System

Abstract

An object of a system according to an embodiment is to provide learning content according to an individual's interest or level.SOLUTION: A system includes a reception unit, an analysis unit, and a response unit. The reception part inputs learning contents of a user. The analysis unit analyzes the information input by the reception unit and creates an explanation using a specific example according to the user's interest or level. The response unit performs question-and-answer session based on the explanation created by the analysis unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies do not adequately provide learning content that is tailored to an individual's interests and level, which can lead to a decrease in learning efficiency.

[0005] The system according to the embodiment aims to provide learning content that is suited to the interests and level of each individual. [Means for solving the problem]

[0006] The system according to the embodiment includes a reception unit, an analysis unit, and a response unit. The reception unit inputs the user's learning content. The analysis unit analyzes the information input by the reception unit and creates an explanation using specific examples according to the user's interests and level. The response unit conducts a question-and-answer session based on the explanation created by the analysis unit. [Effects of the Invention]

[0007] The system according to the embodiment can provide learning content according to the interests and level of an individual. [Brief explanation of the drawings]

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support system according to an embodiment of the present invention automatically analyzes a user's learning content, and a generation AI creates explanations tailored to the user's interests and level, engaging in Q&A. The learning support system allows users to input the content they want to learn, and the generation AI analyzes the content and creates explanations using analogies tailored to the user's interests and level. Furthermore, the system also supports Q&A, allowing users to input questions and receive appropriate answers from the generation AI. For example, a user might input "I want to learn differential and integral calculus in mathematics." This information is then input to the generation AI. The generation AI then analyzes the input information and generates explanations using analogies tailored to the user's interests and level. The generation AI selects the most appropriate analogy, taking into account the user's past learning history and interests. For example, the system can explain the concept of differential and integral calculus by comparing it to "mountain climbing." Furthermore, the system also supports Q&A, allowing users to input questions and receive appropriate answers from the generation AI. For example, if a user asks, "What are some applications of differential and integral calculus?", the generation AI might respond, "Differential and integral calculus is widely used in physics, economics, and other fields." This allows the learning support system to allow users to study at their own pace, making studying more enjoyable. In addition, because the generation AI responds individually, it provides the most appropriate explanation based on the user's level of understanding. This makes it easier to achieve results in their studies, and the praise they receive further increases their motivation to study. This allows the learning support system to make learning more enjoyable and effective for users. For example, it allows users to study at their own pace, making studying more enjoyable. In addition, because the generation AI responds individually, it provides the most appropriate explanation based on the user's level of understanding. This makes it easier to achieve results in their studies, and the praise they receive further increases their motivation to study.

[0029] A learning support system according to an embodiment includes a reception unit, an analysis unit, and a response unit. The reception unit inputs a user's learning content. The user's learning content may include, but is not limited to, subjects or topics such as mathematics, science, and history. For example, the user may input "I want to learn differential and integral calculus" into the reception unit. This information is input to the generation AI. The analysis unit uses the generation AI to analyze the information input by the reception unit and create an explanation using specific examples tailored to the user's interests and level. For example, the generation AI may select an optimal analogy based on the user's past learning history and interests. For example, the generation AI may explain the concept of differential and integral calculus using the analogy of "mountain climbing." The analysis unit may also use the generation AI to create an explanation tailored to the user's interests and level. For example, the generation AI may use natural language processing technology to create an explanation tailored to the user's interests and level. The response unit conducts a question-and-answer session based on the explanation created by the analysis unit. For example, when a user inputs a question, the response unit causes the generation AI to provide an appropriate answer to the question. For example, if a user asks, "Please tell me some applications of differential and integral calculus," the generation AI will answer, "Differential and integral calculus is widely applied in physics, economics, etc." This allows the learning support system according to the embodiment to efficiently analyze the user's learning content and provide appropriate explanations and Q&A sessions.

[0030] The reception unit analyzes the user's past learning history and selects an appropriate input method. For example, if the user has frequently used voice input in the past, the reception unit may preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit may preferentially suggest text input. Furthermore, if the user has used image input in the past, the reception unit may preferentially suggest image input. This improves user convenience by selecting the optimal input method based on the user's past learning history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past learning history data into the generation AI and cause the generation AI to select the optimal input method.

[0031] When inputting learning content, the reception unit filters the learning content based on the user's current learning progress and areas of interest. For example, the reception unit may exclude content that the user has already learned and prioritize displaying unlearned content. The reception unit may also prioritize displaying learning content related to areas of interest to the user. The reception unit may also display learning content of an appropriate level of difficulty according to the user's current learning progress. This allows for filtering based on the user's learning progress and areas of interest, thereby providing more appropriate learning content. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's learning progress data to a generation AI and have the generation AI perform filtering.

[0032] When inputting learning content, the reception unit selects an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the learning content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input the learning content using keyboard input. Furthermore, if the user selects image input, the reception unit can also input the learning content using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0033] When inputting study content, the reception unit prioritizes inputting highly relevant content, taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting study content related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize inputting study content related to the travel destination. Furthermore, if the user is at home, the reception unit can also prioritize inputting content that is easy to study at home. In this way, by taking the user's geographical location information into consideration, highly relevant study content can be provided. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant content.

[0034] When inputting learning content, the reception unit analyzes the user's social media activity and inputs related content. For example, the reception unit inputs content that the user has shown interest in on social media as learning content. The reception unit can also analyze the user's social media posts and input related learning content. The reception unit can also input related learning content by referring to the activities of the user's friends on social media. In this way, related learning content can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to select related content.

[0035] The reception unit customizes the input method by reflecting the user's past feedback when inputting learning content. For example, if the user has preferred voice input in the past, the reception unit may preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit may preferentially suggest text input. Furthermore, if the user has preferred image input in the past, the reception unit may preferentially suggest image input. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0036] When creating an explanation, the analysis unit adjusts the level of detail of the explanation based on the importance of the learning content. For example, the analysis unit provides a detailed explanation for important learning content. The analysis unit can also provide a concise explanation for less important learning content. The analysis unit can also provide a detailed explanation for learning content in which the user is particularly interested. In this way, by adjusting the level of detail of the explanation based on the importance of the learning content, an appropriate explanation can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the learning content to the generation AI and cause the generation AI to adjust the level of detail of the explanation.

[0037] When creating an explanation, the analysis unit applies different analogies depending on the category of the learning content. For example, the analysis unit applies analogies using examples from everyday life to an explanation of mathematics. The analysis unit can also apply analogies using examples from sports to an explanation of physics. The analysis unit can also apply analogies using examples from business to an explanation of economics. In this way, by applying different analogies depending on the category of the learning content, it is possible to provide explanations that are easy to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the learning content into a generation AI and cause the generation AI to apply the analogy.

[0038] When creating an explanation, the analysis unit refers to the user's past learning history to improve the accuracy of the explanation. The analysis unit, for example, provides a relevant explanation based on the content the user has learned in the past. The analysis unit can also provide an explanation based on the user's past learning history according to the user's level of understanding. The analysis unit can also analyze the user's past learning history to provide an optimal explanation. This improves the accuracy of the explanation by referring to the user's past learning history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past learning history data into the generation AI and cause the generation AI to improve the accuracy of the explanation.

[0039] When creating explanations, the analysis unit determines the priority of explanations based on the submission date of the learning content. For example, the analysis unit prioritizes providing explanations for learning content with an approaching deadline. The analysis unit can also postpone providing explanations for learning content with a distant submission date. The analysis unit can also provide explanations as a top priority for learning content for which the user is particularly in a hurry. In this way, by determining the priority of explanations based on the submission date of the learning content, explanations can be provided at an appropriate time. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of learning content to the generation AI and have the generation AI determine the priority of explanations.

[0040] When creating an explanation, the analysis unit adjusts the order of explanations based on the relevance of the learning content. For example, the analysis unit prioritizes explanations of highly relevant learning content. The analysis unit can also postpone explanations of less relevant learning content. The analysis unit can also prioritize explanations of learning content in which the user is particularly interested. This enables more effective learning by adjusting the order of explanations based on the relevance of the learning content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the learning content into the generation AI and cause the generation AI to adjust the order of explanations.

[0041] When creating an explanation, the analysis unit adjusts the use of technical terms in the explanation according to the user's level of expertise. For example, if the user is a beginner, the analysis unit may avoid using technical terms when providing the explanation. Furthermore, if the user is an intermediate user, the analysis unit may also use technical terms moderately when providing the explanation. Furthermore, if the user is an advanced user, the analysis unit may also use a lot of technical terms to provide a detailed explanation. By adjusting the use of technical terms in the explanation according to the user's level of expertise, an easy-to-understand explanation can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0042] When answering a question, the response unit adjusts the level of detail of the answer based on the importance of the question. For example, the response unit provides a detailed answer for an important question. The response unit can also provide a concise answer for a less important question. The response unit can also provide a detailed answer for a question in which the user is particularly interested. In this way, by adjusting the level of detail of the answer based on the importance of the question, an appropriate answer can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit may input question importance data to a generation AI and cause the generation AI to adjust the level of detail of the answer.

[0043] When answering a question, the response unit applies different answering algorithms depending on the category of the question. For example, the response unit applies an answering algorithm using a mathematical formula to a mathematics question. The response unit can also apply an answering algorithm using experimental data to a physics question. The response unit can also apply an answering algorithm using statistical data to an economics question. In this way, by applying different answering algorithms depending on the question category, an appropriate answer can be provided. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input question category data to the generation AI and cause the generation AI to apply the answering algorithm.

[0044] When answering a question, the response unit improves the accuracy of the answer by referring to the user's past question history. The response unit provides a relevant answer, for example, based on the content of questions asked by the user in the past. The response unit can also provide an answer based on the user's past question history according to the user's level of understanding. The response unit can also analyze the user's past question history and provide an optimal answer. This improves the accuracy of the answer by referring to the user's past question history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past question history data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0045] When answering a question, the response unit determines the priority of the answers based on the time the question was submitted. For example, the response unit provides answers preferentially to questions with an approaching deadline. The response unit can also provide answers later to questions with a more distant submission date. The response unit can also provide answers as a top priority to questions for which the user is particularly urgent. In this way, by determining the priority of answers based on the time the question was submitted, answers can be provided at an appropriate time. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input question submission time data into the generation AI and have the generation AI determine the priority of answers.

[0046] When answering a question, the response unit adjusts the order of the answers based on the relevance of the question. For example, the response unit prioritizes answers to highly relevant questions. The response unit can also postpone answers to less relevant questions. The response unit can also prioritize answers to questions in which the user is particularly interested. This enables more effective learning by adjusting the order of answers based on the relevance of the questions. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of the answers.

[0047] When answering a question, the response unit adjusts the use of technical terminology in the answer depending on the user's level of expertise. For example, if the user is a beginner, the response unit may avoid using technical terminology when providing an answer. Furthermore, if the user is an intermediate user, the response unit may use technical terminology appropriately when providing an answer. Furthermore, if the user is an advanced user, the response unit may use a lot of technical terminology to provide a detailed answer. By adjusting the use of technical terminology in the answer depending on the user's level of expertise, an answer that is easy to understand can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit may input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology.

[0048] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0049] The analysis unit can also estimate the user's learning style and adjust the format of the explanation based on the estimated learning style. For example, if the user is a visual learner, the analysis unit can provide explanations that make extensive use of diagrams and graphs. If the user is an auditory learner, the analysis unit can preferentially provide audio explanations. Furthermore, if the user is a hands-on learner, the analysis unit can provide explanations through actual problem-solving. This provides optimal explanations according to the user's learning style, improving learning effectiveness.

[0050] The reception unit can also analyze the user's past learning history and set appropriate learning goals. For example, it can suggest new goals based on learning goals the user has achieved in the past. It can also set goals to help the user overcome areas in which the user has struggled in the past. It can also set goals to help the user delve deeper into areas in which the user is particularly interested. This allows optimal learning goals to be set based on the user's past learning history, making the direction of learning clear.

[0051] When inputting study content, the reception unit can also suggest an appropriate study method taking into account the user's current study environment. For example, if the user is in a quiet environment, it can suggest a study method that allows the user to concentrate. Also, if the user is on the move, it can suggest a method that allows for quick study. Furthermore, if the user is studying in a group, it can suggest a study method that allows for collaborative work. This provides the optimal study method according to the user's study environment, improving study efficiency.

[0052] The reception unit can also monitor the user's input speed in real time when entering study content and provide appropriate feedback. For example, if the user's input speed is slow, it can suggest improvements to the input method. Also, if the user's input speed is fast, it can encourage the user to proceed to the next step. Furthermore, if the user is having difficulty inputting, it can provide an input support tool. This allows the user to receive optimal feedback according to their input speed, enabling smooth progress in their studies.

[0053] When inputting study content, the reception unit can provide content that includes relevant cultural background, taking into account the user's geographical location information. For example, if the user is in a particular country, study content related to the culture and history of that country can be provided. If the user is traveling, study content related to tourist destinations and famous places at the travel destination can be provided. Furthermore, if the user is at home, content that is easy to study at home can be provided. This provides highly relevant study content based on the user's geographical location information, increasing the user's interest in learning.

[0054] When the user enters their learning content, the reception unit can analyze the user's social media activity and suggest related learning communities. For example, if the user shows interest in a particular topic, online forums and groups related to that topic can be suggested. Also, if the user prefers a particular learning method, communities related to that method can be suggested. Furthermore, if the user follows a particular expert, communities in which that expert participates can be suggested. This allows the reception unit to suggest optimal learning communities based on the user's social media activity, improving motivation for learning.

[0055] When creating explanations, the analysis unit can also customize the format of the explanations by referring to the user's past learning history. For example, if the user has previously preferred visual explanations, the analysis unit can provide explanations that make extensive use of diagrams and graphs. Also, if the user has previously preferred audio explanations, the analysis unit can provide audio explanations preferentially. Furthermore, if the user has previously preferred practical explanations, the analysis unit can provide explanations that involve solving actual problems. This provides the optimal explanation format based on the user's past learning history, improving learning effectiveness.

[0056] The processing flow of the first embodiment will be briefly explained below.

[0057] Step 1: The reception unit inputs the user's learning content. The user's learning content includes subjects and topics such as mathematics, science, and history. The user inputs, "I want to learn calculus in mathematics." This information is input into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit and create an explanation using specific examples that match the user's interests and level. For example, the generation AI considers the user's past learning history and interests and selects the most appropriate analogy. The generation AI explains the concept of differential and integral calculus by comparing it to "mountain climbing." The generation AI also uses natural language processing technology to create an explanation that matches the user's interests and level. Step 3: The response unit answers questions based on the explanations created by the analysis unit. For example, when a user inputs a question, the generation AI provides an appropriate answer to that question. If the user asks, "Please tell me some applications of differential and integral calculus," the generation AI will respond, "Differential and integral calculus is widely used in physics, economics, and other fields."

[0058] (Example 2) A learning support system according to an embodiment of the present invention automatically analyzes a user's learning content, and a generation AI creates explanations tailored to the user's interests and level, engaging in Q&A. The learning support system allows users to input the content they want to learn, and the generation AI analyzes the content and creates explanations using analogies tailored to the user's interests and level. Furthermore, the system also supports Q&A, allowing users to input questions and receive appropriate answers from the generation AI. For example, a user might input "I want to learn differential and integral calculus in mathematics." This information is then input to the generation AI. The generation AI then analyzes the input information and generates explanations using analogies tailored to the user's interests and level. The generation AI selects the most appropriate analogy, taking into account the user's past learning history and interests. For example, the system can explain the concept of differential and integral calculus by comparing it to "mountain climbing." Furthermore, the system also supports Q&A, allowing users to input questions and receive appropriate answers from the generation AI. For example, if a user asks, "What are some applications of differential and integral calculus?", the generation AI might respond, "Differential and integral calculus is widely used in physics, economics, and other fields." This allows the learning support system to allow users to study at their own pace, making studying more enjoyable. In addition, because the generation AI responds individually, it provides the most appropriate explanation based on the user's level of understanding. This makes it easier to achieve results in their studies, and the praise they receive further increases their motivation to study. This allows the learning support system to make learning more enjoyable and effective for users. For example, it allows users to study at their own pace, making studying more enjoyable. In addition, because the generation AI responds individually, it provides the most appropriate explanation based on the user's level of understanding. This makes it easier to achieve results in their studies, and the praise they receive further increases their motivation to study.

[0059] A learning support system according to an embodiment includes a reception unit, an analysis unit, and a response unit. The reception unit inputs a user's learning content. The user's learning content may include, but is not limited to, subjects or topics such as mathematics, science, and history. For example, the user may input "I want to learn differential and integral calculus" into the reception unit. This information is input to the generation AI. The analysis unit uses the generation AI to analyze the information input by the reception unit and create an explanation using specific examples tailored to the user's interests and level. For example, the generation AI may select an optimal analogy based on the user's past learning history and interests. For example, the generation AI may explain the concept of differential and integral calculus using the analogy of "mountain climbing." The analysis unit may also use the generation AI to create an explanation tailored to the user's interests and level. For example, the generation AI may use natural language processing technology to create an explanation tailored to the user's interests and level. The response unit conducts a question-and-answer session based on the explanation created by the analysis unit. For example, when a user inputs a question, the response unit causes the generation AI to provide an appropriate answer to the question. For example, if a user asks, "Please tell me some applications of differential and integral calculus," the generation AI will answer, "Differential and integral calculus is widely applied in physics, economics, etc." This allows the learning support system according to the embodiment to efficiently analyze the user's learning content and provide appropriate explanations and Q&A sessions.

[0060] The reception unit estimates the user's emotions and adjusts the timing of inputting the learning content based on the estimated user emotions. For example, if the user is feeling stressed, the reception unit prompts the user to input the learning content during a time when the user can relax. Furthermore, if the user is concentrating, the reception unit can adjust the input timing so that the user can continue studying. Furthermore, if the user is tired, the reception unit can prompt the user to take a break before inputting the learning content. This allows for more effective learning by adjusting the input timing of the learning content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0061] The reception unit analyzes the user's past learning history and selects an appropriate input method. For example, if the user has frequently used voice input in the past, the reception unit may preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit may preferentially suggest text input. Furthermore, if the user has used image input in the past, the reception unit may preferentially suggest image input. This improves user convenience by selecting the optimal input method based on the user's past learning history. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past learning history data into the generation AI and cause the generation AI to select the optimal input method.

[0062] When inputting learning content, the reception unit filters the learning content based on the user's current learning progress and areas of interest. For example, the reception unit may exclude content that the user has already learned and prioritize displaying unlearned content. The reception unit may also prioritize displaying learning content related to areas of interest to the user. The reception unit may also display learning content of an appropriate level of difficulty according to the user's current learning progress. This allows for filtering based on the user's learning progress and areas of interest, thereby providing more appropriate learning content. Some or all of the above-described processing by the reception unit may be performed using, or without, AI. For example, the reception unit may input the user's learning progress data to a generation AI and have the generation AI perform filtering.

[0063] When inputting learning content, the reception unit selects an appropriate input means according to the user's input method. For example, if the user selects voice input, the reception unit inputs the learning content using voice recognition technology. Furthermore, if the user selects text input, the reception unit can also input the learning content using keyboard input. Furthermore, if the user selects image input, the reception unit can also input the learning content using image recognition technology. This improves input efficiency by selecting the optimal input means according to the user's input method. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's input method data to the generation AI and cause the generation AI to select the optimal input means.

[0064] The reception unit estimates the user's emotions and determines the priority of the learning content to be input based on the estimated user emotions. For example, when the user is excited, the reception unit prioritizes input of more difficult learning content. Furthermore, when the user is relaxed, the reception unit can also prioritize input of learning content in areas of interest to the user. Furthermore, when the user is tired, the reception unit can also prioritize input of easier learning content. This improves the effectiveness of learning by determining the priority of learning content according to the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the reception unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the reception unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0065] When inputting study content, the reception unit prioritizes inputting highly relevant content, taking into account the user's geographical location information. For example, if the user is in a specific area, the reception unit prioritizes inputting study content related to that area. Furthermore, if the user is traveling, the reception unit can also prioritize inputting study content related to the travel destination. Furthermore, if the user is at home, the reception unit can also prioritize inputting content that is easy to study at home. In this way, by taking the user's geographical location information into consideration, highly relevant study content can be provided. Some or all of the above-described processing by the reception unit may be performed using AI, for example, or may be performed without using AI. For example, the reception unit can input the user's geographical location information data to the generation AI and cause the generation AI to select highly relevant content.

[0066] When inputting learning content, the reception unit analyzes the user's social media activity and inputs related content. For example, the reception unit inputs content that the user has shown interest in on social media as learning content. The reception unit can also analyze the user's social media posts and input related learning content. The reception unit can also input related learning content by referring to the activities of the user's friends on social media. In this way, related learning content can be provided by analyzing the user's social media activity. Some or all of the above-described processing by the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit can input the user's social media data into the generation AI and cause the generation AI to select related content.

[0067] The reception unit customizes the input method by reflecting the user's past feedback when inputting learning content. For example, if the user has preferred voice input in the past, the reception unit may preferentially suggest voice input. Furthermore, if the user has preferred text input in the past, the reception unit may preferentially suggest text input. Furthermore, if the user has preferred image input in the past, the reception unit may preferentially suggest image input. In this way, the input method can be customized by reflecting the user's past feedback. Some or all of the above-described processing in the reception unit may be performed using, for example, AI, or may be performed without using AI. For example, the reception unit may input the user's past feedback data into the generation AI and cause the generation AI to customize the input method.

[0068] The analysis unit estimates the user's emotions and adjusts the way the commentary is expressed based on the estimated user emotions. For example, if the user is relaxed, the analysis unit may provide the commentary in a relaxed manner. Furthermore, if the user is excited, the analysis unit may provide the commentary in an energetic manner. Furthermore, if the user is tired, the analysis unit may provide the commentary in a concise and easy-to-understand manner. This allows for more effective commentary by adjusting the way the commentary is expressed based on the user's emotions. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0069] When creating an explanation, the analysis unit adjusts the level of detail of the explanation based on the importance of the learning content. For example, the analysis unit provides a detailed explanation for important learning content. The analysis unit can also provide a concise explanation for less important learning content. The analysis unit can also provide a detailed explanation for learning content in which the user is particularly interested. In this way, by adjusting the level of detail of the explanation based on the importance of the learning content, an appropriate explanation can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input importance data of the learning content to the generation AI and cause the generation AI to adjust the level of detail of the explanation.

[0070] When creating an explanation, the analysis unit applies different analogies depending on the category of the learning content. For example, the analysis unit applies analogies using examples from everyday life to an explanation of mathematics. The analysis unit can also apply analogies using examples from sports to an explanation of physics. The analysis unit can also apply analogies using examples from business to an explanation of economics. In this way, by applying different analogies depending on the category of the learning content, it is possible to provide explanations that are easy to understand. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input category data of the learning content into a generation AI and cause the generation AI to apply the analogy.

[0071] When creating an explanation, the analysis unit refers to the user's past learning history to improve the accuracy of the explanation. The analysis unit, for example, provides a relevant explanation based on the content the user has learned in the past. The analysis unit can also provide an explanation based on the user's past learning history according to the user's level of understanding. The analysis unit can also analyze the user's past learning history to provide an optimal explanation. This improves the accuracy of the explanation by referring to the user's past learning history. Some or all of the above-mentioned processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input the user's past learning history data into the generation AI and cause the generation AI to improve the accuracy of the explanation.

[0072] The analysis unit estimates the user's emotions and adjusts the length of the commentary based on the estimated user emotions. For example, if the user is in a hurry, the analysis unit provides a short, to-the-point commentary. Furthermore, if the user is relaxed, the analysis unit can provide a longer commentary with detailed explanations. Furthermore, if the user is excited, the analysis unit can provide a commentary with visually stimulating effects. By adjusting the length of the commentary according to the user's emotions, more effective commentary can be provided. Emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the analysis unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the analysis unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0073] When creating explanations, the analysis unit determines the priority of explanations based on the submission date of the learning content. For example, the analysis unit prioritizes providing explanations for learning content with an approaching deadline. The analysis unit can also postpone providing explanations for learning content with a distant submission date. The analysis unit can also provide explanations as a top priority for learning content for which the user is particularly in a hurry. In this way, by determining the priority of explanations based on the submission date of the learning content, explanations can be provided at an appropriate time. Some or all of the above-mentioned processing by the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input data on the submission date of learning content to the generation AI and have the generation AI determine the priority of explanations.

[0074] When creating an explanation, the analysis unit adjusts the order of explanations based on the relevance of the learning content. For example, the analysis unit prioritizes explanations of highly relevant learning content. The analysis unit can also postpone explanations of less relevant learning content. The analysis unit can also prioritize explanations of learning content in which the user is particularly interested. This enables more effective learning by adjusting the order of explanations based on the relevance of the learning content. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit can input relevance data of the learning content into the generation AI and cause the generation AI to adjust the order of explanations.

[0075] When creating an explanation, the analysis unit adjusts the use of technical terms in the explanation according to the user's level of expertise. For example, if the user is a beginner, the analysis unit may avoid using technical terms when providing the explanation. Furthermore, if the user is an intermediate user, the analysis unit may also use technical terms moderately when providing the explanation. Furthermore, if the user is an advanced user, the analysis unit may also use a lot of technical terms to provide a detailed explanation. By adjusting the use of technical terms in the explanation according to the user's level of expertise, an easy-to-understand explanation can be provided. Some or all of the above-described processing in the analysis unit may be performed using, for example, AI, or may be performed without using AI. For example, the analysis unit may input the user's level of expertise data into the generation AI and cause the generation AI to adjust the use of technical terms.

[0076] The response unit estimates the user's emotions and adjusts the way the response is expressed based on the estimated user emotions. For example, if the user is relaxed, the response unit may respond in a relaxed manner. Furthermore, if the user is excited, the response unit may respond in an energetic manner. Furthermore, if the user is tired, the response unit may respond in a concise and easy-to-understand manner. By adjusting the way the response is expressed based on the user's emotions, a more effective response can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0077] When answering a question, the response unit adjusts the level of detail of the answer based on the importance of the question. For example, the response unit provides a detailed answer for an important question. The response unit can also provide a concise answer for a less important question. The response unit can also provide a detailed answer for a question in which the user is particularly interested. In this way, by adjusting the level of detail of the answer based on the importance of the question, an appropriate answer can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit may input question importance data to a generation AI and cause the generation AI to adjust the level of detail of the answer.

[0078] When answering a question, the response unit applies different answering algorithms depending on the category of the question. For example, the response unit applies an answering algorithm using a mathematical formula to a mathematics question. The response unit can also apply an answering algorithm using experimental data to a physics question. The response unit can also apply an answering algorithm using statistical data to an economics question. In this way, by applying different answering algorithms depending on the question category, an appropriate answer can be provided. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input question category data to the generation AI and cause the generation AI to apply the answering algorithm.

[0079] When answering a question, the response unit improves the accuracy of the answer by referring to the user's past question history. The response unit provides a relevant answer, for example, based on the content of questions asked by the user in the past. The response unit can also provide an answer based on the user's past question history according to the user's level of understanding. The response unit can also analyze the user's past question history and provide an optimal answer. This improves the accuracy of the answer by referring to the user's past question history. Some or all of the above-mentioned processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input the user's past question history data into the generation AI and cause the generation AI to improve the accuracy of the answer.

[0080] The response unit estimates the user's emotions and adjusts the length of the response based on the estimated user emotions. For example, if the user is in a hurry, the response unit provides a short, to-the-point response. If the user is relaxed, the response unit can provide a longer response with detailed explanations. If the user is excited, the response unit can provide a response with a visually stimulating effect. By adjusting the length of the response according to the user's emotions, a more effective response can be provided. The emotion estimation is realized using an emotion estimation function, for example, an emotion engine or a generation AI. The generation AI may be, but is not limited to, a text generation AI (e.g., LLM) or a multimodal generation AI. Some or all of the above-described processing in the response unit may be performed using, for example, an AI, or may be performed without using an AI. For example, the response unit may input the user's facial expression data into the generation AI and cause the generation AI to estimate the emotion.

[0081] When answering a question, the response unit determines the priority of the answers based on the time the question was submitted. For example, the response unit provides answers preferentially to questions with an approaching deadline. The response unit can also provide answers later to questions with a more distant submission date. The response unit can also provide answers as a top priority to questions for which the user is particularly urgent. In this way, by determining the priority of answers based on the time the question was submitted, answers can be provided at an appropriate time. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input question submission time data into the generation AI and have the generation AI determine the priority of answers.

[0082] When answering a question, the response unit adjusts the order of the answers based on the relevance of the question. For example, the response unit prioritizes answers to highly relevant questions. The response unit can also postpone answers to less relevant questions. The response unit can also prioritize answers to questions in which the user is particularly interested. This enables more effective learning by adjusting the order of answers based on the relevance of the questions. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit can input question relevance data to a generation AI and cause the generation AI to adjust the order of the answers.

[0083] When answering a question, the response unit adjusts the use of technical terminology in the answer depending on the user's level of expertise. For example, if the user is a beginner, the response unit may avoid using technical terminology when providing an answer. Furthermore, if the user is an intermediate user, the response unit may use technical terminology appropriately when providing an answer. Furthermore, if the user is an advanced user, the response unit may use a lot of technical terminology to provide a detailed answer. By adjusting the use of technical terminology in the answer depending on the user's level of expertise, an answer that is easy to understand can be provided. Some or all of the above-described processing in the response unit may be performed using, for example, AI, or may be performed without using AI. For example, the response unit may input the user's level of expertise data into the generation AI and have the generation AI adjust the use of technical terminology. === Hard Collateral 1-1 === For example, each of the multiple elements including the reception unit, analysis unit, and response unit is realized by at least one of the smart device 14 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart device 14 and inputs the user's learning content. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI and creates an explanation based on the user's interests and level. The response unit is realized by the control unit 46A of the smart device 14 and conducts a question-and-answer session based on the explanation created by the analysis unit. === Hard Collateral 1-2 === For example, each of the multiple elements including the reception unit, analysis unit, and response unit is realized by at least one of the smart glasses 214 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the smart glasses 214 and inputs the user's learning content. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI and creates explanations according to the user's interests and level. The response unit is realized by the control unit 46A of the smart glasses 214 and conducts question and answer sessions based on the explanations created by the analysis unit. === Hard Collateral 1-3 === For example, each of the multiple elements including the reception unit, analysis unit, and response unit is realized by at least one of the headset type terminal 314 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the headset type terminal 314 and inputs the user's learning content. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI and creates explanations according to the user's interests and level. The response unit is realized by the control unit 46A of the headset type terminal 314 and conducts a question-and-answer session based on the explanations created by the analysis unit. === Hard Collateral 1-4 === For example, each of the multiple elements including the reception unit, analysis unit, and response unit is realized by at least one of the robot 414 and the data processing device 12. For example, the reception unit is realized by the control unit 46A of the robot 414 and inputs the user's learning content. The analysis unit is realized by the specific processing unit 290 of the data processing device 12 and analyzes the input information using a generation AI and creates an explanation according to the user's interests and level. The response unit is realized by the control unit 46A of the robot 414 and conducts a question-and-answer session based on the explanation created by the analysis unit.

[0084] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0085] The analysis unit can also estimate the user's learning style and adjust the format of the explanation based on the estimated learning style. For example, if the user is a visual learner, the analysis unit can provide explanations that make extensive use of diagrams and graphs. If the user is an auditory learner, the analysis unit can preferentially provide audio explanations. Furthermore, if the user is a hands-on learner, the analysis unit can provide explanations through actual problem-solving. This provides optimal explanations according to the user's learning style, improving learning effectiveness.

[0086] The reception unit can also estimate the user's emotions and adjust the difficulty of the learning content based on the estimated user's emotions. For example, if the user is feeling stressed, easy questions can be provided preferentially. If the user is relaxed, more difficult questions can be provided. Furthermore, if the user is excited, more challenging questions can be provided. This provides the user with optimal learning content according to their emotions, maintaining their motivation to learn.

[0087] The reception unit can also analyze the user's past learning history and set appropriate learning goals. For example, it can suggest new goals based on learning goals the user has achieved in the past. It can also set goals to help the user overcome areas in which the user has struggled in the past. It can also set goals to help the user delve deeper into areas in which the user is particularly interested. This allows optimal learning goals to be set based on the user's past learning history, making the direction of learning clear.

[0088] When inputting study content, the reception unit can also suggest an appropriate study method taking into account the user's current study environment. For example, if the user is in a quiet environment, it can suggest a study method that allows the user to concentrate. Also, if the user is on the move, it can suggest a method that allows for quick study. Furthermore, if the user is studying in a group, it can suggest a study method that allows for collaborative work. This provides the optimal study method according to the user's study environment, improving study efficiency.

[0089] The reception unit can also monitor the user's input speed in real time when entering study content and provide appropriate feedback. For example, if the user's input speed is slow, it can suggest improvements to the input method. Also, if the user's input speed is fast, it can encourage the user to proceed to the next step. Furthermore, if the user is having difficulty inputting, it can provide an input support tool. This allows the user to receive optimal feedback according to their input speed, enabling smooth progress in their studies.

[0090] The analysis unit can also estimate the user's emotions and adjust the tone of the commentary based on the estimated user's emotions. For example, if the user is relaxed, the commentary can be provided in a calm tone. If the user is excited, the commentary can be provided in a lively tone. Furthermore, if the user is tired, the commentary can be provided in a calm tone. This allows the commentary to be provided in the optimal tone according to the user's emotions, deepening understanding.

[0091] When inputting study content, the reception unit can provide content that includes relevant cultural background, taking into account the user's geographical location information. For example, if the user is in a particular country, study content related to the culture and history of that country can be provided. If the user is traveling, study content related to tourist destinations and famous places at the travel destination can be provided. Furthermore, if the user is at home, content that is easy to study at home can be provided. This provides highly relevant study content based on the user's geographical location information, increasing the user's interest in learning.

[0092] When the user enters their learning content, the reception unit can analyze the user's social media activity and suggest related learning communities. For example, if the user shows interest in a particular topic, online forums and groups related to that topic can be suggested. Also, if the user prefers a particular learning method, communities related to that method can be suggested. Furthermore, if the user follows a particular expert, communities in which that expert participates can be suggested. This allows the reception unit to suggest optimal learning communities based on the user's social media activity, improving motivation for learning.

[0093] When creating explanations, the analysis unit can also customize the format of the explanations by referring to the user's past learning history. For example, if the user has previously preferred visual explanations, the analysis unit can provide explanations that make extensive use of diagrams and graphs. Also, if the user has previously preferred audio explanations, the analysis unit can provide audio explanations preferentially. Furthermore, if the user has previously preferred practical explanations, the analysis unit can provide explanations that involve solving actual problems. This provides the optimal explanation format based on the user's past learning history, improving learning effectiveness.

[0094] The analysis unit can also estimate the user's emotions and adjust the visual elements of the commentary based on the estimated user's emotions. For example, if the user is relaxed, figures and graphs with calm colors can be used. If the user is excited, figures and graphs with vivid colors can be used. Furthermore, if the user is tired, figures and graphs that are simple and easy to read can be used. This provides optimal visual elements according to the user's emotions, deepening understanding of the commentary.

[0095] The processing flow of the second embodiment will be briefly explained below.

[0096] Step 1: The reception unit inputs the user's learning content. The user's learning content includes subjects and topics such as mathematics, science, and history. The user inputs, "I want to learn calculus in mathematics." This information is input into the generation AI. Step 2: The analysis unit uses the generation AI to analyze the information entered by the reception unit and create an explanation using specific examples that match the user's interests and level. For example, the generation AI considers the user's past learning history and interests and selects the most appropriate analogy. The generation AI explains the concept of differential and integral calculus by comparing it to "mountain climbing." The generation AI also uses natural language processing technology to create an explanation that matches the user's interests and level. Step 3: The response unit answers questions based on the explanations created by the analysis unit. For example, when a user inputs a question, the generation AI provides an appropriate answer to that question. If the user asks, "Please tell me some applications of differential and integral calculus," the generation AI will respond, "Differential and integral calculus is widely used in physics, economics, and other fields."

[0097] 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.

[0098] 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 generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0099] 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.

[0100] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0101] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0102] 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.

[0103] 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.

[0104] 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.

[0105] 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.

[0106] 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).

[0107] 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.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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.

[0114] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0115] 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.

[0116] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0117] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0118] 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.

[0119] 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.

[0120] 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.

[0121] 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.

[0122] 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).

[0123] 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.

[0124] Fig. 6 shows an example of the main functions of the data processing device 12 and the headset terminal 314. As shown in Fig. 6, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32.

[0125] 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.

[0126] 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.

[0127] 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.

[0128] 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.

[0129] 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.

[0130] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0131] 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.

[0132] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0133] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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).

[0139] 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.

[0140] 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.

[0141] 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.

[0142] 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.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AI other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0148] 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.

[0149] The correspondence between each part and the device or control part is not limited to the above example, and various modifications are possible.

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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).

[0154] 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.

[0155] 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."

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] 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.

[0166] 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, in order to avoid confusion and to 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.

[0167] 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.

[0168] [Explanation of symbols]

[0169] 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 for inputting user learning content; an analysis unit that analyzes the information input by the reception unit and creates an explanation using specific examples according to the user's interests and level; a response unit that conducts a question and answer session based on the explanation created by the analysis unit. A system characterized by:

2. The reception unit Estimates the user's emotions and adjusts the timing of learning content input based on the estimated user emotions.

2. The system of claim 1.

3. The reception unit Analyze the user's past learning history and select the appropriate input method 2. The system of claim 1.

4. The reception unit Filter learning content based on the user's current learning progress and areas of interest 2. The system of claim 1.

5. The reception unit When inputting learning content, select the appropriate input method according to the user's input method.

2. The system of claim 1.

6. The reception unit Estimate the user's emotions and prioritize the learning content to be input based on the estimated user emotions.

2. The system of claim 1.

7. The reception unit When entering learning content, the system takes into account the user's geographic location information to prioritize the most relevant content.

2. The system of claim 1.

8. The reception unit When entering learning content, analyze the user's social media activity and enter related content 2. The system of claim 1.

Images