System

Abstract

An object of a system according to an embodiment is to efficiently output contents learned by a learner.SOLUTION: A system includes a teaching material provision part, a question generation part, and an answer analysis part. The teaching material providing section provides a teaching material to the learner. The question generating section generates a question based on the teaching material provided by the teaching material providing section. The answer analysis unit analyzes the learner's answer to the question generated by the question generation 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 technology has the problem that it is difficult for learners to efficiently output what they have learned by teaching it to others.

[0005] The system according to the embodiment aims to enable learners to efficiently output what they have learned. [Means for solving the problem]

[0006] The system according to the embodiment includes a learning material providing unit, a question generating unit, and an answer analyzing unit. The learning material providing unit provides learning material to a learner. The question generating unit generates questions based on the learning material provided by the learning material providing unit. The answer analyzing unit analyzes answers from the learner to the questions generated by the question generating unit. [Effects of the Invention]

[0007] The system according to the embodiment allows a learner to efficiently output what he or she has learned. [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) The children's educational AI according to the embodiment of the present invention is a system in which the AI ​​asks the learner questions in a conversational format after learning about a learning topic, and can efficiently output the output of "teaching others." This allows the children's educational AI to deepen the learner's understanding and improve the learning retention rate.

[0029] The children's educational AI according to the embodiment includes a teaching material providing unit, a question generating unit, and an answer analyzing unit. The teaching material providing unit provides teaching materials to a learner. For example, the teaching material providing unit can have a learner study a specific learning topic using teaching materials in the system. The teaching material providing unit can also provide teaching materials selected by the learner. Furthermore, the teaching material providing unit can recommend appropriate teaching materials based on the learner's learning history. The question generating unit generates questions based on the teaching materials provided by the teaching material providing unit. For example, the question generating unit generates basic questions related to content learned by the learner. The question generating unit can also adjust the difficulty of the questions according to the learner's level of understanding. Furthermore, the question generating unit can generate additional questions based on the learner's answers. The answer analyzing unit analyzes the learner's answers to the questions generated by the question generating unit. For example, the answer analyzing unit can evaluate the accuracy of the learner's answers. The answer analyzing unit can also analyze the content of the learner's answers and evaluate the learner's level of understanding. Furthermore, the answer analyzing unit can provide feedback based on the learner's answers. As a result, the children's educational AI according to the embodiment can deepen learners' understanding and improve their learning retention rate.

[0030] The teaching material providing unit can automatically generate individually customized teaching materials based on the learner's past learning history. For example, the teaching material providing unit analyzes the learner's past learning history and automatically generates individually customized teaching materials based on the learner's level of understanding and interests. For example, based on the learner's history of learning history, the teaching material providing unit provides teaching materials including other historical figures and events related to Oda Nobunaga. The teaching material providing unit also automatically generates special teaching materials to strengthen weak points based on the learner's past test results and learning logs. For example, if the learner lacks understanding of Oda Nobunaga's strategies, the teaching material providing unit provides teaching materials to strengthen that area. The teaching material providing unit also automatically generates individually customized teaching materials to suit the learner's learning style and pace. For example, for learners who prefer visual learning, the teaching material explaining the life of Oda Nobunaga using illustrations and videos is provided. This makes it possible to provide teaching materials that meet the individual needs of learners.

[0031] The teaching material providing unit can analyze the learner's interests in real time and dynamically change the content of the teaching materials based on that analysis. For example, the teaching material providing unit analyzes the learner's real-time reactions and behavior and dynamically changes the content of the teaching materials based on the learner's interest. For example, if the learner shows interest in Oda Nobunaga's tactics, the teaching material providing unit provides teaching materials that explain that section in detail. The teaching material providing unit also analyzes the learner's click and scroll patterns to identify the section of interest and dynamically generate teaching materials that reinforce that content. For example, if the learner is interested in Oda Nobunaga's alliances, the teaching material providing unit provides a detailed explanation of that section. The teaching material providing unit also analyzes the learner's questions and comments in real time and provides additional information related to the topic of interest. For example, if there are many questions about Oda Nobunaga's economic policies, the teaching material providing unit provides teaching materials that explain that section in detail. This makes it possible to provide teaching materials that meet the learner's interests.

[0032] The question generation unit can dynamically adjust the difficulty of questions according to the learner's answers and provide an appropriate challenge. The question generation unit, for example, builds a system that dynamically adjusts the difficulty of questions based on the learner's answers. For example, if a learner correctly answers a basic question, the difficulty of the next question is increased. The question generation unit also analyzes the learner's level of understanding in real time and provides questions of an appropriate level of difficulty. For example, if a learner understands basic information about Oda Nobunaga, it asks difficult questions about strategy and policy. The question generation unit also develops a system that individually adjusts the difficulty of questions based on the learner's answer history. For example, if past answers are accurate, the difficulty of the next question is increased. This makes it possible to provide learners with questions of an appropriate level of difficulty.

[0033] The question generation unit can provide additional information or background knowledge related to the learner's answer to deepen understanding. For example, the question generation unit builds a system that provides additional information or background knowledge related to the learner's answer. For example, after answering a question about Oda Nobunaga's strategy, the background and impact of the strategy are explained in detail. The question generation unit also introduces historical events or people related to the learner's answer to deepen understanding. For example, after answering a question about Oda Nobunaga's alliances, the background and impact of the alliances are explained. The question generation unit also develops a system that provides additional information related to the learner's answer in real time. For example, after answering a question about Oda Nobunaga's economic policies, the background and impact of the policies are explained in detail. This makes it possible to provide additional information to deepen the learner's understanding.

[0034] The answer analysis unit can present relevant real-world examples or case studies based on the learner's answers to deepen understanding. For example, the answer analysis unit builds a system that presents relevant real-world examples based on the learner's answers. For example, for an answer regarding Oda Nobunaga's strategies, similar examples in modern business strategies are introduced. The answer analysis unit also develops a system that presents relevant case studies based on the learner's answers. For example, for an answer regarding Oda Nobunaga's alliances, case studies of other historical alliances are introduced. The answer analysis unit also builds a system that provides real-world examples and case studies in real time based on the learner's answers. For example, for an answer regarding Oda Nobunaga's economic policies, similar examples in modern economic policies are introduced. This makes it possible to provide real-world examples and case studies to deepen the learner's understanding.

[0035] The answer analysis unit can analyze the learner's answers, identify misunderstandings or insufficient knowledge, and provide supplementary explanations. The answer analysis unit, for example, analyzes the learner's answers and builds a system to identify misunderstandings or insufficient knowledge. For example, it identifies misunderstandings regarding Oda Nobunaga's strategies and provides correct information. The answer analysis unit also develops a system to analyze the learner's answers, identify insufficient knowledge, and provide supplementary explanations. For example, it supplements insufficient knowledge regarding Oda Nobunaga's alliances. The answer analysis unit also analyzes the learner's answers in real time, identifies misunderstandings or insufficient knowledge, and builds a system to provide supplementary explanations. For example, it identifies misunderstandings regarding Oda Nobunaga's economic policies and provides correct information. This makes it possible to provide explanations to supplement the learner's misunderstandings or insufficient knowledge.

[0036] The answer analysis unit can dig deeper into understanding in a group discussion format and promote the exchange of opinions with other learners. The answer analysis unit, for example, builds a system for digging deeper into understanding in a group discussion format and promotes the exchange of opinions with other learners. For example, a group discussion about Oda Nobunaga's strategies. The answer analysis unit also develops a system for learners to deepen their understanding through group discussion. For example, a group exchange of opinions about Oda Nobunaga's alliances. The answer analysis unit also builds a system for digging deeper into understanding in a group discussion format and promotes the exchange of opinions with other learners. For example, a group discussion about Oda Nobunaga's economic policies. This promotes the exchange of opinions among learners and deepens understanding.

[0037] The answer analysis unit can ask in-depth questions from different perspectives or positions to encourage multifaceted understanding. For example, the answer analysis unit builds a system that asks in-depth questions from different perspectives or positions to encourage multifaceted understanding. For example, it asks questions that consider Oda Nobunaga's strategies from the perspective of his enemies. The answer analysis unit also develops a system that provides questions from different perspectives or positions to help learners deepen their multifaceted understanding. For example, it asks questions that consider Oda Nobunaga's alliances from the perspective of other daimyo. The answer analysis unit also builds a system that asks in-depth questions from different perspectives or positions to encourage multifaceted understanding. For example, it asks questions that consider Oda Nobunaga's economic policies from the perspective of ordinary people. This can encourage learners to understand from multiple perspectives.

[0038] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0039] The teaching material providing unit can also provide teaching materials that incorporate game elements to attract the learner's interest. For example, in the study of history, teaching materials can be provided that allow the learner to experience the life of Oda Nobunaga in a game format. The teaching material providing unit can also provide teaching materials that allow the learner to check the content they have learned in the form of a quiz. For example, a learner can check their level of understanding through a quiz about Oda Nobunaga's strategies. The teaching material providing unit can also provide teaching materials that allow the learner to compete with other learners. For example, teaching materials can be provided in an online competitive format that allows learners to compete in knowledge of Oda Nobunaga. This can attract the learner's interest and increase their motivation to learn.

[0040] The teaching material providing unit can also provide customized teaching materials according to the learner's learning style. For example, for a learner who prefers visual learning, teaching materials explaining the life of Oda Nobunaga using illustrations and videos can be provided. Also, for a learner who prefers auditory learning, teaching materials explaining the history of Oda Nobunaga through audio can be provided. Furthermore, for a learner who prefers tactile learning, teaching materials that allow them to experience Oda Nobunaga's strategies through a simulation game can be provided. In this way, it is possible to provide optimal teaching materials according to the learner's learning style.

[0041] The learning material provider can also personalize learning content based on the learner's real-time responses. For example, if a learner shows interest in a particular topic, it can provide additional learning materials related to that topic. Also, if the learner is having difficulty understanding a part, it can provide learning materials that explain that part in detail. Furthermore, it can adjust the learning progress speed based on the learner's responses. For example, if the learner wants to proceed quickly, it can provide the next learning material sooner, and if the learner wants to proceed more slowly, it can add detailed explanations. This makes it possible to provide a flexible learning experience that meets the learner's needs.

[0042] The question generator can dynamically adjust the difficulty of questions based on the learner's answers to provide an appropriate challenge. For example, if a learner answers a basic question correctly, the difficulty of the next question can be increased. It can also analyze the learner's level of understanding in real time to provide questions of an appropriate level of difficulty. For example, if a learner understands basic information about Oda Nobunaga, it can ask difficult questions about strategy and policy. It can also individually adjust the difficulty of questions based on the learner's answer history. For example, if past answers are accurate, the difficulty of the next question can be increased. This allows learners to be provided with questions of an appropriate level of difficulty.

[0043] The question generation unit can provide additional information or background knowledge related to the learner's answer to deepen understanding. For example, after answering a question about Oda Nobunaga's strategy, the background and impact of that strategy can be explained in detail. The question generation unit can also introduce related historical events or people to deepen understanding in response to the learner's answer. For example, after answering a question about Oda Nobunaga's alliances, the background and impact of those alliances can be explained. Furthermore, based on the learner's answer, related additional information can be provided in real time. For example, after answering a question about Oda Nobunaga's economic policies, the background and impact of those policies can be explained in detail. This makes it possible to provide additional information to deepen the learner's understanding.

[0044] The answer analysis unit can present relevant real-world examples or case studies based on the learner's answers to deepen their understanding. For example, for an answer about Oda Nobunaga's strategies, similar examples in modern business strategies can be introduced. It can also present related case studies based on the learner's answers. For example, for an answer about Oda Nobunaga's alliances, case studies of other historical alliances can be introduced. Furthermore, real-world examples and case studies can be provided in real time based on the learner's answers. For example, for an answer about Oda Nobunaga's economic policies, similar examples in modern economic policies can be introduced. In this way, it is possible to provide real-world examples and case studies to deepen the learner's understanding.

[0045] The answer analysis unit can analyze the learner's answers, identify misunderstandings or insufficient knowledge, and provide supplementary explanations for them. For example, it can identify misunderstandings about Oda Nobunaga's strategies and provide correct information. It can also analyze the learner's answers, identify insufficient knowledge, and provide supplementary explanations for them. For example, it can supplement insufficient knowledge about Oda Nobunaga's alliances. Furthermore, it can analyze the learner's answers in real time, identify misunderstandings or insufficient knowledge, and provide supplementary explanations for them. For example, it can identify misunderstandings about Oda Nobunaga's economic policies and provide correct information. This makes it possible to provide explanations that supplement the learner's misunderstandings or insufficient knowledge.

[0046] The processing flow of the first embodiment will be briefly explained below.

[0047] Step 1: The teaching material provider provides learning materials to the learner. For example, the teaching material provider can have the learner study a specific learning topic using teaching materials in the system. It can also provide learning materials selected by the learner, and can also recommend appropriate learning materials based on the learner's learning history. Step 2: The question generator generates questions based on the learning materials provided by the learning material provider. For example, it can generate basic questions about the content the learner has learned and adjust the difficulty of the questions according to the learner's level of understanding. It can also generate additional questions based on the learner's answers. Step 3: The answer analysis unit analyzes the learner's answers to the questions generated by the question generation unit. For example, it can evaluate the accuracy of the learner's answers and evaluate the learner's understanding by analyzing the content of the answers. Furthermore, it can provide feedback based on the learner's answers.

[0048] (Example 2) The children's educational AI according to the embodiment of the present invention is a system in which the AI ​​asks the learner questions in a conversational format after learning about a learning topic, and can efficiently output the output of "teaching others." This allows the children's educational AI to deepen the learner's understanding and improve the learning retention rate.

[0049] The children's educational AI according to the embodiment includes a teaching material providing unit, a question generating unit, and an answer analyzing unit. The teaching material providing unit provides teaching materials to a learner. For example, the teaching material providing unit can have a learner study a specific learning topic using teaching materials in the system. The teaching material providing unit can also provide teaching materials selected by the learner. Furthermore, the teaching material providing unit can recommend appropriate teaching materials based on the learner's learning history. The question generating unit generates questions based on the teaching materials provided by the teaching material providing unit. For example, the question generating unit generates basic questions related to content learned by the learner. The question generating unit can also adjust the difficulty of the questions according to the learner's level of understanding. Furthermore, the question generating unit can generate additional questions based on the learner's answers. The answer analyzing unit analyzes the learner's answers to the questions generated by the question generating unit. For example, the answer analyzing unit can evaluate the accuracy of the learner's answers. The answer analyzing unit can also analyze the content of the learner's answers and evaluate the learner's level of understanding. Furthermore, the answer analyzing unit can provide feedback based on the learner's answers. As a result, the children's educational AI according to the embodiment can deepen learners' understanding and improve their learning retention rate.

[0050] The teaching material providing unit can automatically generate individually customized teaching materials based on the learner's past learning history. For example, the teaching material providing unit analyzes the learner's past learning history and automatically generates individually customized teaching materials based on the learner's level of understanding and interests. For example, based on the learner's history of learning history, the teaching material providing unit provides teaching materials including other historical figures and events related to Oda Nobunaga. The teaching material providing unit also automatically generates special teaching materials to strengthen weak points based on the learner's past test results and learning logs. For example, if the learner lacks understanding of Oda Nobunaga's strategies, the teaching material providing unit provides teaching materials to strengthen that area. The teaching material providing unit also automatically generates individually customized teaching materials to suit the learner's learning style and pace. For example, for learners who prefer visual learning, the teaching material explaining the life of Oda Nobunaga using illustrations and videos is provided. This makes it possible to provide teaching materials that meet the individual needs of learners.

[0051] The teaching material providing unit can analyze the learner's interests in real time and dynamically change the content of the teaching materials based on that analysis. For example, the teaching material providing unit analyzes the learner's real-time reactions and behavior and dynamically changes the content of the teaching materials based on the learner's interest. For example, if the learner shows interest in Oda Nobunaga's tactics, the teaching material providing unit provides teaching materials that explain that section in detail. The teaching material providing unit also analyzes the learner's click and scroll patterns to identify the section of interest and dynamically generate teaching materials that reinforce that content. For example, if the learner is interested in Oda Nobunaga's alliances, the teaching material providing unit provides a detailed explanation of that section. The teaching material providing unit also analyzes the learner's questions and comments in real time and provides additional information related to the topic of interest. For example, if there are many questions about Oda Nobunaga's economic policies, the teaching material providing unit provides teaching materials that explain that section in detail. This makes it possible to provide teaching materials that meet the learner's interests.

[0052] The teaching material providing unit uses the emotion estimation function to select an input method according to the learner's emotional state, thereby increasing motivation to learn. The teaching material providing unit, for example, analyzes the learner's facial expressions and voice and selects an input method according to the learner's emotional state. For example, if the learner is tired, the learning material providing unit plays relaxing music while the learner studies. The teaching material providing unit also uses the emotion estimation function to provide challenging problems when the learner is excited, and conversely, to promote learning in a relaxed atmosphere when the learner is calm. The teaching material providing unit also monitors the learner's emotional state in real time and selects an input method to elicit positive emotions. For example, if the learner is happy, the learning material providing unit displays praise or encouraging messages. This makes it possible to provide an input method according to the learner's emotional state.

[0053] The question generation unit can dynamically adjust the difficulty of questions according to the learner's answers and provide an appropriate challenge. The question generation unit, for example, builds a system that dynamically adjusts the difficulty of questions based on the learner's answers. For example, if a learner correctly answers a basic question, the difficulty of the next question is increased. The question generation unit also analyzes the learner's level of understanding in real time and provides questions of an appropriate level of difficulty. For example, if a learner understands basic information about Oda Nobunaga, it asks difficult questions about strategy and policy. The question generation unit also develops a system that individually adjusts the difficulty of questions based on the learner's answer history. For example, if past answers are accurate, the difficulty of the next question is increased. This makes it possible to provide learners with questions of an appropriate level of difficulty.

[0054] The question generation unit can provide additional information or background knowledge related to the learner's answer to deepen understanding. For example, the question generation unit builds a system that provides additional information or background knowledge related to the learner's answer. For example, after answering a question about Oda Nobunaga's strategy, the background and impact of the strategy are explained in detail. The question generation unit also introduces historical events or people related to the learner's answer to deepen understanding. For example, after answering a question about Oda Nobunaga's alliances, the background and impact of the alliances are explained. The question generation unit also develops a system that provides additional information related to the learner's answer in real time. For example, after answering a question about Oda Nobunaga's economic policies, the background and impact of the policies are explained in detail. This makes it possible to provide additional information to deepen the learner's understanding.

[0055] The question generation unit uses the emotion estimation function to select questions that correspond to the learner's emotional state, thereby reducing learning stress. The question generation unit, for example, builds a system that analyzes the learner's emotional state and selects questions to reduce stress. For example, if the learner is tired, it asks questions that will help them relax. The question generation unit also uses the emotion estimation function to select questions in real time that correspond to the learner's emotional state. For example, if the learner is excited, it asks challenging questions. The question generation unit also develops a system that monitors the learner's emotional state and dynamically selects questions to reduce stress. For example, if the learner is calm, it asks questions that will help them relax. This makes it possible to provide questions that reduce the learner's stress.

[0056] The answer analysis unit can present relevant real-world examples or case studies based on the learner's answers to deepen understanding. For example, the answer analysis unit builds a system that presents relevant real-world examples based on the learner's answers. For example, for an answer regarding Oda Nobunaga's strategies, similar examples in modern business strategies are introduced. The answer analysis unit also develops a system that presents relevant case studies based on the learner's answers. For example, for an answer regarding Oda Nobunaga's alliances, case studies of other historical alliances are introduced. The answer analysis unit also builds a system that provides real-world examples and case studies in real time based on the learner's answers. For example, for an answer regarding Oda Nobunaga's economic policies, similar examples in modern economic policies are introduced. This makes it possible to provide real-world examples and case studies to deepen the learner's understanding.

[0057] The answer analysis unit can analyze the learner's answers, identify misunderstandings or insufficient knowledge, and provide supplementary explanations. The answer analysis unit, for example, analyzes the learner's answers and builds a system to identify misunderstandings or insufficient knowledge. For example, it identifies misunderstandings regarding Oda Nobunaga's strategies and provides correct information. The answer analysis unit also develops a system to analyze the learner's answers, identify insufficient knowledge, and provide supplementary explanations. For example, it supplements insufficient knowledge regarding Oda Nobunaga's alliances. The answer analysis unit also analyzes the learner's answers in real time, identifies misunderstandings or insufficient knowledge, and builds a system to provide supplementary explanations. For example, it identifies misunderstandings regarding Oda Nobunaga's economic policies and provides correct information. This makes it possible to provide explanations to supplement the learner's misunderstandings or insufficient knowledge.

[0058] The answer analysis unit uses the emotion estimation function to ask in-depth questions related to topics that interest the learner most, thereby increasing the depth of learning. The answer analysis unit, for example, analyzes the learner's emotional state and builds a system that asks in-depth questions related to the topic that interests the learner most. For example, if the learner shows a strong interest in Oda Nobunaga's tactics, the answer analysis unit asks questions that delve deeper into that part. The answer analysis unit also uses the emotion estimation function to develop a system that asks in-depth questions related to topics that interest the learner in real time. For example, if the learner is interested in Oda Nobunaga's alliances, the answer analysis unit asks questions that delve deeper into that part. The answer analysis unit also monitors the learner's emotional state and builds a system that dynamically asks in-depth questions related to topics of interest. For example, if there are many questions about Oda Nobunaga's economic policies, the answer analysis unit asks questions that delve deeper into that part. This makes it possible to provide in-depth questions that match the learner's interests.

[0059] The answer analysis unit can dig deeper into understanding in a group discussion format and promote the exchange of opinions with other learners. The answer analysis unit, for example, builds a system for digging deeper into understanding in a group discussion format and promotes the exchange of opinions with other learners. For example, a group discussion about Oda Nobunaga's strategies. The answer analysis unit also develops a system for learners to deepen their understanding through group discussion. For example, a group exchange of opinions about Oda Nobunaga's alliances. The answer analysis unit also builds a system for digging deeper into understanding in a group discussion format and promotes the exchange of opinions with other learners. For example, a group discussion about Oda Nobunaga's economic policies. This promotes the exchange of opinions among learners and deepens understanding.

[0060] The answer analysis unit can ask in-depth questions from different perspectives or positions to encourage multifaceted understanding. For example, the answer analysis unit builds a system that asks in-depth questions from different perspectives or positions to encourage multifaceted understanding. For example, it asks questions that consider Oda Nobunaga's strategies from the perspective of his enemies. The answer analysis unit also develops a system that provides questions from different perspectives or positions to help learners deepen their multifaceted understanding. For example, it asks questions that consider Oda Nobunaga's alliances from the perspective of other daimyo. The answer analysis unit also builds a system that asks in-depth questions from different perspectives or positions to encourage multifaceted understanding. For example, it asks questions that consider Oda Nobunaga's economic policies from the perspective of ordinary people. This can encourage learners to understand from multiple perspectives.

[0061] The answer analysis unit uses the emotion estimation function to ask in-depth questions when the learner is most relaxed, thereby promoting effective learning. The answer analysis unit, for example, analyzes the emotional state of the learner and builds a system that asks in-depth questions when the learner is most relaxed. For example, when the learner is relaxed, it asks in-depth questions about Oda Nobunaga's strategies. The answer analysis unit also uses the emotion estimation function to develop a system that asks in-depth questions when the learner is relaxed. For example, when the learner is relaxed, it asks in-depth questions about Oda Nobunaga's alliances. The answer analysis unit also monitors the emotional state of the learner and builds a system that asks in-depth questions when the learner is most relaxed. For example, when the learner is relaxed, it asks in-depth questions about Oda Nobunaga's economic policies. In this way, by asking in-depth questions when the learner is relaxed, it is possible to promote effective learning.

[0062] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0063] The teaching material providing unit can also provide teaching materials that incorporate game elements to attract the learner's interest. For example, in the study of history, teaching materials can be provided that allow the learner to experience the life of Oda Nobunaga in a game format. The teaching material providing unit can also provide teaching materials that allow the learner to check the content they have learned in the form of a quiz. For example, a learner can check their level of understanding through a quiz about Oda Nobunaga's strategies. The teaching material providing unit can also provide teaching materials that allow the learner to compete with other learners. For example, teaching materials can be provided in an online competitive format that allows learners to compete in knowledge of Oda Nobunaga. This can attract the learner's interest and increase their motivation to learn.

[0064] The teaching material providing unit can also provide customized teaching materials according to the learner's learning style. For example, for a learner who prefers visual learning, teaching materials explaining the life of Oda Nobunaga using illustrations and videos can be provided. Also, for a learner who prefers auditory learning, teaching materials explaining the history of Oda Nobunaga through audio can be provided. Furthermore, for a learner who prefers tactile learning, teaching materials that allow them to experience Oda Nobunaga's strategies through a simulation game can be provided. In this way, it is possible to provide optimal teaching materials according to the learner's learning style.

[0065] The learning material provider can also personalize learning content based on the learner's real-time responses. For example, if a learner shows interest in a particular topic, it can provide additional learning materials related to that topic. Also, if the learner is having difficulty understanding a part, it can provide learning materials that explain that part in detail. Furthermore, it can adjust the learning progress speed based on the learner's responses. For example, if the learner wants to proceed quickly, it can provide the next learning material sooner, and if the learner wants to proceed more slowly, it can add detailed explanations. This makes it possible to provide a flexible learning experience that meets the learner's needs.

[0066] The learning material provider can use the emotion estimation function to select an input method according to the learner's emotional state and increase their motivation to learn. For example, if a learner is tired, they can study while listening to relaxing music. If a learner is excited, they can be provided with challenging problems, and conversely, if they are calm, they can be taught in a relaxed atmosphere. Furthermore, it is possible to monitor the learner's emotional state in real time and select an input method that will elicit positive emotions. For example, if a learner is happy, they can be shown praise or encouraging messages. This makes it possible to provide an input method that suits the learner's emotional state.

[0067] The question generator can dynamically adjust the difficulty of questions based on the learner's answers to provide an appropriate challenge. For example, if a learner answers a basic question correctly, the difficulty of the next question can be increased. It can also analyze the learner's level of understanding in real time to provide questions of an appropriate level of difficulty. For example, if a learner understands basic information about Oda Nobunaga, it can ask difficult questions about strategy and policy. It can also individually adjust the difficulty of questions based on the learner's answer history. For example, if past answers are accurate, the difficulty of the next question can be increased. This allows learners to be provided with questions of an appropriate level of difficulty.

[0068] The question generation unit can provide additional information or background knowledge related to the learner's answer to deepen understanding. For example, after answering a question about Oda Nobunaga's strategy, the background and impact of that strategy can be explained in detail. The question generation unit can also introduce related historical events or people to deepen understanding in response to the learner's answer. For example, after answering a question about Oda Nobunaga's alliances, the background and impact of those alliances can be explained. Furthermore, based on the learner's answer, related additional information can be provided in real time. For example, after answering a question about Oda Nobunaga's economic policies, the background and impact of those policies can be explained in detail. This makes it possible to provide additional information to deepen the learner's understanding.

[0069] The question generation unit uses the emotion estimation function to select questions that correspond to the learner's emotional state, thereby reducing learning stress. For example, if the learner is tired, it can ask questions that will help them relax. If the learner is excited, it can ask challenging questions. Furthermore, it can monitor the learner's emotional state and dynamically select questions to reduce stress. For example, if the learner is calm, it can ask questions that will help them relax. This makes it possible to provide questions that will reduce the learner's stress.

[0070] The answer analysis unit can present relevant real-world examples or case studies based on the learner's answers to deepen their understanding. For example, for an answer about Oda Nobunaga's strategies, similar examples in modern business strategies can be introduced. It can also present related case studies based on the learner's answers. For example, for an answer about Oda Nobunaga's alliances, case studies of other historical alliances can be introduced. Furthermore, real-world examples and case studies can be provided in real time based on the learner's answers. For example, for an answer about Oda Nobunaga's economic policies, similar examples in modern economic policies can be introduced. In this way, it is possible to provide real-world examples and case studies to deepen the learner's understanding.

[0071] The answer analysis unit can analyze the learner's answers, identify misunderstandings or insufficient knowledge, and provide supplementary explanations for them. For example, it can identify misunderstandings about Oda Nobunaga's strategies and provide correct information. It can also analyze the learner's answers, identify insufficient knowledge, and provide supplementary explanations for them. For example, it can supplement insufficient knowledge about Oda Nobunaga's alliances. Furthermore, it can analyze the learner's answers in real time, identify misunderstandings or insufficient knowledge, and provide supplementary explanations for them. For example, it can identify misunderstandings about Oda Nobunaga's economic policies and provide correct information. This makes it possible to provide explanations that supplement the learner's misunderstandings or insufficient knowledge.

[0072] The answer analysis unit can use the emotion estimation function to ask in-depth questions related to the topics that the learner is most interested in, thereby increasing the depth of learning. For example, a system can be built that analyzes the learner's emotional state and asks in-depth questions related to the topics that interest them most. For example, if the learner shows a strong interest in Oda Nobunaga's tactics, questions that delve deeper into that area can be asked. It is also possible to develop a system that asks in-depth questions related to topics that interest the learner in real time. For example, if the learner is interested in Oda Nobunaga's alliances, questions that delve deeper into that area can be asked. Furthermore, it is possible to build a system that monitors the learner's emotional state and dynamically asks in-depth questions related to topics that interest them. For example, if there are many questions about Oda Nobunaga's economic policies, questions that delve deeper into that area can be asked. This makes it possible to provide in-depth questions that match the learner's interests.

[0073] The processing flow of the second embodiment will be briefly explained below.

[0074] Step 1: The teaching material provider provides learning materials to the learner. For example, the teaching material provider can have the learner study a specific learning topic using teaching materials in the system. It can also provide learning materials selected by the learner, and can also recommend appropriate learning materials based on the learner's learning history. Step 2: The question generator generates questions based on the learning materials provided by the learning material provider. For example, it can generate basic questions about the content the learner has learned and adjust the difficulty of the questions according to the learner's level of understanding. It can also generate additional questions based on the learner's answers. Step 3: The answer analysis unit analyzes the learner's answers to the questions generated by the question generation unit. For example, it can evaluate the accuracy of the learner's answers and evaluate the learner's understanding by analyzing the content of the answers. Furthermore, it can provide feedback based on the learner's answers.

[0075] 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.

[0076] 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.

[0077] 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.

[0078] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0079] 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.

[0080] 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.

[0081] 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.

[0082] 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.

[0083] 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).

[0084] 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.

[0085] 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.

[0086] 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.

[0087] 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.

[0088] 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.

[0089] 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.

[0090] 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.

[0091] 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.

[0092] 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.

[0093] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0094] 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.

[0095] 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.

[0096] 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.

[0097] 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.

[0098] 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).

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] In the headset type terminal 314, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the 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 specific processing unit 290 using these models.

[0104] 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.

[0105] 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.

[0106] 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.

[0107] 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.

[0108] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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.

[0113] 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).

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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.

[0119] In the robot 414, 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 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 processing similar to that of the specific processing unit 290 using these models.

[0120] 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.

[0121] 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.

[0122] 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.

[0123] 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.

[0124] 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.

[0125] 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.

[0126] 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.

[0127] 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).

[0128] 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.

[0129] 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."

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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.

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] 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.

[0139] 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.

[0140] 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.

[0141] All publications, patent applications, and technical standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, or technical standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0142] 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 teaching material providing unit that provides teaching materials to learners; a question generation unit that generates questions based on the teaching materials provided by the teaching material provision unit; a response analysis unit that analyzes the learner's response to the question generated by the question generation unit. A system characterized by:

2. The teaching material providing unit Automatically generate individually customized learning materials based on the learner's past learning history.

2. The system of claim 1.

3. The teaching material providing unit Analyzing the learner's interests or concerns in real time and dynamically changing the content of the learning material based thereon.

2. The system of claim 1.

4. The teaching material providing unit Select an input method that suits the learner's emotional state and increase their motivation to learn.

2. The system of claim 1.

5. The question generation unit Dynamically adjusting the difficulty of the questions depending on the learner's answers to provide an appropriate challenge 2. The system of claim 1.

6. The question generation unit Providing additional information or background knowledge related to the learner's answer to deepen understanding 2. The system of claim 1.

7. The question generation unit Selecting the questions according to the learner's emotional state to reduce learning stress 2. The system of claim 1.

8. The answer analysis unit Provide relevant real-world examples or case studies based on the learner's answers to deepen their understanding 2. The system of claim 1.

Images