System

Abstract

An object of the system according to the embodiment is to enable a learner to immediately obtain an answer to a question, to make an individualized learning plan, and to provide abundant learning resources.SOLUTION: A system according to an embodiment includes a question answering unit, a learning plan formulation unit, and a resource provision unit. The question answering unit provides an immediate answer to the question of the learner. The learning plan drafting unit drafts an individualized learning plan according to a learning history and a goal of the learner based on the answer provided by the question answering unit. The resource providing unit provides the learner with abundant learning resources based on the learning plan made by the learning plan making unit.SELECTED DRAWING: Figure 1

Description

[Technical Field]

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

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

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

[0004] Conventional technologies have had the drawback of making it difficult for learners to get answers to their questions immediately, and they do not adequately provide personalized learning plans or abundant learning resources.

[0005] The system according to the embodiment aims to enable learners to get instant answers to their questions, create personalized learning plans, and provide a wealth of learning resources. [Means for solving the problem]

[0006] The system according to the embodiment includes a question and answering unit, a learning plan creation unit, and a resource providing unit. The question and answering unit provides immediate answers to questions from a learner. The learning plan creation unit creates an individualized learning plan according to the learner's learning history and goals based on the answers provided by the question and answering unit. The resource providing unit provides the learner with abundant learning resources based on the learning plan created by the learning plan creation unit. [Effects of the Invention]

[0007] The system according to the embodiment allows learners to get instant answers to their questions, develop personalized learning plans, and provide a wealth of learning resources. [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 nonvolatile storage devices that store various programs, various parameters, etc. Examples of nonvolatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

[0028] (Example 1) A learning support community service according to an embodiment of the present invention is a community service that provides learning support using generative AI. This service uses Discord as a platform, allowing learners to support each other while aiming to improve learning efficiency through instant question and answering provided by generative AI, the creation of personalized learning plans, and the sharing of abundant resources. As a result, the learning support community service allows learners to progress in their studies efficiently and effectively.

[0029] A learning support community service according to an embodiment includes a question-answering unit, a learning plan creation unit, and a resource provision unit. The question-answering unit provides instant answers to learners' questions. For example, when a learner posts a question on Discord, the generation AI immediately provides an answer. The generation AI is fine-tuned in advance based on learning data and generates appropriate answers to the learner's questions. For example, when a learner asks a question about a math problem, the generation AI analyzes the problem and presents a solution. The input to the generation AI is a prompt containing instructions on what the learner wants the generation AI to do, and the generation AI generates an answer based on the prompt. The learning plan creation unit creates an individualized learning plan based on the learner's learning history and goals. For example, if a learner wants to improve their English grammar, the generation AI proposes a learning plan tailored to the learner's current level and goals. This plan includes specific learning content and schedule. The input to the generation AI is information about the learner's learning history and goals, and the generation AI generates a learning plan based on that information. The resource provision unit provides the learner with a wealth of learning resources, such as reference books, online learning materials, and video lectures. When a learner wants to learn about a specific topic, the generation AI searches for and provides resources related to that topic. The input to the generation AI is information about the topic the learner wants to learn, and the generation AI suggests resources based on that information. This allows the learning support community service according to the embodiment to enable learners to progress with their studies efficiently and effectively. For example, immediate question and answering can quickly resolve doubts, and personalized learning plans can help learners progress efficiently toward their goals. Furthermore, the sharing of abundant resources makes it easy to obtain the information and learning materials they need.

[0030] The question answering unit can refer to past question history and generate the best answer based on answers to similar questions. For example, when a learner posts a question, the generation AI searches past question history to identify similar questions. For example, if the same math problem has been posted in the past, a new answer will be generated based on that answer. This makes it possible to quickly provide the best answer to similar questions by referring to past question history.

[0031] The question-answering unit can suggest relevant video tutorials and interactive simulations depending on the content of the question. For example, when a learner posts a question, the generative AI analyzes the content of the question and suggests relevant video tutorials. For example, in response to a question about chemistry experiment procedures, a video of the experiment can be provided. This allows the learner to deepen their understanding by suggesting relevant video tutorials and interactive simulations depending on the content of the question.

[0032] The question and answer unit can support not only text but also voice input, enabling questions and answers via voice. For example, the question and answer unit allows learners to post questions via voice, and the generation AI analyzes the voice to provide an answer. For example, it uses voice recognition technology to convert the question into text and generate an answer. This allows learners to ask and answer questions in a more diverse way by supporting voice input.

[0033] The question and answering unit can create a database of question and answer history and automatically generate an FAQ section that other learners can refer to. For example, the question and answering unit stores the question and answer history in a database, and the generation AI automatically generates an FAQ section based on that data. For example, it creates a page that compiles frequently asked questions and their answers. In this way, by creating a database of question and answer history and automatically generating an FAQ section, other learners can refer to past questions and answers.

[0034] The study plan creation unit can propose optimal study times by taking into account the learner's lifestyle and schedule. For example, the study plan creation unit analyzes the learner's lifestyle and schedule, and the generation AI proposes optimal study times. For example, it sets efficient study times by taking into account the learner's sleep time and commute time. This makes it possible to propose optimal study times by taking into account the learner's lifestyle and schedule.

[0035] The learning plan creation unit can incorporate break times and refreshment methods recommended by the generation AI into the learning plan. For example, the learning plan creation unit incorporates break times into the learning plan and suggests refreshment methods recommended by the generation AI. For example, it may suggest taking short breaks at regular intervals and doing stretching or meditation. In this way, by incorporating break times and refreshment methods, the learner's concentration can be maintained.

[0036] The learning plan creation unit can generate a group learning plan for sharing the learning plan with other learners and advancing learning together. For example, the learning plan creation unit generates a group learning plan to be shared with other learners based on the learner's individual learning plan using a generation AI. For example, learners with the same goal are grouped together to advance learning together. In this way, generating a group learning plan promotes cooperation between learners and improves learning effectiveness.

[0037] The learning plan creation unit can incorporate external learning events and seminars recommended by the generation AI into the learning plan. For example, the learning plan creation unit incorporates external learning events and seminars into the learning plan and suggests events recommended by the generation AI. For example, it introduces seminars and workshops on a specific topic. In this way, by incorporating external learning events and seminars, the learner's learning opportunities are expanded.

[0038] The resource providing unit can provide customized, optimal resources by taking into account the learner's past learning history and interests. For example, the resource providing unit analyzes the learner's past learning history, and the generation AI suggests optimal resources based on that history. For example, it provides new learning materials related to topics previously studied. This makes it possible to provide optimal resources by taking into account the learner's past learning history and interests.

[0039] The resource providing unit can include exercises and quizzes automatically generated by the generation AI in the resources it provides. For example, the resource providing unit includes exercises and quizzes automatically generated by the generation AI in the resources to enable learners to learn practically. For example, it provides a collection of mathematics problems and English quizzes. In this way, by including exercises and quizzes automatically generated by the generation AI, learners can learn practically.

[0040] The resource provider can expand resource suggestions beyond text to include interactive content and game-style teaching materials. For example, the resource provider can expand the resources suggested by the generative AI beyond text to include interactive content and game-style teaching materials. For example, in programming learning, the resource provider can provide an interactive code editor. This allows learners to learn while having fun by suggesting interactive content and game-style teaching materials.

[0041] The resource providing unit can add a collaboration function that enables the provided resources to be shared with other learners and that allows for joint learning. For example, the resource providing unit adds a collaboration function that enables the resources suggested by the generation AI to be shared with other learners and that allows for joint learning. For example, a group discussion can be held using the same learning materials. This promotes cooperation between learners by sharing resources and jointly learning.

[0042] The question answering unit can suggest appropriate topics and discussion themes to promote communication between learners. For example, the generative AI can suggest appropriate topics and discussion themes to promote communication between learners. For example, it can suggest discussions on specific topics. This promotes communication between learners by suggesting appropriate topics and discussion themes.

[0043] The question and answering unit automatically analyzes feedback between learners, and the generation AI can provide suggestions for improvement and advice. For example, the question and answering unit automatically analyzes feedback between learners and provides suggestions for improvement and advice. For example, it analyzes the feedback content and makes specific suggestions for improvement. In this way, the learners' learning effectiveness is improved by automatically analyzing feedback between learners and providing suggestions for improvement and advice.

[0044] The question and answering unit can also make the function of support between learners compatible with real-time video chat and voice chat. For example, the question and answering unit can also make the function of support between learners compatible with real-time video chat and voice chat. For example, learners can communicate directly through video chat. This allows support between learners to be more effective by supporting real-time video chat and voice chat.

[0045] The question and answering unit can compile a database of support provided between learners and build a knowledge base that other learners can refer to. For example, the question and answering unit can save support provided between learners in a database and build a knowledge base that other learners can refer to. For example, it can create a page that summarizes past discussions and feedback. In this way, by compile- ing support content into a database and building a knowledge base, other learners can refer to past support content.

[0046] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0047] The question-answering unit provides instant answers to learners' questions. For example, when a learner posts a question on Discord, the generative AI immediately provides a response. The generative AI is fine-tuned based on prior training data and generates appropriate answers to learners' questions. For example, when a learner asks about a math problem, the generative AI analyzes the problem and presents a solution. The generative AI receives input from the learner, including prompts containing instructions on what the learner wants the generative AI to do, and generates an answer based on the prompt. The learning plan creation unit creates an individualized learning plan based on the learner's learning history and goals. For example, if a learner wants to improve their English grammar, the generative AI proposes a learning plan based on the learner's current level and goals. This plan includes specific learning content and schedule. The generative AI receives input from the learner's learning history and goals, and generates a learning plan based on that information. The resource provision unit provides learners with a wealth of learning resources. For example, it suggests reference books, online materials, video lectures, etc. If a learner wants to learn about a specific topic, the generative AI searches for and provides resources related to that topic. The input to the generation AI is information about the topic the learner wants to learn, and the generation AI suggests resources based on that information. This allows the learning support community service according to the embodiment to enable learners to progress with their studies efficiently and effectively. For example, immediate question and answering can quickly resolve doubts, and personalized learning plans can help learners progress efficiently toward their goals. Furthermore, the sharing of abundant resources makes it easy to obtain the information and learning materials they need.

[0048] The question answering unit can refer to past question history and generate the best answer based on answers to similar questions. For example, when a learner posts a question, the generation AI searches past question history to identify similar questions. For example, if the same math problem has been posted in the past, a new answer can be generated based on that answer. This makes it possible to quickly provide the best answer to similar questions by referring to past question history.

[0049] The question-answering unit can suggest relevant video tutorials and interactive simulations depending on the content of the question. For example, when a learner posts a question, the generative AI analyzes the question and suggests relevant video tutorials. For example, in response to a question about chemistry experiment procedures, a video of the experiment can be provided. This allows the learner to deepen their understanding by suggesting relevant video tutorials and interactive simulations depending on the content of the question.

[0050] The question-and-answer unit can handle voice input as well as text, enabling questions and answers via voice. For example, learners can post questions via voice, and the generation AI can analyze the voice and provide answers. For example, it can use voice recognition technology to convert the question into text and generate answers. This allows learners to ask and answer questions in a more diverse way by supporting voice input.

[0051] The question and answer section can create a database of question and answer history and automatically generate an FAQ section that other learners can refer to. For example, the question and answer history can be saved in a database, and the generation AI can automatically generate an FAQ section based on that data. For example, a page can be created that compiles frequently asked questions and their answers. This allows other learners to refer to past questions and answers by creating a database of question and answer history and automatically generating an FAQ section.

[0052] The study plan creation unit can propose optimal study times by taking into account the learner's lifestyle and schedule. For example, the generation AI can analyze the learner's lifestyle and schedule and propose optimal study times. For example, it can set efficient study times by taking into account the learner's sleep time and commute time. This makes it possible to propose optimal study times by taking into account the learner's lifestyle and schedule.

[0053] The learning plan creation unit can incorporate break times and refreshment methods recommended by the generation AI into the learning plan. For example, break times can be incorporated into the learning plan, and the generation AI can suggest recommended refreshment methods. For example, it can suggest taking short breaks at regular intervals and doing stretching or meditation. In this way, incorporating break times and refreshment methods can help the learner maintain their concentration.

[0054] The processing flow of the first embodiment will be briefly explained below.

[0055] Step 1: The question-answering unit provides instant answers to learners' questions. For example, when a learner posts a question on Discord, the generative AI provides an immediate answer. The generative AI is fine-tuned in advance based on training data, and generates appropriate answers to the learner's questions. For example, if a question is about a math problem, the generative AI analyzes the problem and presents a solution. The input to the generative AI is a prompt containing instructions on what the learner wants the generative AI to do, and the generative AI generates an answer based on that prompt. Step 2: The learning plan creation unit creates an individualized learning plan based on the learner's learning history and goals. For example, if a learner wants to improve their English grammar, the generation AI will propose a learning plan that suits the learner's current level and goals. This plan will include specific learning content and schedule. The input to the generation AI is information about the learner's learning history and goals, and the generation AI will generate a learning plan based on that information. Step 3: The resource provider provides the learner with a wide range of learning resources. For example, it suggests reference books, online learning materials, video lectures, etc. If a learner wants to learn about a specific topic, the generation AI searches for and provides resources related to that topic. The input to the generation AI is information about the topic the learner wants to learn, and the generation AI suggests resources based on that information.

[0056] (Example 2) A learning support community service according to an embodiment of the present invention is a community service that provides learning support using generative AI. This service uses Discord as a platform, allowing learners to support each other while aiming to improve learning efficiency through instant question and answering provided by generative AI, the creation of personalized learning plans, and the sharing of abundant resources. As a result, the learning support community service allows learners to progress in their studies efficiently and effectively.

[0057] A learning support community service according to an embodiment includes a question-answering unit, a learning plan creation unit, and a resource provision unit. The question-answering unit provides instant answers to learners' questions. For example, when a learner posts a question on Discord, the generation AI immediately provides an answer. The generation AI is fine-tuned in advance based on learning data and generates appropriate answers to the learner's questions. For example, when a learner asks a question about a math problem, the generation AI analyzes the problem and presents a solution. The input to the generation AI is a prompt containing instructions on what the learner wants the generation AI to do, and the generation AI generates an answer based on the prompt. The learning plan creation unit creates an individualized learning plan based on the learner's learning history and goals. For example, if a learner wants to improve their English grammar, the generation AI proposes a learning plan tailored to the learner's current level and goals. This plan includes specific learning content and schedule. The input to the generation AI is information about the learner's learning history and goals, and the generation AI generates a learning plan based on that information. The resource provision unit provides the learner with a wealth of learning resources, such as reference books, online learning materials, and video lectures. When a learner wants to learn about a specific topic, the generation AI searches for and provides resources related to that topic. The input to the generation AI is information about the topic the learner wants to learn, and the generation AI suggests resources based on that information. This allows the learning support community service according to the embodiment to enable learners to progress with their studies efficiently and effectively. For example, immediate question and answering can quickly resolve doubts, and personalized learning plans can help learners progress efficiently toward their goals. Furthermore, the sharing of abundant resources makes it easy to obtain the information and learning materials they need.

[0058] The question answering unit can refer to past question history and generate the best answer based on answers to similar questions. For example, when a learner posts a question, the generation AI searches past question history to identify similar questions. For example, if the same math problem has been posted in the past, a new answer will be generated based on that answer. This makes it possible to quickly provide the best answer to similar questions by referring to past question history.

[0059] The question-answering unit can suggest relevant video tutorials and interactive simulations depending on the content of the question. For example, when a learner posts a question, the generative AI analyzes the content of the question and suggests relevant video tutorials. For example, in response to a question about chemistry experiment procedures, a video of the experiment can be provided. This allows the learner to deepen their understanding by suggesting relevant video tutorials and interactive simulations depending on the content of the question.

[0060] The question answering unit can use the emotion estimation function to analyze the emotional state of the questioner and provide an answer in an appropriate tone and expression. For example, the question answering unit analyzes the emotional state of the questioner in real time, and the generation AI provides an answer in a tone that corresponds to that emotion. For example, if the questioner is feeling anxious, the answer will be given in a gentle tone. This increases the questioner's satisfaction by providing an answer in a tone and expression that corresponds to the questioner's emotional state.

[0061] The question and answer unit can support not only text but also voice input, enabling questions and answers via voice. For example, the question and answer unit allows learners to post questions via voice, and the generation AI analyzes the voice to provide an answer. For example, it uses voice recognition technology to convert the question into text and generate an answer. This allows learners to ask and answer questions in a more diverse way by supporting voice input.

[0062] The question and answering unit can create a database of question and answer history and automatically generate an FAQ section that other learners can refer to. For example, the question and answering unit stores the question and answer history in a database, and the generation AI automatically generates an FAQ section based on that data. For example, it creates a page that compiles frequently asked questions and their answers. In this way, by creating a database of question and answer history and automatically generating an FAQ section, other learners can refer to past questions and answers.

[0063] The question answering unit can use the emotion estimation function to provide encouragement and advice according to the questioner's emotions. For example, the question answering unit uses the emotion estimation function to analyze the questioner's emotional state, and the generation AI provides encouragement and advice according to those emotions. For example, if the questioner is feeling down, the answering unit will include encouraging words. This helps maintain the questioner's motivation by providing encouragement and advice according to the questioner's emotions.

[0064] The study plan creation unit can propose optimal study times by taking into account the learner's lifestyle and schedule. For example, the study plan creation unit analyzes the learner's lifestyle and schedule, and the generation AI proposes optimal study times. For example, it sets efficient study times by taking into account the learner's sleep time and commute time. This makes it possible to propose optimal study times by taking into account the learner's lifestyle and schedule.

[0065] The learning plan creation unit can incorporate break times and refreshment methods recommended by the generation AI into the learning plan. For example, the learning plan creation unit incorporates break times into the learning plan and suggests refreshment methods recommended by the generation AI. For example, it may suggest taking short breaks at regular intervals and doing stretching or meditation. In this way, by incorporating break times and refreshment methods, the learner's concentration can be maintained.

[0066] The learning plan creation unit can use the emotion estimation function to analyze the learner's motivation and stress level and adjust the learning plan accordingly. For example, the learning plan creation unit uses the emotion estimation function to analyze the learner's motivation and stress level in real time, and the generation AI adjusts the learning plan accordingly. For example, when stress is high, it suggests lighter learning. This improves learning efficiency by providing a learning plan that suits the learner's motivation and stress level.

[0067] The learning plan creation unit can generate a group learning plan for sharing the learning plan with other learners and advancing learning together. For example, the learning plan creation unit generates a group learning plan to be shared with other learners based on the learner's individual learning plan using a generation AI. For example, learners with the same goal are grouped together to advance learning together. In this way, generating a group learning plan promotes cooperation between learners and improves learning effectiveness.

[0068] The learning plan creation unit can incorporate external learning events and seminars recommended by the generation AI into the learning plan. For example, the learning plan creation unit incorporates external learning events and seminars into the learning plan and suggests events recommended by the generation AI. For example, it introduces seminars and workshops on a specific topic. In this way, by incorporating external learning events and seminars, the learner's learning opportunities are expanded.

[0069] The learning plan creation unit can use the emotion estimation function to provide messages and rewards to increase motivation according to the learner's emotions. For example, the learning plan creation unit uses the emotion estimation function to analyze the learner's emotional state, and the generation AI provides messages to increase motivation. For example, when the learner is tired, an encouraging message is sent. In this way, by providing messages and rewards according to the learner's emotions, the learner's motivation is maintained.

[0070] The resource providing unit can provide customized, optimal resources by taking into account the learner's past learning history and interests. For example, the resource providing unit analyzes the learner's past learning history, and the generation AI suggests optimal resources based on that history. For example, it provides new learning materials related to topics previously studied. This makes it possible to provide optimal resources by taking into account the learner's past learning history and interests.

[0071] The resource providing unit can include exercises and quizzes automatically generated by the generation AI in the resources it provides. For example, the resource providing unit includes exercises and quizzes automatically generated by the generation AI in the resources to enable learners to learn practically. For example, it provides a collection of mathematics problems and English quizzes. In this way, by including exercises and quizzes automatically generated by the generation AI, learners can learn practically.

[0072] The resource providing unit can use the emotion estimation function to suggest resources that match the learner's interests and concerns, thereby increasing their motivation to learn. For example, the resource providing unit uses the emotion estimation function to analyze the learner's interests and concerns, and the generation AI suggests resources accordingly. For example, it provides learning materials related to topics that interest the learner. In this way, suggesting resources that match the learner's interests and concerns increases their motivation to learn.

[0073] The resource provider can expand resource suggestions beyond text to include interactive content and game-style teaching materials. For example, the resource provider can expand the resources suggested by the generative AI beyond text to include interactive content and game-style teaching materials. For example, in programming learning, the resource provider can provide an interactive code editor. This allows learners to learn while having fun by suggesting interactive content and game-style teaching materials.

[0074] The resource providing unit can add a collaboration function that enables the provided resources to be shared with other learners and that allows for joint learning. For example, the resource providing unit adds a collaboration function that enables the resources suggested by the generation AI to be shared with other learners and that allows for joint learning. For example, a group discussion can be held using the same learning materials. This promotes cooperation between learners by sharing resources and jointly learning.

[0075] The resource providing unit can use the emotion estimation function to adjust the difficulty and content of resources according to the learner's emotions. For example, the resource providing unit uses the emotion estimation function to analyze the learner's emotional state, and the generation AI adjusts the difficulty and content of resources. For example, when the learner is tired, it provides easy learning materials. In this way, adjusting the difficulty and content of resources according to the learner's emotions deepens the learner's understanding.

[0076] The question answering unit can suggest appropriate topics and discussion themes to promote communication between learners. For example, the generative AI can suggest appropriate topics and discussion themes to promote communication between learners. For example, it can suggest discussions on specific topics. This promotes communication between learners by suggesting appropriate topics and discussion themes.

[0077] The question and answering unit automatically analyzes feedback between learners, and the generation AI can provide suggestions for improvement and advice. For example, the question and answering unit automatically analyzes feedback between learners and provides suggestions for improvement and advice. For example, it analyzes the feedback content and makes specific suggestions for improvement. In this way, the learners' learning effectiveness is improved by automatically analyzing feedback between learners and providing suggestions for improvement and advice.

[0078] The question answering unit can use the emotion estimation function to analyze the emotional state between learners and make suggestions to promote positive communication. For example, the question answering unit uses the emotion estimation function to analyze the emotional state between learners, and the generation AI makes suggestions to promote positive communication. For example, it can suggest words of encouragement when emotions are running high. This helps maintain good relationships between learners by analyzing the emotional state between learners and making suggestions to promote positive communication.

[0079] The question and answering unit can also make the function of support between learners compatible with real-time video chat and voice chat. For example, the question and answering unit can also make the function of support between learners compatible with real-time video chat and voice chat. For example, learners can communicate directly through video chat. This allows support between learners to be more effective by supporting real-time video chat and voice chat.

[0080] The question and answering unit can compile a database of support provided between learners and build a knowledge base that other learners can refer to. For example, the question and answering unit can save support provided between learners in a database and build a knowledge base that other learners can refer to. For example, it can create a page that summarizes past discussions and feedback. In this way, by compile- ing support content into a database and building a knowledge base, other learners can refer to past support content.

[0081] The question-answering unit can use the emotion estimation function to provide encouragement and advice according to the emotions of the learners. For example, the question-answering unit uses the emotion estimation function to analyze the emotional state of the learners, and the generation AI provides encouragement and advice according to those emotions. For example, when a learner is feeling down, the generation AI can send words of encouragement. This helps maintain the learners' motivation by providing encouragement and advice according to their emotions.

[0082] The system according to the embodiment is not limited to the above-described example, and various modifications are possible, for example, as follows.

[0083] The question-answering unit provides instant answers to learners' questions. For example, when a learner posts a question on Discord, the generative AI immediately provides a response. The generative AI is fine-tuned based on prior training data and generates appropriate answers to learners' questions. For example, when a learner asks about a math problem, the generative AI analyzes the problem and presents a solution. The generative AI receives input from the learner, including prompts containing instructions on what the learner wants the generative AI to do, and generates an answer based on the prompt. The learning plan creation unit creates an individualized learning plan based on the learner's learning history and goals. For example, if a learner wants to improve their English grammar, the generative AI proposes a learning plan based on the learner's current level and goals. This plan includes specific learning content and schedule. The generative AI receives input from the learner's learning history and goals, and generates a learning plan based on that information. The resource provision unit provides learners with a wealth of learning resources. For example, it suggests reference books, online materials, video lectures, etc. If a learner wants to learn about a specific topic, the generative AI searches for and provides resources related to that topic. The input to the generation AI is information about the topic the learner wants to learn, and the generation AI suggests resources based on that information. This allows the learning support community service according to the embodiment to enable learners to progress with their studies efficiently and effectively. For example, immediate question and answering can quickly resolve doubts, and personalized learning plans can help learners progress efficiently toward their goals. Furthermore, the sharing of abundant resources makes it easy to obtain the information and learning materials they need.

[0084] The question answering unit can refer to past question history and generate the best answer based on answers to similar questions. For example, when a learner posts a question, the generation AI searches past question history to identify similar questions. For example, if the same math problem has been posted in the past, a new answer can be generated based on that answer. This makes it possible to quickly provide the best answer to similar questions by referring to past question history.

[0085] The question-answering unit can suggest relevant video tutorials and interactive simulations depending on the content of the question. For example, when a learner posts a question, the generative AI analyzes the question and suggests relevant video tutorials. For example, in response to a question about chemistry experiment procedures, a video of the experiment can be provided. This allows the learner to deepen their understanding by suggesting relevant video tutorials and interactive simulations depending on the content of the question.

[0086] The question answering unit uses an emotion estimation function to analyze the emotional state of the questioner and provide an answer in an appropriate tone and expression. For example, the questioner's emotional state can be analyzed in real time, and the generation AI can provide an answer in a tone that corresponds to that emotion. For example, if the questioner is feeling anxious, the answer will be in a gentle tone. This increases the questioner's satisfaction by providing an answer in a tone and expression that corresponds to the questioner's emotional state.

[0087] The question-and-answer unit can handle voice input as well as text, enabling questions and answers via voice. For example, learners can post questions via voice, and the generation AI can analyze the voice and provide answers. For example, it can use voice recognition technology to convert the question into text and generate answers. This allows learners to ask and answer questions in a more diverse way by supporting voice input.

[0088] The question and answer section can create a database of question and answer history and automatically generate an FAQ section that other learners can refer to. For example, the question and answer history can be saved in a database, and the generation AI can automatically generate an FAQ section based on that data. For example, a page can be created that compiles frequently asked questions and their answers. This allows other learners to refer to past questions and answers by creating a database of question and answer history and automatically generating an FAQ section.

[0089] The question answering unit can use the emotion estimation function to provide encouragement and advice according to the questioner's emotions. For example, the emotion estimation function can be used to analyze the questioner's emotional state, and the generation AI can provide encouragement and advice according to those emotions. For example, if the questioner is feeling down, the AI ​​can respond with encouraging words. This helps maintain the questioner's motivation by providing encouragement and advice according to their emotions.

[0090] The study plan creation unit can propose optimal study times by taking into account the learner's lifestyle and schedule. For example, the generation AI can analyze the learner's lifestyle and schedule and propose optimal study times. For example, it can set efficient study times by taking into account the learner's sleep time and commute time. This makes it possible to propose optimal study times by taking into account the learner's lifestyle and schedule.

[0091] The learning plan creation unit can incorporate break times and refreshment methods recommended by the generation AI into the learning plan. For example, break times can be incorporated into the learning plan, and the generation AI can suggest recommended refreshment methods. For example, it can suggest taking short breaks at regular intervals and doing stretching or meditation. In this way, incorporating break times and refreshment methods can help the learner maintain their concentration.

[0092] The learning plan creation unit can use the emotion estimation function to analyze the learner's motivation and stress level and adjust the learning plan accordingly. For example, the emotion estimation function can be used to analyze the learner's motivation and stress level in real time, and the generation AI can adjust the learning plan accordingly. For example, when stress is high, it can suggest lighter learning. This improves learning efficiency by providing a learning plan that suits the learner's motivation and stress level.

[0093] The processing flow of the second embodiment will be briefly explained below.

[0094] Step 1: The question-answering unit provides instant answers to learners' questions. For example, when a learner posts a question on Discord, the generative AI provides an immediate answer. The generative AI is fine-tuned in advance based on training data, and generates appropriate answers to the learner's questions. For example, if a question is about a math problem, the generative AI analyzes the problem and presents a solution. The input to the generative AI is a prompt containing instructions on what the learner wants the generative AI to do, and the generative AI generates an answer based on that prompt. Step 2: The learning plan creation unit creates an individualized learning plan based on the learner's learning history and goals. For example, if a learner wants to improve their English grammar, the generation AI will propose a learning plan that suits the learner's current level and goals. This plan will include specific learning content and schedule. The input to the generation AI is information about the learner's learning history and goals, and the generation AI will generate a learning plan based on that information. Step 3: The resource provider provides the learner with a wide range of learning resources. For example, it suggests reference books, online learning materials, video lectures, etc. If a learner wants to learn about a specific topic, the generation AI searches for and provides resources related to that topic. The input to the generation AI is information about the topic the learner wants to learn, and the generation AI suggests resources based on that information.

[0095] 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.

[0096] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet Search<URL: https: / / openai.com / blog / chatgpt> Examples of generative AIs include the data generation model 58, such as a neural network model (e.g., a neural network model), and a neural network model (e.g., a neural network model). The data generation model 58 is obtained by performing deep learning on a neural network. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating speech, text data indicating text, and image data indicating an image is also input to the data generation model 58. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The specification processing unit 290 performs the above-mentioned specification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0097] 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.

[0098] [Second embodiment] FIG. 3 shows an example of the configuration of a data processing system 210 according to the second embodiment.

[0099] 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.

[0100] 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.

[0101] 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.

[0102] 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.

[0103] 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).

[0104] 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.

[0105] 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.

[0106] 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.

[0107] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0108] In the smart glasses 214, the specific processing is performed by the processor 46. A specific processing program 60 is stored in the storage 50. The processor 46 reads the specific processing program 60 from the storage 50 and executes the read specific processing program 60 on the RAM 48. The specific processing is realized by the processor 46 operating as the control unit 46A in accordance with the specific processing program 60 executed on the RAM 48. Note that the smart glasses 214 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0109] 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.

[0110] 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.

[0111] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0112] 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.

[0113] [Third embodiment] FIG. 5 shows an example of the configuration of a data processing system 310 according to the third embodiment.

[0114] 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.

[0115] 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.

[0116] 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.

[0117] 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.

[0118] 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).

[0119] 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.

[0120] 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.

[0121] 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.

[0122] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0123] In the headset type terminal 314, the identification process is performed by the processor 46. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. Note that the headset type terminal 314 may also have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0124] 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.

[0125] 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.

[0126] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0127] 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.

[0128] [Fourth embodiment] FIG. 7 shows an example of the configuration of a data processing system 410 according to the fourth embodiment.

[0129] 7, the data processing system 410 includes a data processing device 12 and a robot 414. An example of the data processing device 12 is a server.

[0130] 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.

[0131] 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.

[0132] 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.

[0133] 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).

[0134] 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.

[0135] 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.

[0136] 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.

[0137] 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.

[0138] The storage 32 stores a data generation model 58 and an emotion identification model 59. The data generation model 58 and the emotion identification model 59 are used by the identification processing unit 290. The identification processing unit 290 can estimate the user's emotion using the emotion identification model 59 and perform identification processing using the user's emotion.

[0139] In the robot 414, the processor 46 performs the identification process. A identification program 60 is stored in the storage 50. The processor 46 reads the identification program 60 from the storage 50 and executes the read identification program 60 on the RAM 48. The identification process is realized by the processor 46 operating as a control unit 46A in accordance with the identification program 60 executed on the RAM 48. The robot 414 may have a data generation model and an emotion identification model similar to the data generation model 58 and the emotion identification model 59.

[0140] 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.

[0141] 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.

[0142] The data generation model 58 is a so-called generative AI. An example of the data generation model 58 is a generative AI such as ChatGPT. The data generation model 58 is obtained by performing deep learning on a neural network. The data generation model 58 receives a prompt containing an instruction, as well as inference data such as voice data representing speech, text data representing text, and image data representing an image. The data generation model 58 performs inference on the input inference data in accordance with the instruction indicated by the prompt and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization. The identification processing unit 290 performs the above-mentioned identification processing using the data generation model 58. The data generation model 58 may be a fine-tuned model so as to output an inference result from a prompt that does not include an instruction. In this case, the data generation model 58 can output an inference result from a prompt that does not include an instruction. The data processing device 12 and the like include multiple types of data generation models 58, and the data generation model 58 includes AIs other than the generative AI. The AI ​​other than the generative AI may be, for example, linear regression, logistic regression, decision tree, random forest, support vector machine (SVM), k-means clustering, convolutional neural network (CNN), recurrent neural network (RNN), generative adversarial network (GAN), or naive Bayes, and can perform various processes, but is not limited to these examples. The AI ​​may also be an AI agent. When the processes of each of the above-mentioned parts are performed by AI, the processes may be performed in part or entirely by AI, but are not limited to these examples. The processes performed by AI, including the generative AI, may be replaced with rule-based processes.

[0143] 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.

[0144] 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.

[0145] 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.

[0146] 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.

[0147] 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).

[0148] 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.

[0149] 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."

[0150] 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.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] The hardware resource for executing a specific process can be any of the following processors: A CPU is a general-purpose processor that functions as a hardware resource for executing a specific process by executing software, i.e., a program. A dedicated electrical circuit, such as a field-programmable gate array (FPGA), a programmable logic device (PLD), or an application-specific integrated circuit (ASIC), is a processor with a circuit configuration specifically designed to execute a specific process. Each processor has built-in or connected memory, and uses the memory to execute the specific process.

[0156] 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.

[0157] 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.

[0158] 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.

[0159] 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.

[0160] The above-described description and illustrations are a detailed explanation of the parts related to the technology of the present disclosure and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, functions, actions, and effects is an explanation of an example of the configuration, functions, actions, and effects of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above-described description and illustrations within the scope of the gist of the technology of the present disclosure. Furthermore, to avoid confusion and facilitate understanding of the parts related to the technology of the present disclosure, the above-described description and illustrations omit explanations of common technical knowledge that do not require particular explanation to enable the implementation of the technology of the present disclosure.

[0161] 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]

[0162] 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 question answering unit that provides immediate answers to questions from learners; a learning plan creation unit that creates an individualized learning plan according to the learning history and goals of the learner based on the answers provided by the question answering unit; a resource providing unit that provides a wealth of learning resources to the learner based on the learning plan formulated by the learning plan formulating unit. A system characterized by:

2. The question answering unit Refer to past question history and generate the best answer based on answers to similar questions 2. The system of claim 1.

3. The learning plan creation unit Considering the learner's daily rhythm and schedule, we suggest optimal study times 2. The system of claim 1.

4. The resource providing unit Customize and provide the most appropriate resources based on the learner's past learning history and interests 2. The system of claim 1.

5. The question answering unit Emotion estimation function analyzes the questioner's emotional state and provides an answer with the appropriate tone and expression 2. The system of claim 1.

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