into account the user's geographical location information.

[0077] When reading a question, the reception unit can analyze the user's social media activity and read related question statements. The reception unit, for example, retrieves the user's social media activity from a database and analyzes it using data mining technology. For example, the reception unit can read question statements related to topics in which the user has shown interest on social media. The reception unit can also read question statements related to the content of educational accounts the user follows on social media. For example, the reception unit can analyze the content of posts on educational accounts the user follows and select related question statements. The reception unit can also read question statements related to articles the user shared on social media. For example, the reception unit can analyze the content of articles shared by the user and select related question statements. In this way, the reception unit can analyze the user's social media activity and provide related question statements. Some or all of the above-described processing in the reception unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reception unit can analyze the user's social media activity using a generation AI and select optimal question statements. In this way, the reception unit can analyze the user's social media activity and provide related question statements.

[0078] The analysis unit can estimate the user's emotion and adjust the analysis presentation method based on the estimated user's emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the analysis presentation method. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the analysis presentation method. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the analysis presentation method. This allows the analysis unit to adjust the analysis presentation method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit may input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions. This allows the analysis unit to adjust the method of expression of the analysis according to the user's emotions.

[0079] During analysis, the analysis unit can adjust the level of detail of the analysis based on the importance of the question. The analysis unit, for example, evaluates the importance of the question and adjusts the level of detail of the analysis based on the evaluation result. For example, the analysis unit performs a detailed analysis on a question with high importance. Specifically, the analysis unit analyzes each element of the question in detail to extract information necessary for the answer. Furthermore, the analysis unit performs a concise analysis on a question with low importance. Specifically, the analysis unit analyzes only the main elements of the question to extract information necessary for the answer. Furthermore, the analysis unit performs an analysis with a moderate level of detail on a question with medium importance. Specifically, the analysis unit analyzes the main elements and some detailed elements of the question to extract information necessary for the answer. This allows the analysis unit to adjust the level of detail of the analysis based on the importance of the question. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or may be performed without using a generation AI. For example, the analysis unit can use the generation AI to evaluate the importance of the question sentence and adjust the level of analysis detail based on the evaluation results, allowing the analysis unit to perform efficient analysis based on the importance of the question sentence.

[0080] During analysis, the analysis unit can apply different analysis algorithms depending on the category of the problem. For example, the analysis unit classifies the category of the problem and applies an appropriate analysis algorithm depending on the category. For example, the analysis unit applies a mathematical expression analysis algorithm to a mathematics problem. Specifically, the analysis unit analyzes the structure of the mathematical expression and extracts information necessary for the answer. Furthermore, the analysis unit applies a text analysis algorithm to a history problem. Specifically, the analysis unit analyzes information about historical events and people and extracts information necessary for the answer. Furthermore, the analysis unit applies a data analysis algorithm to a science problem. Specifically, the analysis unit analyzes experimental data and observational data and extracts information necessary for the answer. This allows the analysis unit to apply different analysis algorithms depending on the category of the problem. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can classify the category of the problem using a generation AI and apply an appropriate analysis algorithm depending on the category. This allows the analysis unit to perform accurate analysis according to the category of the question sentence.

[0081] The analysis unit can estimate the user's emotion and adjust the length of the analysis based on the estimated user emotion. For example, the analysis unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on changes in facial expression and adjusts the length of the analysis. The analysis unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the analysis unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the analysis. The analysis unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the analysis unit calculates an emotion score based on heart rate fluctuations and adjusts the length of the analysis. This allows the analysis unit to adjust the length of the analysis according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generative AI. The generative AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the analysis unit may input image data of a user taken with a camera into the generation AI and have the generation AI estimate the user's emotions. This allows the analysis unit to adjust the length of the analysis depending on the user's emotions.

[0082] During analysis, the analysis unit can determine the priority of analysis based on the submission date of the question. The analysis unit, for example, evaluates the submission date of the question and determines the priority of analysis based on the evaluation result. For example, the analysis unit prioritizes analysis of question statements with an upcoming submission deadline. Specifically, the analysis unit quickly analyzes question statements with an upcoming submission deadline and generates answers. The analysis unit also postpones question statements with a distant submission deadline. Specifically, the analysis unit analyzes question statements with a distant submission deadline after completing analysis of other question statements. Furthermore, the analysis unit gives moderate priority to question statements with a medium submission deadline. Specifically, the analysis unit analyzes question statements with a medium submission deadline in parallel with analysis of other question statements. This allows the analysis unit to determine the priority of analysis based on the submission date of the question. Some or all of the above-mentioned processing in the analysis unit may be performed, for example, using a generation AI or without using a generation AI. For example, the analysis unit can use the generation AI to evaluate the timing of question submission and determine the analysis priority based on the evaluation results, allowing the analysis unit to perform efficient analysis based on the timing of question submission.

[0083] The analysis unit can adjust the order of analysis based on the relevance of the questions during analysis. The analysis unit, for example, evaluates the relevance of the questions and adjusts the order of analysis based on the evaluation results. For example, the analysis unit prioritizes analysis of questions related to the user's learning goals. Specifically, the analysis unit quickly analyzes questions related to the user's learning goals and generates answers. The analysis unit can also prioritize analysis of questions related to the user's interests. Specifically, the analysis unit analyzes questions related to the user's interests in parallel with the analysis of other questions. Furthermore, the analysis unit can prioritize analysis of questions related to the user's learning progress. Specifically, the analysis unit analyzes questions related to the user's learning progress after the analysis of other questions has been completed. This allows the analysis unit to adjust the order of analysis based on the relevance of the questions. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the analysis unit can evaluate the relevance of questions using a generation AI and adjust the order of analysis based on the evaluation results. This allows the analysis unit to perform efficient analysis based on the relevance of the question sentences.

[0084] The generation unit can estimate the user's emotion and adjust the answer generation method based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and adjusts the answer generation method. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the answer generation method. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and adjusts the answer generation method. This allows the generation unit to adjust the answer generation method according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the generation unit to adjust the method of generating answers according to the user's emotions.

[0085] When generating an answer, the generation unit can adjust the level of detail of the answer based on the importance of the question. The generation unit, for example, evaluates the importance of the question and adjusts the level of detail of the answer based on the evaluation result. For example, the generation unit generates a detailed answer for a question with high importance. Specifically, the generation unit analyzes each element of the question in detail and provides information necessary for the answer. Furthermore, the generation unit generates a concise answer for a question with low importance. Specifically, the generation unit analyzes only the main elements of the question and provides information necessary for the answer. Furthermore, the generation unit generates an answer with a moderate level of detail for a question with medium importance. Specifically, the generation unit analyzes the main elements and some of the detailed elements of the question and provides information necessary for the answer. This allows the generation unit to adjust the level of detail of the answer based on the importance of the question. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generator can use a generation AI to evaluate the importance of the question and adjust the level of detail of the answer based on the evaluation results, thereby enabling the generator to generate an efficient answer based on the importance of the question.

[0086] When generating an answer, the generation unit can apply different generation algorithms depending on the category of the question. For example, the generation unit classifies the category of the question and applies an appropriate generation algorithm depending on the category. For example, the generation unit applies a mathematical formula generation algorithm to a mathematics question. Specifically, the generation unit analyzes the structure of the mathematical formula and provides information necessary for the answer. Furthermore, the generation unit applies a text generation algorithm to a history question. Specifically, the generation unit analyzes information about historical events and people and provides information necessary for the answer. Furthermore, the generation unit applies a data generation algorithm to a science question. Specifically, the generation unit analyzes experimental data and observational data and provides information necessary for the answer. This allows the generation unit to apply different generation algorithms depending on the category of the question. Some or all of the above-mentioned processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit can classify the category of the question using a generation AI and apply an appropriate generation algorithm depending on the category. This allows the generator to generate an accurate answer according to the category of the question sentence.

[0087] The generation unit can estimate the user's emotion and adjust the length of the answer based on the estimated user emotion. For example, the generation unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on changes in facial expression and adjusts the length of the answer. The generation unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the generation unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the length of the answer. The generation unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the generation unit calculates an emotion score based on heart rate fluctuations and adjusts the length of the answer. This allows the generation unit to adjust the length of the answer according to the user's emotion. Emotion estimation is achieved using an emotion estimation function, for example, using an emotion engine or generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the generation unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the generation unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the generation unit to adjust the length of the answer depending on the user's emotions.

[0088] When generating answers, the generation unit can determine the priority of answers based on the submission time of the question. The generation unit, for example, evaluates the submission time of the question and determines the priority of answers based on the evaluation result. For example, the generation unit prioritizes generating answers for question statements with an upcoming submission deadline. Specifically, the generation unit quickly generates answers for question statements with an upcoming submission deadline. Furthermore, the generation unit postpones answering of questions with a distant submission deadline. Specifically, the generation unit generates answers for question statements with a distant submission deadline after the answers for other question statements have been completed. Furthermore, the generation unit moderately prioritizes answers for question statements with a medium submission deadline. Specifically, the generation unit generates answers for question statements with a medium submission deadline in parallel with the answers for other question statements. This allows the generation unit to determine the priority of answers based on the submission time of the question. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generation unit can use the generation AI to evaluate the timing of question submission and determine the priority of answers based on the evaluation results, thereby enabling the generation unit to generate efficient answers based on the timing of question submission.

[0089] When generating answers, the generation unit can adjust the order of answers based on the relevance of the questions. For example, the generation unit evaluates the relevance of the questions and adjusts the order of answers based on the evaluation result. For example, the generation unit prioritizes generating answers for questions related to the user's learning goals. Specifically, the generation unit quickly generates answers for questions related to the user's learning goals. The generation unit can also prioritize generating answers for questions related to the user's interests. Specifically, the generation unit generates answers for questions related to the user's interests in parallel with answers for other questions. Furthermore, the generation unit can prioritize generating answers for questions related to the user's learning progress. Specifically, the generation unit generates answers for questions related to the user's learning progress after the answers for the other questions have been completed. This allows the generation unit to adjust the order of answers based on the relevance of the questions. Some or all of the above-mentioned processing in the generation unit may be performed, for example, using a generation AI, or may be performed without using a generation AI. For example, the generator can use a generation AI to evaluate the relevance of the question sentence and adjust the order of the answers based on the evaluation results, thereby enabling the generator to generate efficient answers based on the relevance of the question sentence.

[0090] The display unit can estimate the user's emotion and adjust the display expression method based on the estimated user's emotion. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on changes in facial expression and adjusts the display expression method. The display unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display expression method. The display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on heart rate fluctuations and adjusts the display expression method. This allows the display unit to adjust the display expression method according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the display unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the display unit to adjust the display expression method according to the user's emotions.

[0091] The display unit can adjust the level of detail of the display based on the importance of the answer when displaying the answer. The display unit, for example, evaluates the importance of the answer and adjusts the level of detail of the display based on the evaluation result. For example, the display unit provides a detailed display for an answer with high importance. Specifically, the display unit displays each element of the answer in detail to provide the user with necessary information. The display unit also provides a concise display for an answer with low importance. Specifically, the display unit displays only the main elements of the answer to provide the user with necessary information. Furthermore, the display unit provides a display with an appropriate level of detail for an answer with medium importance. Specifically, the display unit displays the main elements of the answer and some detailed elements to provide the user with necessary information. This allows the display unit to adjust the level of detail of the display based on the importance of the answer. Some or all of the above-mentioned processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can evaluate the importance of the answer using a generation AI and adjust the level of detail of the display based on the evaluation result. This allows the display unit to perform efficient display based on the importance of the answer.

[0092] The display unit can apply different display algorithms depending on the category of the answer when displaying the answer. For example, the display unit classifies the category of the answer and applies an appropriate display algorithm depending on the category. For example, the display unit applies a mathematical formula display algorithm to a mathematics answer. Specifically, the display unit analyzes the structure of the mathematical formula and provides the user with the necessary information. Furthermore, the display unit applies a text display algorithm to a history answer. Specifically, the display unit analyzes information about historical events and people and provides the user with the necessary information. Furthermore, the display unit applies a data display algorithm to a science answer. Specifically, the display unit analyzes experimental data and observational data and provides the user with the necessary information. This allows the display unit to apply different display algorithms depending on the category of the answer. Some or all of the above-mentioned processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can classify the category of the answer using a generation AI and apply an appropriate display algorithm depending on the category. This allows the display unit to display an accurate answer according to the category of the answer.

[0093] The display unit can estimate the user's emotion and adjust the display length based on the estimated user emotion. For example, the display unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on changes in facial expression and adjusts the display length. The display unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display length. The display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the display unit calculates an emotion score based on heart rate fluctuations and adjusts the display length. This allows the display unit to adjust the display length according to the user's emotion. Emotion estimation is realized using an emotion estimation function using, for example, an emotion engine or a generation AI. The generation AI can be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using the generation AI. For example, the display unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the display unit to adjust the length of the display according to the user's emotions.

[0094] The display unit can determine the display priority based on the submission time of the answers when displaying them. The display unit, for example, evaluates the submission time of the answers and determines the display priority based on the evaluation result. For example, the display unit prioritizes displaying answers with an upcoming submission deadline. Specifically, the display unit quickly displays answers with an upcoming submission deadline. Furthermore, the display unit postpones answers with a distant submission deadline. Specifically, the display unit displays answers with a distant submission deadline after the display of other answers has been completed. Furthermore, the display unit gives moderate priority to answers with a medium submission deadline. Specifically, the display unit displays answers with a medium submission deadline in parallel with the display of other answers. This allows the display unit to determine the display priority based on the submission time of the answers. Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can evaluate the submission time of the answers using a generation AI and determine the display priority based on the evaluation result. This allows the display unit to efficiently display the answers based on the time of submission.

[0095] The display unit can adjust the display order based on the relevance of the answers when displaying them. The display unit, for example, evaluates the relevance of the answers and adjusts the display order based on the evaluation result. For example, the display unit prioritizes displaying answers related to the user's learning goals. Specifically, the display unit quickly displays answers related to the user's learning goals. The display unit can also prioritize displaying answers related to the user's interests. Specifically, the display unit displays answers related to the user's interests in parallel with the display of other answers. Furthermore, the display unit can also prioritize displaying answers related to the user's learning progress. Specifically, the display unit displays answers related to the user's learning progress after the display of other answers has been completed. This allows the display unit to adjust the display order based on the relevance of the answers. Some or all of the above-described processing in the display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the display unit can evaluate the relevance of the answers using a generation AI and adjust the display order based on the evaluation result. This allows the display unit to efficiently display answers based on their relevance.

[0096] The character display unit can estimate the user's emotions and adjust the display method of the character based on the estimated user emotions. For example, the character display unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the character display unit calculates an emotion score based on changes in facial expressions and adjusts the display method of the character. The character display unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the character display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display method of the character. The character display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the character display unit calculates an emotion score based on fluctuations in heart rate and adjusts the display method of the character. This allows the character display unit to adjust the display method of the character according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the character display unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the character display unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the character display unit to adjust the way the character is displayed depending on the user's emotions.

[0097] When displaying a character, the character display unit can select an appropriate character by referring to the user's past character selection history. The character display unit, for example, retrieves the user's past character selection history from a database and analyzes it using data mining technology. For example, the character display unit preferentially displays characters previously selected by the user. Specifically, the character display unit suggests new characters that have the characteristics of characters the user previously preferred. The character display unit can also automatically select an optimal character from the user's past selection history. Specifically, the character display unit analyzes the user's past selection history and selects an optimal character. This allows the character display unit to select an appropriate character by referring to the user's past character selection history. Some or all of the above-described processing in the character display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character display unit can analyze the user's past character selection history using a generation AI and select an optimal character. This allows the character display unit to select an appropriate character by referring to the user's past character selection history.

[0098] The character display unit can estimate the user's emotions and adjust the display order of the characters based on the estimated user emotions. For example, the character display unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the character display unit calculates an emotion score based on changes in facial expressions and adjusts the display order of the characters. The character display unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the character display unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the display order of the characters. The character display unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the character display unit calculates an emotion score based on fluctuations in heart rate and adjusts the display order of the characters. This allows the character display unit to adjust the display order of the characters according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the character display unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the character display unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the character display unit to adjust the display order of characters according to the user's emotions.

[0099] When displaying a character, the character display unit can select an appropriate character based on the user's geographical location information. The character display unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects a highly relevant character. For example, if the user is in a specific area, the character display unit displays a character related to that area. Specifically, the character display unit selects a character related to the culture or history of that area. Furthermore, if the user is traveling, the character display unit can also display a character related to the travel destination. Specifically, the character display unit selects a character related to tourist attractions and specialties of the travel destination. Furthermore, if the user is at school, the character display unit can also display a character related to the school curriculum. Specifically, the character display unit selects a character related to school lessons and events. In this way, the character display unit can provide a highly relevant character taking the user's geographical location information into consideration. Some or all of the above-described processing in the character display unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the character display unit can analyze the user's geographical location information using a generation AI and select an optimal character. This allows the character display unit to provide highly relevant characters taking into account the user's geographical location information.

[0100] The reaction acquisition unit can estimate the user's emotion and adjust the reaction acquisition method based on the estimated user emotion. For example, the reaction acquisition unit captures the user's facial expression with a camera and estimates the emotion using an emotion estimation algorithm. For example, the reaction acquisition unit calculates an emotion score based on changes in facial expression and adjusts the reaction acquisition method. The reaction acquisition unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the reaction acquisition unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the reaction acquisition method. The reaction acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the reaction acquisition unit calculates an emotion score based on fluctuations in heart rate and adjusts the reaction acquisition method. This allows the reaction acquisition unit to adjust the reaction acquisition method according to the user's emotion. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reaction acquisition unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the reaction acquisition unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the reaction acquisition unit to adjust the reaction acquisition method according to the user's emotions.

[0101] When acquiring a reaction, the reaction acquisition unit can select an appropriate acquisition method by referring to the user's past reaction history. The reaction acquisition unit, for example, acquires the user's past reaction history from a database and analyzes it using data mining technology. For example, if the user has provided a detailed reaction in the past, the reaction acquisition unit acquires a similar detailed reaction. Specifically, the reaction acquisition unit analyzes the content of the reaction provided by the user in the past and selects an appropriate acquisition method. Furthermore, if the user has provided a brief reaction in the past, the reaction acquisition unit can also acquire a similar brief reaction. Specifically, the reaction acquisition unit analyzes the content of the reaction provided by the user in the past and selects an appropriate acquisition method. Furthermore, the reaction acquisition unit can automatically select an optimal acquisition method from the user's past reaction history. Specifically, the reaction acquisition unit analyzes the user's past reaction history and selects an optimal acquisition method. In this way, the reaction acquisition unit can select an appropriate acquisition method by referring to the user's past reaction history. Some or all of the above-described processing in the reaction acquisition unit may be performed, for example, using a generation AI or without using a generation AI. For example, the reaction acquisition unit can use the generation AI to analyze the user's past reaction history and select the optimal acquisition method. This allows the reaction acquisition unit to select an appropriate acquisition method by referring to the user's past reaction history.

[0102] The reaction acquisition unit can estimate the user's emotions and determine the priority of reactions based on the estimated user emotions. For example, the reaction acquisition unit captures the user's facial expression with a camera and estimates the emotions using an emotion estimation algorithm. For example, the reaction acquisition unit calculates an emotion score based on changes in facial expression and determines the priority of reactions. The reaction acquisition unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the reaction acquisition unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of reactions. The reaction acquisition unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the reaction acquisition unit calculates an emotion score based on fluctuations in heart rate and determines the priority of reactions. This allows the reaction acquisition unit to determine the priority of reactions according to the user's emotions. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the reaction acquisition unit may be performed using the generation AI, for example, or may be performed without using the generation AI. For example, the reaction acquisition unit may input image data of a user captured by a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the reaction acquisition unit to determine the priority of reactions according to the user's emotions.

[0103] When acquiring a reaction, the reaction acquisition unit can acquire an appropriate reaction based on the user's geographical location information. The reaction acquisition unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects a highly relevant reaction. For example, if the user is in a specific area, the reaction acquisition unit acquires reactions related to that area. Specifically, the reaction acquisition unit selects reactions related to the culture and history of that area. Furthermore, if the user is traveling, the reaction acquisition unit can also acquire reactions related to the travel destination. Specifically, the reaction acquisition unit selects reactions related to tourist attractions and specialties of the travel destination. Furthermore, if the user is at school, the reaction acquisition unit can also acquire reactions related to the school curriculum. Specifically, the reaction acquisition unit selects reactions related to school class content and events. This allows the reaction acquisition unit to provide a highly relevant reaction taking the user's geographical location information into consideration. Some or all of the above-described processing in the reaction acquisition unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the reaction acquisition unit can analyze the user's geographical location information using a generation AI and select an optimal reaction. This allows the reaction acquisition unit to provide highly relevant reactions taking into account the user's geographical location information.

[0104] The history management unit can estimate the user's emotions and adjust the history management method based on the estimated user emotions. For example, the history management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the history management unit calculates an emotion score based on changes in facial expressions and adjusts the history management method. The history management unit can also record the user's voice and estimate the emotions using voice analysis technology. For example, the history management unit analyzes the tone and speed of the voice, calculates an emotion score, and adjusts the history management method. The history management unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotions using an emotion estimation algorithm. For example, the history management unit calculates an emotion score based on fluctuations in heart rate and adjusts the history management method. This allows the history management unit to adjust the history management method according to the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the history management unit may be performed using, for example, the generation AI, or may be performed without using the generation AI. For example, the history management unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the history management unit to adjust the history management method according to the user's emotions.

[0105] During history management, the history management unit can select an appropriate management method by referring to the user's past learning history. The history management unit, for example, retrieves the user's past learning history from a database and analyzes it using data mining technology. For example, if the user previously managed a detailed history, the history management unit manages a similar detailed history. Specifically, the history management unit analyzes the content of the history previously managed by the user and selects an appropriate management method. Furthermore, if the user previously managed a concise history, the history management unit can also manage a similar concise history. Specifically, the history management unit analyzes the content of the history previously managed by the user and selects an appropriate management method. Furthermore, the history management unit can automatically select an optimal management method from the user's past learning history. Specifically, the history management unit analyzes the user's past learning history and selects an optimal management method. This allows the history management unit to select an appropriate management method by referring to the user's past learning history. Some or all of the above-described processing in the history management unit may be performed, for example, using a generation AI or without using a generation AI. For example, the history management unit can use the generation AI to analyze the user's past learning history and select the optimal management method. This allows the history management unit to select an appropriate management method by referring to the user's past learning history.

[0106] The history management unit can estimate the user's emotions and prioritize the history based on the estimated user emotions. For example, the history management unit captures the user's facial expressions with a camera and estimates the emotions using an emotion estimation algorithm. For example, the history management unit calculates an emotion score based on changes in facial expressions and determines the priority of the history. The history management unit can also record the user's voice and estimate the emotion using voice analysis technology. For example, the history management unit analyzes the tone and speed of the voice, calculates an emotion score, and determines the priority of the history. The history management unit can also collect the user's biometric data (heart rate and electrodermal activity) with a sensor and estimate the emotion using an emotion estimation algorithm. For example, the history management unit calculates an emotion score based on heart rate fluctuations and determines the priority of the history. This allows the history management unit to prioritize the history based on the user's emotions. Emotion estimation is achieved using an emotion estimation function, for example, an emotion engine or a generative AI. The generation AI may be a text generation AI (e.g., LLM) or a multimodal generation AI, but is not limited to such examples. Some or all of the above-described processing in the history management unit may be performed, for example, using the generation AI, or may be performed without using the generation AI. For example, the history management unit may input image data of a user taken with a camera into the generation AI and cause the generation AI to estimate the user's emotions. This allows the history management unit to determine the priority of history according to the user's emotions.

[0107] During history management, the history management unit can manage appropriate history based on the user's geographical location information. The history management unit, for example, acquires the user's geographical location information from GPS data or an IP address and selects highly relevant history. For example, if the user is in a specific area, the history management unit manages history related to that area. Specifically, the history management unit selects history related to the culture and history of that area. Furthermore, if the user is traveling, the history management unit can also manage history related to the user's travel destination. Specifically, the history management unit selects history related to tourist attractions and specialties at the travel destination. Furthermore, if the user is at school, the history management unit can also manage history related to the school curriculum. Specifically, the history management unit selects history related to school class content and events. This allows the history management unit to provide highly relevant history taking the user's geographical location information into consideration. Some or all of the above-described processing in the history management unit may be performed using, for example, a generation AI, or may be performed without using a generation AI. For example, the history management unit can analyze the user's geographical location information using a generation AI and select the most appropriate history. This allows the history management unit to provide highly relevant history taking into account the user's geographical location information. === Hard Collateral 1-1 === Each of the above-described elements, including the reception unit, analysis unit, generation unit, display unit, character display unit, reaction acquisition unit, history management unit, and emotion estimation function, is implemented, for example, by at least one of the smart device 14 and the data processing device 12. For example, the reception unit reads the user's question using the camera 42 or microphone 38B of the smart device 14 and receives the information via the control unit 46A. The analysis unit, implemented by the specific processing unit 290 of the data processing device 12, analyzes the question and extracts information for generating an answer. The generation unit, implemented by the specific processing unit 290 of the data processing device 12, generates an answer based on the information obtained from the analysis unit. The display unit visually presents the generated answer and explanation using the display 40A and speaker 40B of the smart device 14. The character display unit displays the user's favorite character using the display 40A of the smart device 14 and explains the answer and explanation. The reaction acquisition unit acquires the user's reaction using the camera 42 and microphone 38B of the smart device 14, and transmits the data to the analysis unit via the control unit 46A. The history management unit is realized by the specific processing unit 290 of the data processing device 12 and manages the user's learning history. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12 and estimates the user's emotion and adjusts the timing of reading the question sentence. === Hard Collateral 1-2 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, display unit, character display unit, reaction acquisition unit, history management unit, and emotion estimation function, is realized, for example, by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit reads the user's question sentence using the camera 42 or microphone 238 of the smart glasses 214 and receives the information via the control unit 46A. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the question sentence and extracts information for generating an answer. The generation unit, realized by the specific processing unit 290 of the data processing device 12, generates an answer based on the information obtained from the analysis unit. The display unit visually provides the generated answer and explanation using the display of the smart glasses 214. The character display unit displays the user's favorite character using the display of the smart glasses 214 and explains the answer and explanation. The reaction acquisition unit acquires the user's reaction using the camera 42 or microphone 238 of the smart glasses 214 and transmits the data to the analysis unit via the control unit 46A. The history management unit is realized by the specific processing unit 290 of the data processing device 12, and manages the user's learning history. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of reading the question sentences. === Hard Collateral 1-3 === Each of the multiple elements, including the above-described reception unit, analysis unit, generation unit, display unit, character display unit, reaction acquisition unit, history management unit, and emotion estimation function, is realized, for example, by at least one of the headset-type terminal 314 and the data processing device 12. For example, the reception unit reads the user's question statement using the camera 42 or microphone 238 of the headset-type terminal 314 and receives the information using the control unit 46A. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the question statement and extracts information for generating an answer. The generation unit, realized by the specific processing unit 290 of the data processing device 12, generates an answer based on the information obtained from the analysis unit. The display unit visually provides the generated answer and explanation using the display 343 of the headset-type terminal 314. The character display unit displays the user's favorite character using the display 343 of the headset-type terminal 314 and explains the answer and explanation. The reaction acquisition unit acquires the user's reaction using the camera 42 and microphone 238 of the headset terminal 314, and transmits the data to the analysis unit via the control unit 46A. The history management unit is realized by the specific processing unit 290 of the data processing device 12, and manages the user's learning history. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of reading the question sentences. === Hard Collateral 1-4 === Each of the multiple elements, including the above-mentioned reception unit, analysis unit, generation unit, display unit, character display unit, reaction acquisition unit, history management unit, and emotion estimation function, is realized, for example, by at least one of the robot 414 and the data processing device 12. For example, the reception unit reads the user's question sentence using the camera 42 or microphone 238 of the robot 414 and receives the information via the control unit 46A. The analysis unit, realized by the specific processing unit 290 of the data processing device 12, analyzes the question sentence and extracts information for generating an answer. The generation unit, realized by the specific processing unit 290 of the data processing device 12, generates an answer based on the information obtained from the analysis unit. The display unit visually presents the generated answer and explanation using the display of the robot 414. The character display unit displays the user's favorite character using the display of the robot 414 and explains the answer and explanation. The reaction acquisition unit acquires the user's reaction using the camera 42 or microphone 238 of the robot 414 and transmits the data to the analysis unit via the control unit 46A. The history management unit is realized by the specific processing unit 290 of the data processing device 12, and manages the user's learning history. The emotion estimation function is realized by the specific processing unit 290 of the data processing device 12, and estimates the user's emotion and adjusts the timing of reading the question sentences.

[0108] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0109] The learning assistance system may further include a learning style analysis unit that analyzes the user's learning style. The learning style analysis unit analyzes the user's preferred learning method based on the user's past learning history and response data. For example, if the user prefers visual information, the learning style analysis unit may instruct the display unit to provide visual content preferentially. If the user prefers auditory information, the learning style analysis unit may intensify audio commentary. Furthermore, if the user prefers interactive learning, the learning assistance system may instruct the character display unit to increase dialogue with the user. This allows the learning assistance system to provide an optimal learning experience tailored to the user's learning style.

[0110] The learning assistance system may further include a goal setting unit that sets learning goals for the user. The goal setting unit sets learning goals that the user wants to achieve and creates a learning plan based on those goals. For example, if the user's goal is to pass a specific exam, the goal setting unit creates a learning plan that covers the knowledge and skills required for that exam. Also, if the user's goal is to acquire a specific skill, questions related to that skill can be provided preferentially. Furthermore, the goal setting unit can monitor the user's progress and provide feedback toward goal achievement. This allows the learning assistance system to provide effective learning assistance tailored to the user's learning goals.

[0111] The learning assistance system can further include an environment optimization unit that optimizes the user's learning environment. The environment optimization unit monitors the user's learning environment (e.g., lighting, volume, temperature, etc.) and provides an optimal learning environment. For example, the environment optimization unit can adjust the brightness of the lighting to make it easier for the user to concentrate. It can also play music that allows the user to study in a relaxed environment. Furthermore, the environment optimization unit can issue an alert if the user's learning environment is not appropriate. In this way, the learning assistance system can support the user in studying in an optimal environment.

[0112] The learning assistance system can further include a motivation improvement unit that improves the user's motivation to learn. The motivation improvement unit provides appropriate motivation improvement measures based on the user's learning progress and achievement status. For example, if the user achieves a goal, the motivation improvement unit can provide the user with praise or a reward. Also, if the user loses motivation to study, the motivation improvement unit can provide an encouraging message or advice. Furthermore, the motivation improvement unit can provide the user with an opportunity to compete with other users to increase their motivation to study. In this way, the learning assistance system can maintain and improve the user's motivation to study.

[0113] The learning assistance system may further include an evaluation unit that evaluates the user's learning outcomes. The evaluation unit evaluates the accuracy and level of understanding of the questions answered by the user and provides feedback on the results. For example, the evaluation unit may calculate the percentage of correct answers for the questions answered by the user and provide feedback to the user. The evaluation unit may also evaluate the user's level of understanding in a specific field and propose a learning plan based on the user's level of understanding. Furthermore, the evaluation unit may visually display the user's learning outcomes, allowing the user to intuitively grasp their progress. This allows the learning assistance system to appropriately evaluate the user's learning outcomes and provide effective feedback.

[0114] The learning assistance system may further include an emotion adjustment unit that estimates the user's emotions and adjusts the learning content based on the estimated emotions. The emotion adjustment unit analyzes the user's facial expressions, voice, and biometric data to estimate the user's emotions. For example, if the user is feeling stressed, the emotion adjustment unit may provide learning content that helps the user relax. Alternatively, if the user is excited, the emotion adjustment unit may provide learning content that helps the user increase their concentration. Furthermore, the emotion adjustment unit may adjust the pace of learning according to the user's emotions to provide an optimal learning experience. This allows the learning assistance system to provide flexible learning assistance that is tailored to the user's emotions.

[0115] The learning assistance system may further include a character emotion adjustment unit that estimates the user's emotions and adjusts the character's behavior based on the estimated emotions. The character emotion adjustment unit analyzes the user's facial expressions, voice, and biometric data to estimate the user's emotions. For example, if the user is happy, the character emotion adjustment unit can make the character express joy. Also, if the user is sad, the character can offer words of encouragement. Furthermore, if the user is tired, the character can suggest taking a break. In this way, the learning assistance system can adjust the character's behavior according to the user's emotions and provide a more friendly learning experience.

[0116] The learning assistance system may further include a progress adjustment unit that estimates the user's emotions and adjusts the learning progress based on the estimated emotions. The progress adjustment unit analyzes the user's facial expressions, voice, and biometric data to estimate the user's emotions. For example, if the user is impatient, the progress adjustment unit can slow down the pace of learning. Also, if the user is relaxed, the progress adjustment unit can speed up the pace of learning. Furthermore, if the user is concentrating, the progress adjustment unit can provide more difficult questions. In this way, the learning assistance system can adjust the learning progress according to the user's emotions and provide an optimal learning experience.

[0117] The learning assistance system may further include a feedback adjustment unit that estimates the user's emotions and adjusts learning feedback based on the estimated emotions. The feedback adjustment unit analyzes the user's facial expressions, voice, and biometric data to estimate the user's emotions. For example, if the user is depressed, the feedback adjustment unit may provide encouraging feedback. If the user is confident, the feedback adjustment unit may provide challenging feedback. Furthermore, if the user is tired, the feedback adjustment unit may provide feedback suggesting that the user take a break. In this way, the learning assistance system can provide feedback according to the user's emotions and maximize the effectiveness of learning.

[0118] The learning assistance system may further include an interface adjustment unit that estimates the user's emotions and adjusts the learning interface based on the estimated emotions. The interface adjustment unit analyzes the user's facial expressions, voice, and biometric data to estimate the user's emotions. For example, if the user is feeling stressed, the interface adjustment unit may provide a simple and intuitive interface. If the user is relaxed, the interface adjustment unit may provide an interface that provides detailed information. Furthermore, if the user is excited, the interface adjustment unit may provide an interactive interface. In this way, the learning assistance system may provide an interface that corresponds to the user's emotions, thereby improving the effectiveness of learning.

[0119] The processing flow of the second embodiment will be briefly explained below.

[0120] Step 1: The reception unit receives the information when the user reads the question. For example, the user can scan the question using a smartphone or tablet, or input it digitally. Step 2: The analysis unit analyzes the question text read by the reception unit and extracts information to generate an answer. For example, it uses natural language processing technology to analyze the content of the question text and extract important keywords and context. Step 3: The generator generates an answer based on the information obtained from the analyzer. For example, a generator AI is used to generate an appropriate answer to the question. The generator AI can be a text generator AI (e.g., LLM) or a multimodal generator AI. Step 4: The display unit visually displays the answer and explanation generated by the generator. For example, using AR technology, a character the user likes may appear and explain the answer and explanation. Furthermore, the display unit may display the generated answer and explanation in the form of text, audio, video, or the like.

[0121] 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.

[0122] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (registered trademark) (Internet search engine).<URL: https: / / openai.com / blog / chatgpt> Examples of the generative AI include a neural network (NN) and a neural network (NN). The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats of voice data, text data, image data, etc. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and may perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-mentioned parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. The processing performed by an AI including the generative AI may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI including the generative AI.

[0123] 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.

[0124] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0125] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] 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).

[0131] 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.

[0132] 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.

[0133] 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.

[0134] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0135] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. The smart glasses 214 also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform processing similar to that of the specific processing unit 290 using these models.

[0136] 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.

[0137] 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.

[0138] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0139] 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.

[0140] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0141] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0142] 5, the data processing system 310 includes the data processing device 12 and a headset terminal 314. An example of the data processing device 12 is a server.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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).

[0147] 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.

[0148] 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.

[0149] 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.

[0150] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0151] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can also perform processing similar to that of the identification processing unit 290 using these models.

[0152] 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.

[0153] 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.

[0154] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0155] 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.

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

[0157] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0158] 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.

[0159] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, RAM 30, and storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a WAN and / or a LAN.

[0160] The 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.

[0161] The microphone 238 receives instructions and the like from the user by receiving voice uttered by the user. The microphone 238 captures the voice uttered by the user, converts the captured voice into audio data, and outputs it to the processor 46. The speaker 240 outputs audio according to instructions from the processor 46.

[0162] Camera 42 is a small digital camera equipped with an optical system including a lens, aperture, and shutter, and an imaging element such as a CMOS 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).

[0163] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for the exchange of various information between the processor 46 and the processor 28 via the network 54. The exchange of various information between the processor 46 and the processor 28 using the communication I / Fs 44 and 26 is carried out in a secure state.

[0164] 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.

[0165] 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.

[0166] 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.

[0167] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate a user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion. The emotion estimation function (emotion identification function) using the emotion identification model 59 performs various estimations and predictions regarding the user's emotion, including estimation and prediction of the user's emotion, but is not limited to these examples. Furthermore, the estimation and prediction of emotion also includes, for example, emotion analysis.

[0168] In the robot 414, the processor 46 performs the identification process. The storage 50 stores the identification program 60. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as the control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 also has a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59, and can perform the same process as the identification processing unit 290 using these models.

[0169] 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.

[0170] 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.

[0171] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt including an instruction, as well as inference data such as audio data indicating speech, text data indicating text, and image data indicating an image (e.g., still image data or video data). The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt, and outputs the inference result in one or more data formats, such as audio data, text data, and image data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation models 58 include AIs other than the generation AI. Examples of AIs other than the generation AI include, but are not limited to, linear regression, logistic regression, decision trees, random forests, support vector machines (SVMs), k-means clustering, convolutional neural networks (CNNs), recurrent neural networks (RNNs), generative adversarial networks (GANs), and naive Bayes. These AIs can perform various types of processing, but are not limited to these examples. The AI ​​may also be an AI agent. When the processing of each of the above-described parts is performed by an AI, the processing may be performed in part or entirely by the AI, but is not limited to these examples. Processing performed by an AI, including the generation AI, may be replaced with rule-based processing, and rule-based processing may be replaced with processing performed by an AI, including the generation AI.

[0172] 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.

[0173] The correspondence between each part and the device or control part is not limited to the example described above, and various modifications are possible.

[0174] 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.

[0175] 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.

[0176] 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.

[0177] 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).

[0178] 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.

[0179] 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."

[0180] 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.

[0181] 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.

[0182] 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.

[0183] 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.

[0184] 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.

[0185] 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.

[0186] 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.

[0187] 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.

[0188] 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.

[0189] 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.

[0190] 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.

[0191] 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.

[0192] [Explanation of symbols]

[0193] 10, 210, 310, 410 Data Processing Systems 12 Data Processing Device 14 Smart Devices 214 Smart Glasses 314 Headset-type terminal 414 Robot

Claims

1. A reception unit that reads the question text; an analysis unit that analyzes the question sentence read by the reception unit; a generation unit that generates an answer based on the information analyzed by the analysis unit; a display unit that visually displays the answers and explanations generated by the generation unit; Equipped with A system characterized by:

2. Equipped with a character display section that displays characters 2. The system of claim 1.

3. Equipped with a reaction acquisition unit that acquires user reactions 2. The system of claim 1.

4. Equipped with a history management unit that manages learning history 2. The system of claim 1.

5. The analysis unit Analyze the question and extract information to generate an answer 2. The system of claim 1.

6. The generation unit Generate an answer based on the information obtained from the analysis unit 2. The system of claim 1.

7. The reception unit Estimate the user's emotions and adjust the timing of reading the questions based on the estimated user emotions.

2. The system of claim 1.

8. The reception unit Analyze the user's past learning history and select the appropriate method for loading questions 2. The system of claim 1.

9. The reception unit When loading questions, filter them based on the user's current learning status and areas of interest.

2. The system of claim 1.

Images